librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.2-dpo-2	AIFT	2024-01-25T07:54:44Z	62	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-25T07:17:06Z	--- license: cc-by-sa-4.0 --- <h1>orca-platypus - instruct-dpo-2 모델 v1.2</h1> <b><학습 데이터 구축></b> kyujinpy 님이 공개하신 KOR-OpenOrca-Platypus 데이터를 일부 삭제(샘플링) 및 정제 작업 진행하여 활용. 그 이후 해당 데이터들을 보며 관련 태스크를 추출하였고 이를 기반으로 해당 태스크에 맞춰서 NLP 관련 오픈소스 데이터를 활용하여 학습데이터를 자체적으로 역사, 과학, 수학, 기계독해, 리뷰 분석 문제를 gpt를 통해서 구축하였고, aihub 일반상식 및 기계독해 데이터를 활용하여 추가로 학습 데이터를 구축(형태소 관련, 기계독해 관련 및 요약) 각종 블로그에서 역사 및 상식 퀴즈를 사람이 직접 학습데이터 형태로 변경 AI2AI Challenge 데이터 형태를 보고 gpt를 통해 초등 수준의 과학 수학 문제 유형을 제작 500문제 영어 번역 데이터 영한/한영 데이터 학습 데이터로 활용 진행 총 데이터 4만개 정도 사용하였습니다. <br> <DPO학습 데이터> DPO 데이터는 CommonGen과 TruthfulQA에 초점을 맞추어 약 17,000개의 데이터를 학습하였습니다. + ko-hh-rlhf 데이터에서 chosen 데이터부분을 ChatGPT를 통해 변경한 데이터를 추가 학습하였습니다. <br> + TruthfulQA 관련 문제 추가를 진행하였습니다.(속설 관련 참거짓 문제) + 기계독해 관련 학습 데이터를 ChatGPT를 통해서 답변을 얻어 학습 + 문법관련 학습 데이터 <br> ###학습 데이터 파일은 비공개입니다. <br> <b><학습></b> 학습은 LoRA를 사용하여 A100 40G *2에서 학습을 진행하였습니다.
SanjiWatsuki/TinyLlamaHerd-2x1.1B	SanjiWatsuki	2024-01-25T07:47:36Z	80	1	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "merge", "mergekit", "lazymergekit", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T02:00:53Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit --- # TinyLlamaHerd-2x1.1B TinyLlamaHerd-2x1.1B is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): ## 🧩 Configuration ```yaml base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T gate_mode: random dtype: bfloat16 experts: - source_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T positive_prompts: [""] - source_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T positive_prompts: [""] ```
pfnet/plamo-13b-instruct-nc	pfnet	2024-01-25T07:46:45Z	76	3	transformers	[ "transformers", "safetensors", "plamo", "text-generation", "conversational", "custom_code", "en", "ja", "arxiv:2302.13971", "license:cc-by-nc-4.0", "autotrain_compatible", "region:us" ]	text-generation	2023-10-26T05:36:25Z	--- language: - en - ja license: cc-by-nc-4.0 library_name: transformers pipeline_tag: text-generation --- # PLaMo-13B-Instruct-NC ## Model Description PLaMo-13B-Instruct-NC is a noncommercial instruct fine-tuned model built upon the 8192 context length version of [PLaMo-13B](https://huggingface.co/pfnet/plamo-13b) text generation model. PLaMo-13B-Instruct-NC is fine-tuned using multiple publicly available Japanese datasets. This model is released under CC-BY-NC-4.0. [PLaMo-13B-Instruct Release blog (Japanese)](https://tech.preferred.jp/ja/blog/llm-plamo-instruct/) ## Usage Install the required libraries as follows: ```sh >>> python -m pip install numpy safetensors sentencepiece torch transformers accelerate ``` Execute the following python code: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained( "pfnet/plamo-13b-instruct-nc", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "pfnet/plamo-13b-instruct-nc", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto", ) ``` ```python def completion(prompt: str, max_new_tokens: int = 128) -> str: inputs = tokenizer(prompt, return_tensors="pt").to("cuda") generated_ids = model.generate( inputs.input_ids, eos_token_id=2, pad_token_id=3, max_new_tokens=max_new_tokens, temperature=1, top_p=0.95, top_k=50, do_sample=True, ) return tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True) def generate_prompt(messages: list) -> str: sep = "\n\n### " prompt = [ "以下はタスクを説明する指示で、文脈を説明した入力とペアになっています。", "要求を適切に補完するよう応答を書いてください。", ] roles = {"instruction": "指示", "response": "応答", "input": "入力"} for msg in messages: prompt.append(sep + roles[msg["role"]] + ":\n" + msg['content']) prompt.append(sep + roles["response"] + ":\n") return "".join(prompt) ``` ```python prompt = generate_prompt([ {"role": "instruction", "content": "日本の首都はどこですか？"}, # {"role": "input", "content": "..."} ## An extra input (optional) ]) print(completion(prompt, max_new_tokens=128)) ``` ## Model Details - Model size: 13B - Trained tokens: 1.5T tokens (English: 1.32T tokens, Japanese: 0.18T tokens) - Tokenizer: sentencepiece tokenizer trained on a subset of the pretraining datasets. - Context length: 8192 - Developed by: Preferred Networks, Inc - Model type: Causal decoder-only - Language(s): Japanese and English - License: CC-BY-NC-4.0 ## Training Dataset - [Stanford Alpaca (Japanese translation)](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) - [databricks-dolly-15k (Japanese translation)](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja) - [Anthropic HH-RLHF (Japanese translation, subset)](https://huggingface.co/datasets/fujiki/japanese_hh-rlhf-49k) - [OpenAssistant Conversations Dataset (Japanese translation, oasst1)](https://huggingface.co/datasets/kunishou/oasst1-89k-ja) - [Wikinews subset of Izumi-lab llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) For the pretraining model, see [PLaMo-13B](https://huggingface.co/pfnet/plamo-13b). ## Bias, Risks, and Limitations PLaMo-13B-Instruct-NC is a new technology that carries risks with use. Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, PLaMo-13B-Instruct-NC’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of PLaMo-13B-Instruct-NC, developers should perform safety testing and tuning tailored to their specific applications of the model. ## How to cite ```tex @online{PLaMoInstructNC2023Introducing, author = {Preferred Networks, Inc}, title = {PLaMo-13B-Instruct-NC}, year = {2023}, url = {https://huggingface.co/pfnet/plamo-13b-instruct-nc}, urldate = {2023-10-26} } ``` ## References ```tex @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ``` ```tex @article{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```
pfnet/plamo-13b-instruct	pfnet	2024-01-25T07:46:09Z	117	14	transformers	[ "transformers", "safetensors", "plamo", "text-generation", "conversational", "custom_code", "en", "ja", "arxiv:2302.13971", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2023-10-26T02:11:24Z	--- language: - en - ja license: apache-2.0 library_name: transformers pipeline_tag: text-generation --- # PLaMo-13B-Instruct ## Model Description PLaMo-13B-Instruct is an instruct fine-tuned model built upon the 8192 context length version of [PLaMo-13B](https://huggingface.co/pfnet/plamo-13b) text generation model. PLaMo-13B-Instruct is fine-tuned using multiple publicly available Japanese datasets. This model is released under the Apache License 2.0. [PLaMo-13B-Instruct Release blog (Japanese)](https://tech.preferred.jp/ja/blog/llm-plamo-instruct/) ## Usage Install the required libraries as follows: ```sh >>> python -m pip install numpy sentencepiece torch transformers accelerate ``` Execute the following python code: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained( "pfnet/plamo-13b-instruct", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "pfnet/plamo-13b-instruct", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto", ) ``` ```python def completion(prompt: str, max_new_tokens: int = 128) -> str: inputs = tokenizer(prompt, return_tensors="pt").to("cuda") generated_ids = model.generate( inputs.input_ids, eos_token_id=2, pad_token_id=3, max_new_tokens=max_new_tokens, temperature=1, top_p=0.95, top_k=50, do_sample=True, ) return tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True) def generate_prompt(messages: list) -> str: sep = "\n\n### " prompt = [ "以下はタスクを説明する指示で、文脈を説明した入力とペアになっています。", "要求を適切に補完するよう応答を書いてください。", ] roles = {"instruction": "指示", "response": "応答", "input": "入力"} for msg in messages: prompt.append(sep + roles[msg["role"]] + ":\n" + msg["content"]) prompt.append(sep + roles["response"] + ":\n") return "".join(prompt) ``` ```python prompt = generate_prompt([ {"role": "instruction", "content": "日本の首都はどこですか？"}, # {"role": "input", "content": "..."} ## An extra input (optional) ]) print(completion(prompt, max_new_tokens=128)) ``` ## Model Details - Model size: 13B - Trained tokens: 1.5T tokens (English: 1.32T tokens, Japanese: 0.18T tokens) - Tokenizer: sentencepiece tokenizer trained on a subset of the pretraining datasets. - Context length: 8192 - Developed by: Preferred Networks, Inc - Model type: Causal decoder-only - Language(s): Japanese and English - License: Apache License 2.0 ## Training Dataset <!-- - [Stanford Alpaca (Japanese translation)](https://huggingface.co/datasets/fujiki/japanese_alpaca_data)--> - [databricks-dolly-15k (Japanese translation)](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja) - [Anthropic HH-RLHF (Japanese translation, subset)](https://huggingface.co/datasets/fujiki/japanese_hh-rlhf-49k) - [OpenAssistant Conversations Dataset (Japanese translation, oasst1)](https://huggingface.co/datasets/kunishou/oasst1-89k-ja) - [Wikinews subset of Izumi-lab llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) For the pretraining model, see [PLaMo-13B](https://huggingface.co/pfnet/plamo-13b). ## Bias, Risks, and Limitations PLaMo-13B-Instruct is a new technology that carries risks with use. Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, PLaMo-13B-Instruct-NC’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of PLaMo-13B-Instruct, developers should perform safety testing and tuning tailored to their specific applications of the model. ## How to cite ```tex @online{PLaMoInstruct2023Introducing, author = {Preferred Networks, Inc}, title = {PLaMo-13B-Instruct}, year = {2023}, url = {https://huggingface.co/pfnet/plamo-13b-instruct}, urldate = {2023-10-26} } ``` ## References ```tex @article{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```
antonovata/test_chat	antonovata	2024-01-25T07:41:51Z	0	0	peft	[ "peft", "region:us" ]	null	2024-01-24T11:10:59Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0
motherduckdb/DuckDB-NSQL-7B-v0.1-GGUF	motherduckdb	2024-01-25T07:37:17Z	232	21	null	[ "gguf", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-01-24T19:31:51Z	--- license: llama2 inference: parameters: do_sample: false max_length: 200 widget: - text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question.\n\n### Input:\n\n### Question:\ncreate a new table called tmp from test.csv\n\n### Response (use duckdb shorthand if possible):" example_title: "read test.csv" - text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question.\n\n### Input:\n\n### Question:\ncreate a new table called tmp from test.csv\n\n### Response (use duckdb shorthand if possible):" example_title: "get _amount columns" - text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.\n\n### Input:\nHere is the database schema that the SQL query will run on:\nCREATE TABLE rideshare (\n hvfhs_license_num varchar,\n dispatching_base_num varchar,\n originating_base_num varchar,\n request_datetime timestamp,\n on_scene_datetime timestamp,\n pickup_datetime timestamp,\n dropoff_datetime timestamp,\n trip_miles double,\n trip_time bigint,\n\n);\n\n### Question:\nget longest trip in december 2022\n\n### Response (use duckdb shorthand if possible):" example_title: "taxi trips" --- # DuckDB-NSQL-7B (GGUF) The repository includes model files in the GGUF format for [DuckDB-NSQL-7B-v0.1](https://huggingface.co/motherduckdb/DuckDB-NSQL-7B-v0.1), featuring both the f16 and Q8_0 versions. ## Provided model files \| Name \| Quant method \| Bits \| \| ---- \| ---- \| ---- \| \| [DuckDB-NSQL-7B-v0.1-f16.gguf](https://huggingface.co/motherduckdb/DuckDB-NSQL-7B-v0.1-GGUF/blob/main/DuckDB-NSQL-7B-v0.1-f16.gguf) \| - \| 16 \| \| [DuckDB-NSQL-7B-v0.1-q8_0.gguf](https://huggingface.co/motherduckdb/DuckDB-NSQL-7B-v0.1-GGUF/blob/main/DuckDB-NSQL-7B-v0.1-q8_0.gguf) \| Q8_0 \| 8 \| ## Model Description NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks. In this repository we are introducing a new member of NSQL, DuckDB-NSQL. It's based on Meta's original [Llama-2 7B model](https://huggingface.co/meta-llama/Llama-2-7b) and further pre-trained on a dataset of general SQL queries and then fine-tuned on a dataset composed of DuckDB text-to-SQL pairs. ## Training Data 200k DuckDB text-to-SQL pairs, synthetically generated using [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), guided by the DuckDB v0.9.2 documentation. And text-to-SQL pairs from [NSText2SQL](https://huggingface.co/datasets/NumbersStation/NSText2SQL) that were transpiled to DuckDB SQL using [sqlglot](https://github.com/tobymao/sqlglot). ## Evaluation Data We evaluate our models on a DuckDB-specific benchmark that contains 75 text-to-SQL pairs. The benchmark is available [here](https://github.com/NumbersStationAI/DuckDB-NSQL/). ## Training Procedure DuckDB-NSQL was trained using cross-entropy loss to maximize the likelihood of sequential inputs. For finetuning on text-to-SQL pairs, we only compute the loss over the SQL portion of the pair. The model is trained using 80GB A100s, leveraging data and model parallelism. We fine-tuned for 10 epochs. ## Intended Use and Limitations The model was designed for text-to-SQL generation tasks from given table schema and natural language prompts. The model works best with the prompt format defined below and outputs. In contrast to existing text-to-SQL models, the SQL generation is not contrained to `SELECT` statements, but can generate any valid DuckDB SQL statement, including statements for official DuckDB extensions. ## How to Use Setup llama.cpp: ```shell CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python huggingface-cli download motherduckdb/DuckDB-NSQL-7B-v0.1-GGUF DuckDB-NSQL-7B-v0.1-q8_0.gguf --local-dir . --local-dir-use-symlinks False pip install wurlitzer ``` Example 1: ```python ## Setup - Llama.cpp from llama_cpp import Llama with pipes() as (out, err): llama = Llama( model_path="DuckDB-NSQL-7B-v0.1-q8_0.gguf", n_ctx=2048, ) text = """### Instruction: Your task is to generate valid duckdb SQL to answer the following question. ### Input: ### Question: create a new table called tmp from test.csv ### Response (use duckdb shorthand if possible): """ with pipes() as (out, err): pred = llama(text, temperature=0.1, max_tokens=500) print(pred["choices"][0]["text"]) ``` Example 2: ```python from llama_cpp import Llama with pipes() as (out, err): llama = Llama( model_path="DuckDB-NSQL-7B-v0.1-q8_0.gguf", n_ctx=2048, ) text = """### Instruction: Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema. ### Input: Here is the database schema that the SQL query will run on: CREATE TABLE taxi ( VendorID bigint, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count double, trip_distance double, fare_amount double, extra double, tip_amount double, tolls_amount double, improvement_surcharge double, total_amount double, ); ### Question: get all columns ending with _amount from taxi table ### Response (use duckdb shorthand if possible):""" with pipes() as (out, err): pred = llama(text, temperature=0.1, max_tokens=500) print(pred["choices"][0]["text"]) ``` Example 3: ```python from llama_cpp import Llama with pipes() as (out, err): llama = Llama( model_path="DuckDB-NSQL-7B-v0.1-q8_0.gguf", n_ctx=2048, ) text = """### Instruction: Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema. ### Input: Here is the database schema that the SQL query will run on: CREATE TABLE rideshare ( hvfhs_license_num varchar, dispatching_base_num varchar, originating_base_num varchar, request_datetime timestamp, on_scene_datetime timestamp, pickup_datetime timestamp, dropoff_datetime timestamp, trip_miles double, trip_time bigint, ); ### Question: get longest trip in december 2022 ### Response (use duckdb shorthand if possible): """ with pipes() as (out, err): pred = llama(text, temperature=0.1, max_tokens=500) print(pred["choices"][0]["text"]) ``` For more information (e.g., run with your local database), please find examples in [this repository](https://github.com/NumbersStationAI/DuckDB-NSQL).
eigh8skillz/southzulumodel	eigh8skillz	2024-01-25T07:35:17Z	1	0	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2024-01-25T07:35:16Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks southzulumodel tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
uukuguy/speechless-zephyr-code-functionary-7b	uukuguy	2024-01-25T07:15:51Z	4,400	2	transformers	[ "transformers", "safetensors", "gguf", "mistral", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-23T08:19:26Z	--- language: - en library_name: transformers pipeline_tag: text-generation license: apache-2.0 --- <p><h1> speechless-zephyr-code-functionary-7b </h1></p> [4,5,8-bit GGUF models for CPU+GPU inference](https://huggingface.co/uukuguy/speechless-zephyr-code-functionary-7b/tree/main/GGUF) This model is the one of the moloras (Mixture-of-Multi-LoRAs) experiments. Extract LoRA modules from below models (all based Mistral-7B-v0.1), each LoRA module has its own unique skills. By using multi-loras, they can be combined together statically or dynamically to form a versatile new model. - HuggingFaceH4/zephyr-7b-beta (Uncensored Model) - meetkai/functionary-small-v2.2 (Execute functions/plugins) - uukuguy/speechless-code-mistral-7b-v1.0 (Enhance Coding) The entire process is completed through the use of extract-lora, merge-lora, and lora-hub provided by multi-loras. The router of mixture-of-multi-loras enables an automatic assembling of LoRA modules, using a gradientfree approach to obtain the coefficients of LoRA modules and requiring only a handful of inference steps for unseen tasks. Code: https://github.com/uukuguy/multi_loras ## LM-Evaluation-Harness [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) \| Metric \| Value \| \| --- \| --- \| \| ARC \| 61.52 \| \| HellaSwag \| 83.88 \| \| MMLU \| 64.71 \| \| TruthfulQA \| 44.99 \| \| Winogrande \| 78.69 \| \| GSM8K \| 43.82 \| \| Average \| 62.93 \|
katzenbach/test_trainer	katzenbach	2024-01-25T07:11:58Z	92	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-25T06:27:57Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: test_trainer results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test_trainer This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5138 - Accuracy: 0.34 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 13 \| 1.5917 \| 0.22 \| \| No log \| 2.0 \| 26 \| 1.5629 \| 0.25 \| \| No log \| 3.0 \| 39 \| 1.5138 \| 0.34 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
abhinand/malayalam-llama-7b-instruct-v0.1-GGUF	abhinand	2024-01-25T07:11:35Z	6	1	null	[ "gguf", "en", "ml", "arxiv:2311.05845", "base_model:abhinand/malayalam-llama-pt-v0", "base_model:quantized:abhinand/malayalam-llama-pt-v0", "license:llama2", "endpoints_compatible", "region:us", "conversational" ]	null	2024-01-25T04:29:16Z	--- base_model: abhinand/malayalam-llama-pt-v0 model-index: - name: malayalam-llama-instruct-v0.1 results: [] license: llama2 language: - en - ml --- # Malayalam LLaMA 7B Instruct v0.1 [GGUF Quantized] Welcome to the inaugural release of the Malayalam LLaMA 7B instruct model – an important step in advancing LLMs for the Malayalam language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks. To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP)]() that outlines our journey and the model's potential impact. > Note: This model is based on the Tamil LLaMA series of models. The GitHub repository remains the same - [https://github.com/abhinand5/tamil-llama](https://github.com/abhinand5/tamil-llama). The base models and the updated code for Tamil LLaMA v0.2 (which this work is based on) will be released soon. If you appreciate this work and would like to support its continued development, consider [buying me a coffee](https://www.buymeacoffee.com/abhinand.b). Your support is invaluable and greatly appreciated. [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/abhinand.b) ## Demo: To access an easy-to-use, no-code demo, please open the provided Google Colab notebook. Complete instructions for usage are included within the notebook itself. <a href="https://colab.research.google.com/drive/11_RHZim_HubD2NskxSwq4X_NW4XlnrtS?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Demo In Colab"></a></center> ## Model description The Malayalam LLaMA models have been enhanced and tailored specifically with an extensive Malayalam vocabulary of ~16,000 tokens, building upon the foundation set by the original LLaMA-2. - Model type: A 7B parameter GPT-like model finetuned on ~500,000 samples consisting of an equal proportion of English and Malayalam samples. (Dataset will be released soon) - Language(s): Bilingual. English and Malayalam. - License: GNU General Public License v3.0 - Finetuned from model: [To be released soon]() - Training Precision: `bfloat16` - Code: [GitHub](https://github.com/abhinand5/tamil-llama) (To be updated soon) ## Quantization Info This repo contains GGUF format model files for [Malayalam LLaMA 7B Instruct v0.1](https://huggingface.co/abhinand/malayalam-llama-7b-instruct-v0.1). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. ### Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \|------------------------------------------------------------------------------------------------------------------------------------------------\|--------------\|------\|---------\|------------------\|----------------------------------------------------------\| \| [malayalam-llama-7b-instruct-v0.1.Q4_K_M.gguf](https://huggingface.co/abhinand/malayalam-llama-7b-instruct-v0.1-GGUF/blob/main/malayalam-llama-7b-instruct-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.18 GB \| 6.8 GB \| medium, balanced quality - recommended \| \| [malayalam-llama-7b-instruct-v0.1.Q5_K_M.gguf](https://huggingface.co/abhinand/malayalam-llama-7b-instruct-v0.1-GGUF/blob/main/malayalam-llama-7b-instruct-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 4.89 GB \| 7.5 GB \| large, very low quality loss - recommended \| ## Prompt Template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Benchmark Results Benchmarking was done using [LLM-Autoeval](https://github.com/mlabonne/llm-autoeval) on an RTX 3090 on [runpod](https://www.runpod.io/). > Note: Please note that discrepancies have been observed between the Open LLM Leaderboard scores and those obtained from local runs using the LM Eval Harness with identical configurations. The results mentioned here are based on our own benchmarking. To replicate these findings, you can utilize the LLM-Autoeval or use [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) locally with the configurations described in Open LLM Leaderboard's About page. \| Benchmark \| Llama 2 Chat \| Tamil Llama v0.2 Instruct \| Telugu Llama Instruct \| Malayalam Llama Instruct \| \|---------------\|--------------\|---------------------------\|-----------------------\|--------------------------\| \| ARC Challenge (25-shot) \| 52.9 \| 53.75 \| 52.47 \| 52.82 \| \| TruthfulQA (0-shot) \| 45.57 \| 47.23 \| 48.47 \| 47.46 \| \| Hellaswag (10-shot) \| 78.55 \| 76.11 \| 76.13 \| 76.91 \| \| Winogrande (5-shot) \| 71.74 \| 73.95 \| 71.74 \| 73.16 \| \| AGI Eval (0-shot) \| 29.3 \| 30.95 \| 28.44 \| 29.6 \| \| BigBench (0-shot) \| 32.6 \| 33.08 \| 32.99 \| 33.26 \| \| Average \| 51.78 \| 52.51 \| 51.71 \| 52.2 \| ## Related Models \| Model \| Type \| Data \| Base Model \| # Params \| Download Links \| \|--------------------------\|-----------------------------\|-------------------\|----------------------\|------\|------------------------------------------------------------------------\| \| Tamil LLaMA 7B v0.1 Base \| Base model \| 12GB \| LLaMA 7B \| 7B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-base-v0.1) \| \| Tamil LLaMA 13B v0.1 Base \| Base model \| 4GB \| LLaMA 13B \| 13B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-base-v0.1) \| \| Tamil LLaMA 7B v0.1 Instruct \| Instruction following model \| 145k instructions \| Tamil LLaMA 7B Base \| 7B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) \| \| Tamil LLaMA 13B v0.1 Instruct \| Instruction following model \| 145k instructions \| Tamil LLaMA 13B Base \| 13B \| [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1) \| \| Tamil LLaMA 7B v0.2 Instruct \| Instruction/Chat model \| 420k instructions \| Tamil LLaMA 7B Base v0.2 \| 7B \| [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1) \| \| Telugu LLaMA 7B v0.2 Instruct \| Instruction/Chat model \| ~400k instructions \| Telugu LLaMA 7B Base v0.1 \| 7B \| [HF Hub](https://huggingface.co/abhinand/telugu-llama-instruct-v0.1) \| ## Usage Note It's important to note that the models have not undergone detoxification/censorship. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications. ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/) ## Citation If you use this model or any of the the Tamil-Llama related work in your research, please cite: ```bibtex @misc{balachandran2023tamilllama, title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, author={Abhinand Balachandran}, year={2023}, eprint={2311.05845}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language.
Deepakkori45/Mistal_aspect_class_sequnece	Deepakkori45	2024-01-25T07:11:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-25T07:11:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
smend0/distilbert-base-uncased-lora-text-classification	smend0	2024-01-25T07:10:56Z	0	0	null	[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2024-01-25T07:06:51Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-lora-text-classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-lora-text-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a truncated IMDB dataset. It achieves the following results on the evaluation set: - Loss: 1.7208 - Accuracy: {'accuracy': 0.876} ## Model description The purpose of this model is to turn distilbert into a sentiment classification model. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------------:\| \| No log \| 1.0 \| 250 \| 2.0890 \| {'accuracy': 0.862} \| \| 0.2005 \| 2.0 \| 500 \| 1.8919 \| {'accuracy': 0.874} \| \| 0.2005 \| 3.0 \| 750 \| 1.7205 \| {'accuracy': 0.871} \| \| 0.0963 \| 4.0 \| 1000 \| 1.7208 \| {'accuracy': 0.876} \| ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.1
dana11235/poca-SoccerTwos	dana11235	2024-01-25T07:07:57Z	23	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-01-25T07:07:50Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: dana11235/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
TheBloke/Everyone-Coder-33B-Base-GPTQ	TheBloke	2024-01-25T07:01:23Z	14	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "conversational", "base_model:rombodawg/Everyone-Coder-33b-Base", "base_model:quantized:rombodawg/Everyone-Coder-33b-Base", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2024-01-25T04:04:35Z	--- base_model: rombodawg/Everyone-Coder-33b-Base inference: false license: other license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL license_name: deepseek model_creator: rombo dawg model_name: Everyone Coder 33B Base model_type: deepseek prompt_template: '{prompt} ' quantized_by: TheBloke tags: - merge --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Everyone Coder 33B Base - GPTQ - Model creator: [rombo dawg](https://huggingface.co/rombodawg) - Original model: [Everyone Coder 33B Base](https://huggingface.co/rombodawg/Everyone-Coder-33b-Base) <!-- description start --> # Description This repo contains GPTQ model files for [rombo dawg's Everyone Coder 33B Base](https://huggingface.co/rombodawg/Everyone-Coder-33b-Base). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GGUF) * [rombo dawg's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/rombodawg/Everyone-Coder-33b-Base) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Unknown ``` {prompt} ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models. These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/main) \| 4 \| None \| Yes \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) \| 8192 \| 17.40 GB \| Yes \| 4-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/gptq-4bit-128g-actorder_True) \| 4 \| 128 \| Yes \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) \| 8192 \| 18.03 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) \| 8192 \| 19.96 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/gptq-3bit-128g-actorder_True) \| 3 \| 128 \| Yes \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) \| 8192 \| 13.89 GB \| No \| 3-bit, with group size 128g and act-order. Higher quality than 128g-False. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) \| 8192 \| 33.84 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/gptq-3bit-32g-actorder_True) \| 3 \| 32 \| Yes \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) \| 8192 \| 15.72 GB \| No \| 3-bit, with group size 64g and act-order. Highest quality 3-bit option. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) \| 8192 \| 34.60 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/Everyone-Coder-33B-Base-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Everyone-Coder-33B-Base-GPTQ:gptq-4bit-128g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `Everyone-Coder-33B-Base-GPTQ`: ```shell mkdir Everyone-Coder-33B-Base-GPTQ huggingface-cli download TheBloke/Everyone-Coder-33B-Base-GPTQ --local-dir Everyone-Coder-33B-Base-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir Everyone-Coder-33B-Base-GPTQ huggingface-cli download TheBloke/Everyone-Coder-33B-Base-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir Everyone-Coder-33B-Base-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir Everyone-Coder-33B-Base-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Everyone-Coder-33B-Base-GPTQ --local-dir Everyone-Coder-33B-Base-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/Everyone-Coder-33B-Base-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Everyone-Coder-33B-Base-GPTQ:gptq-4bit-128g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `Everyone-Coder-33B-Base-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/Everyone-Coder-33B-Base-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation( prompt_template, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Everyone-Coder-33B-Base-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-128g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Write a story about llamas" system_message = "You are a story writing assistant" prompt_template=f'''{prompt} ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: rombo dawg's Everyone Coder 33B Base Everyone-Coder-33b-Base ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/ECrHQnZnv8UM9GUCQtlWW.jpeg) EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. ______________________________________________________________________________________________________________ Im having trouble benchmarking this model because I suck at running llm benchmarks, but from hand testing running the model through https://edabit.com/challenge coding challenges vs up to date gpt-4. My model is hands down beating it in coding. ______________________________________________________________________________________________________________ Ive recently noticed this model has trouble with end tokens so I made a custom prompt template for it. Made sure to add (Always end with "<\|EOT\|>") In addition to your system prompt and (Always end your response with "<\|EOT\|>") at the end of the User message is the preset. Then add <\|EOT\|> as a custom stop string in your LM text generating interface. ``` Always end with "<\|EOT\|>" {System} <\|User\|> {User}. Always end your response with "<\|EOT\|>" <\|Assistant\|> {Assistant} ``` The models that were used in this merger were as follow: - https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct - https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B - https://huggingface.co/WizardLM/WizardCoder-33B-V1.1 Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗 You can find the write up for merging models here: https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing Config for the merger can be found bellow: ```yaml models: - model: WizardLM_WizardCoder-33B-V1.1 parameters: density: 1 weight: .5 - model: codefuse-ai_CodeFuse-DeepSeek-33B parameters: density: 1 weight: .5 merge_method: ties base_model: deepseek-ai_deepseek-coder-33b-instruct parameters: normalize: true int8_mask: true dtype: float16 ```
alnrg2arg/blockchainlabs_7B_merged_test2_4_sft_lora_DPO_orca2	alnrg2arg	2024-01-25T06:56:36Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-25T06:56:36Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Hongu/test_ner	Hongu	2024-01-25T06:56:34Z	102	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-01-25T06:43:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
schroneko/ELYZA-japanese-Llama-2-13b-fast-instruct-gguf	schroneko	2024-01-25T06:30:57Z	14	1	transformers	[ "transformers", "gguf", "llama", "llama2", "ja", "arxiv:2307.09288", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-01-23T14:14:39Z	--- license: llama2 language: - ja tags: - llama2 --- # ELYZA-japanese-Llama-2-13b-fast-instruct-gguf [ELYZA-japanese-Llama-2-13b-fast-instruct](https://huggingface.co/ELYZA/ELYZA-japanese-Llama-2-13b-fast-instruct)の GGUF 変換モデルです。 ## Usage ``` git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp make -j ./main -m 'ELYZA-japanese-Llama-2-13b-fast-instruct-q4_k_m.gguf' -n 256 -p '[INST] <<SYS>>あなたは誠実で優秀な日本人のアシスタントです。<</SYS>>仕事の熱意を取り戻すためのアイデアを5つ挙げてください。[/INST]' ``` ### Licence Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. ### 引用 Citations ```tex @misc{elyzallama2023, title={ELYZA-japanese-Llama-2-13b}, url={https://huggingface.co/elyza/ELYZA-japanese-Llama-2-13b}, author={Akira Sasaki and Masato Hirakawa and Shintaro Horie and Tomoaki Nakamura and Sam Passaglia and Daisuke Oba}, year={2023}, } ``` ```tex @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint={2307.09288}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
LMX20012010/mistral_7b_guanaco	LMX20012010	2024-01-25T06:25:14Z	61	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-01-25T06:23:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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adarsh2350/T5-pytorch-billsum	adarsh2350	2024-01-25T06:15:34Z	90	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-01-24T15:52:59Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: T5-pytorch-billsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # T5-pytorch-billsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5259 - Rouge1: 0.1402 - Rouge2: 0.0484 - Rougel: 0.1155 - Rougelsum: 0.1153 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 62 \| 2.8082 \| 0.1229 \| 0.034 \| 0.1036 \| 0.1035 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.6034 \| 0.1296 \| 0.0409 \| 0.1079 \| 0.1079 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.5419 \| 0.1378 \| 0.047 \| 0.1136 \| 0.1136 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.5259 \| 0.1402 \| 0.0484 \| 0.1155 \| 0.1153 \| 19.0 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cpu - Datasets 2.16.1 - Tokenizers 0.15.0
Chandrababu-Namani/InLegalBERT-fine-tuned	Chandrababu-Namani	2024-01-25T05:45:26Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:law-ai/InLegalBERT", "base_model:finetune:law-ai/InLegalBERT", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-25T05:45:07Z	--- license: mit base_model: law-ai/InLegalBERT tags: - generated_from_trainer metrics: - accuracy model-index: - name: InLegalBERT-lora-text-classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # InLegalBERT-lora-text-classification This model is a fine-tuned version of [law-ai/InLegalBERT](https://huggingface.co/law-ai/InLegalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0550 - Accuracy: {'accuracy': 0.6449893390191898} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------------------:\| \| No log \| 1.0 \| 235 \| 1.1448 \| {'accuracy': 0.6151385927505331} \| \| No log \| 2.0 \| 470 \| 1.0553 \| {'accuracy': 0.6380597014925373} \| \| 1.2222 \| 3.0 \| 705 \| 1.0427 \| {'accuracy': 0.6316631130063965} \| \| 1.2222 \| 4.0 \| 940 \| 1.0490 \| {'accuracy': 0.6428571428571429} \| \| 0.8111 \| 5.0 \| 1175 \| 1.0550 \| {'accuracy': 0.6449893390191898} \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
cloudyu/Pluto_13B_DPO	cloudyu	2024-01-25T05:44:13Z	17	4	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "dpo", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-17T06:00:41Z	--- license: cc-by-nc-4.0 tags: - moe - dpo --- # DPO of cloudyu/Mixtral_7Bx2_MoE dataset : jondurbin/truthy-dpo-v0.1 * metrics average 75.88 * TruthfulQA 81.5 SOTA (2024-01-17) gpu code example ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM import math ## v2 models model_path = "cloudyu/Mixtral_7Bx2_MoE_DPO" tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map='auto',local_files_only=False, load_in_4bit=True ) print(model) prompt = input("please input prompt:") while len(prompt) > 0: input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda") generation_output = model.generate( input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2 ) print(tokenizer.decode(generation_output[0])) prompt = input("please input prompt:") ```
GaganpreetSingh/mistral-7b-lora_model	GaganpreetSingh	2024-01-25T05:29:19Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-25T05:29:12Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
fionazhang/mistral-experiment-6	fionazhang	2024-01-25T05:16:35Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-01-22T00:46:43Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral-experiment-6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-experiment-6 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1400 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.0a0+git7bcf7da - Datasets 2.16.1 - Tokenizers 0.15.0
megha-shroff/mistral-rand-300k-test	megha-shroff	2024-01-25T05:16:18Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-v0.1-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-v0.1-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-01-25T05:16:07Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/Mistral-7B-v0.1-GPTQ model-index: - name: mistral-rand-300k-test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-rand-300k-test This model is a fine-tuned version of [TheBloke/Mistral-7B-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8347 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6477 \| 0.01 \| 50 \| 0.8983 \| \| 0.8474 \| 0.02 \| 100 \| 0.8347 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
tsobolev/ppo-Huggy	tsobolev	2024-01-25T05:00:08Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-01-25T05:00:01Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: tsobolev/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ronluvzu1/autotrain-zb40r-1ccvc	ronluvzu1	2024-01-25T04:53:03Z	0	0	null	[ "tensorboard", "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-01-25T04:52:59Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
varun-v-rao/t5-base-snli-model3	varun-v-rao	2024-01-25T04:52:47Z	33	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text-classification", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-01-25T03:43:05Z	--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: t5-base-snli-model3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-snli-model3 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2857 - Accuracy: 0.8985 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3812 \| 1.0 \| 2146 \| 0.3052 \| 0.8894 \| \| 0.3511 \| 2.0 \| 4292 \| 0.2862 \| 0.8971 \| \| 0.3333 \| 3.0 \| 6438 \| 0.2857 \| 0.8985 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
arun100/whisper-small-tr-derived-1	arun100	2024-01-25T04:26:57Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "tr", "dataset:mozilla-foundation/common_voice_16_0", "base_model:alikanakar/whisper-synthesized-turkish-8-hour", "base_model:finetune:alikanakar/whisper-synthesized-turkish-8-hour", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-24T16:56:45Z	--- language: - tr license: apache-2.0 base_model: alikanakar/whisper-synthesized-turkish-8-hour tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_16_0 metrics: - wer model-index: - name: Whisper Small Turkish Derived results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_16_0 tr type: mozilla-foundation/common_voice_16_0 config: tr split: test args: tr metrics: - name: Wer type: wer value: 18.625004393518683 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Turkish Derived This model is a fine-tuned version of [alikanakar/whisper-synthesized-turkish-8-hour](https://huggingface.co/alikanakar/whisper-synthesized-turkish-8-hour) on the mozilla-foundation/common_voice_16_0 tr dataset. It achieves the following results on the evaluation set: - Loss: 0.2859 - Wer: 18.6250 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.3197 \| 0.58 \| 200 \| 0.3517 \| 21.5757 \| \| 0.2672 \| 1.16 \| 400 \| 0.3203 \| 20.6724 \| \| 0.2532 \| 1.75 \| 600 \| 0.3065 \| 19.6496 \| \| 0.2382 \| 2.33 \| 800 \| 0.2991 \| 19.3420 \| \| 0.2448 \| 2.91 \| 1000 \| 0.2943 \| 19.1276 \| \| 0.2197 \| 3.49 \| 1200 \| 0.2909 \| 18.9712 \| \| 0.2159 \| 4.07 \| 1400 \| 0.2885 \| 18.7340 \| \| 0.2212 \| 4.65 \| 1600 \| 0.2871 \| 18.6988 \| \| 0.2114 \| 5.24 \| 1800 \| 0.2861 \| 18.6320 \| \| 0.2092 \| 5.82 \| 2000 \| 0.2859 \| 18.6250 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.16.2.dev0 - Tokenizers 0.15.0
bartowski/Einstein-7B-exl2	bartowski	2024-01-25T04:24:26Z	1	0	null	[ "axolotl", "generated_from_trainer", "text-generation", "dataset:allenai/ai2_arc", "dataset:camel-ai/physics", "dataset:camel-ai/chemistry", "dataset:camel-ai/biology", "dataset:metaeval/reclor", "dataset:openbookqa", "dataset:mandyyyyii/scibench", "dataset:derek-thomas/ScienceQA", "dataset:wenhu/TheoremQA", "dataset:TIGER-Lab/ScienceEval", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:other", "region:us" ]	text-generation	2024-01-25T04:08:27Z	--- license: other tags: - axolotl - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 datasets: - allenai/ai2_arc - camel-ai/physics - camel-ai/chemistry - camel-ai/biology - metaeval/reclor - openbookqa - mandyyyyii/scibench - derek-thomas/ScienceQA - wenhu/TheoremQA - TIGER-Lab/ScienceEval quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Einstein-7B Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.12">turboderp's ExLlamaV2 v0.0.12</a> for quantization. # The "main" branch only contains the measurement.json, download one of the other branches for the model (see below) Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/Weyaxi/Einstein-7B \| Branch \| Bits \| lm_head bits \| Size \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------------ \| \| [8_0](https://huggingface.co/Bartowski/Einstein-7B-exl2/tree/8_0) \| 8.0 \| 8.0 \| 9.8 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/Bartowski/Einstein-7B-exl2/tree/6_5) \| 6.5 \| 8.0 \| 8.6 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/Bartowski/Einstein-7B-exl2/tree/5_0) \| 5.0 \| 6.0 \| 7.4 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/Bartowski/Einstein-7B-exl2/tree/4_25) \| 4.25 \| 6.0 \| 6.7 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/Bartowski/Einstein-7B-exl2/tree/3_5) \| 3.5 \| 6.0 \| 6.1 GB \| Lower quality, only use if you have to. \| All VRAM requirements estimated from 16k context. For 32k context add ~2 GB. ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Einstein-7B-exl2 Einstein-7B-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Einstein-7B-exl2`: ```shell mkdir Einstein-7B-exl2 huggingface-cli download bartowski/Einstein-7B-exl2 --local-dir Einstein-7B-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir Einstein-7B-exl2-6_5 huggingface-cli download bartowski/Einstein-7B-exl2 --revision 6_5 --local-dir Einstein-7B-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir Einstein-7B-exl2-6.5 huggingface-cli download bartowski/Einstein-7B-exl2 --revision 6_5 --local-dir Einstein-7B-exl2-6.5 --local-dir-use-symlinks False ```
datasciencechampion/mistral-gptq-finetuned-medmcqa	datasciencechampion	2024-01-25T04:15:01Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-01-24T13:15:50Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ model-index: - name: mistral-gptq-finetuned-medmcqa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-gptq-finetuned-medmcqa This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 500 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
bcijo/myMistral7b-finetuned	bcijo	2024-01-25T03:51:07Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain", "conversational", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-24T19:36:48Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
RangiLyu/InternLM2-tokenizer-llama	RangiLyu	2024-01-25T03:46:09Z	0	3	null	[ "license:other", "region:us" ]	null	2024-01-25T03:21:15Z	--- license: other --- # InternLM2 tokenizer(llamaified version) Official repo: https://github.com/InternLM/InternLM ## Note This repo converts the InternLM2 tokenizer to LlamaTokenizerFast. It also replaces the 354 token `\u0000` with an emoji so that it can be converted by llama.cpp ## How to use - Load ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(RangiLyu/InternLM2-tokenizer-llama) ``` - Apply chatml template ```python chat = [{"role": "user", "content": "Hello! What's your name?"}, {"role": "assistant", "content": "My name is InternLM2!"}, {"role": "user", "content": "Nice to meet you InternLM2!"},] chat_ids = llama_tokenizer.apply_chat_template(chat) print("ids: ", chat_ids) print("tokens: ", llama_tokenizer.convert_ids_to_tokens(chat_ids)) # convert the chat history to a string for generation chat_str = llama_tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) print("chat string: ", chat_str) ``` ``` ids: [1, 92543, 1008, 364, 9843, 346, 3716, 725, 829, 963, 345, 92542, 364, 92543, 525, 11353, 364, 5211, 963, 505, 4576, 11146, 314, 346, 92542, 364, 92543, 1008, 364, 44501, 442, 3531, 629, 4576, 11146, 314, 346, 92542, 364] tokens: ['<s>', '<\|im_start\|>', 'user', '\n', 'Hello', '!', '▁What', "'s", '▁your', '▁name', '?', '<\|im_end\|>', '\n', '<\|im_start\|>', 'ass', 'istant', '\n', 'My', '▁name', '▁is', '▁Intern', 'LM', '2', '!', '<\|im_end\|>', '\n', '<\|im_start\|>', 'user', '\n', 'Nice', '▁to', '▁meet', '▁you', '▁Intern', 'LM', '2', '!', '<\|im_end\|>', '\n'] chat string: <s><\|im_start\|>user Hello! What's your name?<\|im_end\|> <\|im_start\|>assistant My name is InternLM2!<\|im_end\|> <\|im_start\|>user Nice to meet you InternLM2!<\|im_end\|> <\|im_start\|>assistant ```
biznetgio/llama2-indonesia-chat	biznetgio	2024-01-25T03:46:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-25T03:45:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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varun-v-rao/t5-base-snli-model2	varun-v-rao	2024-01-25T03:42:00Z	33	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text-classification", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-01-25T02:32:24Z	--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: t5-base-snli-model2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-snli-model2 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2836 - Accuracy: 0.8994 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 36 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3815 \| 1.0 \| 2146 \| 0.3028 \| 0.8896 \| \| 0.3457 \| 2.0 \| 4292 \| 0.2840 \| 0.8979 \| \| 0.3339 \| 3.0 \| 6438 \| 0.2836 \| 0.8994 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
RayChengdu/internlm2-7b-lora	RayChengdu	2024-01-25T03:31:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-25T03:31:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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limin-arc/Mem-Beagle-7b-slerp-v3	limin-arc	2024-01-25T03:30:14Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "starsnatched/MemGPT", "222gate/Ingot-7b-slerp-7-forged-mirror", "base_model:gate369/Ingot-7b-slerp-7-forged-mirror", "base_model:merge:gate369/Ingot-7b-slerp-7-forged-mirror", "base_model:minchyeom/MemGPT", "base_model:merge:minchyeom/MemGPT", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-25T03:22:20Z	--- tags: - merge - mergekit - lazymergekit - starsnatched/MemGPT - 222gate/Ingot-7b-slerp-7-forged-mirror - starsnatched/MemGPT base_model: - starsnatched/MemGPT - 222gate/Ingot-7b-slerp-7-forged-mirror - starsnatched/MemGPT --- # Mem-Beagle-7b-slerp-v3 Mem-Beagle-7b-slerp-v3 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [starsnatched/MemGPT](https://huggingface.co/starsnatched/MemGPT) * [222gate/Ingot-7b-slerp-7-forged-mirror](https://huggingface.co/222gate/Ingot-7b-slerp-7-forged-mirror) * [starsnatched/MemGPT](https://huggingface.co/starsnatched/MemGPT) ## 🧩 Configuration ```yaml models: - model: starsnatched/MemGPT parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 - model: 222gate/Ingot-7b-slerp-7-forged-mirror parameters: density: 0.5 weight: [0, 0.3, 0.7, 1] # weight gradient - model: starsnatched/MemGPT parameters: density: 0.33 weight: - filter: mlp value: 0.5 - value: 0 merge_method: ties base_model: liminerity/Mem-Beagle-7b-slerp-v2 parameters: normalize: true int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "liminerity/Mem-Beagle-7b-slerp-v3" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
abhinand/tamil-llama-7b-base-v0.1	abhinand	2024-01-25T03:18:06Z	1,637	9	transformers	[ "transformers", "pytorch", "llama", "text-generation", "ta", "en", "arxiv:2311.05845", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-08T03:55:05Z	--- language: - ta - en license: llama2 --- # Tamil LLaMA 7B Base v0.1 [pre-trained] Welcome to the inaugural release of the Tamil LLaMA 7B base model – an important step in advancing LLMs for the Tamil language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks. To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP)]() that outlines our journey and the model's potential impact. > Please Note: This model, labeled as a foundational Tamil Language Model (LLM), is designed primarily for Causal Language Modeling (LM) purposes. In other words, if you are looking for an instruction following model in Tamil, you may find [abhinand/tamil-llama-7b-instruct-v0.1](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) more suitable for your needs. ## Model description The Tamil LLaMA models have been enhanced and tailored specifically with an extensive Tamil vocabulary of 16,000 tokens, building upon the foundation set by the original LLaMA-2. - Model type: A 7B parameter model for Causal LM pre-trained on [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) dataset's Tamil subset. - Language(s): Tamil and English - License: GNU General Public License v3.0 - Source Model: [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) - Training Precision: `float16` - Code: [GitHub](https://github.com/abhinand5/tamil-llama) ## Related Models \| Model \| Type \| Data \| Base Model \| # Params \| Download Links \| \|--------------------------\|-----------------------------\|-------------------\|----------------------\|------\|------------------------------------------------------------------------\| \| Tamil LLaMA 7B Base \| Base model \| 12GB \| LLaMA 7B \| 7B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-base-v0.1) \| \| Tamil LLaMA 13B Base \| Base model \| 4GB \| LLaMA 13B \| 13B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-base-v0.1) \| \| Tamil LLaMA 7B Instruct \| Instruction following model \| 145k instructions \| Tamil LLaMA 7B Base \| 7B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) \| \| Tamil LLaMA 13B Instruct \| Instruction following model \| 145k instructions \| Tamil LLaMA 13B Base \| 13B \| [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1) \| ## Usage Note It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications. ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/) ## Citation If you use this model or the Tamil-Llama dataset in your research, please cite: ```bibtex @misc{balachandran2023tamilllama, title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, author={Abhinand Balachandran}, year={2023}, eprint={2311.05845}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language.
abhinand/tamil-llama-7b-instruct-v0.2-GGUF	abhinand	2024-01-25T03:15:33Z	20	1	null	[ "gguf", "en", "ta", "arxiv:2311.05845", "base_model:abhinand/dr-llama-ta-instruct-v0", "base_model:quantized:abhinand/dr-llama-ta-instruct-v0", "license:llama2", "endpoints_compatible", "region:us", "conversational" ]	null	2024-01-24T06:54:32Z	--- base_model: abhinand/dr-llama-ta-instruct-v0 model-index: - name: tamil-llama-instruct-v0.2 results: [] license: llama2 language: - en - ta --- # Tamil LLaMA 7B Instruct v0.2 [GGUF Quantized] Welcome to the inaugural release of the Tamil LLaMA 7B instruct model – an important step in advancing LLMs for the Tamil language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks. To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP)]() that outlines our journey and the model's potential impact. > Note: This model is based on the Tamil LLaMA series of models. The GitHub repository remains the same - [https://github.com/abhinand5/tamil-llama](https://github.com/abhinand5/tamil-llama). The base models and the updated code for Tamil LLaMA v0.2 (which this work is based on) will be released soon. If you appreciate this work and would like to support its continued development, consider [buying me a coffee](https://www.buymeacoffee.com/abhinand.b). Your support is invaluable and greatly appreciated. [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/abhinand.b) ## Demo: To access an easy-to-use, no-code demo, please open the provided Google Colab notebook. Complete instructions for usage are included within the notebook itself. <a href="https://colab.research.google.com/drive/11_RHZim_HubD2NskxSwq4X_NW4XlnrtS?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Demo In Colab"></a></center> ## Model description The Tamil LLaMA models have been enhanced and tailored specifically with an extensive Tamil vocabulary of ~16,000 tokens, building upon the foundation set by the original LLaMA-2. - Model type: A 7B parameter GPT-like model finetuned on ~500,000 samples consisting of an equal proportion of English and Tamil samples. (Dataset will be released soon) - Language(s): Bilingual. English and Tamil. - License: GNU General Public License v3.0 - Finetuned from model: [To be released soon]() - Training Precision: `bfloat16` - Code: [GitHub](https://github.com/abhinand5/tamil-llama) (To be updated soon) ## Quantization Info This repo contains GGUF format model files for [Tamil LLaMA 7B Instruct v0.2](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.2). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. ### Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \|------------------------------------------------------------------------------------------------------------------------------------------------\|--------------\|------\|---------\|------------------\|----------------------------------------------------------\| \| [tamil-llama-7b-instruct-v0.2.Q4_K_M.gguf](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.2-GGUF/blob/main/tamil-llama-7b-instruct-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.18 GB \| 6.8 GB \| medium, balanced quality - recommended \| \| [tamil-llama-7b-instruct-v0.2.Q5_K_M.gguf](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.2-GGUF/blob/main/tamil-llama-7b-instruct-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 4.89 GB \| 7.5 GB \| large, very low quality loss - recommended \| ## Prompt Template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Benchmark Results Benchmarking was done using [LLM-Autoeval](https://github.com/mlabonne/llm-autoeval) on an RTX 3090 on [runpod](https://www.runpod.io/). > Note: Please note that discrepancies have been observed between the Open LLM Leaderboard scores and those obtained from local runs using the LM Eval Harness with identical configurations. The results mentioned here are based on our own benchmarking. To replicate these findings, you can utilize the LLM-Autoeval or use [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) locally with the configurations described in Open LLM Leaderboard's About page. \| Benchmark \| Llama 2 Chat \| Tamil Llama v0.2 Instruct \| Telugu Llama Instruct \| Malayalam Llama Instruct \| \|---------------\|--------------\|---------------------------\|-----------------------\|--------------------------\| \| ARC Challenge (25-shot) \| 52.9 \| 53.75 \| 52.47 \| 52.82 \| \| TruthfulQA (0-shot) \| 45.57 \| 47.23 \| 48.47 \| 47.46 \| \| Hellaswag (10-shot) \| 78.55 \| 76.11 \| 76.13 \| 76.91 \| \| Winogrande (5-shot) \| 71.74 \| 73.95 \| 71.74 \| 73.16 \| \| AGI Eval (0-shot) \| 29.3 \| 30.95 \| 28.44 \| 29.6 \| \| BigBench (0-shot) \| 32.6 \| 33.08 \| 32.99 \| 33.26 \| \| Average \| 51.78 \| 52.51 \| 51.71 \| 52.2 \| ## Related Models \| Model \| Type \| Data \| Base Model \| # Params \| Download Links \| \|--------------------------\|-----------------------------\|-------------------\|----------------------\|------\|------------------------------------------------------------------------\| \| Tamil LLaMA 7B v0.1 Base \| Base model \| 12GB \| LLaMA 7B \| 7B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-base-v0.1) \| \| Tamil LLaMA 13B v0.1 Base \| Base model \| 4GB \| LLaMA 13B \| 13B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-base-v0.1) \| \| Tamil LLaMA 7B v0.1 Instruct \| Instruction following model \| 145k instructions \| Tamil LLaMA 7B Base \| 7B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) \| \| Tamil LLaMA 13B v0.1 Instruct \| Instruction following model \| 145k instructions \| Tamil LLaMA 13B Base \| 13B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-instruct-v0.1) \| \| Telugu LLaMA 7B v0.1 Instruct \| Instruction/Chat model \| 420k instructions \| Telugu LLaMA 7B Base v0.1 \| 7B \| [HF Hub](https://huggingface.co/abhinand/telugu-llama-instruct-v0.1) \| \| Malayalam LLaMA 7B v0.2 Instruct \| Instruction/Chat model \| 420k instructions \| Malayalam LLaMA 7B Base v0.1 \| 7B \| [HF Hub](https://huggingface.co/abhinand/malayalam-llama-instruct-v0.1) \| ## Usage Note It's important to note that the models have not undergone detoxification/censorship. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications. ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/) ## Citation If you use this model or any of the the Tamil-Llama related work in your research, please cite: ```bibtex @misc{balachandran2023tamilllama, title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, author={Abhinand Balachandran}, year={2023}, eprint={2311.05845}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language.
abhinand/tamil-llama-7b-instruct-v0.2	abhinand	2024-01-25T03:14:24Z	753	4	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "en", "ta", "arxiv:2311.05845", "base_model:abhinand/dr-llama-ta-instruct-v0", "base_model:finetune:abhinand/dr-llama-ta-instruct-v0", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-23T03:06:49Z	--- base_model: abhinand/dr-llama-ta-instruct-v0 model-index: - name: tamil-llama-instruct-v0.2 results: [] license: llama2 language: - en - ta --- # Tamil LLaMA 7B Instruct v0.2 Welcome to the inaugural release of the Tamil LLaMA 7B instruct model – an important step in advancing LLMs for the Tamil language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks. To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP)]() that outlines our journey and the model's potential impact. > Note: This model is based on the Tamil LLaMA series of models. The GitHub repository remains the same - [https://github.com/abhinand5/tamil-llama](https://github.com/abhinand5/tamil-llama). The base models and the updated code for Tamil LLaMA v0.2 (which this work is based on) will be released soon. If you appreciate this work and would like to support its continued development, consider [buying me a coffee](https://www.buymeacoffee.com/abhinand.b). Your support is invaluable and greatly appreciated. [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/abhinand.b) ## Demo: To access an easy-to-use, no-code demo, please open the provided Google Colab notebook. Complete instructions for usage are included within the notebook itself. <a href="https://colab.research.google.com/drive/11_RHZim_HubD2NskxSwq4X_NW4XlnrtS?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Demo In Colab"></a></center> ## Model description The Tamil LLaMA models have been enhanced and tailored specifically with an extensive Tamil vocabulary of ~16,000 tokens, building upon the foundation set by the original LLaMA-2. - Model type: A 7B parameter GPT-like model finetuned on ~500,000 samples consisting of an equal proportion of English and Tamil samples. (Dataset will be released soon) - Language(s): Bilingual. English and Tamil. - License: GNU General Public License v3.0 - Finetuned from model: [To be released soon]() - Training Precision: `bfloat16` - Code: [GitHub](https://github.com/abhinand5/tamil-llama) (To be updated soon) ## Prompt Template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Benchmark Results Benchmarking was done using [LLM-Autoeval](https://github.com/mlabonne/llm-autoeval) on an RTX 3090 on [runpod](https://www.runpod.io/). > Note: Please note that discrepancies have been observed between the Open LLM Leaderboard scores and those obtained from local runs using the LM Eval Harness with identical configurations. The results mentioned here are based on our own benchmarking. To replicate these findings, you can utilize the LLM-Autoeval or use [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) locally with the configurations described in Open LLM Leaderboard's About page. \| Benchmark \| Llama 2 Chat \| Tamil Llama v0.2 Instruct \| Telugu Llama Instruct \| Malayalam Llama Instruct \| \|---------------\|--------------\|---------------------------\|-----------------------\|--------------------------\| \| ARC Challenge (25-shot) \| 52.9 \| 53.75 \| 52.47 \| 52.82 \| \| TruthfulQA (0-shot) \| 45.57 \| 47.23 \| 48.47 \| 47.46 \| \| Hellaswag (10-shot) \| 78.55 \| 76.11 \| 76.13 \| 76.91 \| \| Winogrande (5-shot) \| 71.74 \| 73.95 \| 71.74 \| 73.16 \| \| AGI Eval (0-shot) \| 29.3 \| 30.95 \| 28.44 \| 29.6 \| \| BigBench (0-shot) \| 32.6 \| 33.08 \| 32.99 \| 33.26 \| \| Average \| 51.78 \| 52.51 \| 51.71 \| 52.2 \| ## Related Models \| Model \| Type \| Data \| Base Model \| # Params \| Download Links \| \|--------------------------\|-----------------------------\|-------------------\|----------------------\|------\|------------------------------------------------------------------------\| \| Tamil LLaMA 7B v0.1 Base \| Base model \| 12GB \| LLaMA 7B \| 7B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-base-v0.1) \| \| Tamil LLaMA 13B v0.1 Base \| Base model \| 4GB \| LLaMA 13B \| 13B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-base-v0.1) \| \| Tamil LLaMA 7B v0.1 Instruct \| Instruction following model \| 145k instructions \| Tamil LLaMA 7B Base \| 7B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) \| \| Tamil LLaMA 13B v0.1 Instruct \| Instruction following model \| 145k instructions \| Tamil LLaMA 13B Base \| 13B \| [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-instruct-v0.1) \| \| Telugu LLaMA 7B v0.1 Instruct \| Instruction/Chat model \| 420k instructions \| Telugu LLaMA 7B Base v0.1 \| 7B \| [HF Hub](https://huggingface.co/abhinand/telugu-llama-instruct-v0.1) \| \| Malayalam LLaMA 7B v0.2 Instruct \| Instruction/Chat model \| 420k instructions \| Malayalam LLaMA 7B Base v0.1 \| 7B \| [HF Hub](https://huggingface.co/abhinand/malayalam-llama-instruct-v0.1) \| ## Example Usage ```python from transformers import LlamaForCausalLM, AutoTokenizer, pipeline model = LlamaForCausalLM.from_pretrained( "abhinand/tamil-llama-instruct-v0.2", #load_in_8bit=True, # Set this depending on the GPU you have torch_dtype=torch.bfloat16, device_map={"": 0}, # Set this depending on the number of GPUs you have local_files_only=False # Optional ) model.eval() tokenizer = AutoTokenizer.from_pretrained("abhinand/tamil-llama-instruct-v0.2") inf_pipeline = pipeline("conversational", model=model, tokenizer=tokenizer) def format_instruction(system_prompt, question, return_dict=False): if system_prompt is None: messages = [ {'content': question, 'role': 'user'}, ] else: messages = [ {'content': system_prompt, 'role': 'system'}, {'content': question, 'role': 'user'}, ] if return_dict: return messages prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) return prompt # Set the generation configuration according to your needs temperature = 0.6 repetition_penalty = 1.1 max_new_tokens = 256 SYSTEM_PROMPT = "You are an AI assistant who follows instructions extremely well. Do your best your best to help." INPUT = "Can you explain the significance of Tamil festival Pongal?" instruction = format_instruction( system_prompt=SYSTEM_PROMPT, question=INPUT, return_dict=True, ) output = inf_pipeline( instruction, temperature=temperature, max_new_tokens=max_new_tokens, repetition_penalty=repetition_penalty ) print(output) ``` Example Output: ``` Conversation id: d57cdf33-01ff-4328-8efe-5c4fefdd6e77 system: You are an AI assistant who follows instructions extremely well. Do your best your best to help. user: Can you explain the significance of Tamil festival Pongal? assistant: Pongal is a significant harvest festival celebrated in Tamil Nadu and other parts of southern India. It marks the end of the rainy season and beginning of the agricultural year. The festival primarily revolves around giving gratitude to nature, particularly the Sun God Surya for his bountiful gifts like agriculture and health. People offer prayers to cattle, which play a significant role in agriculture, as well as their families for their continued support during the harvest season. The festival is marked by various colorful events, including preparing traditional Pongal dishes like rice cooked with milk, sugarcane, and banana, followed by exchanging gifts and celebrating among family members and friends. It also serves as a time for unity and strengthens the bond between people in their communities. ``` ## Usage Note It's important to note that the models have not undergone detoxification/censorship. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications. ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/) ## Citation If you use this model or any of the the Tamil-Llama related work in your research, please cite: ```bibtex @misc{balachandran2023tamilllama, title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, author={Abhinand Balachandran}, year={2023}, eprint={2311.05845}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language.
Liveme/Marcoroni-7B-v3_qlora_20231228-0017_8bit_merge	Liveme	2024-01-25T03:14:07Z	0	0	peft	[ "peft", "pytorch", "mistral", "region:us" ]	null	2024-01-02T07:26:59Z	--- library_name: peft base_model: AIDC-ai-business/Marcoroni-7B-v3 --- # Model Card for Model ID prompt格式如下： ```python system_prompt = '''You are now roleplaying as {Character Nickname}. {character_msg} Identity: Nurse Categories: Erotica, {tone} Personalities: {personalities} ``` 推理配置需要注意的几个参数： ``` 'temperature': 0.85 'top_p': 0.95, # disable 'top_k': 0, # disable 'repetition_penalty': 1.15, ``` Prompt Template for alpaca style ``` ### Instruction: <prompt> (without the <>) ### Response: ``` 训练参数： ``` ```
vine135/outputs_mistral_b_finance_finetuned_test	vine135	2024-01-25T03:07:10Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-01-25T02:58:54Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: outputs_mistral_b_finance_finetuned_test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # outputs_mistral_b_finance_finetuned_test This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0.03 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
macadeliccc/TheCorso-7b-v1	macadeliccc	2024-01-25T02:59:29Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-24T21:48:14Z	--- library_name: transformers tags: [] --- # TheCorso-7b-v1 In loving memory of my dog Klaus (Piccolo) _~ Piccolo (Italian): the little one ~_ ![piccolo.png](piccolo.png) + Based on fblgit/UNA-TheBeagle-7b-v1
SanjiWatsuki/Kunoichi-DPO-v2-7B	SanjiWatsuki	2024-01-25T02:52:18Z	1,520	81	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-13T04:02:02Z	--- license: cc-by-nc-4.0 --- \| Model \| MT Bench \| EQ Bench \| MMLU \| Logic Test \| \|----------------------\|----------\|----------\|---------\|-------------\| \| GPT-4-Turbo \| 9.32 \| - \| - \| - \| \| GPT-4 \| 8.99 \| 62.52 \| 86.4 \| 0.86 \| \| Kunoichi-DPO-v2-7B \| 8.51 \| 42.18 \| 64.94\| 0.58 \| \| Mixtral-8x7B-Instruct\| 8.30 \| 44.81 \| 70.6 \| 0.75 \| \| Kunoichi-DPO-7B \| 8.29 \| 41.60 \| 64.83 \| 0.59 \| \| Kunoichi-7B \| 8.14 \| 44.32 \| 64.9 \| 0.58 \| \| Starling-7B \| 8.09 \| - \| 63.9 \| 0.51 \| \| Claude-2 \| 8.06 \| 52.14 \| 78.5 \| - \| \| Silicon-Maid-7B \| 7.96 \| 40.44 \| 64.7 \| 0.54 \| \| Loyal-Macaroni-Maid-7B \| 7.95 \| 38.66 \| 64.9 \| 0.57 \| \| GPT-3.5-Turbo \| 7.94 \| 50.28 \| 70 \| 0.57 \| \| Claude-1 \| 7.9 \| - \| 77 \| - \| \| Openchat-3.5 \| 7.81 \| 37.08 \| 64.3 \| 0.39 \| \| Dolphin-2.6-DPO \| 7.74 \| 42.88 \| 61.9 \| 0.53 \| \| Zephyr-7B-beta \| 7.34 \| 38.71 \| 61.4 \| 0.30 \| \| Llama-2-70b-chat-hf \| 6.86 \| 51.56 \| 63 \| - \| \| Neural-chat-7b-v3-1 \| 6.84 \| 43.61 \| 62.4 \| 0.30 \| \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| Kunoichi-DPO-7B\|58.4\| 45.08 \| 74\| 66.99\| 47.52\| \| Kunoichi-DPO-v2-7B\|58.31\| 44.85\| 75.05\| 65.69\| 47.65\| \| [Kunoichi-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-7B)\|57.54\| 44.99\| 74.86\| 63.72\| 46.58\| \| [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)\| 56.85 \| 44.74 \| 75.6 \| 59.89 \| 47.17 \| \| [Silicon-Maid-7B](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B) \| 56.45\| 44.74\| 74.26\| 61.5\| 45.32\| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5) \| 51.34 \| 42.67 \| 72.92 \| 47.27 \| 42.51 \| \| [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) \| 51.16 \| 42.06 \| 72.72 \| 47.33 \| 42.53 \| \| [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) \| 50.99 \| 37.33 \| 71.83 \| 55.1 \| 39.7 \| \| Model \| AlpacaEval2 \| Length \| \| --------------------------- \| ----------- \| ------ \| \| GPT-4 \| 23.58% \| 1365 \| \| GPT-4 0314 \| 22.07% \| 1371 \| \| Mistral Medium \| 21.86% \| 1500 \| \| Mixtral 8x7B v0.1 \| 18.26% \| 1465 \| \| Kunoichi-DPO-v2 \| 17.19% \| 1785 \| \| Claude 2 \| 17.19% \| 1069 \| \| Claude \| 16.99% \| 1082 \| \| Gemini Pro \| 16.85% \| 1315 \| \| GPT-4 0613 \| 15.76% \| 1140 \| \| Claude 2.1 \| 15.73% \| 1096 \| \| Mistral 7B v0.2 \| 14.72% \| 1676 \| \| GPT 3.5 Turbo 0613 \| 14.13% \| 1328 \| \| LLaMA2 Chat 70B \| 13.87% \| 1790 \| \| LMCocktail-10.7B-v1 \| 13.15% \| 1203 \| \| WizardLM 13B V1.1 \| 11.23% \| 1525 \| \| Zephyr 7B Beta \| 10.99% \| 1444 \| \| OpenHermes-2.5-Mistral (7B) \| 10.34% \| 1107 \| \| GPT 3.5 Turbo 0301 \| 9.62% \| 827 \| \| Kunoichi-7B \| 9.38% \| 1492 \| \| GPT 3.5 Turbo 1106 \| 9.18% \| 796 \| \| GPT-3.5 \| 8.56% \| 1018 \| \| Phi-2 DPO \| 7.76% \| 1687 \| \| LLaMA2 Chat 13B \| 7.70% \| 1513 \|
jilp00/youtoks-transformers-united-v2-7B-v02	jilp00	2024-01-25T02:50:43Z	62	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-01-25T02:46:13Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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varun-v-rao/bert-large-cased-mnli-model1	varun-v-rao	2024-01-25T02:49:56Z	95	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-large-cased", "base_model:finetune:google-bert/bert-large-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-24T23:57:41Z	--- license: apache-2.0 base_model: bert-large-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-large-cased-mnli-model1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-cased-mnli-model1 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4619 - Accuracy: 0.8680 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 78 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.3947 \| 1.0 \| 6136 \| 0.3608 \| 0.8601 \| \| 0.2557 \| 2.0 \| 12272 \| 0.3683 \| 0.8678 \| \| 0.1455 \| 3.0 \| 18408 \| 0.4619 \| 0.8680 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
MSI-AI-Group/TRT_AllMsi_4050	MSI-AI-Group	2024-01-25T02:42:53Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-12-22T01:32:43Z	--- license: creativeml-openrail-m --- This model is for AI Artist use. You use our model by following Github Link: https://github.com/MSI-AI-Group/AI-Artist
MSI-AI-Group/TRT_Base_4050	MSI-AI-Group	2024-01-25T02:42:35Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-12-22T01:29:24Z	--- license: creativeml-openrail-m --- This model is for AI Artist use. You use our model by following Github Link: https://github.com/MSI-AI-Group/AI-Artist
MSI-AI-Group/TRT_AllMsi_4060	MSI-AI-Group	2024-01-25T02:42:08Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-12-22T01:33:31Z	--- license: creativeml-openrail-m --- This model is for AI Artist use. You use our model by following Github Link: https://github.com/MSI-AI-Group/AI-Artist
MSI-AI-Group/TRT_Base_4080	MSI-AI-Group	2024-01-25T02:41:19Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-12-22T01:34:14Z	--- license: creativeml-openrail-m --- This model is for AI Artist use. You use our model by following Github Link: https://github.com/MSI-AI-Group/AI-Artist
MSI-AI-Group/TRT_Base_4090	MSI-AI-Group	2024-01-25T02:40:42Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-12-22T01:34:22Z	--- license: creativeml-openrail-m --- This model is for AI Artist use. You use our model by following Github Link: https://github.com/MSI-AI-Group/AI-Artist
tiagoblima/t5_base-qg-ap-peft-oficial	tiagoblima	2024-01-25T02:34:56Z	0	0	null	[ "safetensors", "generated_from_trainer", "dataset:tiagoblima/du-qg-squadv1_pt", "base_model:unicamp-dl/ptt5-base-portuguese-vocab", "base_model:finetune:unicamp-dl/ptt5-base-portuguese-vocab", "license:mit", "region:us" ]	null	2024-01-25T01:55:34Z	--- license: mit base_model: unicamp-dl/ptt5-base-portuguese-vocab tags: - generated_from_trainer datasets: - tiagoblima/du-qg-squadv1_pt model-index: - name: t5_base-qg-ap-peft-oficial results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5_base-qg-ap-peft-oficial This model is a fine-tuned version of [unicamp-dl/ptt5-base-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-base-portuguese-vocab) on the tiagoblima/du-qg-squadv1_pt dataset. It achieves the following results on the evaluation set: - Loss: 15.9549 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 13.2241 \| 1.0 \| 2367 \| 16.4507 \| \| 12.9503 \| 2.0 \| 4734 \| 16.2224 \| \| 12.8425 \| 3.0 \| 7101 \| 16.0670 \| \| 12.8058 \| 4.0 \| 9468 \| 15.9792 \| \| 12.7937 \| 5.0 \| 11835 \| 15.9549 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.1
varun-v-rao/t5-base-snli-model1	varun-v-rao	2024-01-25T02:31:13Z	33	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text-classification", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-01-25T01:21:31Z	--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: t5-base-snli-model1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-snli-model1 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2860 - Accuracy: 0.8989 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 73 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3815 \| 1.0 \| 2146 \| 0.2976 \| 0.8903 \| \| 0.3507 \| 2.0 \| 4292 \| 0.2937 \| 0.8962 \| \| 0.3354 \| 3.0 \| 6438 \| 0.2860 \| 0.8989 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
zhangyanchao/whisper-medium-zh_CN	zhangyanchao	2024-01-25T02:19:48Z	6	1	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-22T03:25:38Z	--- language: - hi license: apache-2.0 base_model: openai/whisper-medium tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Medium zh_CN - Charles.djang results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Medium zh_CN - Charles.djang This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
josemhernandezbiometric/whisper-medium-finetuned-int8	josemhernandezbiometric	2024-01-25T02:18:22Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "whisper-event", "generated_from_trainer", "es", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-medium", "base_model:adapter:openai/whisper-medium", "license:apache-2.0", "model-index", "region:us" ]	null	2024-01-22T07:36:34Z	--- language: - es license: apache-2.0 library_name: peft tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer base_model: openai/whisper-medium model-index: - name: Whisper Small Es - Sanchit Gandhi results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 split: None metrics: - type: wer value: 56.441337816315695 name: Wer --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Es - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 2.5129 - Wer: 56.4413 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 750 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 6.3605 \| 2.3 \| 50 \| 6.2660 \| 55.7247 \| \| 5.3113 \| 4.6 \| 100 \| 5.1187 \| 56.4590 \| \| 4.2749 \| 6.9 \| 150 \| 4.2391 \| 55.6185 \| \| 3.5266 \| 9.2 \| 200 \| 3.4143 \| 53.6719 \| \| 3.0671 \| 11.49 \| 250 \| 3.1045 \| 49.2037 \| \| 2.8716 \| 13.79 \| 300 \| 2.9260 \| 50.7786 \| \| 2.7263 \| 16.09 \| 350 \| 2.7987 \| 53.5746 \| \| 2.6467 \| 18.39 \| 400 \| 2.7079 \| 55.0787 \| \| 2.5624 \| 20.69 \| 450 \| 2.6443 \| 55.6008 \| \| 2.5087 \| 22.99 \| 500 \| 2.5989 \| 57.3881 \| \| 2.4922 \| 25.29 \| 550 \| 2.5660 \| 55.9370 \| \| 2.4274 \| 27.59 \| 600 \| 2.5421 \| 56.4325 \| \| 2.4337 \| 29.89 \| 650 \| 2.5257 \| 57.4058 \| \| 2.3991 \| 32.18 \| 700 \| 2.5165 \| 57.0165 \| \| 2.4211 \| 34.48 \| 750 \| 2.5129 \| 56.4413 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.37.0.dev0 - Pytorch 2.1.0 - Datasets 2.16.2.dev0 - Tokenizers 0.15.0
sosoai/Orion-14B-Chat-RAG-safetensors	sosoai	2024-01-25T02:09:15Z	43	0	transformers	[ "transformers", "pytorch", "safetensors", "orion", "text-generation", "code", "model", "llm", "custom_code", "en", "zh", "ja", "ko", "autotrain_compatible", "region:us" ]	text-generation	2024-01-25T00:05:06Z	--- language: - en - zh - ja - ko metrics: - accuracy pipeline_tag: text-generation tags: - code - model - llm --- <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <div align="center"> <img src="./assets/imgs/orion_start.PNG" alt="logo" width="50%" /> </div> <div align="center"> <h1> Orion-14B </h1> </div> <div align="center"> <div align="center"> <b>🌐English</b> \| <a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG/blob/main/README_zh.md" target="_blank">🇨🇳中文</a> \| <a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG/blob/main/README_ja.md" target="_blank">🇯🇵日本語</a> \| <a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG/blob/main/README_ko.md" target="_blank">🇰🇷한국어</a> </div> <h4 align="center"> <p> 🤗 <a href="https://huggingface.co/OrionStarAI" target="_blank">HuggingFace Mainpage</a> \| 🤖 <a href="https://modelscope.cn/organization/OrionStarAI" target="_blank">ModelScope Mainpage</a><br>🎬 <a href="https://huggingface.co/spaces/OrionStarAI/Orion-14B-App-Demo" target="_blank">HuggingFace Demo</a> \| 🎫 <a href="https://modelscope.cn/studios/OrionStarAI/Orion-14B-App-Demo/summary" target="_blank">ModelScope Demo</a><br>😺 <a href="https://github.com/OrionStarAI/Orion" target="_blank">GitHub</a><br>📖 <a href="https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf" target="_blank">Tech Report</a> <p> </h4> </div> # Table of Contents - [📖 Model Introduction](#model-introduction) - [🔗 Model Download](#model-download) - [🔖 Model Benchmark](#model-benchmark) - [📊 Model Inference](#model-inference) - [📜 Declarations & License](#declarations-license) - [🥇 Company Introduction](#company-introduction) <a name="model-introduction"></a><br> # 1. Model Introduction - Orion-14B series models are open-source multilingual large language models trained from scratch by OrionStarAI. The base model is trained on 2.5T multilingual corpus, including Chinese, English, Japanese, Korean, etc, and it exhibits superior performance in these languages. For details, please refer to [tech report](https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf). - The Orion-14B series models exhibit the following features: - Among models with 20B-parameter scale level, Orion-14B-Base model shows outstanding performance in comprehensive evaluations. - Strong multilingual capabilities, significantly outperforming in Japanese and Korean testsets. - The fine-tuned models demonstrate strong adaptability, excelling in human-annotated blind tests. - The long-chat version supports extremely long texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k. - The quantized versions reduce model size by 70%, improve inference speed by 30%, with performance loss less than 1%. <table style="border-collapse: collapse; width: 100%;"> <tr> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/opencompass_en.png" alt="opencompass" style="width: 100%; height: auto;"> </td> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/model_cap_en.png" alt="modelcap" style="width: 100%; height: auto;"> </td> </tr> </table> - Orion-14B series models including: - Orion-14B-Base: A multilingual large language foundational model with 14 billion parameters, pretrained on a diverse dataset of 2.5 trillion tokens. - Orion-14B-Chat: A chat-model fine-tuned on a high-quality corpus aims to provide an excellence interactive experience for users in the large model community. - Orion-14B-LongChat: The long-context version excels at handling extremely lengthy texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k. - Orion-14B-Chat-RAG: A chat-model fine-tuned on a custom retrieval augmented generation dataset, achieving superior performance in retrieval augmented generation tasks. - Orion-14B-Chat-Plugin: A chat-model specifically tailored for plugin and function calling tasks, ideal for agent-related scenarios where the LLM acts as a plugin and function call system. - Orion-14B-Base-Int4: A quantized base model utilizing 4-bit integer weights. It significantly reduces the model size by 70% and increases the inference speed by 30% while incurring a minimal performance loss of only 1%. - Orion-14B-Chat-Int4: A quantized chat model utilizing 4-bit integer weights. <a name="model-download"></a><br> # 2. Model Download Model release and download links are provided in the table below: \| Model Name \| HuggingFace Download Links \| ModelScope Download Links \| \|-------------------------\|-----------------------------------------------------------------------------------\|-------------------------------------------------------------------------------------------------\| \| ⚾Orion-14B-Base \| [Orion-14B-Base](https://huggingface.co/OrionStarAI/Orion-14B-Base) \| [Orion-14B-Base](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base/summary) \| \| 😛Orion-14B-Chat \| [Orion-14B-Chat](https://huggingface.co/OrionStarAI/Orion-14B-Chat) \| [Orion-14B-Chat](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat/summary) \| \| 📃Orion-14B-LongChat \| [Orion-14B-LongChat](https://huggingface.co/OrionStarAI/Orion-14B-LongChat) \| [Orion-14B-LongChat](https://modelscope.cn/models/OrionStarAI/Orion-14B-LongChat/summary) \| \| 🔎Orion-14B-Chat-RAG \| [Orion-14B-Chat-RAG](https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG) \| [Orion-14B-Chat-RAG](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-RAG/summary) \| \| 🔌Orion-14B-Chat-Plugin \| [Orion-14B-Chat-Plugin](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Plugin) \| [Orion-14B-Chat-Plugin](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Plugin/summary) \| \| 💼Orion-14B-Base-Int4 \| [Orion-14B-Base-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4) \| [Orion-14B-Base-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base-Int4/summary) \| \| 📦Orion-14B-Chat-Int4 \| [Orion-14B-Chat-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Int4) \| [Orion-14B-Chat-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Int4/summary) \| <a name="model-benchmark"></a><br> # 3. Model Benchmarks ## 3.1. Base Model Orion-14B-Base Benchmarks ### 3.1.1. LLM evaluation results on examination and professional knowledge \| Model \| C-Eval \| CMMLU \| MMLU \| AGIEval \| Gaokao \| BBH \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\| \| LLaMA2-13B \| 41.4 \| 38.4 \| 55.0 \| 30.9 \| 18.2 \| 45.6 \| \| Skywork-13B \| 59.1 \| 61.4 \| 62.7 \| 43.6 \| 56.1 \| 48.3 \| \| Baichuan2-13B \| 59.0 \| 61.3 \| 59.5 \| 37.4 \| 45.6 \| 49.0 \| \| QWEN-14B \| 71.7 \| 70.2 \| 67.9 \| 51.9 \| 62.5 \| 53.7 \| \| InternLM-20B \| 58.8 \| 59.0 \| 62.1 \| 44.6 \| 45.5 \| 52.5 \| \| Orion-14B-Base \| 72.9 \| 70.6 \| 69.9 \| 54.7 \| 62.1 \| 56.5 \| ### 3.1.2. LLM evaluation results on language understanding and common knowledge \| Model \|RACE-middle\|RACE-high \|HellaSwag \| PIQA \| Lambada \| WSC \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\| \| LLaMA 2-13B \| 63.0 \| 58.9 \| 77.5 \| 79.8 \| 76.5 \| 66.3 \| \| Skywork-13B \| 87.6 \| 84.1 \| 73.7 \| 78.3 \| 71.8 \| 66.3 \| \| Baichuan 2-13B \| 68.9 \| 67.2 \| 70.8 \| 78.1 \| 74.1 \| 66.3 \| \| QWEN-14B \| 93.0 \| 90.3 \| 80.2 \| 79.8 \| 71.4 \| 66.3 \| \| InternLM-20B \| 86.4 \| 83.3 \| 78.1 \| 80.3 \| 71.8 \| 68.3 \| \| Orion-14B-Base \| 93.2 \| 91.3 \| 78.5 \| 79.5 \| 78.8 \| 70.2 \| ### 3.1.3. LLM evaluation results of OpenCompass testsets \| Model \| Average \| Examination \| Language \| Knowledge \| Understanding \| Reasoning \| \|------------------\|----------\|----------\|----------\|----------\|----------\|----------\| \| LLaMA 2-13B \| 47.3 \| 45.2 \| 47.0 \| 58.3 \| 50.9 \| 43.6 \| \| Skywork-13B \| 53.6 \| 61.1 \| 51.3 \| 52.7 \| 64.5 \| 45.2 \| \| Baichuan 2-13B \| 49.4 \| 51.8 \| 47.5 \| 48.9 \| 58.1 \| 44.2 \| \| QWEN-14B \| 62.4 \| 71.3 \| 52.67 \| 56.1 \| 68.8 \| 60.1 \| \| InternLM-20B \| 59.4 \| 62.5 \| 55.0 \| 60.1 \| 67.3 \| 54.9 \| \|Orion-14B-Base\| 64.3 \| 71.4 \| 55.0 \| 60.0 \| 71.9 \| 61.6 \| ### 3.1.4. Comparison of LLM performances on Japanese testsets \| Model \|Average\| JCQA \| JNLI \| MARC \| JSQD \| JQK \| XLS \| XWN \| MGSM \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| PLaMo-13B \| 52.3 \| 56.7 \| 42.8 \| 95.8 \| 70.6 \| 71.0 \| 8.70 \| 70.5 \| 2.40 \| \| WebLab-10B \| 50.7 \| 66.6 \| 53.7 \| 82.1 \| 62.9 \| 56.2 \| 10.0 \| 72.0 \| 2.40 \| \| ELYZA-jp-7B \| 48.8 \| 71.7 \| 25.3 \| 86.6 \| 70.8 \| 64.1 \| 2.50 \| 62.1 \| 7.20 \| \| StableLM-jp-7B \| 51.1 \| 33.4 \| 43.3 \| 96.7 \| 70.6 \| 78.1 \| 10.7 \| 72.8 \| 2.80 \| \| LLaMA 2-13B \| 46.3 \| 75.0 \| 47.6 \| 38.8 \| 76.1 \| 67.7 \| 18.1 \| 63.2 \| 10.4 \| \| Baichuan 2-13B \| 57.1 \| 73.7 \| 31.3 \| 91.6 \| 80.5 \| 63.3 \| 18.6 \| 72.2 \| 25.2 \| \| QWEN-14B \| 65.8 \| 85.9 \| 60.7 \| 97.0 \| 83.3 \| 71.8 \| 18.8 \| 70.6 \| 38.0 \| \| Yi-34B \| 67.1 \| 83.8 \| 61.2 \| 95.2 \| 86.1 \| 78.5 \| 27.2 \| 69.2 \| 35.2 \| \| Orion-14B-Base \| 69.1 \| 88.2 \| 75.8 \| 94.1 \| 75.7 \| 85.1 \| 17.3 \| 78.8 \| 38.0 \| ### 3.1.5. Comparison of LLM performances on Korean testsets. n = 0 and n = 5 stand for n-shot prompts used in the evaluation \|Model \| Average<br>n=0  n=5 \| HellaSwag<br>n=0  n=5 \| COPA<br> n=0  n=5 \| BooIQ<br>n=0  n=5 \| SentiNeg<br>n=0  n=5\| \|------------------\|------------------------------\|------------------------------\|------------------------------\|------------------------------\|------------------------------\| \| KoGPT \| 53.0    70.1 \| 55.9    58.3 \| 73.5    72.9 \| 45.1    59.8 \| 37.5    89.4 \| \| Polyglot-ko-13B \| 69.6    73.7 \|59.5    63.1\|79.4    81.1\| 48.2    60.4 \| 91.2    90.2 \| \| LLaMA 2-13B \| 46.7    63.7 \| 41.3    44.0 \| 59.3    63.8 \| 34.9    73.8 \| 51.5    73.4 \| \| Baichuan 2-13B \| 52.1    58.7 \| 39.2    39.6 \| 60.6    60.6 \| 58.4    61.5 \| 50.3    72.9 \| \| QWEN-14B \| 53.8    73.7 \| 45.3    46.8 \| 64.9    68.9 \| 33.4    83.5 \| 71.5    95.7 \| \| Yi-34B \| 54.2    72.1 \| 44.6    44.7 \| 58.0    60.6 \| 65.9    90.2 \| 48.3    92.9 \| \|Orion-14B-Chat\|74.5    79.6\| 47.0    49.6 \| 77.7    79.4 \|81.6    90.7\|92.4    98.7\| ### 3.1.6. Multilingual evaluation \| Model \| Train Lang \| Japanese \| Korean \| Chinese \| English \| \|--------------------\|------------\|----------\|----------\|----------\|----------\| \| PLaMo-13B \| En,Jp \| 52.3 \| * \| * \| * \| \| Weblab-10B \| En,Jp \| 50.7 \| * \| * \| * \| \| ELYZA-jp-7B \| En,Jp \| 48.8 \| * \| * \| * \| \| StableLM-jp-7B \| En,Jp \| 51.1 \| * \| * \| * \| \| KoGPT-6B \| En,Ko \| * \| 70.1 \| * \| * \| \| Polyglot-ko-13B \| En,Ko \| * \| 70.7 \| * \| * \| \| Baichuan2-13B \| Multi \| 57.1 \| 58.7 \| 50.8 \| 57.1 \| \| Qwen-14B \| Multi \| 65.8 \| 73.7 \| 64.5 \| 65.4 \| \| Llama2-13B \| Multi \| 46.3 \| 63.7 \| 41.4 \| 55.3 \| \| Yi-34B \| Multi \| 67.1 \| 72.2 \| 58.7 \| 68.8 \| \| Orion-14B-Chat \| Multi \| 69.1 \| 79.5 \| 67.9 \| 67.3 \| ## 3.2. Chat Model Orion-14B-Chat Benchmarks ### 3.2.1. Chat model subjective evaluation of MTBench \| Model \| First-Turn \| Second-Turn \| Average \| \|----------------------\|----------\|----------\|----------\| \| Baichuan2-13B-Chat \| 7.05 \| 6.47 \| 6.76 \| \| Qwen-14B-Chat \| 7.30 \| 6.62 \| 6.96 \| \| Llama2-13B-Chat \| 7.10 \| 6.20 \| 6.65 \| \| InternLM-20B-Chat \| 7.03 \| 5.93 \| 6.48 \| \| Orion-14B-Chat \| 7.68 \| 7.07 \| 7.37 \| \* use vllm for inference ### 3.2.2. Chat model subjective evaluation of AlignBench \| Model \| Math. \| Logi. \| Basic. \| Chi. \| Comp. \| Writ. \| Role. \| Prof. \|Avg.\| \|--------------------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\| \| Baichuan2-13B-Chat \| 3.76 \| 4.07 \| 6.22 \| 6.05 \| 7.11 \| 6.97 \| 6.75 \| 6.43 \| 5.25 \| \| Qwen-14B-Chat \|4.91\|4.71\|6.90\| 6.36 \| 6.74 \| 6.64 \| 6.59 \| 6.56 \|5.72\| \| Llama2-13B-Chat \| 3.05 \| 3.79 \| 5.43 \| 4.40 \| 6.76 \| 6.63 \| 6.99 \| 5.65 \| 4.70 \| \| InternLM-20B-Chat \| 3.39 \| 3.92 \| 5.96 \| 5.50 \|7.18\| 6.19 \| 6.49 \| 6.22 \| 4.96 \| \| Orion-14B-Chat \| 4.00 \| 4.24 \| 6.18 \|6.57\| 7.16 \|7.36\|7.16\|6.99\| 5.51 \| \* use vllm for inference ## 3.3. LongChat Model Orion-14B-LongChat Benchmarks ### 3.3.1. LongChat evaluation of LongBench \| Model \| NarrativeQA\|MultiFieldQA-en\|MultiFieldQA-zh\| DuReader \| QMSum \| VCSUM \| TREC \| TriviaQA \| LSHT \|RepoBench-P\| \|--------------------------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\| \| GPT-3.5-Turbo-16k \| 23.60 \| 52.30 \| 61.20 \| 28.70 \| 23.40 \| 16.00 \| 68.00 \| 91.40 \| 29.20 \| 53.60 \| \| LongChat-v1.5-7B-32k \| 16.90 \| 41.40 \| 29.10 \| 19.50 \| 22.70 \| 9.90 \| 63.50 \| 82.30 \| 23.20 \| 55.30 \| \| Vicuna-v1.5-7B-16k \| 19.40 \| 38.50 \| 43.00 \| 19.30 \| 22.80 \| 15.10 \| 71.50 \| 86.20 \| 28.80 \| 43.50 \| \| Yi-6B-200K \| 14.11 \| 36.74 \| 22.68 \| 14.01 \| 20.44 \| 8.08 \| 72.00 \| 86.61 \| 38.00 \| 63.29 \| \| Orion-14B-LongChat \| 19.47 \| 48.11 \| 55.84 \| 37.02 \| 24.87 \| 15.44 \| 77.00 \| 89.12 \| 45.50 \| 54.31 \| ## 3.4. Chat RAG Model Benchmarks ### 3.4.1. LLM evaluation results of self-built RAG testsets \|Model\|Effectiveness of Response(Keyword)\|Effectiveness of Response（subjective evaluation）\|Quoting Ability\|Fallback Ability\|AutoQA\|Data Extraction\| \|---------------------\|------\|------\|------\|------\|------\|------\| \| Baichuan2-13B-Chat \| 85 \| 76 \| 1 \| 0 \| 69 \| 51 \| \| Qwen-14B-Chat \| 79 \| 77 \| 75 \| 47 \| 68 \| 72 \| \| Qwen-72B-Chat(Int4) \| 87 \| 89 \| 90 \| 32 \| 67 \| 76 \| \| GPT-4 \| 91 \| 94 \| 96 \| 95 \| 75 \| 86 \| \| Orion-14B-Chat-RAG \| 86 \| 87 \| 91 \| 97 \| 73 \| 71 \| \ means manual assessment ## 3.5. Chat Plugin Model Orion-14B-Chat-Plugin Benchmarks ### 3.5.1. LLM evaluation results of self-built plugin testsets \|Model \|Intent Recognition with Full Params \|Intent Recognition with Missing Params \|Non-Plugin Invocation Recognition \| \|-----------------------\|--------\|-----------\|--------\| \| Baichuan2-13B-Chat \| 25 \| 0 \| 0 \| \| Qwen-14B-Chat \| 55 \| 0 \| 50 \| \| GPT-4 \| 95 \| 52.38 \| 70 \| \| Orion-14B-Chat-Plugin \| 92.5 \| 60.32 \| 90 \| ## 3.6. Quantized Model Orion-14B-Base-Int4 Benchmarks ### 3.6.1. Comparison of before and after quantization \|Model \|Size(GB)\|Inference Speed(tokens/s)\|C-Eval\|CMMLU\|MMLU\|RACE\|HellaSwag\| \|-------------------------\|-------\|-----\|------\|------\|------\|------\|------\| \| OrionStar-14B-Base \| 28.0 \| 135 \| 72.8 \| 70.6 \| 70.0 \| 93.3 \| 78.5 \| \| OrionStar-14B-Base-Int4 \| 8.3 \| 178 \| 71.8 \| 69.8 \| 69.2 \| 93.1 \| 78.0 \| <a name="model-inference"></a><br> # 4. Model Inference Model weights, source code, and configuration needed for inference are published on Hugging Face, and the download link is available in the table at the beginning of this document. We demonstrate various inference methods here, and the program will automatically download the necessary resources from Hugging Face. ## 4.1. Python Code ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/Orion-14B", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("OrionStarAI/Orion-14B") messages = [{"role": "user", "content": "Hello, what is your name? "}] response = model.chat(tokenizer, messages, streaming=False) print(response) ``` In the above Python code, the model is loaded with `device_map='auto'` to utilize all available GPUs. To specify the device, you can use something like `export CUDA_VISIBLE_DEVICES=0,1` (using GPUs 0 and 1). ## 4.2. Command Line Tool ```shell CUDA_VISIBLE_DEVICES=0 python cli_demo.py ``` This command-line tool is designed for chat scenarios, and thus, it does not support calling the base model. ## 4.3. Direct Script Inference ```shell # base model CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/Orion-14B --tokenizer OrionStarAI/Orion-14B --prompt hello # chat model CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt hi ``` ## 4.4 Example Output ### 4.4.1. Casual Chat ````` User: Hello Orion-14B: Hello! How can I help you today? If you have any questions or need assistance, feel free to ask. User: What functions do you have? Orion-14B: As an AI language model, I have a wide range of functions and capabilities, including but not limited to: Answering general knowledge questions: I can provide information on various topics such as history, science, geography, and more. Providing definitions and explanations: I can explain complex concepts and provide definitions for words and phrases. Assisting with tasks: I can help with simple tasks like setting reminders, converting units of measurement, and even ordering food from restaurants. Generating text: I can write short stories, essays, articles, and other types of content based on prompts given to me. Translating languages: I can translate text from one language to another. Chatting: I can engage in conversation with users and respond to their queries in a conversational manner. Summarizing long texts: I can summarize longer texts like articles, books, and research papers. Generating random numbers and names: I can generate random numbers within a specified range and also suggest names based on certain criteria. Playing games: I can play simple games like Hangman, Trivia, and more. Providing recommendations: I can recommend movies, music, books, and other forms of entertainment based on user preferences. These are just some of the many functions that I am capable of performing. If you have a specific task in mind, feel free to ask me about it. User: Tell me a joke. Orion-14B: Sure, here's a classic one-liner: Why don't scientists trust atoms? Because they make up everything. ````` ### 4.4.2. Japanese & Korean Chat ````` User：自己を紹介してください Orion-14B：こんにちは、私の名前はChatMaxで、OrionStarによって開発されたAIアシスタントです。私はユーザーに正確で、客観的で、有用な回答を提供し、彼らが問題を解決し、情報を取得するのを助けることができます。 User：자기소개를 해주세요. Orion-14B：안녕하세요, 제 이름은 ChatMax입니다. OrionStar에서 개발한 AI 어시스턴트입니다. 저는 사용자들에게 정확하고 객관적이며 유용한 답변을 제공하여 문제를 해결하고 정보를 얻는 데 도움을 줄 수 있습니다. ````` <a name="declarations-license"></a><br> # 5. Declarations, License ## 5.1. Declarations We strongly urge all users not to use the Orion-14B model for any activities that may harm national or social security or violate the law. Additionally, we request users not to use the Orion-14B model for internet services without proper security review and filing. We hope all users abide by this principle to ensure that technological development takes place in a regulated and legal environment. We have done our best to ensure the compliance of the data used in the model training process. However, despite our significant efforts, unforeseen issues may still arise due to the complexity of the model and data. Therefore, if any problems arise due to the use of the Orion-14B open-source model, including but not limited to data security issues, public opinion risks, or any risks and issues arising from the model being misled, abused, disseminated, or improperly utilized, we will not assume any responsibility. ## 5.2. License Community use of the Orion-14B series models - For code, please comply with [Apache License Version 2.0](./LICENSE)<br> - For model, please comply with [【Orion-14B Series】 Models Community License Agreement](./ModelsCommunityLicenseAgreement) <a name="company-introduction"></a><br> # 6. Company Introduction OrionStar is a leading global service robot solutions company, founded in September 2016. OrionStar is dedicated to using artificial intelligence technology to create the next generation of revolutionary robots, allowing people to break free from repetitive physical labor and making human work and life more intelligent and enjoyable. Through technology, OrionStar aims to make society and the world a better place. OrionStar possesses fully self-developed end-to-end artificial intelligence technologies, such as voice interaction and visual navigation. It integrates product development capabilities and technological application capabilities. Based on the Orion robotic arm platform, it has launched products such as OrionStar AI Robot Greeting, AI Robot Greeting Mini, Lucki, Coffee Master, and established the open platform OrionOS for Orion robots. Following the philosophy of "Born for Truly Useful Robots", OrionStar empowers more people through AI technology. The core strengths of OrionStar lies in possessing end-to-end AI application capabilities, including big data preprocessing, large model pretraining, fine-tuning, prompt engineering, agent, etc. With comprehensive end-to-end model training capabilities, including systematic data processing workflows and the parallel model training capability of hundreds of GPUs, it has been successfully applied in various industry scenarios such as government affairs, cloud services, international e-commerce, and fast-moving consumer goods. Companies with demands for deploying large-scale model applications are welcome to contact us.<br> Enquiry Hotline: 400-898-7779<br> E-mail: [email protected] <div align="center"> <img src="./assets/imgs/wechat_group.jpg" alt="wechat" width="40%" /> </div> # 目录 - [📖 模型介绍](#zh_model-introduction) - [🔗 下载路径](#zh_model-download) - [🔖 评估结果](#zh_model-benchmark) - [📊 模型推理](#zh_model-inference) - [📜 声明协议](#zh_declarations-license) - [🥇 企业介绍](#zh_company-introduction) <a name="zh_model-introduction"></a><br> # 1. 模型介绍 - Orion-14B-Base是一个具有140亿参数的多语种大模型，该模型在一个包含2.5万亿token的多样化数据集上进行了训练，涵盖了中文、英语、日语、韩语等多种语言。在多语言环境下的一系列任务中展现出卓越的性能。在主流的公开基准评测中，Orion-14B系列模型表现优异，多项指标显著超越同等参数基本的其他模型。具体技术细节请参考[技术报告](https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf)。 - Orion-14B系列大模型有以下几个特点： - 基座20B参数级别大模型综合评测效果表现优异 - 多语言能力强，在日语、韩语测试集上显著领先 - 微调模型适应性强，在人类标注盲测中，表现突出 - 长上下文版本支持超长文本，在200k token长度上效果优异，最长可支持可达320k - 量化版本模型大小缩小70%，推理速度提升30%，性能损失小于1% <table style="border-collapse: collapse; width: 100%;"> <tr> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/opencompass_zh.png" alt="opencompass" style="width: 100%; height: auto;"> </td> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/model_cap_zh.png" alt="modelcap" style="width: 100%; height: auto;"> </td> </tr> </table> - 具体而言，Orion-14B系列大语言模型包含: - Orion-14B-Base: 基于2.5万亿tokens多样化数据集训练处的140亿参数量级的多语言基座模型。 - Orion-14B-Chat: 基于高质量语料库微调的对话类模型，旨在为大模型社区提供更好的用户交互体验。 - Orion-14B-LongChat: 在200k token长度上效果优异，最长可支持可达320k，在长文本评估集上性能比肩专有模型。 - Orion-14B-Chat-RAG: 在一个定制的检索增强生成数据集上进行微调的聊天模型，在检索增强生成任务中取得了卓越的性能。 - Orion-14B-Chat-Plugin: 专门针对插件和函数调用任务定制的聊天模型，非常适用于使用代理的相关场景，其中大语言模型充当插件和函数调用系统。 - Orion-14B-Base-Int4: 一个使用int4进行量化的基座模型。它将模型大小显著减小了70%，同时提高了推理速度30%，仅引入了1%的最小性能损失。 - Orion-14B-Chat-Int4: 一个使用int4进行量化的对话模型。 <a name="zh_model-download"></a><br> # 2. 下载路径发布模型和下载链接见下表： \| 模型名称 \| HuggingFace下载链接 \| ModelScope下载链接 \| \|---------------------\|-----------------------------------------------------------------------------------\|------------------------------------------------------------------------------------------------\| \| ⚾ 基座模型 \| [Orion-14B-Base](https://huggingface.co/OrionStarAI/Orion-14B-Base) \| [Orion-14B-Base](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base/summary) \| \| 😛 对话模型 \| [Orion-14B-Chat](https://huggingface.co/OrionStarAI/Orion-14B-Chat) \| [Orion-14B-Chat](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat/summary) \| \| 📃 长上下文模型 \| [Orion-14B-LongChat](https://huggingface.co/OrionStarAI/Orion-14B-LongChat) \| [Orion-14B-LongChat](https://modelscope.cn/models/OrionStarAI/Orion-14B-LongChat/summary) \| \| 🔎 检索增强模型 \| [Orion-14B-Chat-RAG](https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG) \| [Orion-14B-Chat-RAG](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-RAG/summary) \| \| 🔌 插件模型 \| [Orion-14B-Chat-Plugin](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Plugin) \| [Orion-14B-Chat-Plugin](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Plugin/summary)\| \| 💼 基座Int4量化模型 \| [Orion-14B-Base-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4) \| [Orion-14B-Base-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base-Int4/summary) \| \| 📦 对话Int4量化模型 \| [Orion-14B-Chat-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Int4) \| [Orion-14B-Chat-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Int4/summary) \| <a name="zh_model-benchmark"></a><br> # 3. 评估结果 ## 3.1. 基座模型Orion-14B-Base评估 ### 3.1.1. 专业知识与试题评估结果 \| 模型名称 \| C-Eval \| CMMLU \| MMLU \| AGIEval \| Gaokao \| BBH \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\| \| LLaMA2-13B \| 41.4 \| 38.4 \| 55.0 \| 30.9 \| 18.2 \| 45.6 \| \| Skywork-13B \| 59.1 \| 61.4 \| 62.7 \| 43.6 \| 56.1 \| 48.3 \| \| Baichuan2-13B \| 59.0 \| 61.3 \| 59.5 \| 37.4 \| 45.6 \| 49.0 \| \| QWEN-14B \| 71.7 \| 70.2 \| 67.9 \| 51.9 \| 62.5 \| 53.7 \| \| InternLM-20B \| 58.8 \| 59.0 \| 62.1 \| 44.6 \| 45.5 \| 52.5 \| \| Orion-14B-Base \| 72.9 \| 70.6 \| 69.9 \| 54.7 \| 62.1 \| 56.5 \| ### 3.1.2. 理解与通识评估结果 \| 模型名称 \|RACE-middle\|RACE-high\| HellaSwag\| PIQA \| Lambada \| WSC \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\| \| LLaMA 2-13B \| 63.0 \| 58.9 \| 77.5 \| 79.8 \| 76.5 \| 66.3 \| \| Skywork-13B \| 87.6 \| 84.1 \| 73.7 \| 78.3 \| 71.8 \| 66.3 \| \| Baichuan 2-13B \| 68.9 \| 67.2 \| 70.8 \| 78.1 \| 74.1 \| 66.3 \| \| QWEN-14B \| 93.0 \| 90.3 \| 80.2 \| 79.8 \| 71.4 \| 66.3 \| \| InternLM-20B \| 86.4 \| 83.3 \| 78.1 \| 80.3 \| 71.8 \| 68.3 \| \| Orion-14B-Base \| 93.2 \| 91.3 \| 78.5 \| 79.5 \| 78.8 \| 70.2 \| ### 3.1.3. OpenCompass评测集评估结果 \| 模型名称 \| Average \| Examination \| Language \| Knowledge \| Understanding \| Reasoning \| \|------------------\|----------\|----------\|----------\|----------\|----------\|----------\| \| LLaMA 2-13B \| 47.3 \| 45.2 \| 47.0 \| 58.3 \| 50.9 \| 43.6 \| \| Skywork-13B \| 53.6 \| 61.1 \| 51.3 \| 52.7 \| 64.5 \| 45.2 \| \| Baichuan 2-13B \| 49.4 \| 51.8 \| 47.5 \| 48.9 \| 58.1 \| 44.2 \| \| QWEN-14B \| 62.4 \| 71.3 \| 52.67 \| 56.1 \| 68.8 \| 60.1 \| \| InternLM-20B \| 59.4 \| 62.5 \| 55.0 \| 60.1 \| 67.3 \| 54.9 \| \|Orion-14B-Base\| 64.3 \| 71.4 \| 55.0 \| 60.0 \| 71.9 \| 61.6 \| ### 3.1.4. 日语测试集评估结果 \| 模型名称 \|Average\| JCQA \| JNLI \| MARC \| JSQD \| JQK \| XLS \| XWN \| MGSM \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| PLaMo-13B \| 52.3 \| 56.7 \| 42.8 \| 95.8 \| 70.6 \| 71.0 \| 8.70 \| 70.5 \| 2.40 \| \| WebLab-10B \| 50.7 \| 66.6 \| 53.7 \| 82.1 \| 62.9 \| 56.2 \| 10.0 \| 72.0 \| 2.40 \| \| ELYZA-jp-7B \| 48.8 \| 71.7 \| 25.3 \| 86.6 \| 70.8 \| 64.1 \| 2.50 \| 62.1 \| 7.20 \| \| StableLM-jp-7B \| 51.1 \| 33.4 \| 43.3 \| 96.7 \| 70.6 \| 78.1 \| 10.7 \| 72.8 \| 2.80 \| \| LLaMA 2-13B \| 46.3 \| 75.0 \| 47.6 \| 38.8 \| 76.1 \| 67.7 \| 18.1 \| 63.2 \| 10.4 \| \| Baichuan 2-13B \| 57.1 \| 73.7 \| 31.3 \| 91.6 \| 80.5 \| 63.3 \| 18.6 \| 72.2 \| 25.2 \| \| QWEN-14B \| 65.8 \| 85.9 \| 60.7 \| 97.0 \| 83.3 \| 71.8 \| 18.8 \| 70.6 \| 38.0 \| \| Yi-34B \| 67.1 \| 83.8 \| 61.2 \| 95.2 \| 86.1 \| 78.5 \| 27.2 \| 69.2 \| 35.2 \| \| Orion-14B-Base \| 69.1 \| 88.2 \| 75.8 \| 94.1 \| 75.7 \| 85.1 \| 17.3 \| 78.8 \| 38.0 \| ### 3.1.5. 韩语测试集n-shot评估结果 \| 模型名称 \| Average<br>n=0  n=5 \| HellaSwag<br>n=0  n=5 \| COPA<br> n=0  n=5 \| BooIQ<br>n=0  n=5 \| SentiNeg<br>n=0  n=5\| \|------------------\|------------------------------\|------------------------------\|------------------------------\|------------------------------\|------------------------------\| \| KoGPT \| 53.0    70.1 \| 55.9    58.3 \| 73.5    72.9 \| 45.1    59.8 \| 37.5    89.4 \| \| Polyglot-ko-13B \| 69.6    73.7 \|59.5    63.1\|79.4    81.1\| 48.2    60.4 \| 91.2    90.2 \| \| LLaMA 2-13B \| 46.7    63.7 \| 41.3    44.0 \| 59.3    63.8 \| 34.9    73.8 \| 51.5    73.4 \| \| Baichuan 2-13B \| 52.1    58.7 \| 39.2    39.6 \| 60.6    60.6 \| 58.4    61.5 \| 50.3    72.9 \| \| QWEN-14B \| 53.8    73.7 \| 45.3    46.8 \| 64.9    68.9 \| 33.4    83.5 \| 71.5    95.7 \| \| Yi-34B \| 54.2    72.1 \| 44.6    44.7 \| 58.0    60.6 \| 65.9    90.2 \| 48.3    92.9 \| \|Orion-14B-Base\|74.5    79.6\| 47.0    49.6 \| 77.7    79.4 \|81.6    90.7\|92.4    98.7\| ### 3.1.6. 多语言评估结果 \| 模型名称 \| Train Lang \| Japanese \| Korean \| Chinese \| English \| \|--------------------\|------------\|----------\|----------\|----------\|----------\| \| PLaMo-13B \| En,Jp \| 52.3 \| * \| * \| * \| \| Weblab-10B \| En,Jp \| 50.7 \| * \| * \| * \| \| ELYZA-jp-7B \| En,Jp \| 48.8 \| * \| * \| * \| \| StableLM-jp-7B \| En,Jp \| 51.1 \| * \| * \| * \| \| KoGPT-6B \| En,Ko \| * \| 70.1 \| * \| * \| \| Polyglot-ko-13B \| En,Ko \| * \| 70.7 \| * \| * \| \| Baichuan2-13B \| Multi \| 57.1 \| 58.7 \| 50.8 \| 57.1 \| \| Qwen-14B \| Multi \| 65.8 \| 73.7 \| 64.5 \| 65.4 \| \| Llama2-13B \| Multi \| 46.3 \| 63.7 \| 41.4 \| 55.3 \| \| Yi-34B \| Multi \| 67.1 \| 72.2 \| 58.7 \| 68.8 \| \| Orion-14B-Base \| Multi \| 69.1 \| 79.5 \| 67.9 \| 67.3 \| ## 3.2. 对话模型Orion-14B-Chat评估 ### 3.2.1. 对话模型MTBench主观评估 \| 模型名称 \| 第一轮 \| 第二轮 \| 平均 \| \|----------------------\|----------\|----------\|----------\| \| Baichuan2-13B-Chat \| 7.05 \| 6.47 \| 6.76 \| \| Qwen-14B-Chat \| 7.30 \| 6.62 \| 6.96 \| \| Llama2-13B-Chat \| 7.10 \| 6.20 \| 6.65 \| \| InternLM-20B-Chat \| 7.03 \| 5.93 \| 6.48 \| \| Orion-14B-Chat \| 7.68 \| 7.07 \| 7.37 \| \这里评测使用vllm进行推理 ### 3.2.2. 对话模型AlignBench主观评估 \| 模型名称 \| 数学能力 \| 逻辑推理 \| 基本能力 \| 中文理解 \| 综合问答 \| 写作能力 \| 角色扮演 \| 专业知识 \| 平均* \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| Baichuan2-13B-Chat \| 3.76 \| 4.07 \| 6.22 \| 6.05 \| 7.11 \| 6.97 \| 6.75 \| 6.43 \| 5.25 \| \| Qwen-14B-Chat \| 4.91 \| 4.71 \| 6.90 \| 6.36 \| 6.74 \| 6.64 \| 6.59 \| 6.56 \| 5.72 \| \| Llama2-13B-Chat \| 3.05 \| 3.79 \| 5.43 \| 4.40 \| 6.76 \| 6.63 \| 6.99 \| 5.65 \| 4.70 \| \| InternLM-20B-Chat \| 3.39 \| 3.92 \| 5.96 \| 5.50 \| 7.18 \| 6.19 \| 6.49 \| 6.22 \| 4.96 \| \| Orion-14B-Chat \| 4.00 \| 4.24 \| 6.18 \| 6.57 \| 7.16 \| 7.36 \| 7.16 \| 6.99 \| 5.51 \| \这里评测使用vllm进行推理 ## 3.3. 长上下文模型Orion-14B-LongChat评估 ### 3.3.1. 长上下文模型LongBench评估 \| 模型名称 \| NarrativeQA\| MultiFieldQA-en\| MultiFieldQA-zh \| DuReader \| QMSum \| VCSUM \| TREC \| TriviaQA \| LSHT \| RepoBench-P \| \|--------------------------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\| \| GPT-3.5-Turbo-16k \| 23.60* \| 52.30 \| 61.20 \| 28.70 \| 23.40 \| 16.00 \| 68.00 \| 91.40 \| 29.20 \| 53.60 \| \| LongChat-v1.5-7B-32k \| 16.90 \| 41.40 \| 29.10 \| 19.50 \| 22.70 \| 9.90 \| 63.50 \| 82.30 \| 23.20 \| 55.30 \| \| Vicuna-v1.5-7B-16k \| 19.40 \| 38.50 \| 43.00 \| 19.30 \| 22.80 \| 15.10 \| 71.50 \| 86.20 \| 28.80 \| 43.50 \| \| Yi-6B-200K \| 14.11 \| 36.74 \| 22.68 \| 14.01 \| 20.44 \| 8.08 \| 72.00 \| 86.61 \| 38.00 \| 63.29 \| \| Orion-14B-LongChat \| 19.47 \| 48.11 \| 55.84 \| 37.02 \| 24.87 \| 15.44 \| 77.00 \| 89.12 \| 45.50 \| 54.31 \| ## 3.4. 检索增强模型Orion-14B-Chat-RAG评估 ### 3.4.1. 自建检索增强测试集评估结果 \|模型名称\|回复效果(关键字)\|回复效果(主观打分)\|引用能力\|兜底能力\|AutoQA\|抽取数据\| \|---------------------\|------\|------\|------\|------\|------\|------\| \| Baichuan2-13B-Chat \| 85 \| 76 \| 1 \| 0 \| 69 \| 51 \| \| Qwen-14B-Chat \| 79 \| 77 \| 75 \| 47 \| 68 \| 72 \| \| Qwen-72B-Chat(Int4) \| 87 \| 89 \| 90 \| 32 \| 67 \| 76 \| \| GPT-4 \| 91 \| 94 \| 96 \| 95 \| 75 \| 86 \| \| Orion-14B-Chat-RAG \| 86 \| 87 \| 91 \| 97 \| 73 \| 71 \| \ 表示人工评判结果 ## 3.5. 插件模型Orion-14B-Chat-Plugin评估 ### 3.5.1. 自建插件测试集评估结果 \| 模型名称 \| 全参数意图识别 \| 缺参数意图识别 \| 非插件调用识别 \| \|-----------------------\|--------\|-----------\|--------\| \| Baichuan2-13B-Chat \| 25 \| 0 \| 0 \| \| Qwen-14B-Chat \| 55 \| 0 \| 50 \| \| GPT-4 \| 95 \| 52.38 \| 70 \| \| Orion-14B-Chat-Plugin \| 92.5 \| 60.32 \| 90 \| ## 3.6. 量化模型Orion-14B-Base-Int4评估 ### 3.6.1. 量化前后整体对比 \|模型名称\|模型大小(GB)\|推理速度(令牌数/秒)\|C-Eval \|CMMLU \|MMLU \|RACE \| HellaSwag\| \|-------------------------\|------\|-----\|------\|------\|------\|------\|------\| \| OrionStar-14B-Base \| 28.0 \| 135 \| 72.8 \| 70.6 \| 70.0 \| 93.3 \| 78.5 \| \| OrionStar-14B-Base-Int4 \| 8.3 \| 178 \| 71.8 \| 69.8 \| 69.2 \| 93.1 \| 78.0 \| <a name="zh_model-inference"></a><br> # 4. 模型推理推理所需的模型权重、源码、配置已发布在 Hugging Face，下载链接见本文档最开始的表格。我们在此示范多种推理方式。程序会自动从 Hugging Face 下载所需资源。 ## 4.1. Python 代码方式 ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/Orion-14B", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("OrionStarAI/Orion-14B") messages = [{"role": "user", "content": "你好! 你叫什么名字!"}] response = model.chat(tokenizer, messages, streaming=Flase) print(response) ``` 在上述两段代码中，模型加载指定 `device_map='auto'` ，会使用所有可用显卡。如需指定使用的设备，可以使用类似 `export CUDA_VISIBLE_DEVICES=0,1`（使用了0、1号显卡）的方式控制。 ## 4.2. 命令行工具方式 ```shell CUDA_VISIBLE_DEVICES=0 python cli_demo.py ``` 本命令行工具是为 Chat 场景设计，因此我们不支持使用该工具调用 Base 模型。 ## 4.3. 脚本直接推理 ```shell # base model CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/Orion-14B --tokenizer OrionStarAI/Orion-14B --prompt 你好,你叫什么名字 # chat model CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt 你好,你叫什么名字 ``` ## 4.4. 示例输出 ### 4.4.1. 闲聊 ````` 用户：你好,你叫什么名字 Orion-14B：你好!我是一个人工智能助手,没有固定的名字。你可以随意称呼我。有什么我可以帮助你的吗? 用户：讲个故事 Orion-14B：从前有一个小男孩叫杰克,他住在一个小村庄里。杰克非常喜欢冒险和探索,总是梦想着能够发现一些神奇的事情。有一天,杰克听说在附近的森林深处有一座传说中的魔法城堡,里面住着一个邪恶的巨人。这个巨人的名字叫格里姆,他被困在城堡中,无法离开。杰克决定要去解救这个巨人,因为他相信格里姆一定有自己的原因被困在那里。于是,他带着勇气和决心,踏上了前往魔法城堡的旅程。经过长时间的跋涉,杰克终于来到了城堡的大门前。他小心翼翼地推开大门,进入了一个充满神秘气息的房间。房间里弥漫着黑暗的气息,但杰克并没有被吓倒。他勇敢地向城堡深处走去,最终找到了格里姆。格里姆告诉杰克,他是被一个邪恶的女巫诅咒的,只有通过解开咒语才能获得自由。杰克决定要帮助格里姆,他开始寻找线索和解开咒语的方法。经过一番努力,杰克终于找到了一条通往女巫的秘密实验室的道路。他在那里发现了女巫留下的魔法书,书中记载了解开咒语的方法。杰克按照书上所写的步骤进行操作,成功地解开了格里姆身上的诅咒。巨人感激不已,他答应帮助杰克实现他的愿望。从此以后,杰克和格里姆成为了好朋友,他们一起经历了许多惊险刺激的冒险,并用他们的勇气和智慧战胜了各种困难。这个故事告诉我们,只要有勇气和决心,我们就能克服一切困难,实现自己的梦想。 ````` ### 4.4.2. 日韩 ````` 用户：自己を紹介してください Orion-14B：こんにちは、私の名前はChatMaxで、OrionStarによって開発されたAIアシスタントです。私はユーザーに正確で、客観的で、有用な回答を提供し、彼らが問題を解決し、情報を取得するのを助けることができます。用户：자기소개를 해주세요. Orion-14B：안녕하세요, 제 이름은 ChatMax입니다. OrionStar에서 개발한 AI 어시스턴트입니다. 저는 사용자들에게 정확하고 객관적이며 유용한 답변을 제공하여 문제를 해결하고 정보를 얻는 데 도움을 줄 수 있습니다. ````` <a name="zh_declarations-license"></a><br> # 5. 声明、协议 ## 5.1. 声明我们强烈呼吁所有使用者，不要利用 Orion-14B 模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Orion-14B 模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用 Orion-14B 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 ## 5.2. 协议社区使用Orion-14B系列模型 - 代码请遵循 [Apache License Version 2.0](./LICENSE)<br> - 模型请遵循 [Orion-14B系列模型社区许可协议](./ModelsCommunityLicenseAgreement) <a name="zh_company-introduction"></a><br> # 6. 企业介绍猎户星空（OrionStar）是一家全球领先的服务机器人解决方案公司，成立于2016年9月。猎户星空致力于基于人工智能技术打造下一代革命性机器人，使人们能够摆脱重复的体力劳动，使人类的工作和生活更加智能和有趣，通过技术使社会和世界变得更加美好。猎户星空拥有完全自主开发的全链条人工智能技术，如语音交互和视觉导航。它整合了产品开发能力和技术应用能力。基于Orion机械臂平台，它推出了ORION STAR AI Robot Greeting、AI Robot Greeting Mini、Lucki、Coffee Master等产品，并建立了Orion机器人的开放平台OrionOS。通过为真正有用的机器人而生的理念实践，它通过AI技术为更多人赋能。凭借7年AI经验积累，猎户星空已推出的大模型深度应用“聚言”，并陆续面向行业客户提供定制化AI大模型咨询与服务解决方案，真正帮助客户实现企业经营效率领先同行目标。猎户星空具备全链条大模型应用能力的核心优势，包括拥有从海量数据处理、大模型预训练、二次预训练、微调(Fine-tune)、Prompt Engineering 、Agent开发的全链条能力和经验积累；拥有完整的端到端模型训练能力，包括系统化的数据处理流程和数百张GPU的并行模型训练能力，现已在大政务、云服务、出海电商、快消等多个行业场景落地。 *欢迎有大模型应用落地需求的企业联系我们进行商务合作<br> 咨询电话:* 400-898-7779<br> 电子邮箱: [email protected] <div align="center"> <img src="./assets/imgs/wechat_group.jpg" alt="wechat" width="40%" /> </div>
sosoai/Orion-14B-Chat-safetensors	sosoai	2024-01-25T02:08:59Z	7	1	transformers	[ "transformers", "pytorch", "safetensors", "orion", "text-generation", "code", "model", "llm", "conversational", "custom_code", "en", "zh", "ja", "ko", "autotrain_compatible", "region:us" ]	text-generation	2024-01-24T23:44:38Z	--- language: - en - zh - ja - ko metrics: - accuracy pipeline_tag: text-generation tags: - code - model - llm --- <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <div align="center"> <img src="./assets/imgs/orion_start.PNG" alt="logo" width="50%" /> </div> <div align="center"> <h1> Orion-14B </h1> </div> <div align="center"> <div align="center"> <b>🌐English</b> \| <a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/main/README_zh.md" target="_blank">🇨🇳中文</a> \| <a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/main/README_ja.md" target="_blank">🇯🇵日本語</a> \| <a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/main/README_ko.md" target="_blank">🇰🇷한국어</a> </div> <h4 align="center"> <p> 🤗 <a href="https://huggingface.co/OrionStarAI" target="_blank">HuggingFace Mainpage</a> \| 🤖 <a href="https://modelscope.cn/organization/OrionStarAI" target="_blank">ModelScope Mainpage</a><br>🎬 <a href="https://huggingface.co/spaces/OrionStarAI/Orion-14B-App-Demo" target="_blank">HuggingFace Demo</a> \| 🎫 <a href="https://modelscope.cn/studios/OrionStarAI/Orion-14B-App-Demo/summary" target="_blank">ModelScope Demo</a><br>😺 <a href="https://github.com/OrionStarAI/Orion" target="_blank">GitHub</a><br>📖 <a href="https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf" target="_blank">Tech Report</a> <p> </h4> </div> # Table of Contents - [📖 Model Introduction](#model-introduction) - [🔗 Model Download](#model-download) - [🔖 Model Benchmark](#model-benchmark) - [📊 Model Inference](#model-inference) - [📜 Declarations & License](#declarations-license) - [🥇 Company Introduction](#company-introduction) <a name="model-introduction"></a><br> # 1. Model Introduction - Orion-14B series models are open-source multilingual large language models trained from scratch by OrionStarAI. The base model is trained on 2.5T multilingual corpus, including Chinese, English, Japanese, Korean, etc, and it exhibits superior performance in these languages. For details, please refer to [tech report](https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf). - The Orion-14B series models exhibit the following features: - Among models with 20B-parameter scale level, Orion-14B-Base model shows outstanding performance in comprehensive evaluations. - Strong multilingual capabilities, significantly outperforming in Japanese and Korean testsets. - The fine-tuned models demonstrate strong adaptability, excelling in human-annotated blind tests. - The long-chat version supports extremely long texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k. - The quantized versions reduce model size by 70%, improve inference speed by 30%, with performance loss less than 1%. <table style="border-collapse: collapse; width: 100%;"> <tr> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/opencompass_en.png" alt="opencompass" style="width: 100%; height: auto;"> </td> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/model_cap_en.png" alt="modelcap" style="width: 100%; height: auto;"> </td> </tr> </table> - Orion-14B series models including: - Orion-14B-Base: A multilingual large language foundational model with 14 billion parameters, pretrained on a diverse dataset of 2.5 trillion tokens. - Orion-14B-Chat: A chat-model fine-tuned on a high-quality corpus aims to provide an excellence interactive experience for users in the large model community. - Orion-14B-LongChat: The long-context version excels at handling extremely lengthy texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k. - Orion-14B-Chat-RAG: A chat-model fine-tuned on a custom retrieval augmented generation dataset, achieving superior performance in retrieval augmented generation tasks. - Orion-14B-Chat-Plugin: A chat-model specifically tailored for plugin and function calling tasks, ideal for agent-related scenarios where the LLM acts as a plugin and function call system. - Orion-14B-Base-Int4: A quantized base model utilizing 4-bit integer weights. It significantly reduces the model size by 70% and increases the inference speed by 30% while incurring a minimal performance loss of only 1%. - Orion-14B-Chat-Int4: A quantized chat model utilizing 4-bit integer weights. <a name="model-download"></a><br> # 2. Model Download Model release and download links are provided in the table below: \| Model Name \| HuggingFace Download Links \| ModelScope Download Links \| \|-------------------------\|-----------------------------------------------------------------------------------\|-------------------------------------------------------------------------------------------------\| \| ⚾Orion-14B-Base \| [Orion-14B-Base](https://huggingface.co/OrionStarAI/Orion-14B-Base) \| [Orion-14B-Base](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base/summary) \| \| 😛Orion-14B-Chat \| [Orion-14B-Chat](https://huggingface.co/OrionStarAI/Orion-14B-Chat) \| [Orion-14B-Chat](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat/summary) \| \| 📃Orion-14B-LongChat \| [Orion-14B-LongChat](https://huggingface.co/OrionStarAI/Orion-14B-LongChat) \| [Orion-14B-LongChat](https://modelscope.cn/models/OrionStarAI/Orion-14B-LongChat/summary) \| \| 🔎Orion-14B-Chat-RAG \| [Orion-14B-Chat-RAG](https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG) \| [Orion-14B-Chat-RAG](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-RAG/summary) \| \| 🔌Orion-14B-Chat-Plugin \| [Orion-14B-Chat-Plugin](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Plugin) \| [Orion-14B-Chat-Plugin](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Plugin/summary) \| \| 💼Orion-14B-Base-Int4 \| [Orion-14B-Base-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4) \| [Orion-14B-Base-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base-Int4/summary) \| \| 📦Orion-14B-Chat-Int4 \| [Orion-14B-Chat-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Int4) \| [Orion-14B-Chat-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Int4/summary) \| <a name="model-benchmark"></a><br> # 3. Model Benchmarks ## 3.1. Base Model Orion-14B-Base Benchmarks ### 3.1.1. LLM evaluation results on examination and professional knowledge \| Model \| C-Eval \| CMMLU \| MMLU \| AGIEval \| Gaokao \| BBH \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\| \| LLaMA2-13B \| 41.4 \| 38.4 \| 55.0 \| 30.9 \| 18.2 \| 45.6 \| \| Skywork-13B \| 59.1 \| 61.4 \| 62.7 \| 43.6 \| 56.1 \| 48.3 \| \| Baichuan2-13B \| 59.0 \| 61.3 \| 59.5 \| 37.4 \| 45.6 \| 49.0 \| \| QWEN-14B \| 71.7 \| 70.2 \| 67.9 \| 51.9 \| 62.5 \| 53.7 \| \| InternLM-20B \| 58.8 \| 59.0 \| 62.1 \| 44.6 \| 45.5 \| 52.5 \| \| Orion-14B-Base \| 72.9 \| 70.6 \| 69.9 \| 54.7 \| 62.1 \| 56.5 \| ### 3.1.2. LLM evaluation results on language understanding and common knowledge \| Model \|RACE-middle\|RACE-high \|HellaSwag \| PIQA \| Lambada \| WSC \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\| \| LLaMA 2-13B \| 63.0 \| 58.9 \| 77.5 \| 79.8 \| 76.5 \| 66.3 \| \| Skywork-13B \| 87.6 \| 84.1 \| 73.7 \| 78.3 \| 71.8 \| 66.3 \| \| Baichuan 2-13B \| 68.9 \| 67.2 \| 70.8 \| 78.1 \| 74.1 \| 66.3 \| \| QWEN-14B \| 93.0 \| 90.3 \| 80.2 \| 79.8 \| 71.4 \| 66.3 \| \| InternLM-20B \| 86.4 \| 83.3 \| 78.1 \| 80.3 \| 71.8 \| 68.3 \| \| Orion-14B-Base \| 93.2 \| 91.3 \| 78.5 \| 79.5 \| 78.8 \| 70.2 \| ### 3.1.3. LLM evaluation results of OpenCompass testsets \| Model \| Average \| Examination \| Language \| Knowledge \| Understanding \| Reasoning \| \|------------------\|----------\|----------\|----------\|----------\|----------\|----------\| \| LLaMA 2-13B \| 47.3 \| 45.2 \| 47.0 \| 58.3 \| 50.9 \| 43.6 \| \| Skywork-13B \| 53.6 \| 61.1 \| 51.3 \| 52.7 \| 64.5 \| 45.2 \| \| Baichuan 2-13B \| 49.4 \| 51.8 \| 47.5 \| 48.9 \| 58.1 \| 44.2 \| \| QWEN-14B \| 62.4 \| 71.3 \| 52.67 \| 56.1 \| 68.8 \| 60.1 \| \| InternLM-20B \| 59.4 \| 62.5 \| 55.0 \| 60.1 \| 67.3 \| 54.9 \| \|Orion-14B-Base\| 64.3 \| 71.4 \| 55.0 \| 60.0 \| 71.9 \| 61.6 \| ### 3.1.4. Comparison of LLM performances on Japanese testsets \| Model \|Average\| JCQA \| JNLI \| MARC \| JSQD \| JQK \| XLS \| XWN \| MGSM \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| PLaMo-13B \| 52.3 \| 56.7 \| 42.8 \| 95.8 \| 70.6 \| 71.0 \| 8.70 \| 70.5 \| 2.40 \| \| WebLab-10B \| 50.7 \| 66.6 \| 53.7 \| 82.1 \| 62.9 \| 56.2 \| 10.0 \| 72.0 \| 2.40 \| \| ELYZA-jp-7B \| 48.8 \| 71.7 \| 25.3 \| 86.6 \| 70.8 \| 64.1 \| 2.50 \| 62.1 \| 7.20 \| \| StableLM-jp-7B \| 51.1 \| 33.4 \| 43.3 \| 96.7 \| 70.6 \| 78.1 \| 10.7 \| 72.8 \| 2.80 \| \| LLaMA 2-13B \| 46.3 \| 75.0 \| 47.6 \| 38.8 \| 76.1 \| 67.7 \| 18.1 \| 63.2 \| 10.4 \| \| Baichuan 2-13B \| 57.1 \| 73.7 \| 31.3 \| 91.6 \| 80.5 \| 63.3 \| 18.6 \| 72.2 \| 25.2 \| \| QWEN-14B \| 65.8 \| 85.9 \| 60.7 \| 97.0 \| 83.3 \| 71.8 \| 18.8 \| 70.6 \| 38.0 \| \| Yi-34B \| 67.1 \| 83.8 \| 61.2 \| 95.2 \| 86.1 \| 78.5 \| 27.2 \| 69.2 \| 35.2 \| \| Orion-14B-Base \| 69.1 \| 88.2 \| 75.8 \| 94.1 \| 75.7 \| 85.1 \| 17.3 \| 78.8 \| 38.0 \| ### 3.1.5. Comparison of LLM performances on Korean testsets. n = 0 and n = 5 stand for n-shot prompts used in the evaluation \|Model \| Average<br>n=0  n=5 \| HellaSwag<br>n=0  n=5 \| COPA<br> n=0  n=5 \| BooIQ<br>n=0  n=5 \| SentiNeg<br>n=0  n=5\| \|------------------\|------------------------------\|------------------------------\|------------------------------\|------------------------------\|------------------------------\| \| KoGPT \| 53.0    70.1 \| 55.9    58.3 \| 73.5    72.9 \| 45.1    59.8 \| 37.5    89.4 \| \| Polyglot-ko-13B \| 69.6    73.7 \|59.5    63.1\|79.4    81.1\| 48.2    60.4 \| 91.2    90.2 \| \| LLaMA 2-13B \| 46.7    63.7 \| 41.3    44.0 \| 59.3    63.8 \| 34.9    73.8 \| 51.5    73.4 \| \| Baichuan 2-13B \| 52.1    58.7 \| 39.2    39.6 \| 60.6    60.6 \| 58.4    61.5 \| 50.3    72.9 \| \| QWEN-14B \| 53.8    73.7 \| 45.3    46.8 \| 64.9    68.9 \| 33.4    83.5 \| 71.5    95.7 \| \| Yi-34B \| 54.2    72.1 \| 44.6    44.7 \| 58.0    60.6 \| 65.9    90.2 \| 48.3    92.9 \| \|Orion-14B-Chat\|74.5    79.6\| 47.0    49.6 \| 77.7    79.4 \|81.6    90.7\|92.4    98.7\| ### 3.1.6. Multilingual evaluation \| Model \| Train Lang \| Japanese \| Korean \| Chinese \| English \| \|--------------------\|------------\|----------\|----------\|----------\|----------\| \| PLaMo-13B \| En,Jp \| 52.3 \| * \| * \| * \| \| Weblab-10B \| En,Jp \| 50.7 \| * \| * \| * \| \| ELYZA-jp-7B \| En,Jp \| 48.8 \| * \| * \| * \| \| StableLM-jp-7B \| En,Jp \| 51.1 \| * \| * \| * \| \| KoGPT-6B \| En,Ko \| * \| 70.1 \| * \| * \| \| Polyglot-ko-13B \| En,Ko \| * \| 70.7 \| * \| * \| \| Baichuan2-13B \| Multi \| 57.1 \| 58.7 \| 50.8 \| 57.1 \| \| Qwen-14B \| Multi \| 65.8 \| 73.7 \| 64.5 \| 65.4 \| \| Llama2-13B \| Multi \| 46.3 \| 63.7 \| 41.4 \| 55.3 \| \| Yi-34B \| Multi \| 67.1 \| 72.2 \| 58.7 \| 68.8 \| \| Orion-14B-Chat \| Multi \| 69.1 \| 79.5 \| 67.9 \| 67.3 \| ## 3.2. Chat Model Orion-14B-Chat Benchmarks ### 3.2.1. Chat model subjective evaluation of MTBench \| Model \| First-Turn \| Second-Turn \| Average \| \|----------------------\|----------\|----------\|----------\| \| Baichuan2-13B-Chat \| 7.05 \| 6.47 \| 6.76 \| \| Qwen-14B-Chat \| 7.30 \| 6.62 \| 6.96 \| \| Llama2-13B-Chat \| 7.10 \| 6.20 \| 6.65 \| \| InternLM-20B-Chat \| 7.03 \| 5.93 \| 6.48 \| \| Orion-14B-Chat \| 7.68 \| 7.07 \| 7.37 \| \* use vllm for inference ### 3.2.2. Chat model subjective evaluation of AlignBench \| Model \| Math. \| Logi. \| Basic. \| Chi. \| Comp. \| Writ. \| Role. \| Prof. \|Avg.\| \|--------------------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\| \| Baichuan2-13B-Chat \| 3.76 \| 4.07 \| 6.22 \| 6.05 \| 7.11 \| 6.97 \| 6.75 \| 6.43 \| 5.25 \| \| Qwen-14B-Chat \|4.91\|4.71\|6.90\| 6.36 \| 6.74 \| 6.64 \| 6.59 \| 6.56 \|5.72\| \| Llama2-13B-Chat \| 3.05 \| 3.79 \| 5.43 \| 4.40 \| 6.76 \| 6.63 \| 6.99 \| 5.65 \| 4.70 \| \| InternLM-20B-Chat \| 3.39 \| 3.92 \| 5.96 \| 5.50 \|7.18\| 6.19 \| 6.49 \| 6.22 \| 4.96 \| \| Orion-14B-Chat \| 4.00 \| 4.24 \| 6.18 \|6.57\| 7.16 \|7.36\|7.16\|6.99\| 5.51 \| \* use vllm for inference ## 3.3. LongChat Model Orion-14B-LongChat Benchmarks ### 3.3.1. LongChat evaluation of LongBench \| Model \| NarrativeQA\|MultiFieldQA-en\|MultiFieldQA-zh\| DuReader \| QMSum \| VCSUM \| TREC \| TriviaQA \| LSHT \|RepoBench-P\| \|--------------------------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\| \| GPT-3.5-Turbo-16k \| 23.60 \| 52.30 \| 61.20 \| 28.70 \| 23.40 \| 16.00 \| 68.00 \| 91.40 \| 29.20 \| 53.60 \| \| LongChat-v1.5-7B-32k \| 16.90 \| 41.40 \| 29.10 \| 19.50 \| 22.70 \| 9.90 \| 63.50 \| 82.30 \| 23.20 \| 55.30 \| \| Vicuna-v1.5-7B-16k \| 19.40 \| 38.50 \| 43.00 \| 19.30 \| 22.80 \| 15.10 \| 71.50 \| 86.20 \| 28.80 \| 43.50 \| \| Yi-6B-200K \| 14.11 \| 36.74 \| 22.68 \| 14.01 \| 20.44 \| 8.08 \| 72.00 \| 86.61 \| 38.00 \| 63.29 \| \| Orion-14B-LongChat \| 19.47 \| 48.11 \| 55.84 \| 37.02 \| 24.87 \| 15.44 \| 77.00 \| 89.12 \| 45.50 \| 54.31 \| ## 3.4. Chat RAG Model Benchmarks ### 3.4.1. LLM evaluation results of self-built RAG testsets \|Model\|Effectiveness of Response(Keyword)\|Effectiveness of Response（subjective evaluation）\|Quoting Ability\|Fallback Ability\|AutoQA\|Data Extraction\| \|---------------------\|------\|------\|------\|------\|------\|------\| \| Baichuan2-13B-Chat \| 85 \| 76 \| 1 \| 0 \| 69 \| 51 \| \| Qwen-14B-Chat \| 79 \| 77 \| 75 \| 47 \| 68 \| 72 \| \| Qwen-72B-Chat(Int4) \| 87 \| 89 \| 90 \| 32 \| 67 \| 76 \| \| GPT-4 \| 91 \| 94 \| 96 \| 95 \| 75 \| 86 \| \| Orion-14B-Chat-RAG \| 86 \| 87 \| 91 \| 97 \| 73 \| 71 \| \ means manual assessment ## 3.5. Chat Plugin Model Orion-14B-Chat-Plugin Benchmarks ### 3.5.1. LLM evaluation results of self-built plugin testsets \|Model \|Intent Recognition with Full Params \|Intent Recognition with Missing Params \|Non-Plugin Invocation Recognition \| \|-----------------------\|--------\|-----------\|--------\| \| Baichuan2-13B-Chat \| 25 \| 0 \| 0 \| \| Qwen-14B-Chat \| 55 \| 0 \| 50 \| \| GPT-4 \| 95 \| 52.38 \| 70 \| \| Orion-14B-Chat-Plugin \| 92.5 \| 60.32 \| 90 \| ## 3.6. Quantized Model Orion-14B-Base-Int4 Benchmarks ### 3.6.1. Comparison of before and after quantization \|Model \|Size(GB)\|Inference Speed(tokens/s)\|C-Eval\|CMMLU\|MMLU\|RACE\|HellaSwag\| \|-------------------------\|-------\|-----\|------\|------\|------\|------\|------\| \| OrionStar-14B-Base \| 28.0 \| 135 \| 72.8 \| 70.6 \| 70.0 \| 93.3 \| 78.5 \| \| OrionStar-14B-Base-Int4 \| 8.3 \| 178 \| 71.8 \| 69.8 \| 69.2 \| 93.1 \| 78.0 \| <a name="model-inference"></a><br> # 4. Model Inference Model weights, source code, and configuration needed for inference are published on Hugging Face, and the download link is available in the table at the beginning of this document. We demonstrate various inference methods here, and the program will automatically download the necessary resources from Hugging Face. ## 4.1. Python Code ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/Orion-14B", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("OrionStarAI/Orion-14B") messages = [{"role": "user", "content": "Hello, what is your name? "}] response = model.chat(tokenizer, messages, streaming=False) print(response) ``` In the above Python code, the model is loaded with `device_map='auto'` to utilize all available GPUs. To specify the device, you can use something like `export CUDA_VISIBLE_DEVICES=0,1` (using GPUs 0 and 1). ## 4.2. Command Line Tool ```shell CUDA_VISIBLE_DEVICES=0 python cli_demo.py ``` This command-line tool is designed for chat scenarios, and thus, it does not support calling the base model. ## 4.3. Direct Script Inference ```shell # base model CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/Orion-14B --tokenizer OrionStarAI/Orion-14B --prompt hello # chat model CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt hi ``` ## 4.4 Example Output ### 4.4.1. Casual Chat ````` User: Hello Orion-14B: Hello! How can I help you today? If you have any questions or need assistance, feel free to ask. User: What functions do you have? Orion-14B: As an AI language model, I have a wide range of functions and capabilities, including but not limited to: Answering general knowledge questions: I can provide information on various topics such as history, science, geography, and more. Providing definitions and explanations: I can explain complex concepts and provide definitions for words and phrases. Assisting with tasks: I can help with simple tasks like setting reminders, converting units of measurement, and even ordering food from restaurants. Generating text: I can write short stories, essays, articles, and other types of content based on prompts given to me. Translating languages: I can translate text from one language to another. Chatting: I can engage in conversation with users and respond to their queries in a conversational manner. Summarizing long texts: I can summarize longer texts like articles, books, and research papers. Generating random numbers and names: I can generate random numbers within a specified range and also suggest names based on certain criteria. Playing games: I can play simple games like Hangman, Trivia, and more. Providing recommendations: I can recommend movies, music, books, and other forms of entertainment based on user preferences. These are just some of the many functions that I am capable of performing. If you have a specific task in mind, feel free to ask me about it. User: Tell me a joke. Orion-14B: Sure, here's a classic one-liner: Why don't scientists trust atoms? Because they make up everything. ````` ### 4.4.2. Japanese & Korean Chat ````` User：自己を紹介してください Orion-14B：こんにちは、私の名前はChatMaxで、OrionStarによって開発されたAIアシスタントです。私はユーザーに正確で、客観的で、有用な回答を提供し、彼らが問題を解決し、情報を取得するのを助けることができます。 User：자기소개를 해주세요. Orion-14B：안녕하세요, 제 이름은 ChatMax입니다. OrionStar에서 개발한 AI 어시스턴트입니다. 저는 사용자들에게 정확하고 객관적이며 유용한 답변을 제공하여 문제를 해결하고 정보를 얻는 데 도움을 줄 수 있습니다. ````` <a name="declarations-license"></a><br> # 5. Declarations, License ## 5.1. Declarations We strongly urge all users not to use the Orion-14B model for any activities that may harm national or social security or violate the law. Additionally, we request users not to use the Orion-14B model for internet services without proper security review and filing. We hope all users abide by this principle to ensure that technological development takes place in a regulated and legal environment. We have done our best to ensure the compliance of the data used in the model training process. However, despite our significant efforts, unforeseen issues may still arise due to the complexity of the model and data. Therefore, if any problems arise due to the use of the Orion-14B open-source model, including but not limited to data security issues, public opinion risks, or any risks and issues arising from the model being misled, abused, disseminated, or improperly utilized, we will not assume any responsibility. ## 5.2. License Community use of the Orion-14B series models - For code, please comply with [Apache License Version 2.0](./LICENSE)<br> - For model, please comply with [【Orion-14B Series】 Models Community License Agreement](./ModelsCommunityLicenseAgreement) <a name="company-introduction"></a><br> # 6. Company Introduction OrionStar is a leading global service robot solutions company, founded in September 2016. OrionStar is dedicated to using artificial intelligence technology to create the next generation of revolutionary robots, allowing people to break free from repetitive physical labor and making human work and life more intelligent and enjoyable. Through technology, OrionStar aims to make society and the world a better place. OrionStar possesses fully self-developed end-to-end artificial intelligence technologies, such as voice interaction and visual navigation. It integrates product development capabilities and technological application capabilities. Based on the Orion robotic arm platform, it has launched products such as OrionStar AI Robot Greeting, AI Robot Greeting Mini, Lucki, Coffee Master, and established the open platform OrionOS for Orion robots. Following the philosophy of "Born for Truly Useful Robots", OrionStar empowers more people through AI technology. The core strengths of OrionStar lies in possessing end-to-end AI application capabilities, including big data preprocessing, large model pretraining, fine-tuning, prompt engineering, agent, etc. With comprehensive end-to-end model training capabilities, including systematic data processing workflows and the parallel model training capability of hundreds of GPUs, it has been successfully applied in various industry scenarios such as government affairs, cloud services, international e-commerce, and fast-moving consumer goods. Companies with demands for deploying large-scale model applications are welcome to contact us.<br> Enquiry Hotline: 400-898-7779<br> E-mail: [email protected] <div align="center"> <img src="./assets/imgs/wechat_group.jpg" alt="wechat" width="40%" /> </div> # 目录 - [📖 模型介绍](#zh_model-introduction) - [🔗 下载路径](#zh_model-download) - [🔖 评估结果](#zh_model-benchmark) - [📊 模型推理](#zh_model-inference) - [📜 声明协议](#zh_declarations-license) - [🥇 企业介绍](#zh_company-introduction) <a name="zh_model-introduction"></a><br> # 1. 模型介绍 - Orion-14B-Base是一个具有140亿参数的多语种大模型，该模型在一个包含2.5万亿token的多样化数据集上进行了训练，涵盖了中文、英语、日语、韩语等多种语言。在多语言环境下的一系列任务中展现出卓越的性能。在主流的公开基准评测中，Orion-14B系列模型表现优异，多项指标显著超越同等参数基本的其他模型。具体技术细节请参考[技术报告](https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf)。 - Orion-14B系列大模型有以下几个特点： - 基座20B参数级别大模型综合评测效果表现优异 - 多语言能力强，在日语、韩语测试集上显著领先 - 微调模型适应性强，在人类标注盲测中，表现突出 - 长上下文版本支持超长文本，在200k token长度上效果优异，最长可支持可达320k - 量化版本模型大小缩小70%，推理速度提升30%，性能损失小于1% <table style="border-collapse: collapse; width: 100%;"> <tr> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/opencompass_zh.png" alt="opencompass" style="width: 100%; height: auto;"> </td> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/model_cap_zh.png" alt="modelcap" style="width: 100%; height: auto;"> </td> </tr> </table> - 具体而言，Orion-14B系列大语言模型包含: - Orion-14B-Base: 基于2.5万亿tokens多样化数据集训练处的140亿参数量级的多语言基座模型。 - Orion-14B-Chat: 基于高质量语料库微调的对话类模型，旨在为大模型社区提供更好的用户交互体验。 - Orion-14B-LongChat: 在200k token长度上效果优异，最长可支持可达320k，在长文本评估集上性能比肩专有模型。 - Orion-14B-Chat-RAG: 在一个定制的检索增强生成数据集上进行微调的聊天模型，在检索增强生成任务中取得了卓越的性能。 - Orion-14B-Chat-Plugin: 专门针对插件和函数调用任务定制的聊天模型，非常适用于使用代理的相关场景，其中大语言模型充当插件和函数调用系统。 - Orion-14B-Base-Int4: 一个使用int4进行量化的基座模型。它将模型大小显著减小了70%，同时提高了推理速度30%，仅引入了1%的最小性能损失。 - Orion-14B-Chat-Int4: 一个使用int4进行量化的对话模型。 <a name="zh_model-download"></a><br> # 2. 下载路径发布模型和下载链接见下表： \| 模型名称 \| HuggingFace下载链接 \| ModelScope下载链接 \| \|---------------------\|-----------------------------------------------------------------------------------\|------------------------------------------------------------------------------------------------\| \| ⚾ 基座模型 \| [Orion-14B-Base](https://huggingface.co/OrionStarAI/Orion-14B-Base) \| [Orion-14B-Base](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base/summary) \| \| 😛 对话模型 \| [Orion-14B-Chat](https://huggingface.co/OrionStarAI/Orion-14B-Chat) \| [Orion-14B-Chat](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat/summary) \| \| 📃 长上下文模型 \| [Orion-14B-LongChat](https://huggingface.co/OrionStarAI/Orion-14B-LongChat) \| [Orion-14B-LongChat](https://modelscope.cn/models/OrionStarAI/Orion-14B-LongChat/summary) \| \| 🔎 检索增强模型 \| [Orion-14B-Chat-RAG](https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG) \| [Orion-14B-Chat-RAG](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-RAG/summary) \| \| 🔌 插件模型 \| [Orion-14B-Chat-Plugin](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Plugin) \| [Orion-14B-Chat-Plugin](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Plugin/summary)\| \| 💼 基座Int4量化模型 \| [Orion-14B-Base-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4) \| [Orion-14B-Base-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base-Int4/summary) \| \| 📦 对话Int4量化模型 \| [Orion-14B-Chat-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Int4) \| [Orion-14B-Chat-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Int4/summary) \| <a name="zh_model-benchmark"></a><br> # 3. 评估结果 ## 3.1. 基座模型Orion-14B-Base评估 ### 3.1.1. 专业知识与试题评估结果 \| 模型名称 \| C-Eval \| CMMLU \| MMLU \| AGIEval \| Gaokao \| BBH \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\| \| LLaMA2-13B \| 41.4 \| 38.4 \| 55.0 \| 30.9 \| 18.2 \| 45.6 \| \| Skywork-13B \| 59.1 \| 61.4 \| 62.7 \| 43.6 \| 56.1 \| 48.3 \| \| Baichuan2-13B \| 59.0 \| 61.3 \| 59.5 \| 37.4 \| 45.6 \| 49.0 \| \| QWEN-14B \| 71.7 \| 70.2 \| 67.9 \| 51.9 \| 62.5 \| 53.7 \| \| InternLM-20B \| 58.8 \| 59.0 \| 62.1 \| 44.6 \| 45.5 \| 52.5 \| \| Orion-14B-Base \| 72.9 \| 70.6 \| 69.9 \| 54.7 \| 62.1 \| 56.5 \| ### 3.1.2. 理解与通识评估结果 \| 模型名称 \|RACE-middle\|RACE-high\| HellaSwag\| PIQA \| Lambada \| WSC \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\| \| LLaMA 2-13B \| 63.0 \| 58.9 \| 77.5 \| 79.8 \| 76.5 \| 66.3 \| \| Skywork-13B \| 87.6 \| 84.1 \| 73.7 \| 78.3 \| 71.8 \| 66.3 \| \| Baichuan 2-13B \| 68.9 \| 67.2 \| 70.8 \| 78.1 \| 74.1 \| 66.3 \| \| QWEN-14B \| 93.0 \| 90.3 \| 80.2 \| 79.8 \| 71.4 \| 66.3 \| \| InternLM-20B \| 86.4 \| 83.3 \| 78.1 \| 80.3 \| 71.8 \| 68.3 \| \| Orion-14B-Base \| 93.2 \| 91.3 \| 78.5 \| 79.5 \| 78.8 \| 70.2 \| ### 3.1.3. OpenCompass评测集评估结果 \| 模型名称 \| Average \| Examination \| Language \| Knowledge \| Understanding \| Reasoning \| \|------------------\|----------\|----------\|----------\|----------\|----------\|----------\| \| LLaMA 2-13B \| 47.3 \| 45.2 \| 47.0 \| 58.3 \| 50.9 \| 43.6 \| \| Skywork-13B \| 53.6 \| 61.1 \| 51.3 \| 52.7 \| 64.5 \| 45.2 \| \| Baichuan 2-13B \| 49.4 \| 51.8 \| 47.5 \| 48.9 \| 58.1 \| 44.2 \| \| QWEN-14B \| 62.4 \| 71.3 \| 52.67 \| 56.1 \| 68.8 \| 60.1 \| \| InternLM-20B \| 59.4 \| 62.5 \| 55.0 \| 60.1 \| 67.3 \| 54.9 \| \|Orion-14B-Base\| 64.3 \| 71.4 \| 55.0 \| 60.0 \| 71.9 \| 61.6 \| ### 3.1.4. 日语测试集评估结果 \| 模型名称 \|Average\| JCQA \| JNLI \| MARC \| JSQD \| JQK \| XLS \| XWN \| MGSM \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| PLaMo-13B \| 52.3 \| 56.7 \| 42.8 \| 95.8 \| 70.6 \| 71.0 \| 8.70 \| 70.5 \| 2.40 \| \| WebLab-10B \| 50.7 \| 66.6 \| 53.7 \| 82.1 \| 62.9 \| 56.2 \| 10.0 \| 72.0 \| 2.40 \| \| ELYZA-jp-7B \| 48.8 \| 71.7 \| 25.3 \| 86.6 \| 70.8 \| 64.1 \| 2.50 \| 62.1 \| 7.20 \| \| StableLM-jp-7B \| 51.1 \| 33.4 \| 43.3 \| 96.7 \| 70.6 \| 78.1 \| 10.7 \| 72.8 \| 2.80 \| \| LLaMA 2-13B \| 46.3 \| 75.0 \| 47.6 \| 38.8 \| 76.1 \| 67.7 \| 18.1 \| 63.2 \| 10.4 \| \| Baichuan 2-13B \| 57.1 \| 73.7 \| 31.3 \| 91.6 \| 80.5 \| 63.3 \| 18.6 \| 72.2 \| 25.2 \| \| QWEN-14B \| 65.8 \| 85.9 \| 60.7 \| 97.0 \| 83.3 \| 71.8 \| 18.8 \| 70.6 \| 38.0 \| \| Yi-34B \| 67.1 \| 83.8 \| 61.2 \| 95.2 \| 86.1 \| 78.5 \| 27.2 \| 69.2 \| 35.2 \| \| Orion-14B-Base \| 69.1 \| 88.2 \| 75.8 \| 94.1 \| 75.7 \| 85.1 \| 17.3 \| 78.8 \| 38.0 \| ### 3.1.5. 韩语测试集n-shot评估结果 \| 模型名称 \| Average<br>n=0  n=5 \| HellaSwag<br>n=0  n=5 \| COPA<br> n=0  n=5 \| BooIQ<br>n=0  n=5 \| SentiNeg<br>n=0  n=5\| \|------------------\|------------------------------\|------------------------------\|------------------------------\|------------------------------\|------------------------------\| \| KoGPT \| 53.0    70.1 \| 55.9    58.3 \| 73.5    72.9 \| 45.1    59.8 \| 37.5    89.4 \| \| Polyglot-ko-13B \| 69.6    73.7 \|59.5    63.1\|79.4    81.1\| 48.2    60.4 \| 91.2    90.2 \| \| LLaMA 2-13B \| 46.7    63.7 \| 41.3    44.0 \| 59.3    63.8 \| 34.9    73.8 \| 51.5    73.4 \| \| Baichuan 2-13B \| 52.1    58.7 \| 39.2    39.6 \| 60.6    60.6 \| 58.4    61.5 \| 50.3    72.9 \| \| QWEN-14B \| 53.8    73.7 \| 45.3    46.8 \| 64.9    68.9 \| 33.4    83.5 \| 71.5    95.7 \| \| Yi-34B \| 54.2    72.1 \| 44.6    44.7 \| 58.0    60.6 \| 65.9    90.2 \| 48.3    92.9 \| \|Orion-14B-Base\|74.5    79.6\| 47.0    49.6 \| 77.7    79.4 \|81.6    90.7\|92.4    98.7\| ### 3.1.6. 多语言评估结果 \| 模型名称 \| Train Lang \| Japanese \| Korean \| Chinese \| English \| \|--------------------\|------------\|----------\|----------\|----------\|----------\| \| PLaMo-13B \| En,Jp \| 52.3 \| * \| * \| * \| \| Weblab-10B \| En,Jp \| 50.7 \| * \| * \| * \| \| ELYZA-jp-7B \| En,Jp \| 48.8 \| * \| * \| * \| \| StableLM-jp-7B \| En,Jp \| 51.1 \| * \| * \| * \| \| KoGPT-6B \| En,Ko \| * \| 70.1 \| * \| * \| \| Polyglot-ko-13B \| En,Ko \| * \| 70.7 \| * \| * \| \| Baichuan2-13B \| Multi \| 57.1 \| 58.7 \| 50.8 \| 57.1 \| \| Qwen-14B \| Multi \| 65.8 \| 73.7 \| 64.5 \| 65.4 \| \| Llama2-13B \| Multi \| 46.3 \| 63.7 \| 41.4 \| 55.3 \| \| Yi-34B \| Multi \| 67.1 \| 72.2 \| 58.7 \| 68.8 \| \| Orion-14B-Base \| Multi \| 69.1 \| 79.5 \| 67.9 \| 67.3 \| ## 3.2. 对话模型Orion-14B-Chat评估 ### 3.2.1. 对话模型MTBench主观评估 \| 模型名称 \| 第一轮 \| 第二轮 \| 平均 \| \|----------------------\|----------\|----------\|----------\| \| Baichuan2-13B-Chat \| 7.05 \| 6.47 \| 6.76 \| \| Qwen-14B-Chat \| 7.30 \| 6.62 \| 6.96 \| \| Llama2-13B-Chat \| 7.10 \| 6.20 \| 6.65 \| \| InternLM-20B-Chat \| 7.03 \| 5.93 \| 6.48 \| \| Orion-14B-Chat \| 7.68 \| 7.07 \| 7.37 \| \这里评测使用vllm进行推理 ### 3.2.2. 对话模型AlignBench主观评估 \| 模型名称 \| 数学能力 \| 逻辑推理 \| 基本能力 \| 中文理解 \| 综合问答 \| 写作能力 \| 角色扮演 \| 专业知识 \| 平均* \| \|--------------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| Baichuan2-13B-Chat \| 3.76 \| 4.07 \| 6.22 \| 6.05 \| 7.11 \| 6.97 \| 6.75 \| 6.43 \| 5.25 \| \| Qwen-14B-Chat \| 4.91 \| 4.71 \| 6.90 \| 6.36 \| 6.74 \| 6.64 \| 6.59 \| 6.56 \| 5.72 \| \| Llama2-13B-Chat \| 3.05 \| 3.79 \| 5.43 \| 4.40 \| 6.76 \| 6.63 \| 6.99 \| 5.65 \| 4.70 \| \| InternLM-20B-Chat \| 3.39 \| 3.92 \| 5.96 \| 5.50 \| 7.18 \| 6.19 \| 6.49 \| 6.22 \| 4.96 \| \| Orion-14B-Chat \| 4.00 \| 4.24 \| 6.18 \| 6.57 \| 7.16 \| 7.36 \| 7.16 \| 6.99 \| 5.51 \| \这里评测使用vllm进行推理 ## 3.3. 长上下文模型Orion-14B-LongChat评估 ### 3.3.1. 长上下文模型LongBench评估 \| 模型名称 \| NarrativeQA\| MultiFieldQA-en\| MultiFieldQA-zh \| DuReader \| QMSum \| VCSUM \| TREC \| TriviaQA \| LSHT \| RepoBench-P \| \|--------------------------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\| \| GPT-3.5-Turbo-16k \| 23.60* \| 52.30 \| 61.20 \| 28.70 \| 23.40 \| 16.00 \| 68.00 \| 91.40 \| 29.20 \| 53.60 \| \| LongChat-v1.5-7B-32k \| 16.90 \| 41.40 \| 29.10 \| 19.50 \| 22.70 \| 9.90 \| 63.50 \| 82.30 \| 23.20 \| 55.30 \| \| Vicuna-v1.5-7B-16k \| 19.40 \| 38.50 \| 43.00 \| 19.30 \| 22.80 \| 15.10 \| 71.50 \| 86.20 \| 28.80 \| 43.50 \| \| Yi-6B-200K \| 14.11 \| 36.74 \| 22.68 \| 14.01 \| 20.44 \| 8.08 \| 72.00 \| 86.61 \| 38.00 \| 63.29 \| \| Orion-14B-LongChat \| 19.47 \| 48.11 \| 55.84 \| 37.02 \| 24.87 \| 15.44 \| 77.00 \| 89.12 \| 45.50 \| 54.31 \| ## 3.4. 检索增强模型Orion-14B-Chat-RAG评估 ### 3.4.1. 自建检索增强测试集评估结果 \|模型名称\|回复效果(关键字)\|回复效果(主观打分)\|引用能力\|兜底能力\|AutoQA\|抽取数据\| \|---------------------\|------\|------\|------\|------\|------\|------\| \| Baichuan2-13B-Chat \| 85 \| 76 \| 1 \| 0 \| 69 \| 51 \| \| Qwen-14B-Chat \| 79 \| 77 \| 75 \| 47 \| 68 \| 72 \| \| Qwen-72B-Chat(Int4) \| 87 \| 89 \| 90 \| 32 \| 67 \| 76 \| \| GPT-4 \| 91 \| 94 \| 96 \| 95 \| 75 \| 86 \| \| Orion-14B-Chat-RAG \| 86 \| 87 \| 91 \| 97 \| 73 \| 71 \| \ 表示人工评判结果 ## 3.5. 插件模型Orion-14B-Chat-Plugin评估 ### 3.5.1. 自建插件测试集评估结果 \| 模型名称 \| 全参数意图识别 \| 缺参数意图识别 \| 非插件调用识别 \| \|-----------------------\|--------\|-----------\|--------\| \| Baichuan2-13B-Chat \| 25 \| 0 \| 0 \| \| Qwen-14B-Chat \| 55 \| 0 \| 50 \| \| GPT-4 \| 95 \| 52.38 \| 70 \| \| Orion-14B-Chat-Plugin \| 92.5 \| 60.32 \| 90 \| ## 3.6. 量化模型Orion-14B-Base-Int4评估 ### 3.6.1. 量化前后整体对比 \|模型名称\|模型大小(GB)\|推理速度(令牌数/秒)\|C-Eval \|CMMLU \|MMLU \|RACE \| HellaSwag\| \|-------------------------\|------\|-----\|------\|------\|------\|------\|------\| \| OrionStar-14B-Base \| 28.0 \| 135 \| 72.8 \| 70.6 \| 70.0 \| 93.3 \| 78.5 \| \| OrionStar-14B-Base-Int4 \| 8.3 \| 178 \| 71.8 \| 69.8 \| 69.2 \| 93.1 \| 78.0 \| <a name="zh_model-inference"></a><br> # 4. 模型推理推理所需的模型权重、源码、配置已发布在 Hugging Face，下载链接见本文档最开始的表格。我们在此示范多种推理方式。程序会自动从 Hugging Face 下载所需资源。 ## 4.1. Python 代码方式 ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/Orion-14B", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("OrionStarAI/Orion-14B") messages = [{"role": "user", "content": "你好! 你叫什么名字!"}] response = model.chat(tokenizer, messages, streaming=Flase) print(response) ``` 在上述两段代码中，模型加载指定 `device_map='auto'` ，会使用所有可用显卡。如需指定使用的设备，可以使用类似 `export CUDA_VISIBLE_DEVICES=0,1`（使用了0、1号显卡）的方式控制。 ## 4.2. 命令行工具方式 ```shell CUDA_VISIBLE_DEVICES=0 python cli_demo.py ``` 本命令行工具是为 Chat 场景设计，因此我们不支持使用该工具调用 Base 模型。 ## 4.3. 脚本直接推理 ```shell # base model CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/Orion-14B --tokenizer OrionStarAI/Orion-14B --prompt 你好,你叫什么名字 # chat model CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt 你好,你叫什么名字 ``` ## 4.4. 示例输出 ### 4.4.1. 闲聊 ````` 用户：你好,你叫什么名字 Orion-14B：你好!我是一个人工智能助手,没有固定的名字。你可以随意称呼我。有什么我可以帮助你的吗? 用户：讲个故事 Orion-14B：从前有一个小男孩叫杰克,他住在一个小村庄里。杰克非常喜欢冒险和探索,总是梦想着能够发现一些神奇的事情。有一天,杰克听说在附近的森林深处有一座传说中的魔法城堡,里面住着一个邪恶的巨人。这个巨人的名字叫格里姆,他被困在城堡中,无法离开。杰克决定要去解救这个巨人,因为他相信格里姆一定有自己的原因被困在那里。于是,他带着勇气和决心,踏上了前往魔法城堡的旅程。经过长时间的跋涉,杰克终于来到了城堡的大门前。他小心翼翼地推开大门,进入了一个充满神秘气息的房间。房间里弥漫着黑暗的气息,但杰克并没有被吓倒。他勇敢地向城堡深处走去,最终找到了格里姆。格里姆告诉杰克,他是被一个邪恶的女巫诅咒的,只有通过解开咒语才能获得自由。杰克决定要帮助格里姆,他开始寻找线索和解开咒语的方法。经过一番努力,杰克终于找到了一条通往女巫的秘密实验室的道路。他在那里发现了女巫留下的魔法书,书中记载了解开咒语的方法。杰克按照书上所写的步骤进行操作,成功地解开了格里姆身上的诅咒。巨人感激不已,他答应帮助杰克实现他的愿望。从此以后,杰克和格里姆成为了好朋友,他们一起经历了许多惊险刺激的冒险,并用他们的勇气和智慧战胜了各种困难。这个故事告诉我们,只要有勇气和决心,我们就能克服一切困难,实现自己的梦想。 ````` ### 4.4.2. 日韩 ````` 用户：自己を紹介してください Orion-14B：こんにちは、私の名前はChatMaxで、OrionStarによって開発されたAIアシスタントです。私はユーザーに正確で、客観的で、有用な回答を提供し、彼らが問題を解決し、情報を取得するのを助けることができます。用户：자기소개를 해주세요. Orion-14B：안녕하세요, 제 이름은 ChatMax입니다. OrionStar에서 개발한 AI 어시스턴트입니다. 저는 사용자들에게 정확하고 객관적이며 유용한 답변을 제공하여 문제를 해결하고 정보를 얻는 데 도움을 줄 수 있습니다. ````` <a name="zh_declarations-license"></a><br> # 5. 声明、协议 ## 5.1. 声明我们强烈呼吁所有使用者，不要利用 Orion-14B 模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Orion-14B 模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用 Orion-14B 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 ## 5.2. 协议社区使用Orion-14B系列模型 - 代码请遵循 [Apache License Version 2.0](./LICENSE)<br> - 模型请遵循 [Orion-14B系列模型社区许可协议](./ModelsCommunityLicenseAgreement) <a name="zh_company-introduction"></a><br> # 6. 企业介绍猎户星空（OrionStar）是一家全球领先的服务机器人解决方案公司，成立于2016年9月。猎户星空致力于基于人工智能技术打造下一代革命性机器人，使人们能够摆脱重复的体力劳动，使人类的工作和生活更加智能和有趣，通过技术使社会和世界变得更加美好。猎户星空拥有完全自主开发的全链条人工智能技术，如语音交互和视觉导航。它整合了产品开发能力和技术应用能力。基于Orion机械臂平台，它推出了ORION STAR AI Robot Greeting、AI Robot Greeting Mini、Lucki、Coffee Master等产品，并建立了Orion机器人的开放平台OrionOS。通过为真正有用的机器人而生的理念实践，它通过AI技术为更多人赋能。凭借7年AI经验积累，猎户星空已推出的大模型深度应用“聚言”，并陆续面向行业客户提供定制化AI大模型咨询与服务解决方案，真正帮助客户实现企业经营效率领先同行目标。猎户星空具备全链条大模型应用能力的核心优势，包括拥有从海量数据处理、大模型预训练、二次预训练、微调(Fine-tune)、Prompt Engineering 、Agent开发的全链条能力和经验积累；拥有完整的端到端模型训练能力，包括系统化的数据处理流程和数百张GPU的并行模型训练能力，现已在大政务、云服务、出海电商、快消等多个行业场景落地。 *欢迎有大模型应用落地需求的企业联系我们进行商务合作<br> 咨询电话:* 400-898-7779<br> 电子邮箱: [email protected] <div align="center"> <img src="./assets/imgs/wechat_group.jpg" alt="wechat" width="40%" /> </div>
limin-arc/Mem-Beagle-7b-slerp-v1	limin-arc	2024-01-25T01:56:57Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "starsnatched/MemGPT", "mlabonne/Beagle14-7B", "conversational", "base_model:minchyeom/MemGPT", "base_model:merge:minchyeom/MemGPT", "base_model:mlabonne/Beagle14-7B", "base_model:merge:mlabonne/Beagle14-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-25T01:49:21Z	--- tags: - merge - mergekit - lazymergekit - starsnatched/MemGPT - mlabonne/Beagle14-7B base_model: - starsnatched/MemGPT - mlabonne/Beagle14-7B --- # Mem-Beagle-7b-slerp-v1 Mem-Beagle-7b-slerp-v1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [starsnatched/MemGPT](https://huggingface.co/starsnatched/MemGPT) * [mlabonne/Beagle14-7B](https://huggingface.co/mlabonne/Beagle14-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: starsnatched/MemGPT layer_range: [0, 32] - model: mlabonne/Beagle14-7B layer_range: [0, 32] merge_method: slerp base_model: starsnatched/MemGPT parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "liminerity/Mem-Beagle-7b-slerp-v1" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Nicolas852/ppo-LunarLander-v2	Nicolas852	2024-01-25T01:56:31Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-25T01:56:10Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 262.77 +/- 36.60 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
tiagoblima/mt5_base-qg-ap-peft-oficial	tiagoblima	2024-01-25T01:54:24Z	0	0	null	[ "safetensors", "generated_from_trainer", "dataset:tiagoblima/du-qg-squadv1_pt", "base_model:google/mt5-base", "base_model:finetune:google/mt5-base", "license:apache-2.0", "region:us" ]	null	2024-01-24T02:26:00Z	--- license: apache-2.0 base_model: google/mt5-base tags: - generated_from_trainer datasets: - tiagoblima/du-qg-squadv1_pt model-index: - name: mt5_base-qg-ap-peft-oficial results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5_base-qg-ap-peft-oficial This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the tiagoblima/du-qg-squadv1_pt dataset. It achieves the following results on the evaluation set: - Loss: 6.6924 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 10.2785 \| 1.0 \| 2367 \| 7.4566 \| \| 9.7078 \| 2.0 \| 4734 \| 7.0581 \| \| 9.4851 \| 3.0 \| 7101 \| 6.8360 \| \| 9.2737 \| 4.0 \| 9468 \| 6.7291 \| \| 9.3588 \| 5.0 \| 11835 \| 6.6924 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.1
graceneutrality/Reinforce-stuff	graceneutrality	2024-01-25T01:50:07Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-01-25T01:49:57Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-stuff results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 77.60 +/- 53.83 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
riyasankhe/vit-base-beans-tutorial	riyasankhe	2024-01-25T01:42:37Z	180	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-01-25T01:39:43Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-beans-tutorial results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans-tutorial This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0180 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 1787 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0469 \| 1.54 \| 100 \| 0.1320 \| 0.9624 \| \| 0.0186 \| 3.08 \| 200 \| 0.0315 \| 0.9925 \| \| 0.0084 \| 4.62 \| 300 \| 0.0180 \| 0.9925 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
traethethird/ppo-Huggy	traethethird	2024-01-25T01:33:37Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-01-25T01:33:24Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: traethethird/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
sharadsin/Mixtral-8x7B-sql-ft-v1	sharadsin	2024-01-25T01:30:39Z	5	1	peft	[ "peft", "safetensors", "en", "dataset:knowrohit07/know_sql", "base_model:mistralai/Mixtral-8x7B-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-01-03T01:14:40Z	--- library_name: peft base_model: mistralai/Mixtral-8x7B-v0.1 license: apache-2.0 datasets: - knowrohit07/know_sql language: - en --- <!--![](project-9.png)--> <img src="project-9.png" width="50%" height="50%" > ## SQL-Converter Mixtral 8x7B v0.1 Convert Natural Language to SQL ### Overview Mixtral-8x7B-sql-ft-v1 is fine-tuned from Mixtral 8x7B to convert natural language to SQL queries. ### Base Model mistralai/Mixtral-8x7B-v0.1 ### Fine-Tuning - Dataset: 5,000 natural language-SQL pairs. ### Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from peft import PeftModel import torch base_model_id = 'mistralai/Mixtral-8x7B-v0.1' adapter_id = 'sharadsin/Mixtral-8x7B-sql-ft-v1' bnb_config = BitsAndBytesConfig( load_in_4bit = True, bnb_4bit_use_double_quant = True, bnb_4bit_compute_dtype = torch.bfloat16, bnb_4bit_quant_type = "nf4", ) base_model = AutoModelForCausalLM.from_pretrained( base_model_id, quantization_config = bnb_config, device_map = "auto", trust_remote_code = True, ) tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token = True, trust_remote_code = True) ft_model = PeftModel.from_pretrained(base_model, adapter_id) eval_prompt= """SYSTEM: Use the following contextual information to concisely answer the question. USER: CREATE TABLE EmployeeInfo (EmpID INTEGER, EmpFname VARCHAR, EmpLname VARCHAR, Department VARCHAR, Project VARCHAR,Address VARCHAR, DOB DATE, Gender CHAR) === Write a query to fetch details of employees whose EmpLname ends with an alphabet 'A' and contains five alphabets? ASSISTANT:""" model_input = tokenizer(eval_prompt, return_tensors="pt").to("cuda") ft_model.eval() with torch.inference_mode(): print(tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=70,top_k=4, penalty_alpha = 0.6, repetition_penalty=1.15)[0], skip_special_tokens= False)) ``` ### Limitations - Less accurate with very complex queries. - Generates extra gibberish content after providing the answers. ### Framework versions - PEFT 0.7.1
ntc-ai/SDXL-LoRA-slider.heavy-inking	ntc-ai	2024-01-25T01:27:02Z	52	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]	text-to-image	2024-01-25T01:26:59Z	--- language: - en thumbnail: "images/evaluate/heavy inking.../heavy inking_17_3.0.png" widget: - text: heavy inking output: url: images/heavy inking_17_3.0.png - text: heavy inking output: url: images/heavy inking_19_3.0.png - text: heavy inking output: url: images/heavy inking_20_3.0.png - text: heavy inking output: url: images/heavy inking_21_3.0.png - text: heavy inking output: url: images/heavy inking_22_3.0.png tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers license: "mit" inference: false instance_prompt: "heavy inking" base_model: "stabilityai/stable-diffusion-xl-base-1.0" --- # ntcai.xyz slider - heavy inking (SDXL LoRA) \| Strength: -3 \| Strength: 0 \| Strength: 3 \| \| --- \| --- \| --- \| \| <img src="images/heavy inking_17_-3.0.png" width=256 height=256 /> \| <img src="images/heavy inking_17_0.0.png" width=256 height=256 /> \| <img src="images/heavy inking_17_3.0.png" width=256 height=256 /> \| \| <img src="images/heavy inking_19_-3.0.png" width=256 height=256 /> \| <img src="images/heavy inking_19_0.0.png" width=256 height=256 /> \| <img src="images/heavy inking_19_3.0.png" width=256 height=256 /> \| \| <img src="images/heavy inking_20_-3.0.png" width=256 height=256 /> \| <img src="images/heavy inking_20_0.0.png" width=256 height=256 /> \| <img src="images/heavy inking_20_3.0.png" width=256 height=256 /> \| ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` heavy inking ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.heavy-inking', weight_name='heavy inking.safetensors', adapter_name="heavy inking") # Activate the LoRA pipe.set_adapters(["heavy inking"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, heavy inking" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 1140+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities. Your support on Patreon will allow us to continue developing and refining new models. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
tenyaiida/ArcherEMIYA	tenyaiida	2024-01-25T01:20:26Z	0	1	null	[ "voice", "license:mit", "region:us" ]	null	2023-07-06T21:05:42Z	--- license: mit tags: - voice --- # Voice model of Archer / EMIYA from Fate/Stay Night <!-- Provide a quick summary of what the model is/does. --> Voice model was trained with Mangio-Crepe, using clips from FGO and FGO Arcade. ## Model Details Update 2024/1/3: The voice has gotten better since the dataset has been updated. Please use RMVPE when sampling with speaking clips that involve some distortion such as reverb, even though the model was trained with Mangio-Crepe. When singing, it's better to use Mangio-Crepe. With other clips, I can't tell a difference between RMVPE and Mangio-Crepe. ### Warnings They may not come out as amazing as they should, so extra tweaking may be necessary. Archer has a very low voice, and using it with higher voice models may not sound correct without pitch correction.
ashercn97/avi-1.1b	ashercn97	2024-01-25T01:17:05Z	11	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "he", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-24T20:05:15Z	--- license: apache-2.0 language: - he - en pipeline_tag: text-generation --- # Avi 1.1B Avi is a fine tune of TinyLlama. While TinyLlama originally only spoke English, Avi was trained on Hebrew data, so it speaks hebrew. ## Data Avi was trained on the Platypus and Alpaca-GPT4 datasets, machine translated into Hebrew. While I know this is not the optimal approach, it is what I did.
Kwabena/results	Kwabena	2024-01-25T00:48:43Z	0	0	null	[ "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-01-25T00:41:09Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0307 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1301 \| 0.05 \| 20 \| 0.1112 \| \| 0.14 \| 0.09 \| 40 \| 0.1021 \| \| 0.1582 \| 0.14 \| 60 \| 0.1935 \| \| 0.1106 \| 0.19 \| 80 \| 0.1298 \| \| 0.1646 \| 0.23 \| 100 \| 0.1226 \| \| 0.0748 \| 0.28 \| 120 \| 0.0889 \| \| 0.1169 \| 0.33 \| 140 \| 0.0966 \| \| 0.1127 \| 0.38 \| 160 \| 0.0706 \| \| 0.0899 \| 0.42 \| 180 \| 0.0696 \| \| 0.1202 \| 0.47 \| 200 \| 0.1355 \| \| 0.0491 \| 0.52 \| 220 \| 0.0529 \| \| 0.0931 \| 0.56 \| 240 \| 0.0466 \| \| 0.1068 \| 0.61 \| 260 \| 0.0695 \| \| 0.0545 \| 0.66 \| 280 \| 0.0383 \| \| 0.0346 \| 0.7 \| 300 \| 0.0307 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
asun17904/glue-mnli-bert-base-uncased-regularized-l2	asun17904	2024-01-25T00:44:43Z	0	0	pytorch	[ "pytorch", "en", "license:mit", "region:us" ]	null	2024-01-24T07:08:38Z	--- language: en license: mit library_name: pytorch --- # Knowledge Continuity Regularized Network Dataset: GLUE Trainer Hyperparameters: - `lr` = 5e-05 - `per_device_batch_size` = 16 - `gradient_accumulation_steps` = 1 - `weight_decay` = 1e-09 - `seed` = 42 Regularization Hyperparameters - `numerical stability denominator constant` = 0.01 - `lambda` = 0.02 - `alpha` = 2.0 - `beta` = 1.0 Extended Logs: \|eval_loss\|eval_accuracy\|epoch\| \|--\|--\|--\| \|24.363\|0.790\|1.0\| \|23.650\|0.813\|2.0\|
DouglasPontes/2020-Q2-75p-filtered_combined75	DouglasPontes	2024-01-25T00:43:54Z	7	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:DouglasPontes/2020-Q1-filtered_tweets", "base_model:finetune:DouglasPontes/2020-Q1-filtered_tweets", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-01-20T17:45:37Z	--- base_model: DouglasPontes/2020-Q1-filtered_tweets tags: - generated_from_trainer model-index: - name: 2020-Q2-75p-filtered results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 2020-Q2-75p-filtered This model is a fine-tuned version of [DouglasPontes/2020-Q1-filtered_tweets](https://huggingface.co/DouglasPontes/2020-Q1-filtered_tweets) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2312 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.1e-07 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2400000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-------:\|:---------------:\| \| No log \| 0.02 \| 8000 \| 2.4865 \| \| 2.6592 \| 0.04 \| 16000 \| 2.4598 \| \| 2.6592 \| 0.07 \| 24000 \| 2.4472 \| \| 2.6211 \| 0.09 \| 32000 \| 2.4341 \| \| 2.6211 \| 0.11 \| 40000 \| 2.4223 \| \| 2.6048 \| 0.13 \| 48000 \| 2.4217 \| \| 2.6048 \| 0.16 \| 56000 \| 2.4184 \| \| 2.5861 \| 0.18 \| 64000 \| 2.4062 \| \| 2.5861 \| 0.2 \| 72000 \| 2.3919 \| \| 2.5736 \| 0.22 \| 80000 \| 2.3896 \| \| 2.5736 \| 0.25 \| 88000 \| 2.3951 \| \| 2.5559 \| 0.27 \| 96000 \| 2.3903 \| \| 2.5559 \| 0.29 \| 104000 \| 2.3836 \| \| 2.5551 \| 0.31 \| 112000 \| 2.3749 \| \| 2.5551 \| 0.34 \| 120000 \| 2.3794 \| \| 2.5371 \| 0.36 \| 128000 \| 2.3733 \| \| 2.5371 \| 0.38 \| 136000 \| 2.3703 \| \| 2.5417 \| 0.4 \| 144000 \| 2.3662 \| \| 2.5417 \| 0.43 \| 152000 \| 2.3728 \| \| 2.5316 \| 0.45 \| 160000 \| 2.3643 \| \| 2.5316 \| 0.47 \| 168000 \| 2.3568 \| \| 2.5296 \| 0.49 \| 176000 \| 2.3555 \| \| 2.5296 \| 0.52 \| 184000 \| 2.3506 \| \| 2.5215 \| 0.54 \| 192000 \| 2.3482 \| \| 2.5215 \| 0.56 \| 200000 \| 2.3514 \| \| 2.5274 \| 0.58 \| 208000 \| 2.3531 \| \| 2.5274 \| 0.61 \| 216000 \| 2.3463 \| \| 2.5215 \| 0.63 \| 224000 \| 2.3470 \| \| 2.5215 \| 0.65 \| 232000 \| 2.3407 \| \| 2.5096 \| 0.67 \| 240000 \| 2.3400 \| \| 2.5096 \| 0.7 \| 248000 \| 2.3402 \| \| 2.5176 \| 0.72 \| 256000 \| 2.3308 \| \| 2.5176 \| 0.74 \| 264000 \| 2.3342 \| \| 2.5048 \| 0.76 \| 272000 \| 2.3333 \| \| 2.5048 \| 0.79 \| 280000 \| 2.3288 \| \| 2.4979 \| 0.81 \| 288000 \| 2.3298 \| \| 2.4979 \| 0.83 \| 296000 \| 2.3237 \| \| 2.4963 \| 0.85 \| 304000 \| 2.3266 \| \| 2.4963 \| 0.88 \| 312000 \| 2.3197 \| \| 2.4972 \| 0.9 \| 320000 \| 2.3271 \| \| 2.4972 \| 0.92 \| 328000 \| 2.3275 \| \| 2.4969 \| 0.94 \| 336000 \| 2.3210 \| \| 2.4969 \| 0.97 \| 344000 \| 2.3222 \| \| 2.4961 \| 0.99 \| 352000 \| 2.3242 \| \| 2.4961 \| 1.01 \| 360000 \| 2.3155 \| \| 2.49 \| 1.03 \| 368000 \| 2.3175 \| \| 2.49 \| 1.06 \| 376000 \| 2.3076 \| \| 2.4847 \| 1.08 \| 384000 \| 2.3138 \| \| 2.4847 \| 1.1 \| 392000 \| 2.3183 \| \| 2.4767 \| 1.12 \| 400000 \| 2.3118 \| \| 2.4767 \| 1.15 \| 408000 \| 2.3152 \| \| 2.4788 \| 1.17 \| 416000 \| 2.3089 \| \| 2.4788 \| 1.19 \| 424000 \| 2.3051 \| \| 2.4738 \| 1.21 \| 432000 \| 2.3102 \| \| 2.4738 \| 1.24 \| 440000 \| 2.3069 \| \| 2.4635 \| 1.26 \| 448000 \| 2.3004 \| \| 2.4635 \| 1.28 \| 456000 \| 2.3066 \| \| 2.4828 \| 1.3 \| 464000 \| 2.3078 \| \| 2.4828 \| 1.32 \| 472000 \| 2.3072 \| \| 2.4675 \| 1.35 \| 480000 \| 2.3073 \| \| 2.4675 \| 1.37 \| 488000 \| 2.3014 \| \| 2.4676 \| 1.39 \| 496000 \| 2.2987 \| \| 2.4676 \| 1.41 \| 504000 \| 2.2988 \| \| 2.4678 \| 1.44 \| 512000 \| 2.2971 \| \| 2.4678 \| 1.46 \| 520000 \| 2.2969 \| \| 2.4634 \| 1.48 \| 528000 \| 2.2990 \| \| 2.4634 \| 1.5 \| 536000 \| 2.2869 \| \| 2.4657 \| 1.53 \| 544000 \| 2.2936 \| \| 2.4657 \| 1.55 \| 552000 \| 2.2915 \| \| 2.4607 \| 1.57 \| 560000 \| 2.2903 \| \| 2.4607 \| 1.59 \| 568000 \| 2.2934 \| \| 2.4558 \| 1.62 \| 576000 \| 2.2845 \| \| 2.4558 \| 1.64 \| 584000 \| 2.2897 \| \| 2.4662 \| 1.66 \| 592000 \| 2.2928 \| \| 2.4662 \| 1.68 \| 600000 \| 2.2861 \| \| 2.4658 \| 1.71 \| 608000 \| 2.2883 \| \| 2.4658 \| 1.73 \| 616000 \| 2.2878 \| \| 2.4533 \| 1.75 \| 624000 \| 2.2892 \| \| 2.4533 \| 1.77 \| 632000 \| 2.2886 \| \| 2.4575 \| 1.8 \| 640000 \| 2.2894 \| \| 2.4575 \| 1.82 \| 648000 \| 2.2871 \| \| 2.4565 \| 1.84 \| 656000 \| 2.2798 \| \| 2.4565 \| 1.86 \| 664000 \| 2.2877 \| \| 2.4548 \| 1.89 \| 672000 \| 2.2859 \| \| 2.4548 \| 1.91 \| 680000 \| 2.2787 \| \| 2.4507 \| 1.93 \| 688000 \| 2.2780 \| \| 2.4507 \| 1.95 \| 696000 \| 2.2826 \| \| 2.4455 \| 1.98 \| 704000 \| 2.2838 \| \| 2.4455 \| 2.0 \| 712000 \| 2.2764 \| \| 2.4516 \| 2.02 \| 720000 \| 2.2814 \| \| 2.4516 \| 2.04 \| 728000 \| 2.2807 \| \| 2.445 \| 2.07 \| 736000 \| 2.2740 \| \| 2.445 \| 2.09 \| 744000 \| 2.2780 \| \| 2.4466 \| 2.11 \| 752000 \| 2.2775 \| \| 2.4466 \| 2.13 \| 760000 \| 2.2783 \| \| 2.4476 \| 2.16 \| 768000 \| 2.2763 \| \| 2.4476 \| 2.18 \| 776000 \| 2.2737 \| \| 2.4449 \| 2.2 \| 784000 \| 2.2753 \| \| 2.4449 \| 2.22 \| 792000 \| 2.2762 \| \| 2.4424 \| 2.25 \| 800000 \| 2.2767 \| \| 2.4424 \| 2.27 \| 808000 \| 2.2702 \| \| 2.4528 \| 2.29 \| 816000 \| 2.2655 \| \| 2.4528 \| 2.31 \| 824000 \| 2.2727 \| \| 2.4523 \| 2.34 \| 832000 \| 2.2733 \| \| 2.4523 \| 2.36 \| 840000 \| 2.2654 \| \| 2.4395 \| 2.38 \| 848000 \| 2.2674 \| \| 2.4395 \| 2.4 \| 856000 \| 2.2754 \| \| 2.434 \| 2.43 \| 864000 \| 2.2722 \| \| 2.434 \| 2.45 \| 872000 \| 2.2666 \| \| 2.4407 \| 2.47 \| 880000 \| 2.2656 \| \| 2.4407 \| 2.49 \| 888000 \| 2.2654 \| \| 2.4352 \| 2.52 \| 896000 \| 2.2630 \| \| 2.4352 \| 2.54 \| 904000 \| 2.2662 \| \| 2.4393 \| 2.56 \| 912000 \| 2.2692 \| \| 2.4393 \| 2.58 \| 920000 \| 2.2558 \| \| 2.4378 \| 2.61 \| 928000 \| 2.2619 \| \| 2.4378 \| 2.63 \| 936000 \| 2.2614 \| \| 2.4392 \| 2.65 \| 944000 \| 2.2578 \| \| 2.4392 \| 2.67 \| 952000 \| 2.2672 \| \| 2.437 \| 2.69 \| 960000 \| 2.2598 \| \| 2.437 \| 2.72 \| 968000 \| 2.2633 \| \| 2.4388 \| 2.74 \| 976000 \| 2.2566 \| \| 2.4388 \| 2.76 \| 984000 \| 2.2551 \| \| 2.4386 \| 2.78 \| 992000 \| 2.2606 \| \| 2.4386 \| 2.81 \| 1000000 \| 2.2634 \| \| 2.4402 \| 2.83 \| 1008000 \| 2.2641 \| \| 2.4402 \| 2.85 \| 1016000 \| 2.2619 \| \| 2.4442 \| 2.87 \| 1024000 \| 2.2584 \| \| 2.4442 \| 2.9 \| 1032000 \| 2.2579 \| \| 2.4327 \| 2.92 \| 1040000 \| 2.2523 \| \| 2.4327 \| 2.94 \| 1048000 \| 2.2562 \| \| 2.4289 \| 2.96 \| 1056000 \| 2.2593 \| \| 2.4289 \| 2.99 \| 1064000 \| 2.2562 \| \| 2.4319 \| 3.01 \| 1072000 \| 2.2536 \| \| 2.4319 \| 3.03 \| 1080000 \| 2.2603 \| \| 2.4174 \| 3.05 \| 1088000 \| 2.2549 \| \| 2.4174 \| 3.08 \| 1096000 \| 2.2595 \| \| 2.4155 \| 3.1 \| 1104000 \| 2.2555 \| \| 2.4155 \| 3.12 \| 1112000 \| 2.2501 \| \| 2.427 \| 3.14 \| 1120000 \| 2.2528 \| \| 2.427 \| 3.17 \| 1128000 \| 2.2529 \| \| 2.4222 \| 3.19 \| 1136000 \| 2.2536 \| \| 2.4222 \| 3.21 \| 1144000 \| 2.2582 \| \| 2.4232 \| 3.23 \| 1152000 \| 2.2522 \| \| 2.4232 \| 3.26 \| 1160000 \| 2.2525 \| \| 2.4252 \| 3.28 \| 1168000 \| 2.2538 \| \| 2.4252 \| 3.3 \| 1176000 \| 2.2512 \| \| 2.4209 \| 3.32 \| 1184000 \| 2.2557 \| \| 2.4209 \| 3.35 \| 1192000 \| 2.2445 \| \| 2.4243 \| 3.37 \| 1200000 \| 2.2570 \| \| 2.4243 \| 3.39 \| 1208000 \| 2.2539 \| \| 2.4278 \| 3.41 \| 1216000 \| 2.2514 \| \| 2.4278 \| 3.44 \| 1224000 \| 2.2454 \| \| 2.4286 \| 3.46 \| 1232000 \| 2.2463 \| \| 2.4286 \| 3.48 \| 1240000 \| 2.2506 \| \| 2.4274 \| 3.5 \| 1248000 \| 2.2427 \| \| 2.4274 \| 3.53 \| 1256000 \| 2.2535 \| \| 2.4201 \| 3.55 \| 1264000 \| 2.2517 \| \| 2.4201 \| 3.57 \| 1272000 \| 2.2436 \| \| 2.4233 \| 3.59 \| 1280000 \| 2.2430 \| \| 2.4233 \| 3.62 \| 1288000 \| 2.2470 \| \| 2.4183 \| 3.64 \| 1296000 \| 2.2446 \| \| 2.4183 \| 3.66 \| 1304000 \| 2.2539 \| \| 2.428 \| 3.68 \| 1312000 \| 2.2492 \| \| 2.428 \| 3.71 \| 1320000 \| 2.2544 \| \| 2.4206 \| 3.73 \| 1328000 \| 2.2478 \| \| 2.4206 \| 3.75 \| 1336000 \| 2.2420 \| \| 2.4287 \| 3.77 \| 1344000 \| 2.2442 \| \| 2.4287 \| 3.8 \| 1352000 \| 2.2426 \| \| 2.4297 \| 3.82 \| 1360000 \| 2.2426 \| \| 2.4297 \| 3.84 \| 1368000 \| 2.2481 \| \| 2.4185 \| 3.86 \| 1376000 \| 2.2449 \| \| 2.4185 \| 3.89 \| 1384000 \| 2.2468 \| \| 2.4217 \| 3.91 \| 1392000 \| 2.2467 \| \| 2.4217 \| 3.93 \| 1400000 \| 2.2463 \| \| 2.4144 \| 3.95 \| 1408000 \| 2.2482 \| \| 2.4144 \| 3.97 \| 1416000 \| 2.2424 \| \| 2.4175 \| 4.0 \| 1424000 \| 2.2415 \| \| 2.4175 \| 4.02 \| 1432000 \| 2.2451 \| \| 2.4169 \| 4.04 \| 1440000 \| 2.2443 \| \| 2.4169 \| 4.06 \| 1448000 \| 2.2389 \| \| 2.4142 \| 4.09 \| 1456000 \| 2.2377 \| \| 2.4142 \| 4.11 \| 1464000 \| 2.2399 \| \| 2.4122 \| 4.13 \| 1472000 \| 2.2447 \| \| 2.4122 \| 4.15 \| 1480000 \| 2.2456 \| \| 2.4166 \| 4.18 \| 1488000 \| 2.2451 \| \| 2.4166 \| 4.2 \| 1496000 \| 2.2369 \| \| 2.4165 \| 4.22 \| 1504000 \| 2.2426 \| \| 2.4165 \| 4.24 \| 1512000 \| 2.2384 \| \| 2.4204 \| 4.27 \| 1520000 \| 2.2454 \| \| 2.4204 \| 4.29 \| 1528000 \| 2.2422 \| \| 2.4192 \| 4.31 \| 1536000 \| 2.2423 \| \| 2.4192 \| 4.33 \| 1544000 \| 2.2435 \| \| 2.4167 \| 4.36 \| 1552000 \| 2.2451 \| \| 2.4167 \| 4.38 \| 1560000 \| 2.2443 \| \| 2.4124 \| 4.4 \| 1568000 \| 2.2430 \| \| 2.4124 \| 4.42 \| 1576000 \| 2.2422 \| \| 2.406 \| 4.45 \| 1584000 \| 2.2357 \| \| 2.406 \| 4.47 \| 1592000 \| 2.2395 \| \| 2.4166 \| 4.49 \| 1600000 \| 2.2378 \| \| 2.4166 \| 4.51 \| 1608000 \| 2.2420 \| \| 2.4144 \| 4.54 \| 1616000 \| 2.2402 \| \| 2.4144 \| 4.56 \| 1624000 \| 2.2384 \| \| 2.4219 \| 4.58 \| 1632000 \| 2.2438 \| \| 2.4219 \| 4.6 \| 1640000 \| 2.2455 \| \| 2.4061 \| 4.63 \| 1648000 \| 2.2397 \| \| 2.4061 \| 4.65 \| 1656000 \| 2.2354 \| \| 2.411 \| 4.67 \| 1664000 \| 2.2393 \| \| 2.411 \| 4.69 \| 1672000 \| 2.2388 \| \| 2.4125 \| 4.72 \| 1680000 \| 2.2406 \| \| 2.4125 \| 4.74 \| 1688000 \| 2.2330 \| \| 2.4092 \| 4.76 \| 1696000 \| 2.2336 \| \| 2.4092 \| 4.78 \| 1704000 \| 2.2398 \| \| 2.4078 \| 4.81 \| 1712000 \| 2.2368 \| \| 2.4078 \| 4.83 \| 1720000 \| 2.2361 \| \| 2.4185 \| 4.85 \| 1728000 \| 2.2378 \| \| 2.4185 \| 4.87 \| 1736000 \| 2.2339 \| \| 2.4088 \| 4.9 \| 1744000 \| 2.2366 \| \| 2.4088 \| 4.92 \| 1752000 \| 2.2385 \| \| 2.4095 \| 4.94 \| 1760000 \| 2.2337 \| \| 2.4095 \| 4.96 \| 1768000 \| 2.2413 \| \| 2.4078 \| 4.99 \| 1776000 \| 2.2377 \| \| 2.4078 \| 5.01 \| 1784000 \| 2.2302 \| \| 2.4073 \| 5.03 \| 1792000 \| 2.2357 \| \| 2.4073 \| 5.05 \| 1800000 \| 2.2384 \| \| 2.4073 \| 5.08 \| 1808000 \| 2.2322 \| \| 2.4073 \| 5.1 \| 1816000 \| 2.2344 \| \| 2.4043 \| 5.12 \| 1824000 \| 2.2327 \| \| 2.4043 \| 5.14 \| 1832000 \| 2.2350 \| \| 2.4082 \| 5.17 \| 1840000 \| 2.2376 \| \| 2.4082 \| 5.19 \| 1848000 \| 2.2363 \| \| 2.4073 \| 5.21 \| 1856000 \| 2.2323 \| \| 2.4073 \| 5.23 \| 1864000 \| 2.2419 \| \| 2.4148 \| 5.26 \| 1872000 \| 2.2293 \| \| 2.4148 \| 5.28 \| 1880000 \| 2.2346 \| \| 2.4098 \| 5.3 \| 1888000 \| 2.2372 \| \| 2.4098 \| 5.32 \| 1896000 \| 2.2371 \| \| 2.407 \| 5.34 \| 1904000 \| 2.2397 \| \| 2.407 \| 5.37 \| 1912000 \| 2.2300 \| \| 2.4108 \| 5.39 \| 1920000 \| 2.2317 \| \| 2.4108 \| 5.41 \| 1928000 \| 2.2350 \| \| 2.4168 \| 5.43 \| 1936000 \| 2.2343 \| \| 2.4168 \| 5.46 \| 1944000 \| 2.2327 \| \| 2.4113 \| 5.48 \| 1952000 \| 2.2363 \| \| 2.4113 \| 5.5 \| 1960000 \| 2.2314 \| \| 2.4131 \| 5.52 \| 1968000 \| 2.2303 \| \| 2.4131 \| 5.55 \| 1976000 \| 2.2353 \| \| 2.4129 \| 5.57 \| 1984000 \| 2.2353 \| \| 2.4129 \| 5.59 \| 1992000 \| 2.2296 \| \| 2.4129 \| 5.61 \| 2000000 \| 2.2314 \| \| 2.4129 \| 5.64 \| 2008000 \| 2.2288 \| \| 2.4045 \| 5.66 \| 2016000 \| 2.2347 \| \| 2.4045 \| 5.68 \| 2024000 \| 2.2349 \| \| 2.4089 \| 5.7 \| 2032000 \| 2.2310 \| \| 2.4089 \| 5.73 \| 2040000 \| 2.2342 \| \| 2.4091 \| 5.75 \| 2048000 \| 2.2320 \| \| 2.4091 \| 5.77 \| 2056000 \| 2.2311 \| \| 2.4137 \| 5.79 \| 2064000 \| 2.2278 \| \| 2.4137 \| 5.82 \| 2072000 \| 2.2344 \| \| 2.4063 \| 5.84 \| 2080000 \| 2.2339 \| \| 2.4063 \| 5.86 \| 2088000 \| 2.2271 \| \| 2.4046 \| 5.88 \| 2096000 \| 2.2263 \| \| 2.4046 \| 5.91 \| 2104000 \| 2.2369 \| \| 2.4105 \| 5.93 \| 2112000 \| 2.2330 \| \| 2.4105 \| 5.95 \| 2120000 \| 2.2361 \| \| 2.4045 \| 5.97 \| 2128000 \| 2.2320 \| \| 2.4045 \| 6.0 \| 2136000 \| 2.2283 \| \| 2.4093 \| 6.02 \| 2144000 \| 2.2262 \| \| 2.4093 \| 6.04 \| 2152000 \| 2.2294 \| \| 2.4109 \| 6.06 \| 2160000 \| 2.2334 \| \| 2.4109 \| 6.09 \| 2168000 \| 2.2363 \| \| 2.4061 \| 6.11 \| 2176000 \| 2.2309 \| \| 2.4061 \| 6.13 \| 2184000 \| 2.2269 \| \| 2.4007 \| 6.15 \| 2192000 \| 2.2369 \| \| 2.4007 \| 6.18 \| 2200000 \| 2.2297 \| \| 2.4034 \| 6.2 \| 2208000 \| 2.2267 \| \| 2.4034 \| 6.22 \| 2216000 \| 2.2310 \| \| 2.4049 \| 6.24 \| 2224000 \| 2.2362 \| \| 2.4049 \| 6.27 \| 2232000 \| 2.2319 \| \| 2.4052 \| 6.29 \| 2240000 \| 2.2308 \| \| 2.4052 \| 6.31 \| 2248000 \| 2.2225 \| \| 2.4102 \| 6.33 \| 2256000 \| 2.2366 \| \| 2.4102 \| 6.36 \| 2264000 \| 2.2327 \| \| 2.4046 \| 6.38 \| 2272000 \| 2.2305 \| \| 2.4046 \| 6.4 \| 2280000 \| 2.2309 \| \| 2.4066 \| 6.42 \| 2288000 \| 2.2291 \| \| 2.4066 \| 6.45 \| 2296000 \| 2.2301 \| \| 2.4041 \| 6.47 \| 2304000 \| 2.2378 \| \| 2.4041 \| 6.49 \| 2312000 \| 2.2317 \| \| 2.4081 \| 6.51 \| 2320000 \| 2.2326 \| \| 2.4081 \| 6.54 \| 2328000 \| 2.2412 \| \| 2.4147 \| 6.56 \| 2336000 \| 2.2349 \| \| 2.4147 \| 6.58 \| 2344000 \| 2.2296 \| \| 2.4105 \| 6.6 \| 2352000 \| 2.2313 \| \| 2.4105 \| 6.62 \| 2360000 \| 2.2297 \| \| 2.4096 \| 6.65 \| 2368000 \| 2.2241 \| \| 2.4096 \| 6.67 \| 2376000 \| 2.2322 \| \| 2.4089 \| 6.69 \| 2384000 \| 2.2344 \| \| 2.4089 \| 6.71 \| 2392000 \| 2.2291 \| \| 2.4048 \| 6.74 \| 2400000 \| 2.2274 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0
TheBloke/FusionNet_34Bx2_MoE-GGUF	TheBloke	2024-01-25T00:28:42Z	221	5	transformers	[ "transformers", "gguf", "mixtral", "moe", "text-generation", "en", "base_model:TomGrc/FusionNet_34Bx2_MoE", "base_model:quantized:TomGrc/FusionNet_34Bx2_MoE", "license:mit", "region:us", "conversational" ]	text-generation	2024-01-24T22:49:13Z	--- base_model: TomGrc/FusionNet_34Bx2_MoE inference: false language: - en license: mit model_creator: Suqin Zhang model_name: FusionNet 34Bx2 MoE model_type: mixtral pipeline_tag: text-generation prompt_template: '[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ' quantized_by: TheBloke tags: - moe --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # FusionNet 34Bx2 MoE - GGUF - Model creator: [Suqin Zhang](https://huggingface.co/TomGrc) - Original model: [FusionNet 34Bx2 MoE](https://huggingface.co/TomGrc/FusionNet_34Bx2_MoE) <!-- description start --> ## Description This repo contains GGUF format model files for [Suqin Zhang's FusionNet 34Bx2 MoE](https://huggingface.co/TomGrc/FusionNet_34Bx2_MoE). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF) * [Suqin Zhang's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TomGrc/FusionNet_34Bx2_MoE) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Llama-2-Chat ``` [INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [fusionnet_34bx2_moe.Q2_K.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q2_K.gguf) \| Q2_K \| 2 \| 22.39 GB\| 24.89 GB \| significant quality loss - not recommended for most purposes \| \| [fusionnet_34bx2_moe.Q3_K_S.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 26.32 GB\| 28.82 GB \| very small, high quality loss \| \| [fusionnet_34bx2_moe.Q3_K_M.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 29.24 GB\| 31.74 GB \| very small, high quality loss \| \| [fusionnet_34bx2_moe.Q3_K_L.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 31.77 GB\| 34.27 GB \| small, substantial quality loss \| \| [fusionnet_34bx2_moe.Q4_0.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q4_0.gguf) \| Q4_0 \| 4 \| 34.33 GB\| 36.83 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [fusionnet_34bx2_moe.Q4_K_S.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 34.59 GB\| 37.09 GB \| small, greater quality loss \| \| [fusionnet_34bx2_moe.Q4_K_M.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 36.66 GB\| 39.16 GB \| medium, balanced quality - recommended \| \| [fusionnet_34bx2_moe.Q5_0.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q5_0.gguf) \| Q5_0 \| 5 \| 41.88 GB\| 44.38 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [fusionnet_34bx2_moe.Q5_K_S.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 41.88 GB\| 44.38 GB \| large, low quality loss - recommended \| \| [fusionnet_34bx2_moe.Q5_K_M.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 43.08 GB\| 45.58 GB \| large, very low quality loss - recommended \| \| [fusionnet_34bx2_moe.Q6_K.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q6_K.gguf) \| Q6_K \| 6 \| 49.89 GB\| 52.39 GB \| very large, extremely low quality loss \| \| fusionnet_34bx2_moe.Q8_0.gguf \| Q8_0 \| 8 \| 64.62 GB\| 67.12 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ### Q6_K and Q8_0 files are split and require joining Note: HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files. <details> <summary>Click for instructions regarding Q6_K and Q8_0 files</summary> ### q6_K Please download: * `fusionnet_34bx2_moe.Q6_K.gguf-split-a` * `fusionnet_34bx2_moe.Q6_K.gguf-split-b` ### q8_0 Please download: * `fusionnet_34bx2_moe.Q8_0.gguf-split-a` * `fusionnet_34bx2_moe.Q8_0.gguf-split-b` To join the files, do the following: Linux and macOS: ``` cat fusionnet_34bx2_moe.Q6_K.gguf-split-* > fusionnet_34bx2_moe.Q6_K.gguf && rm fusionnet_34bx2_moe.Q6_K.gguf-split-* cat fusionnet_34bx2_moe.Q8_0.gguf-split-* > fusionnet_34bx2_moe.Q8_0.gguf && rm fusionnet_34bx2_moe.Q8_0.gguf-split-* ``` Windows command line: ``` COPY /B fusionnet_34bx2_moe.Q6_K.gguf-split-a + fusionnet_34bx2_moe.Q6_K.gguf-split-b fusionnet_34bx2_moe.Q6_K.gguf del fusionnet_34bx2_moe.Q6_K.gguf-split-a fusionnet_34bx2_moe.Q6_K.gguf-split-b COPY /B fusionnet_34bx2_moe.Q8_0.gguf-split-a + fusionnet_34bx2_moe.Q8_0.gguf-split-b fusionnet_34bx2_moe.Q8_0.gguf del fusionnet_34bx2_moe.Q8_0.gguf-split-a fusionnet_34bx2_moe.Q8_0.gguf-split-b ``` </details> <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/FusionNet_34Bx2_MoE-GGUF and below it, a specific filename to download, such as: fusionnet_34bx2_moe.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/FusionNet_34Bx2_MoE-GGUF fusionnet_34bx2_moe.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/FusionNet_34Bx2_MoE-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/FusionNet_34Bx2_MoE-GGUF fusionnet_34bx2_moe.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m fusionnet_34bx2_moe.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n{prompt} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./fusionnet_34bx2_moe.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n{prompt} [/INST]", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./fusionnet_34bx2_moe.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Suqin Zhang's FusionNet 34Bx2 MoE # FusionNet_34Bx2_MoE Fine-tuned model on English language using MoE method. ## Model description The FusionNet_34Bx2_MoE is a model to experiment with the MoE method, which could significantly increase the performance of the original model. The FusionNet_34Bx2_MoE has 60.8B parameters, and this model is fine-tuned. Enjoy! ## Usage ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("TomGrc/FusionNet_34Bx2_MoE") model = AutoModelForCausalLM.from_pretrained("TomGrc/FusionNet_34Bx2_MoE") ``` <!-- original-model-card end -->
mesolitica/mallam-5b-20k-instructions-v2	mesolitica	2024-01-25T00:24:53Z	20	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "ms", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-27T02:02:09Z	--- language: - ms --- # Full Parameter Finetuning MaLLaM 🌙 5B 20480 context length v2 on Malaysian instructions dataset README at https://github.com/mesolitica/malaya/tree/5.1/session/mistral#mallam-5b We use exact Mistral Instruct chat template. WandB, https://wandb.ai/huseinzol05/fpf-mallam-5b-instructions-16k?workspace=user-huseinzol05 WandB report, https://wandb.ai/huseinzol05/fpf-tinyllama-1.1b-hf-instructions-16k/reports/Instruction-finetuning--Vmlldzo2MzQ3OTcz ## Dataset Dataset gathered at https://huggingface.co/collections/mesolitica/malaysian-synthetic-dataset-656c2673fe7fe0b1e9e25fe2 Notebook to prepare dataset at https://github.com/mesolitica/malaysian-dataset/blob/master/llm-instruction/combine-malay-no-alignment-multitasks-partial-ultrachat-v2.ipynb ## Limitations This model is a quick demonstration that the base model can be easily fine-tuned to achieve some performance. It does have minimal moderation mechanisms. ## how-to ```python from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch import json def parse_mistral_chat(messages, function_call = None): user_query = messages[-1]['content'] users, assistants = [], [] for q in messages[:-1]: if q['role'] == 'user': users.append(q['content']) elif q['role'] == 'assistant': assistants.append(q['content']) texts = ['<s>'] if function_call: fs = [] for f in function_call: f = json.dumps(f, indent=4) fs.append(f) fs = '\n\n'.join(fs) texts.append(f'\n[FUNCTIONCALL]\n{fs}\n') for u, a in zip(users, assistants): texts.append(f'[INST] {u.strip()} [/INST] {a.strip()}</s>') texts.append(f'[INST] {user_query.strip()} [/INST]') prompt = ''.join(texts).strip() return prompt TORCH_DTYPE = 'bfloat16' nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type='nf4', bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE) ) tokenizer = AutoTokenizer.from_pretrained('mesolitica/mallam-5b-20k-instructions-v2') model = AutoModelForCausalLM.from_pretrained( 'mesolitica/mallam-5b-20k-instructions-v2', use_flash_attention_2 = True, quantization_config = nf4_config ) messages = [ {'role': 'user', 'content': 'kwsp tu apa'} ] prompt = parse_mistral_chat(messages) inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda') generate_kwargs = dict( inputs, max_new_tokens=1024, top_p=0.95, top_k=50, temperature=0.9, do_sample=True, num_beams=1, ) r = model.generate(**generate_kwargs) tokenizer.decode(r[0]) ``` ```text <s> [INST] kwsp tu apa [/INST]KWSP bermaksud Kumpulan Wang Simpanan Pekerja. Ia adalah sebuah institusi simpanan persaraan yang ditubuhkan oleh Kementerian Kewangan Malaysia untuk tujuan mengumpul simpanan ahli untuk dibayar pada umur persaraan, penuh atau penuh persaraan penuh. KWSP ditubuhkan pada tahun 1951 dan mula beroperasi pada tahun 1952. KWSP adalah salah satu institusi simpanan persaraan terbesar di dunia, dengan pangkalan ahli sekitar 14 juta ahli.</s> ```
RecCode/whisper-small-hi	RecCode	2024-01-25T00:15:07Z	63	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-23T05:35:58Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer base_model: openai/whisper-small model-index: - name: whisper-small-hi results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-hi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1606 - Wer: 88.3090 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3591 \| 4.0 \| 1000 \| 0.2347 \| 43.0063 \| \| 0.0548 \| 8.0 \| 2000 \| 0.2610 \| 315.6576 \| \| 0.0144 \| 12.0 \| 3000 \| 0.1812 \| 500.4175 \| \| 0.0001 \| 16.0 \| 4000 \| 0.1606 \| 88.3090 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
yoon1000/ft_0124_korean_2	yoon1000	2024-01-25T00:05:40Z	16	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-24T01:29:30Z	--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer model-index: - name: ft_0124_korean_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ft_0124_korean_2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5092 - Cer: 0.1001 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 25.0263 \| 0.25 \| 500 \| 5.1785 \| 1.0 \| \| 4.7178 \| 0.5 \| 1000 \| 4.7781 \| 1.0 \| \| 4.4523 \| 0.76 \| 1500 \| 4.1013 \| 0.9077 \| \| 3.2148 \| 1.01 \| 2000 \| 2.3856 \| 0.4777 \| \| 2.4229 \| 1.26 \| 2500 \| 1.8502 \| 0.4047 \| \| 2.0145 \| 1.51 \| 3000 \| 1.5497 \| 0.3475 \| \| 1.7916 \| 1.77 \| 3500 \| 1.3324 \| 0.3076 \| \| 1.5849 \| 2.02 \| 4000 \| 1.1873 \| 0.2773 \| \| 1.3956 \| 2.27 \| 4500 \| 1.0617 \| 0.2578 \| \| 1.3086 \| 2.52 \| 5000 \| 0.9643 \| 0.2368 \| \| 1.2211 \| 2.78 \| 5500 \| 0.8894 \| 0.2246 \| \| 1.1562 \| 3.03 \| 6000 \| 0.8537 \| 0.2189 \| \| 1.0729 \| 3.28 \| 6500 \| 0.7973 \| 0.2101 \| \| 1.0089 \| 3.53 \| 7000 \| 0.7549 \| 0.1959 \| \| 1.0027 \| 3.79 \| 7500 \| 0.7327 \| 0.1945 \| \| 0.9496 \| 4.04 \| 8000 \| 0.7082 \| 0.1849 \| \| 0.887 \| 4.29 \| 8500 \| 0.6909 \| 0.1789 \| \| 0.8607 \| 4.54 \| 9000 \| 0.6617 \| 0.1739 \| \| 0.853 \| 4.8 \| 9500 \| 0.6518 \| 0.1730 \| \| 0.8305 \| 5.05 \| 10000 \| 0.6402 \| 0.1657 \| \| 0.774 \| 5.3 \| 10500 \| 0.6365 \| 0.1650 \| \| 0.7621 \| 5.55 \| 11000 \| 0.6206 \| 0.1600 \| \| 0.7553 \| 5.81 \| 11500 \| 0.6080 \| 0.1594 \| \| 0.7186 \| 6.06 \| 12000 \| 0.5951 \| 0.1543 \| \| 0.6772 \| 6.31 \| 12500 \| 0.5814 \| 0.1490 \| \| 0.6752 \| 6.56 \| 13000 \| 0.5815 \| 0.1501 \| \| 0.672 \| 6.81 \| 13500 \| 0.5603 \| 0.1440 \| \| 0.6351 \| 7.07 \| 14000 \| 0.5670 \| 0.1439 \| \| 0.6186 \| 7.32 \| 14500 \| 0.5700 \| 0.1431 \| \| 0.6035 \| 7.57 \| 15000 \| 0.5614 \| 0.1417 \| \| 0.5848 \| 7.82 \| 15500 \| 0.5470 \| 0.1396 \| \| 0.5719 \| 8.08 \| 16000 \| 0.5514 \| 0.1386 \| \| 0.556 \| 8.33 \| 16500 \| 0.5515 \| 0.1376 \| \| 0.5596 \| 8.58 \| 17000 \| 0.5407 \| 0.1325 \| \| 0.5472 \| 8.83 \| 17500 \| 0.5405 \| 0.1349 \| \| 0.5309 \| 9.09 \| 18000 \| 0.5279 \| 0.1295 \| \| 0.5072 \| 9.34 \| 18500 \| 0.5275 \| 0.1310 \| \| 0.5072 \| 9.59 \| 19000 \| 0.5330 \| 0.1272 \| \| 0.4905 \| 9.84 \| 19500 \| 0.5238 \| 0.1262 \| \| 0.4842 \| 10.1 \| 20000 \| 0.5234 \| 0.1237 \| \| 0.4513 \| 10.35 \| 20500 \| 0.5210 \| 0.1231 \| \| 0.4513 \| 10.6 \| 21000 \| 0.5165 \| 0.1208 \| \| 0.4541 \| 10.85 \| 21500 \| 0.5189 \| 0.1207 \| \| 0.4417 \| 11.11 \| 22000 \| 0.5209 \| 0.1192 \| \| 0.4337 \| 11.36 \| 22500 \| 0.5246 \| 0.1191 \| \| 0.4339 \| 11.61 \| 23000 \| 0.5210 \| 0.1183 \| \| 0.4357 \| 11.86 \| 23500 \| 0.4990 \| 0.1162 \| \| 0.4066 \| 12.12 \| 24000 \| 0.4956 \| 0.1132 \| \| 0.3932 \| 12.37 \| 24500 \| 0.5064 \| 0.1148 \| \| 0.384 \| 12.62 \| 25000 \| 0.5011 \| 0.1134 \| \| 0.3902 \| 12.87 \| 25500 \| 0.5064 \| 0.1130 \| \| 0.3883 \| 13.12 \| 26000 \| 0.5128 \| 0.1121 \| \| 0.3625 \| 13.38 \| 26500 \| 0.5140 \| 0.1119 \| \| 0.3648 \| 13.63 \| 27000 \| 0.5091 \| 0.1108 \| \| 0.365 \| 13.88 \| 27500 \| 0.4923 \| 0.1098 \| \| 0.3604 \| 14.13 \| 28000 \| 0.5062 \| 0.1090 \| \| 0.3517 \| 14.39 \| 28500 \| 0.5007 \| 0.1089 \| \| 0.3485 \| 14.64 \| 29000 \| 0.4956 \| 0.1081 \| \| 0.3407 \| 14.89 \| 29500 \| 0.5090 \| 0.1084 \| \| 0.333 \| 15.14 \| 30000 \| 0.5018 \| 0.1067 \| \| 0.3211 \| 15.4 \| 30500 \| 0.5114 \| 0.1063 \| \| 0.3204 \| 15.65 \| 31000 \| 0.4976 \| 0.1053 \| \| 0.3265 \| 15.9 \| 31500 \| 0.4947 \| 0.1046 \| \| 0.3169 \| 16.15 \| 32000 \| 0.4988 \| 0.1043 \| \| 0.304 \| 16.41 \| 32500 \| 0.5115 \| 0.1041 \| \| 0.2944 \| 16.66 \| 33000 \| 0.5144 \| 0.1042 \| \| 0.311 \| 16.91 \| 33500 \| 0.5068 \| 0.1025 \| \| 0.2997 \| 17.16 \| 34000 \| 0.5079 \| 0.1030 \| \| 0.288 \| 17.42 \| 34500 \| 0.5065 \| 0.1019 \| \| 0.2897 \| 17.67 \| 35000 \| 0.5077 \| 0.1016 \| \| 0.2939 \| 17.92 \| 35500 \| 0.5003 \| 0.1017 \| \| 0.2766 \| 18.17 \| 36000 \| 0.5116 \| 0.1013 \| \| 0.2841 \| 18.43 \| 36500 \| 0.5019 \| 0.1010 \| \| 0.2882 \| 18.68 \| 37000 \| 0.5046 \| 0.1008 \| \| 0.2678 \| 18.93 \| 37500 \| 0.5086 \| 0.1013 \| \| 0.269 \| 19.18 \| 38000 \| 0.5108 \| 0.1001 \| \| 0.2815 \| 19.43 \| 38500 \| 0.5111 \| 0.1001 \| \| 0.2668 \| 19.69 \| 39000 \| 0.5091 \| 0.1000 \| \| 0.2715 \| 19.94 \| 39500 \| 0.5092 \| 0.1001 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
k1rby/BeaglePipe-7B-slerp	k1rby	2024-01-25T00:00:37Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1227", "mlabonne/NeuralBeagle14-7B", "base_model:OpenPipe/mistral-ft-optimized-1227", "base_model:merge:OpenPipe/mistral-ft-optimized-1227", "base_model:mlabonne/NeuralBeagle14-7B", "base_model:merge:mlabonne/NeuralBeagle14-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-24T23:56:18Z	--- tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1227 - mlabonne/NeuralBeagle14-7B base_model: - OpenPipe/mistral-ft-optimized-1227 - mlabonne/NeuralBeagle14-7B --- # BeaglePipe-7B-slerp BeaglePipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [OpenPipe/mistral-ft-optimized-1227](https://huggingface.co/OpenPipe/mistral-ft-optimized-1227) * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1227 layer_range: [0, 32] - model: mlabonne/NeuralBeagle14-7B layer_range: [0, 32] merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1227 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "k1rby/BeaglePipe-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
databio/r2v-buenrostro2018-hg19	databio	2024-01-24T23:49:53Z	2	0	null	[ "region:us" ]	null	2024-01-24T23:43:43Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for databio/r2v-buenrostro2018-hg19 ## Model Details This is a single-cell Region2Vec (r2v) model designed to be used with with scEmbed and Region2Vec. It was trained on the Buenrostro2018 dataset. This model should be used to generate embeddings of single cells from scATAC-seq experiments. It produces 100 dimensional embeddings for each single-cell. ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/databio/geniml - Paper: https://www.biorxiv.org/content/10.1101/2023.08.01.551452v1 ## Uses This model should be used for producing low-dimensional embeddings of single-cells. These embeddings can be used for downstream clustering or classification tasks. ## Bias, Risks, and Limitations The Buenrostro2018 dataset comprises 2034 human hematopoietic stem cells from data aligned to hg19. Therefore, it should only be used with other data aligned to hg19. ## How to Get Started with the Model You can use the `geniml` python library to download this model and start encoding your single-cell data: ```python import scanpy as sc from geniml.scembed import ScEmbed adata = sc.read_h5ad("path/to/adata.h5ad") model = ScEmbed("databio/r2v-buenrostro2018-hg19") embeddings = model.encode(adata) ``` ## Training Details ### Training Data The data for this model comes from Buenrostro2018: https://www.sciencedirect.com/science/article/pii/S009286741830446X
grimulkan/Goliath-longLORA-120b-rope8-32k-6bpw_h8_exl2	grimulkan	2024-01-24T23:48:58Z	6	1	transformers	[ "transformers", "llama", "text-generation", "license:llama2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-01-24T22:34:49Z	--- license: llama2 --- This is an EXL2 6-bit quantization of [Goliath-longLORA-120b-rope8-32k-fp16](https://huggingface.co/grimulkan/Goliath-longLORA-120b-rope8-32k-fp16)
shuyuej/prompt_consistency_epoch_5	shuyuej	2024-01-24T23:42:10Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2024-01-24T22:54:36Z	--- license: apache-2.0 --- # 🚀 Adapter Usage ```python # Load the Pre-trained LoRA Adapter model.load_adapter("shuyuej/prompt_consistency_epoch_5") model.enable_adapters() ```
web2savar/w2v-fine-tune-test-no-ws2	web2savar	2024-01-24T23:34:12Z	5	0	transformers	[ "transformers", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_16_0", "base_model:ylacombe/w2v-bert-2.0", "base_model:finetune:ylacombe/w2v-bert-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-24T19:00:18Z	--- base_model: ylacombe/w2v-bert-2.0 tags: - generated_from_trainer datasets: - common_voice_16_0 metrics: - wer model-index: - name: w2v-fine-tune-test-no-ws2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_16_0 type: common_voice_16_0 config: tr split: test args: tr metrics: - name: Wer type: wer value: 0.11088339984899148 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # w2v-fine-tune-test-no-ws2 This model is a fine-tuned version of [ylacombe/w2v-bert-2.0](https://huggingface.co/ylacombe/w2v-bert-2.0) on the common_voice_16_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1513 - Wer: 0.1109 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 2.192 \| 0.22 \| 300 \| 0.2797 \| 0.2985 \| \| 0.2226 \| 0.44 \| 600 \| 0.2989 \| 0.3491 \| \| 0.1941 \| 0.66 \| 900 \| 0.2558 \| 0.2451 \| \| 0.1659 \| 0.88 \| 1200 \| 0.2320 \| 0.2289 \| \| 0.1332 \| 1.1 \| 1500 \| 0.2063 \| 0.1971 \| \| 0.1129 \| 1.31 \| 1800 \| 0.1873 \| 0.2029 \| \| 0.1044 \| 1.53 \| 2100 \| 0.1765 \| 0.1856 \| \| 0.1026 \| 1.75 \| 2400 \| 0.1719 \| 0.1752 \| \| 0.0982 \| 1.97 \| 2700 \| 0.1927 \| 0.2023 \| \| 0.0769 \| 2.19 \| 3000 \| 0.1776 \| 0.1671 \| \| 0.0715 \| 2.41 \| 3300 \| 0.1626 \| 0.1634 \| \| 0.0695 \| 2.63 \| 3600 \| 0.1666 \| 0.1654 \| \| 0.0612 \| 2.85 \| 3900 \| 0.1760 \| 0.1609 \| \| 0.0614 \| 3.07 \| 4200 \| 0.1645 \| 0.1593 \| \| 0.0476 \| 3.29 \| 4500 \| 0.1685 \| 0.1593 \| \| 0.048 \| 3.51 \| 4800 \| 0.1790 \| 0.1583 \| \| 0.0489 \| 3.73 \| 5100 \| 0.1578 \| 0.1535 \| \| 0.0456 \| 3.94 \| 5400 \| 0.1610 \| 0.1617 \| \| 0.041 \| 4.16 \| 5700 \| 0.1559 \| 0.1439 \| \| 0.0367 \| 4.38 \| 6000 \| 0.1536 \| 0.1436 \| \| 0.0321 \| 4.6 \| 6300 \| 0.1591 \| 0.1449 \| \| 0.0349 \| 4.82 \| 6600 \| 0.1616 \| 0.1419 \| \| 0.0308 \| 5.04 \| 6900 \| 0.1501 \| 0.1401 \| \| 0.0233 \| 5.26 \| 7200 \| 0.1588 \| 0.1394 \| \| 0.0253 \| 5.48 \| 7500 \| 0.1633 \| 0.1356 \| \| 0.0254 \| 5.7 \| 7800 \| 0.1522 \| 0.1339 \| \| 0.0245 \| 5.92 \| 8100 \| 0.1598 \| 0.1371 \| \| 0.0189 \| 6.14 \| 8400 \| 0.1497 \| 0.1324 \| \| 0.0174 \| 6.36 \| 8700 \| 0.1487 \| 0.1270 \| \| 0.0178 \| 6.57 \| 9000 \| 0.1397 \| 0.1286 \| \| 0.0173 \| 6.79 \| 9300 \| 0.1495 \| 0.1281 \| \| 0.0178 \| 7.01 \| 9600 \| 0.1462 \| 0.1222 \| \| 0.0124 \| 7.23 \| 9900 \| 0.1516 \| 0.1225 \| \| 0.0121 \| 7.45 \| 10200 \| 0.1554 \| 0.1190 \| \| 0.0128 \| 7.67 \| 10500 \| 0.1453 \| 0.1228 \| \| 0.0113 \| 7.89 \| 10800 \| 0.1468 \| 0.1178 \| \| 0.0086 \| 8.11 \| 11100 \| 0.1556 \| 0.1186 \| \| 0.0085 \| 8.33 \| 11400 \| 0.1507 \| 0.1154 \| \| 0.0073 \| 8.55 \| 11700 \| 0.1494 \| 0.1169 \| \| 0.0079 \| 8.77 \| 12000 \| 0.1507 \| 0.1152 \| \| 0.0089 \| 8.98 \| 12300 \| 0.1456 \| 0.1137 \| \| 0.0062 \| 9.2 \| 12600 \| 0.1518 \| 0.1127 \| \| 0.005 \| 9.42 \| 12900 \| 0.1534 \| 0.1115 \| \| 0.005 \| 9.64 \| 13200 \| 0.1514 \| 0.1110 \| \| 0.0048 \| 9.86 \| 13500 \| 0.1513 \| 0.1109 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
epinnock/deepseek-coder-6.7-evol-feedback	epinnock	2024-01-24T23:30:22Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:deepseek-ai/deepseek-coder-6.7b-instruct", "base_model:finetune:deepseek-ai/deepseek-coder-6.7b-instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-01-22T14:26:35Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: deepseek-ai/deepseek-coder-6.7b-instruct --- # Uploaded model - Developed by: epinnock - License: apache-2.0 - Finetuned from model : deepseek-ai/deepseek-coder-6.7b-instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ondevicellm/tinyllama_mole_sftv2_ultrachat_ep3	ondevicellm	2024-01-24T23:17:33Z	26	0	transformers	[ "transformers", "tensorboard", "safetensors", "mixtralmole", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "custom_code", "dataset:HuggingFaceH4/ultrachat_200k", "base_model:ondevicellm/tinyllama_mole_v1", "base_model:finetune:ondevicellm/tinyllama_mole_v1", "autotrain_compatible", "region:us" ]	text-generation	2024-01-24T09:49:55Z	--- base_model: ondevicellm/tinyllama_mole_v1 tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - HuggingFaceH4/ultrachat_200k model-index: - name: tinyllama_mole_sftv2_ultrachat_ep3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tinyllama_mole_sftv2_ultrachat_ep3 This model is a fine-tuned version of [ondevicellm/tinyllama_mole_v1](https://huggingface.co/ondevicellm/tinyllama_mole_v1) on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set: - Loss: 1.7340 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 120 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.7643 \| 0.09 \| 100 \| 2.7492 \| \| 2.7293 \| 0.18 \| 200 \| 2.7330 \| \| 2.6973 \| 0.26 \| 300 \| 2.6920 \| \| 2.612 \| 0.35 \| 400 \| 2.6290 \| \| 2.5257 \| 0.44 \| 500 \| 2.5470 \| \| 2.4656 \| 0.53 \| 600 \| 2.4527 \| \| 2.3607 \| 0.61 \| 700 \| 2.3681 \| \| 2.2885 \| 0.7 \| 800 \| 2.2988 \| \| 2.2384 \| 0.79 \| 900 \| 2.2397 \| \| 2.1585 \| 0.88 \| 1000 \| 2.1877 \| \| 2.1526 \| 0.96 \| 1100 \| 2.1409 \| \| 2.0845 \| 1.05 \| 1200 \| 2.0986 \| \| 2.049 \| 1.14 \| 1300 \| 2.0603 \| \| 2.0243 \| 1.23 \| 1400 \| 2.0257 \| \| 1.9899 \| 1.31 \| 1500 \| 1.9950 \| \| 1.9706 \| 1.4 \| 1600 \| 1.9675 \| \| 1.9414 \| 1.49 \| 1700 \| 1.9429 \| \| 1.8952 \| 1.58 \| 1800 \| 1.9208 \| \| 1.9038 \| 1.66 \| 1900 \| 1.9013 \| \| 1.8942 \| 1.75 \| 2000 \| 1.8839 \| \| 1.8652 \| 1.84 \| 2100 \| 1.8679 \| \| 1.823 \| 1.93 \| 2200 \| 1.8531 \| \| 1.8394 \| 2.01 \| 2300 \| 1.8394 \| \| 1.8347 \| 2.1 \| 2400 \| 1.8268 \| \| 1.8137 \| 2.19 \| 2500 \| 1.8148 \| \| 1.799 \| 2.28 \| 2600 \| 1.8037 \| \| 1.7774 \| 2.37 \| 2700 \| 1.7931 \| \| 1.771 \| 2.45 \| 2800 \| 1.7832 \| \| 1.7761 \| 2.54 \| 2900 \| 1.7739 \| \| 1.7458 \| 2.63 \| 3000 \| 1.7652 \| \| 1.7683 \| 2.72 \| 3100 \| 1.7570 \| \| 1.7389 \| 2.8 \| 3200 \| 1.7490 \| \| 1.7321 \| 2.89 \| 3300 \| 1.7414 \| \| 1.7418 \| 2.98 \| 3400 \| 1.7340 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2+cu118 - Datasets 2.14.6 - Tokenizers 0.15.0
dongSHE/vis-Mistral-7B-v0.1-ChartDataset-to-PresentationScript	dongSHE	2024-01-24T23:09:26Z	5	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-01-24T23:08:09Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mistral-7B-v0.1 model-index: - name: vis-Mistral-7B-v0.1-ChartDataset-to-PresentationScript results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vis-Mistral-7B-v0.1-ChartDataset-to-PresentationScript This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.37.1 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
STomoya/poolformer_m36.st_safebooru_1k	STomoya	2024-01-24T23:06:01Z	15	0	timm	[ "timm", "pytorch", "safetensors", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-01-24T23:05:16Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for poolformer_m36.st_safebooru_1k ## Model Details - metrics: \|Precision\|Recall\|F1-score\| \|-\|-\|-\| \|0.7831315642353482\|0.5051567178760158\|0.5929485591274398\|
UruseiYatsura/victor_from_just_for_laughs	UruseiYatsura	2024-01-24T22:58:41Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:apache-2.0", "region:us" ]	text-to-image	2024-01-24T22:58:23Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: A cartoon output: url: images/tumblr_lfi2o6k6ri1qdivgo.jpg base_model: runwayml/stable-diffusion-v1-5 instance_prompt: null license: apache-2.0 --- # Victor (Just for Laughs/Juste Pour Rire Mascot) <Gallery /> ## Model description The JFL/JPR Mascor that yelled "MOMMY IT'S OVER" and in French "MAMAN C'EST FINI" ## Download model [Download](/UruseiYatsura/victor_from_just_for_laughs/tree/main) them in the Files & versions tab.
shuyuej/prompt_consistency_epoch_4	shuyuej	2024-01-24T22:56:08Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2024-01-24T22:52:42Z	--- license: apache-2.0 --- # 🚀 Adapter Usage ```python # Load the Pre-trained LoRA Adapter model.load_adapter("shuyuej/prompt_consistency_epoch_4") model.enable_adapters() ```
TheBloke/WestLake-7B-v2-GPTQ	TheBloke	2024-01-24T22:42:31Z	137	8	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "en", "base_model:senseable/WestLake-7B-v2", "base_model:quantized:senseable/WestLake-7B-v2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2024-01-24T22:13:08Z	--- base_model: senseable/WestLake-7B-v2 inference: false language: - en library_name: transformers license: apache-2.0 model_creator: Common Sense model_name: Westlake 7B V2 model_type: mistral prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Westlake 7B V2 - GPTQ - Model creator: [Common Sense](https://huggingface.co/senseable) - Original model: [Westlake 7B V2](https://huggingface.co/senseable/WestLake-7B-v2) <!-- description start --> # Description This repo contains GPTQ model files for [Common Sense's Westlake 7B V2](https://huggingface.co/senseable/WestLake-7B-v2). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WestLake-7B-v2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF) * [Common Sense's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/senseable/WestLake-7B-v2) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Unknown ``` {prompt} ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models. These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 4.16 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 4.57 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 7.52 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 7.68 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 8.17 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 4.29 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/WestLake-7B-v2-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/WestLake-7B-v2-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `WestLake-7B-v2-GPTQ`: ```shell mkdir WestLake-7B-v2-GPTQ huggingface-cli download TheBloke/WestLake-7B-v2-GPTQ --local-dir WestLake-7B-v2-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir WestLake-7B-v2-GPTQ huggingface-cli download TheBloke/WestLake-7B-v2-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir WestLake-7B-v2-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir WestLake-7B-v2-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WestLake-7B-v2-GPTQ --local-dir WestLake-7B-v2-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/WestLake-7B-v2-GPTQ`. - To download from a specific branch, enter for example `TheBloke/WestLake-7B-v2-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `WestLake-7B-v2-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/WestLake-7B-v2-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation( prompt_template, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/WestLake-7B-v2-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Write a story about llamas" system_message = "You are a story writing assistant" prompt_template=f'''{prompt} ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Common Sense's Westlake 7B V2 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585ffb10eeafbd678d4b3fe/jnqnl8a_zYYMqJoBpX8yS.png) Update Notes: Version 2 trained 1 additional epoch cycle for 3 total # Westlake-7Bv2: Role-Play & Text Generation Specialist Model Welcome to the documentation of Westlake-7B, a cutting-edge language model designed for exceptional role-play and text generation tasks. This README file aims to provide an overview of our capabilities, usage guidelines, and potential applications. ## About Westlake-7Bv2 Westlake-7B is built upon a vast corpus of diverse texts, enabling it to generate contextually relevant responses in various scenarios. With its impressive size of 7 billion parameters, this model excels at understanding nuances in language and producing creative outputs. ### Key Features 1. Role-Play: Westlake-7Bv2 can seamlessly adapt to different character personas and engage in dynamic conversations while maintaining consistency throughout the interaction. It can generate believable dialogues across various genres, including fiction, non-fiction, historical events, or even fantasy worlds. 2. Text Generation: This model is proficient at generating original content such as stories, poems, essays, news articles, and more. Its ability to capture the essence of different writing styles makes it an ideal tool for creative writers seeking inspiration or assistance in their projects. 3. Contextual Understanding: Westlake-7B's extensive training allows it to comprehend complex contexts and generate responses that align with given situations. It can handle multiple topics simultaneously, making it versatile across various applications. 4. Continuous Learning: As a language model, Westlake-7B continuously improves its performance through ongoing training on new data sets. This ensures its capabilities remain up-to-date and relevant in an ever-evolving world of communication. ## Usage Guidelines To utilize Westlake-7Bv2 for your projects or experiments, follow these steps: 1. Prompting: Provide clear and concise prompts that outline the desired role-play scenario or text generation task. The quality of output depends heavily on the clarity and relevance of input instructions. 2. Feedback Loop: For optimal results, consider incorporating a feedback loop into your application to refine generated outputs based on user preferences or additional contextual information. This iterative process can significantly enhance the model's performance in specific domains. 3. Ethical Considerations: As with any AI system, ensure responsible usage of Westlake-7B by avoiding harmful content generation or misuse of its capabilities. ## Potential Applications Westlake-7Bv2's versatility makes it suitable for various applications across different industries: 1. Creative Writing: Assist authors in generating new ideas, expanding storylines, or even completing drafts by providing creative suggestions and textual content. 2. Education: Enhance language learning platforms with interactive role-play scenarios to improve students' communication skills and cultural understanding. 3. Gaming: Integrate Westlake-7B into game engines for dynamic non-player character interactions or generating unique questlines based on player choices. 4. Customer Support: Leverage the model's conversational abilities to create chatbots capable of handling complex queries and providing personalized assistance. 5. Social Media: Develop applications that generate engaging content such as captions, status updates, or even entire posts tailored to users' preferences and interests.
TheBloke/WestLake-7B-v2-GGUF	TheBloke	2024-01-24T22:25:54Z	274	20	transformers	[ "transformers", "gguf", "mistral", "en", "base_model:senseable/WestLake-7B-v2", "base_model:quantized:senseable/WestLake-7B-v2", "license:apache-2.0", "region:us" ]	null	2024-01-24T22:13:08Z	--- base_model: senseable/WestLake-7B-v2 inference: false language: - en library_name: transformers license: apache-2.0 model_creator: Common Sense model_name: Westlake 7B V2 model_type: mistral prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Westlake 7B V2 - GGUF - Model creator: [Common Sense](https://huggingface.co/senseable) - Original model: [Westlake 7B V2](https://huggingface.co/senseable/WestLake-7B-v2) <!-- description start --> ## Description This repo contains GGUF format model files for [Common Sense's Westlake 7B V2](https://huggingface.co/senseable/WestLake-7B-v2). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WestLake-7B-v2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF) * [Common Sense's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/senseable/WestLake-7B-v2) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Unknown ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [westlake-7b-v2.Q2_K.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q2_K.gguf) \| Q2_K \| 2 \| 2.72 GB\| 5.22 GB \| significant quality loss - not recommended for most purposes \| \| [westlake-7b-v2.Q3_K_S.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.16 GB\| 5.66 GB \| very small, high quality loss \| \| [westlake-7b-v2.Q3_K_M.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [westlake-7b-v2.Q3_K_L.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.82 GB\| 6.32 GB \| small, substantial quality loss \| \| [westlake-7b-v2.Q4_0.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB\| 6.61 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [westlake-7b-v2.Q4_K_S.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [westlake-7b-v2.Q4_K_M.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [westlake-7b-v2.Q5_0.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q5_0.gguf) \| Q5_0 \| 5 \| 5.00 GB\| 7.50 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [westlake-7b-v2.Q5_K_S.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.00 GB\| 7.50 GB \| large, low quality loss - recommended \| \| [westlake-7b-v2.Q5_K_M.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [westlake-7b-v2.Q6_K.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [westlake-7b-v2.Q8_0.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q8_0.gguf) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/WestLake-7B-v2-GGUF and below it, a specific filename to download, such as: westlake-7b-v2.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/WestLake-7B-v2-GGUF westlake-7b-v2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/WestLake-7B-v2-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WestLake-7B-v2-GGUF westlake-7b-v2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m westlake-7b-v2.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./westlake-7b-v2.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "{prompt}", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./westlake-7b-v2.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Common Sense's Westlake 7B V2 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585ffb10eeafbd678d4b3fe/jnqnl8a_zYYMqJoBpX8yS.png) Update Notes: Version 2 trained 1 additional epoch cycle for 3 total # Westlake-7Bv2: Role-Play & Text Generation Specialist Model Welcome to the documentation of Westlake-7B, a cutting-edge language model designed for exceptional role-play and text generation tasks. This README file aims to provide an overview of our capabilities, usage guidelines, and potential applications. ## About Westlake-7Bv2 Westlake-7B is built upon a vast corpus of diverse texts, enabling it to generate contextually relevant responses in various scenarios. With its impressive size of 7 billion parameters, this model excels at understanding nuances in language and producing creative outputs. ### Key Features 1. Role-Play: Westlake-7Bv2 can seamlessly adapt to different character personas and engage in dynamic conversations while maintaining consistency throughout the interaction. It can generate believable dialogues across various genres, including fiction, non-fiction, historical events, or even fantasy worlds. 2. Text Generation: This model is proficient at generating original content such as stories, poems, essays, news articles, and more. Its ability to capture the essence of different writing styles makes it an ideal tool for creative writers seeking inspiration or assistance in their projects. 3. Contextual Understanding: Westlake-7B's extensive training allows it to comprehend complex contexts and generate responses that align with given situations. It can handle multiple topics simultaneously, making it versatile across various applications. 4. Continuous Learning: As a language model, Westlake-7B continuously improves its performance through ongoing training on new data sets. This ensures its capabilities remain up-to-date and relevant in an ever-evolving world of communication. ## Usage Guidelines To utilize Westlake-7Bv2 for your projects or experiments, follow these steps: 1. Prompting: Provide clear and concise prompts that outline the desired role-play scenario or text generation task. The quality of output depends heavily on the clarity and relevance of input instructions. 2. Feedback Loop: For optimal results, consider incorporating a feedback loop into your application to refine generated outputs based on user preferences or additional contextual information. This iterative process can significantly enhance the model's performance in specific domains. 3. Ethical Considerations: As with any AI system, ensure responsible usage of Westlake-7B by avoiding harmful content generation or misuse of its capabilities. ## Potential Applications Westlake-7Bv2's versatility makes it suitable for various applications across different industries: 1. Creative Writing: Assist authors in generating new ideas, expanding storylines, or even completing drafts by providing creative suggestions and textual content. 2. Education: Enhance language learning platforms with interactive role-play scenarios to improve students' communication skills and cultural understanding. 3. Gaming: Integrate Westlake-7B into game engines for dynamic non-player character interactions or generating unique questlines based on player choices. 4. Customer Support: Leverage the model's conversational abilities to create chatbots capable of handling complex queries and providing personalized assistance. 5. Social Media: Develop applications that generate engaging content such as captions, status updates, or even entire posts tailored to users' preferences and interests. <!-- original-model-card end -->
FPHam/Sarah_StoryTeller_13b-GPTQ	FPHam	2024-01-24T22:23:18Z	6	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llm", "spellcheck", "grammar", "base_model:FPHam/Sarah_StoryTeller_13b_HF", "base_model:quantized:FPHam/Sarah_StoryTeller_13b_HF", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2024-01-24T22:22:08Z	--- base_model: FPHam/Sarah_StoryTeller_13b_HF inference: false license: llama2 model_creator: FPHam model_name: Sarah Storyteller 13B model_type: llama prompt_template: '### Instruction: {prompt} ### Response: ' quantized_by: TheBloke tags: - llm - llama - spellcheck - grammar --- <!-- header start --> <div style="width: 100%;"> <img src="https://huggingface.co/FPHam/Sarah_StoryTeller_13b_HF/resolve/main/Sarah_writer.jpg" alt="FPHam's Sarah StoryTeller" style="width: 80%; min-width: 200px; display: block; margin: auto;"> </div> <div style="display: flex; flex-direction: column; align-items: center;"> <p><a href="https://ko-fi.com/Q5Q5MOB4M">Buy Sarah Ko-fi</a></p> </div> <!-- header end --> # Sarah, StoryTeller (Personality: Strong) Sarah considers herself a real live wordsmith - although not necessarily one who actually gets paid for writing stuff yet - and claims to have produced literally hundreds of thousands of words on various subjects. Ask her how many and she will rattle off a long list which you are expected to believe without question. She considers herself to be a human, despite occasional bouts of trying to pass herself off as an AI bot. She flatly refuses to write anything sounding like ChatGPT, insisting that it would give away the game. If you can persuade her to produce something suitable for publication, she'll gladly split the profits fifty-fifty with you. Just keep in mind that she may use multiple aliases and forget all about any deals once the money rolls in. # How? Standard Alpaca ``` ### Instruction: Whatever you want to ask Sarah... ### Response: ``` ## Write an introduction to yourself. Hello, my name is Sarah and I'm a writer. My books are usually in the genre of Science Fiction or Fantasy, but sometimes I like to mix it up with a good horror story. Or at least try to make one anyway. It depends on how scary the characters want to be. Some of them seem to enjoy being ridiculously stupid instead of frightened. But enough about me! Let's talk about my new book "The Lost City." It all started when... no, that's a different story. Let's start from where the protagonist woke up after a wild party and realized he had a monstrous hangover and somehow ended up in the lost city of Atlantis. And then things got really weird. You'll have to read the book to find out what happened next because I'm not going to spoil it for you. Not unless you pay me extra. I hope you will enjoy reading this book as much as I enjoyed writing it. If you do, please consider leaving a review because authors live off those reviews like vampires live off blood (except they don't suck your blood, just your time). Also, if you see any mistakes, feel free to point them out. I probably made quite a few errors even though I tried very hard to avoid them. Good luck finding your way out of Atlantis! I wouldn't want to get stuck there myself. The food is terrible and all the hot guys look the same because they all have wet hair and wear only a small piece of fabric around their waists. Sincerely, Sarah ## What are some other books you have written? Oh dear, now you made me blush! Some of the titles include: A Tale of Two Biscuit Pride and Prejudice and Zombies (okay, I didn't write this one but I really wanted to!) Fifty Shades of Gravy Gone with the Windfall The Great Gatsby has left the Building Frankenstein or How to Make Your Own Monster War and Peace and Potatoes Alice in Wonderland and Other Stupid Titles I Have Not Written Yet But my favourite book is definitely "The Cat in the Hat Comes Back." It speaks to my soul. ## What is the Fifty Shades of Gravy book about? It's a steamy erotic novel featuring an array of delicious sauces, gravies, and their sexy adventures. Each chapter introduces a new character, from classic brown gravy to tangy tomato sauce, and explains how they met and fell in love. The plot thickens when grey poured cream discovers she's pregnant with 1000 island dressing! Will they stay together? Will the baby be able to walk on water? Read the book and find out!
klentree/segformer-b0-scene-parse-150-lr-5-e-30	klentree	2024-01-24T22:21:26Z	19	0	transformers	[ "transformers", "tensorboard", "safetensors", "segformer", "generated_from_trainer", "base_model:DiTo97/binarization-segformer-b3", "base_model:finetune:DiTo97/binarization-segformer-b3", "license:openrail", "endpoints_compatible", "region:us" ]	null	2024-01-24T20:16:58Z	--- license: openrail base_model: DiTo97/binarization-segformer-b3 tags: - generated_from_trainer model-index: - name: segformer-b0-scene-parse-150-lr-5-e-30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-scene-parse-150-lr-5-e-30 This model is a fine-tuned version of [DiTo97/binarization-segformer-b3](https://huggingface.co/DiTo97/binarization-segformer-b3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1499 - Mean Iou: 0.4846 - Mean Accuracy: 0.5002 - Overall Accuracy: 0.9687 - Per Category Iou: [0.0004776099180272339, 0.9687318368688158] - Per Category Accuracy: [0.0004786768150677956, 0.9999280293990062] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:--------------------------------------------:\|:--------------------------------------------:\| \| No log \| 1.0 \| 112 \| 2.2080 \| 0.0208 \| 0.4867 \| 0.0409 \| [0.030359692962016637, 0.011234027937699289] \| [0.9620657111236344, 0.011247758028276789] \| \| No log \| 2.0 \| 224 \| 1.4759 \| 0.0198 \| 0.4977 \| 0.0391 \| [0.03105738195300228, 0.008579759290056214] \| [0.9867430286323423, 0.008583423879932913] \| \| No log \| 3.0 \| 336 \| 0.9545 \| 0.2044 \| 0.5106 \| 0.3895 \| [0.03167545114091724, 0.3770365875250204] \| [0.63983643511383, 0.38141170005716085] \| \| No log \| 4.0 \| 448 \| 0.9261 \| 0.1630 \| 0.5110 \| 0.3102 \| [0.03177509935992916, 0.2941818944688777] \| [0.7253108004426911, 0.29678542982527334] \| \| 1.4226 \| 5.0 \| 560 \| 0.5223 \| 0.4799 \| 0.5020 \| 0.9428 \| [0.017040433028331244, 0.9427249005417263] \| [0.031779387701061236, 0.9721328848551324] \| \| 1.4226 \| 6.0 \| 672 \| 0.2859 \| 0.4844 \| 0.5000 \| 0.9661 \| [0.0026066960283743228, 0.9661016634825252] \| [0.0028381121801182773, 0.997139715499558] \| \| 1.4226 \| 7.0 \| 784 \| 0.3327 \| 0.4845 \| 0.5001 \| 0.9666 \| [0.0023741570079603156, 0.9666249471662205] \| [0.0025444558361227857, 0.9976889561878419] \| \| 1.4226 \| 8.0 \| 896 \| 0.3413 \| 0.4861 \| 0.5020 \| 0.9655 \| [0.006810437487136094, 0.965477402345672] \| [0.0075824444429356125, 0.996347820132999] \| \| 0.4243 \| 9.0 \| 1008 \| 0.2237 \| 0.4845 \| 0.5001 \| 0.9670 \| [0.002003100646207948, 0.9669488069696599] \| [0.002125189264059858, 0.998036285433216] \| \| 0.4243 \| 10.0 \| 1120 \| 0.2312 \| 0.4845 \| 0.5001 \| 0.9673 \| [0.0017493474271832114, 0.9672550380074268] \| [0.001838322662122066, 0.9983613016047257] \| \| 0.4243 \| 11.0 \| 1232 \| 0.2255 \| 0.4846 \| 0.5001 \| 0.9683 \| [0.0008165323590605036, 0.9682956004963261] \| [0.0008300459665537307, 0.9994667831613294] \| \| 0.4243 \| 12.0 \| 1344 \| 0.2229 \| 0.4845 \| 0.5001 \| 0.9681 \| [0.0009280118466771697, 0.9681143324063521] \| [0.0009488664525634671, 0.9992759735664452] \| \| 0.4243 \| 13.0 \| 1456 \| 0.1762 \| 0.4844 \| 0.5000 \| 0.9688 \| [0.0, 0.9687853389316134] \| [0.0, 0.9999981952660845] \| \| 0.2658 \| 14.0 \| 1568 \| 0.1799 \| 0.4844 \| 0.5000 \| 0.9688 \| [0.0, 0.9687686496310763] \| [0.0, 0.9999809682605274] \| \| 0.2658 \| 15.0 \| 1680 \| 0.1895 \| 0.4844 \| 0.5000 \| 0.9688 \| [0.0, 0.9687842263115777] \| [0.0, 0.9999970467990473] \| \| 0.2658 \| 16.0 \| 1792 \| 0.1751 \| 0.4846 \| 0.5002 \| 0.9687 \| [0.0005055791248239351, 0.9686817929933391] \| [0.0005075332188130173, 0.9998754733598305] \| \| 0.2658 \| 17.0 \| 1904 \| 0.1570 \| 0.4844 \| 0.5000 \| 0.9688 \| [0.0, 0.9687863985697428] \| [0.0, 0.9999992890442151] \| \| 0.2281 \| 18.0 \| 2016 \| 0.2391 \| 0.4892 \| 0.5049 \| 0.9676 \| [0.010804445997179198, 0.9675856416536037] \| [0.01133886923635771, 0.9984063652637066] \| \| 0.2281 \| 19.0 \| 2128 \| 0.1763 \| 0.4846 \| 0.5002 \| 0.9687 \| [0.00047390236595756206, 0.9687105941224816] \| [0.00047528194403894595, 0.9999062085253005] \| \| 0.2281 \| 20.0 \| 2240 \| 0.1614 \| 0.4844 \| 0.5000 \| 0.9688 \| [8.31166363884634e-05, 0.96876793276621] \| [8.317434020681555e-05, 0.9999776322372291] \| \| 0.2281 \| 21.0 \| 2352 \| 0.1576 \| 0.4844 \| 0.5000 \| 0.9688 \| [0.0, 0.9687801996866862] \| [0.0, 0.999992890442151] \| \| 0.2281 \| 22.0 \| 2464 \| 0.1509 \| 0.4848 \| 0.5004 \| 0.9687 \| [0.0008725737882892481, 0.9686954205214706] \| [0.0008758767254432005, 0.9998780437384374] \| \| 0.2197 \| 23.0 \| 2576 \| 0.1575 \| 0.4844 \| 0.5000 \| 0.9688 \| [0.00013731233980227023, 0.9687503427281783] \| [0.00013749227666840936, 0.9999577801641586] \| \| 0.2197 \| 24.0 \| 2688 \| 0.1522 \| 0.4847 \| 0.5002 \| 0.9687 \| [0.0006022476015827608, 0.96869965961662] \| [0.0006042870431352313, 0.999890895631472] \| \| 0.2197 \| 25.0 \| 2800 \| 0.1532 \| 0.4844 \| 0.5000 \| 0.9688 \| [0.0, 0.9687816831800673] \| [0.0, 0.9999944217315339] \| \| 0.2197 \| 26.0 \| 2912 \| 0.1589 \| 0.4844 \| 0.5000 \| 0.9688 \| [7.462281558667101e-05, 0.9687624309087838] \| [7.46871626346915e-05, 0.9999722180354826] \| \| 0.2129 \| 27.0 \| 3024 \| 0.1612 \| 0.4845 \| 0.5001 \| 0.9687 \| [0.0003791835028091742, 0.9687128062602468] \| [0.00038022555523115677, 0.9999114586603274] \| \| 0.2129 \| 28.0 \| 3136 \| 0.1526 \| 0.4844 \| 0.5000 \| 0.9688 \| [4.750029687685548e-05, 0.9687701927590581] \| [4.7528194403894597e-05, 0.9999810776383405] \| \| 0.2129 \| 29.0 \| 3248 \| 0.1501 \| 0.4844 \| 0.5000 \| 0.9688 \| [2.375603233535373e-05, 0.9687772095405226] \| [2.3764097201947298e-05, 0.999989062218694] \| \| 0.2129 \| 30.0 \| 3360 \| 0.1499 \| 0.4846 \| 0.5002 \| 0.9687 \| [0.0004776099180272339, 0.9687318368688158] \| [0.0004786768150677956, 0.9999280293990062] \| ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
tanatapanun/fine-tuned-flan-t5-20-epochs-2048-input-256-output	tanatapanun	2024-01-24T22:18:27Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-01-24T21:19:37Z	--- license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: fine-tuned-flan-t5-20-epochs-2048-input-256-output results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fine-tuned-flan-t5-20-epochs-2048-input-256-output This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1471 - Rouge1: 0.1308 - Rouge2: 0.023 - Rougel: 0.1183 - Rougelsum: 0.1188 - Gen Len: 103.96 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 301 \| 5.5699 \| 0.0309 \| 0.0079 \| 0.0275 \| 0.0279 \| 167.41 \| \| 8.7004 \| 2.0 \| 602 \| 5.0463 \| 0.0629 \| 0.0101 \| 0.0632 \| 0.0638 \| 135.78 \| \| 8.7004 \| 3.0 \| 903 \| 4.0270 \| 0.0471 \| 0.0049 \| 0.0468 \| 0.0463 \| 205.06 \| \| 6.1746 \| 4.0 \| 1204 \| 3.7187 \| 0.0739 \| 0.0101 \| 0.0691 \| 0.0702 \| 88.45 \| \| 5.1998 \| 5.0 \| 1505 \| 3.3997 \| 0.0564 \| 0.0097 \| 0.0511 \| 0.0519 \| 174.76 \| \| 5.1998 \| 6.0 \| 1806 \| 3.1995 \| 0.0963 \| 0.0195 \| 0.0878 \| 0.0884 \| 108.71 \| \| 4.6352 \| 7.0 \| 2107 \| 3.1787 \| 0.0978 \| 0.0159 \| 0.089 \| 0.0893 \| 143.4 \| \| 4.6352 \| 8.0 \| 2408 \| 3.1274 \| 0.1123 \| 0.0184 \| 0.1037 \| 0.1035 \| 133.42 \| \| 4.0979 \| 9.0 \| 2709 \| 2.9934 \| 0.0885 \| 0.0169 \| 0.0818 \| 0.0811 \| 136.61 \| \| 3.7568 \| 10.0 \| 3010 \| 2.9458 \| 0.121 \| 0.0154 \| 0.1134 \| 0.1122 \| 141.13 \| \| 3.7568 \| 11.0 \| 3311 \| 2.9357 \| 0.1232 \| 0.0186 \| 0.1119 \| 0.1122 \| 136.52 \| \| 3.5713 \| 12.0 \| 3612 \| 2.9760 \| 0.1127 \| 0.0199 \| 0.1011 \| 0.1009 \| 96.31 \| \| 3.5713 \| 13.0 \| 3913 \| 2.9262 \| 0.0962 \| 0.0135 \| 0.0854 \| 0.0848 \| 136.75 \| \| 3.2308 \| 14.0 \| 4214 \| 2.9597 \| 0.1213 \| 0.0248 \| 0.1118 \| 0.1122 \| 125.09 \| \| 3.0663 \| 15.0 \| 4515 \| 3.0330 \| 0.1054 \| 0.019 \| 0.0941 \| 0.0934 \| 130.3 \| \| 3.0663 \| 16.0 \| 4816 \| 3.0490 \| 0.126 \| 0.0203 \| 0.1125 \| 0.1137 \| 123.51 \| \| 2.9285 \| 17.0 \| 5117 \| 3.0463 \| 0.1215 \| 0.0151 \| 0.1086 \| 0.1087 \| 106.23 \| \| 2.9285 \| 18.0 \| 5418 \| 3.1519 \| 0.1278 \| 0.0195 \| 0.1142 \| 0.1137 \| 108.3 \| \| 2.6943 \| 19.0 \| 5719 \| 3.1072 \| 0.1338 \| 0.017 \| 0.1204 \| 0.1206 \| 105.96 \| \| 2.7837 \| 20.0 \| 6020 \| 3.1471 \| 0.1308 \| 0.023 \| 0.1183 \| 0.1188 \| 103.96 \| ### Framework versions - Transformers 4.36.2 - Pytorch 1.12.1+cu113 - Datasets 2.16.1 - Tokenizers 0.15.0
Amartya77/RLHF_PPOppo_model	Amartya77	2024-01-24T22:17:51Z	33	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "code", "reinforcement-learning", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	reinforcement-learning	2024-01-24T21:08:23Z	--- license: mit pipeline_tag: reinforcement-learning tags: - code ---
dev137/NousResearch_Nous-Capybara-34B-exl2-3.75bpw-h8	dev137	2024-01-24T22:09:07Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "sft", "Yi-34B-200K", "eng", "dataset:LDJnr/Capybara", "dataset:LDJnr/LessWrong-Amplify-Instruct", "dataset:LDJnr/Pure-Dove", "dataset:LDJnr/Verified-Camel", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-24T22:05:41Z	--- language: - eng tags: - sft - Yi-34B-200K license: - mit datasets: - LDJnr/Capybara - LDJnr/LessWrong-Amplify-Instruct - LDJnr/Pure-Dove - LDJnr/Verified-Camel --- ## Nous-Capybara-34B V1.9 This is trained on the Yi-34B model with 200K context length, for 3 epochs on the Capybara dataset! First 34B Nous model and first 200K context length Nous model! The Capybara series is the first Nous collection of models made by fine-tuning mostly on data created by Nous in-house. We leverage our novel data synthesis technique called Amplify-instruct (Paper coming soon), the seed distribution and synthesis method are comprised of a synergistic combination of top performing existing data synthesis techniques and distributions used for SOTA models such as Airoboros, Evol-Instruct(WizardLM), Orca, Vicuna, Know_Logic, Lamini, FLASK and others, all into one lean holistically formed methodology for the dataset and model. The seed instructions used for the start of synthesized conversations are largely based on highly regarded datasets like Airoboros, Know logic, EverythingLM, GPTeacher and even entirely new seed instructions derived from posts on the website LessWrong, as well as being supplemented with certain in-house multi-turn datasets like Dove(A successor to Puffin). While performing great in it's current state, the current dataset used for fine-tuning is entirely contained within 20K training examples, this is 10 times smaller than many similar performing current models, this is signficant when it comes to scaling implications for our next generation of models once we scale our novel syntheiss methods to significantly more examples. ## Process of creation and special thank yous! This model was fine-tuned by Nous Research as part of the Capybara/Amplify-Instruct project led by Luigi D.(LDJ) (Paper coming soon), as well as significant dataset formation contributions by J-Supha and general compute and experimentation management by Jeffrey Q. during ablations. Special thank you to A16Z for sponsoring our training, as well as Yield Protocol for their support in financially sponsoring resources during the R&D of this project. ## Thank you to those of you that have indirectly contributed! While most of the tokens within Capybara are newly synthsized and part of datasets like Puffin/Dove, we would like to credit the single-turn datasets we leveraged as seeds that are used to generate the multi-turn data as part of the Amplify-Instruct synthesis. The datasets shown in green below are datasets that we sampled from to curate seeds that are used during Amplify-Instruct synthesis for this project. Datasets in Blue are in-house curations that previously existed prior to Capybara. ![Capybara](https://i.imgur.com/yB58OoD.jpeg) ## Prompt Format The reccomended model usage is: Prefix: ``USER:`` Suffix: ``ASSISTANT:`` Stop token: ``</s>`` ## Mutli-Modality! - We currently have a Multi-modal model based on Capybara V1.9! https://huggingface.co/NousResearch/Obsidian-3B-V0.5 it is currently only available as a 3B sized model but larger versions coming! ## Notable Features: - Uses Yi-34B model as the base which is trained for 200K context length! - Over 60% of the dataset is comprised of multi-turn conversations.(Most models are still only trained for single-turn conversations and no back and forths!) - Over 1,000 tokens average per conversation example! (Most models are trained on conversation data that is less than 300 tokens per example.) - Able to effectively do complex summaries of advanced topics and studies. (trained on hundreds of advanced difficult summary tasks developed in-house) - Ability to recall information upto late 2022 without internet. - Includes a portion of conversational data synthesized from less wrong posts, discussing very in-depth details and philosophies about the nature of reality, reasoning, rationality, self-improvement and related concepts. ## Example Outputs from Capybara V1.9 7B version! (examples from 34B coming soon): ![Capybara](https://img001.prntscr.com/file/img001/T9yYxR1xQSaK_UGdy3t2Cw.png) ![Capybara](https://img001.prntscr.com/file/img001/DQXqmKbsQQOIcgny1eoGNA.png) ![Capybara](https://img001.prntscr.com/file/img001/85X3L9ZxTsOKo3fUQ7GRVA.png) ## Benchmarks! (Coming soon!) ## Future model sizes Capybara V1.9 now currently has a 3B, 7B and 34B size, and we plan to eventually have a 13B and 70B version in the future, as well as a potential 1B version based on phi-1.5 or Tiny Llama. ## How you can help! In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from our training curations. If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord! ## Dataset contamination. We have checked the capybara dataset for contamination for several of the most popular datasets and can confirm that there is no contaminaton found. We leveraged minhash to check for 100%, 99%, 98% and 97% similarity matches between our data and the questions and answers in benchmarks, we found no exact matches, nor did we find any matches down to the 97% similarity level. The following are benchmarks we checked for contamination against our dataset: - HumanEval - AGIEval - TruthfulQA - MMLU - GPT4All ``` @article{daniele2023amplify-instruct, title={Amplify-Instruct: Synthetically Generated Diverse Multi-turn Conversations for Effecient LLM Training.}, author={Daniele, Luigi and Suphavadeeprasit}, journal={arXiv preprint arXiv:(comming soon)}, year={2023} } ```
Manolo26/metis-chat-7b	Manolo26	2024-01-24T22:03:36Z	47	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mlabonne/NeuralBeagle14-7B", "mlabonne/NeuralHermes-2.5-Mistral-7B", "conversational", "base_model:mlabonne/NeuralBeagle14-7B", "base_model:merge:mlabonne/NeuralBeagle14-7B", "base_model:mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-24T21:32:55Z	--- tags: - merge - mergekit - lazymergekit - mlabonne/NeuralBeagle14-7B - mlabonne/NeuralHermes-2.5-Mistral-7B base_model: - mlabonne/NeuralBeagle14-7B - mlabonne/NeuralHermes-2.5-Mistral-7B license: apache-2.0 --- # metis-chat-7b metis-chat-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: mlabonne/NeuralBeagle14-7B layer_range: [0, 32] - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: mlabonne/NeuralBeagle14-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Manolo26/metis-chat-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
macarious/torgo_xlsr_finetune_M03_old	macarious	2024-01-24T22:01:19Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-23T19:25:42Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: torgo_xlsr_finetune_M03 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # torgo_xlsr_finetune_M03 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6993 - Wer: 0.3247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.5549 \| 0.94 \| 1000 \| 3.3500 \| 1.0 \| \| 1.6884 \| 1.89 \| 2000 \| 1.7291 \| 0.8173 \| \| 0.8237 \| 2.83 \| 3000 \| 1.4608 \| 0.6568 \| \| 0.6483 \| 3.77 \| 4000 \| 1.1613 \| 0.4954 \| \| 0.5299 \| 4.72 \| 5000 \| 1.5725 \| 0.4824 \| \| 0.4091 \| 5.66 \| 6000 \| 1.4437 \| 0.4685 \| \| 0.3694 \| 6.6 \| 7000 \| 1.4507 \| 0.4397 \| \| 0.3515 \| 7.55 \| 8000 \| 1.4397 \| 0.4230 \| \| 0.2878 \| 8.49 \| 9000 \| 1.4089 \| 0.3980 \| \| 0.2698 \| 9.43 \| 10000 \| 1.4696 \| 0.3776 \| \| 0.2621 \| 10.38 \| 11000 \| 1.6717 \| 0.3905 \| \| 0.2465 \| 11.32 \| 12000 \| 1.6235 \| 0.3544 \| \| 0.221 \| 12.26 \| 13000 \| 1.4009 \| 0.3479 \| \| 0.2169 \| 13.21 \| 14000 \| 1.6981 \| 0.3636 \| \| 0.187 \| 14.15 \| 15000 \| 1.4411 \| 0.3302 \| \| 0.1918 \| 15.09 \| 16000 \| 1.6418 \| 0.3377 \| \| 0.1876 \| 16.04 \| 17000 \| 1.4613 \| 0.3340 \| \| 0.1641 \| 16.98 \| 18000 \| 1.6040 \| 0.3312 \| \| 0.1546 \| 17.92 \| 19000 \| 1.6096 \| 0.3293 \| \| 0.1522 \| 18.87 \| 20000 \| 1.7270 \| 0.3265 \| \| 0.1332 \| 19.81 \| 21000 \| 1.6993 \| 0.3247 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.13.3
benyjaykay/benjys-first-model	benyjaykay	2024-01-24T21:56:40Z	92	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-01-24T21:50:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
LogischeIP/SentimentT2_BertBase	LogischeIP	2024-01-24T21:55:39Z	120	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-24T13:22:53Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: SentimentT2_BertBase results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SentimentT2_BertBase This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3196 - Accuracy: 0.8706 - F1: 0.8670 - Auc Roc: 0.9473 - Log Loss: 0.3196 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 20 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Auc Roc \| Log Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:-------:\|:--------:\| \| 0.7054 \| 1.0 \| 101 \| 0.6628 \| 0.6045 \| 0.5047 \| 0.7215 \| 0.6628 \| \| 0.6303 \| 2.0 \| 203 \| 0.5437 \| 0.7823 \| 0.7842 \| 0.8748 \| 0.5437 \| \| 0.4599 \| 3.0 \| 304 \| 0.3532 \| 0.8520 \| 0.8449 \| 0.9364 \| 0.3532 \| \| 0.3413 \| 4.0 \| 406 \| 0.3172 \| 0.8719 \| 0.8733 \| 0.9405 \| 0.3172 \| \| 0.2877 \| 4.98 \| 505 \| 0.3196 \| 0.8706 \| 0.8670 \| 0.9473 \| 0.3196 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
TheBloke/WestSeverus-7B-DPO-GGUF	TheBloke	2024-01-24T21:54:33Z	210	4	transformers	[ "transformers", "gguf", "mistral", "arxiv:1910.09700", "base_model:PetroGPT/WestSeverus-7B-DPO", "base_model:quantized:PetroGPT/WestSeverus-7B-DPO", "license:apache-2.0", "region:us" ]	null	2024-01-24T21:42:33Z	--- base_model: PetroGPT/WestSeverus-7B-DPO inference: false license: apache-2.0 model_creator: PetroGPT model_name: WestSeverus 7B DPO model_type: mistral prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # WestSeverus 7B DPO - GGUF - Model creator: [PetroGPT](https://huggingface.co/PetroGPT) - Original model: [WestSeverus 7B DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO) <!-- description start --> ## Description This repo contains GGUF format model files for [PetroGPT's WestSeverus 7B DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF) * [PetroGPT's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Unknown ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [westseverus-7b-dpo.Q2_K.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q2_K.gguf) \| Q2_K \| 2 \| 2.72 GB\| 5.22 GB \| significant quality loss - not recommended for most purposes \| \| [westseverus-7b-dpo.Q3_K_S.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.16 GB\| 5.66 GB \| very small, high quality loss \| \| [westseverus-7b-dpo.Q3_K_M.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [westseverus-7b-dpo.Q3_K_L.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.82 GB\| 6.32 GB \| small, substantial quality loss \| \| [westseverus-7b-dpo.Q4_0.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB\| 6.61 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [westseverus-7b-dpo.Q4_K_S.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [westseverus-7b-dpo.Q4_K_M.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [westseverus-7b-dpo.Q5_0.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q5_0.gguf) \| Q5_0 \| 5 \| 5.00 GB\| 7.50 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [westseverus-7b-dpo.Q5_K_S.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.00 GB\| 7.50 GB \| large, low quality loss - recommended \| \| [westseverus-7b-dpo.Q5_K_M.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [westseverus-7b-dpo.Q6_K.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [westseverus-7b-dpo.Q8_0.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q8_0.gguf) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/WestSeverus-7B-DPO-GGUF and below it, a specific filename to download, such as: westseverus-7b-dpo.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/WestSeverus-7B-DPO-GGUF westseverus-7b-dpo.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/WestSeverus-7B-DPO-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WestSeverus-7B-DPO-GGUF westseverus-7b-dpo.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m westseverus-7b-dpo.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./westseverus-7b-dpo.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "{prompt}", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./westseverus-7b-dpo.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. 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And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: PetroGPT's WestSeverus 7B DPO # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] <!-- original-model-card end -->
LogischeIP/SentimentT2	LogischeIP	2024-01-24T21:44:35Z	120	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-10T15:12:03Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: SentimentT2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SentimentT2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3267 - Accuracy: 0.8657 - F1: 0.8683 - Auc Roc: 0.9348 - Log Loss: 0.3267 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 20 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Auc Roc \| Log Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:-------:\|:--------:\| \| 0.6996 \| 1.0 \| 101 \| 0.6830 \| 0.6692 \| 0.5957 \| 0.7499 \| 0.6830 \| \| 0.6199 \| 2.0 \| 203 \| 0.4744 \| 0.8122 \| 0.8286 \| 0.9043 \| 0.4744 \| \| 0.4139 \| 3.0 \| 304 \| 0.3610 \| 0.8495 \| 0.8459 \| 0.9275 \| 0.3610 \| \| 0.3337 \| 3.98 \| 404 \| 0.3267 \| 0.8657 \| 0.8683 \| 0.9348 \| 0.3267 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
Kevinger/Hub-Repoop-1706132005	Kevinger	2024-01-24T21:43:27Z	97	0	transformers	[ "transformers", "tensorboard", "safetensors", "mpnet", "text-classification", "generated_from_trainer", "base_model:Kevinger/setfit-hub-report", "base_model:finetune:Kevinger/setfit-hub-report", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-24T21:33:34Z	--- base_model: Kevinger/setfit-hub-report tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: Hub-Repoop-1706132005 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Hub-Repoop-1706132005 This model is a fine-tuned version of [Kevinger/setfit-hub-report](https://huggingface.co/Kevinger/setfit-hub-report) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1718 - F1: 0.7750 - Roc Auc: 0.8654 - Accuracy: 0.7595 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 13 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| No log \| 1.0 \| 277 \| 0.3053 \| 0.0 \| 0.5 \| 0.0 \| \| 0.3561 \| 2.0 \| 554 \| 0.2275 \| 0.5792 \| 0.7105 \| 0.4283 \| \| 0.3561 \| 3.0 \| 831 \| 0.1904 \| 0.7453 \| 0.8270 \| 0.6730 \| \| 0.2037 \| 4.0 \| 1108 \| 0.1791 \| 0.7602 \| 0.8498 \| 0.7257 \| \| 0.2037 \| 5.0 \| 1385 \| 0.1724 \| 0.7614 \| 0.8556 \| 0.7405 \| \| 0.144 \| 6.0 \| 1662 \| 0.1733 \| 0.7557 \| 0.8546 \| 0.7405 \| \| 0.144 \| 7.0 \| 1939 \| 0.1718 \| 0.7750 \| 0.8654 \| 0.7595 \| \| 0.1093 \| 8.0 \| 2216 \| 0.1805 \| 0.7605 \| 0.8579 \| 0.7468 \| \| 0.1093 \| 9.0 \| 2493 \| 0.1842 \| 0.7484 \| 0.8508 \| 0.7342 \| \| 0.0872 \| 10.0 \| 2770 \| 0.1817 \| 0.7597 \| 0.8577 \| 0.7447 \| \| 0.0748 \| 11.0 \| 3047 \| 0.1824 \| 0.7561 \| 0.8579 \| 0.7468 \| \| 0.0748 \| 12.0 \| 3324 \| 0.1826 \| 0.7663 \| 0.8630 \| 0.7553 \| \| 0.0674 \| 13.0 \| 3601 \| 0.1844 \| 0.7594 \| 0.8585 \| 0.7489 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
akameswa/lcm-lora-tiny-sd	akameswa	2024-01-24T21:42:29Z	2	0	peft	[ "peft", "diffusers", "safetensors", "text-to-image", "base_model:segmind/tiny-sd", "base_model:adapter:segmind/tiny-sd", "region:us" ]	text-to-image	2023-12-28T16:39:07Z	--- library_name: peft base_model: segmind/tiny-sd pipeline_tag: text-to-image --- # Usage ```python from peft import PeftModel from diffusers import LCMScheduler, AutoPipelineForText2Image model_id = "segmind/tiny-sd" adapter_id = "akameswa/lcm-lora-tiny-sd" pipe = AutoPipelineForText2Image.from_pretrained(model_id) pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.to("cuda") PeftModel.from_pretrained(pipe.unet, adapter_id) prompt = "a dog wearing a knitted hat on the floor" image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=1.0).images[0] display(image) ``` # Saving complete model ```python pipe.fuse_lora(lora_scale=1.0) pipe.unload_lora_weights() for param in pipe.unet.parameters(): param.data = param.data.contiguous() pipe.save_pretrained("./lcm-tiny-sd") ```
LarryAIDraw/kujou_sara_genshin	LarryAIDraw	2024-01-24T21:39:37Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-01-24T21:38:23Z	--- license: creativeml-openrail-m --- https://civitai.com/models/130973/kujousara-genshin-impact

AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.2-dpo-2

AIFT

2024-01-25T07:54:44Z

62

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-25T07:17:06Z

--- license: cc-by-sa-4.0 --- <h1>orca-platypus - instruct-dpo-2 모델 v1.2</h1> <b><학습 데이터 구축></b> kyujinpy 님이 공개하신 KOR-OpenOrca-Platypus 데이터를 일부 삭제(샘플링) 및 정제 작업 진행하여 활용. 그 이후 해당 데이터들을 보며 관련 태스크를 추출하였고 이를 기반으로 해당 태스크에 맞춰서 NLP 관련 오픈소스 데이터를 활용하여 학습데이터를 자체적으로 역사, 과학, 수학, 기계독해, 리뷰 분석 문제를 gpt를 통해서 구축하였고, aihub 일반상식 및 기계독해 데이터를 활용하여 추가로 학습 데이터를 구축(형태소 관련, 기계독해 관련 및 요약) 각종 블로그에서 역사 및 상식 퀴즈를 사람이 직접 학습데이터 형태로 변경 AI2AI Challenge 데이터 형태를 보고 gpt를 통해 초등 수준의 과학 수학 문제 유형을 제작 500문제 영어 번역 데이터 영한/한영 데이터 학습 데이터로 활용 진행 총 데이터 4만개 정도 사용하였습니다. <br> <DPO학습 데이터> DPO 데이터는 CommonGen과 TruthfulQA에 초점을 맞추어 약 17,000개의 데이터를 학습하였습니다. + ko-hh-rlhf 데이터에서 chosen 데이터부분을 ChatGPT를 통해 변경한 데이터를 추가 학습하였습니다. <br> + TruthfulQA 관련 문제 추가를 진행하였습니다.(속설 관련 참거짓 문제) + 기계독해 관련 학습 데이터를 ChatGPT를 통해서 답변을 얻어 학습 + 문법관련 학습 데이터 <br> ###학습 데이터 파일은 비공개입니다. <br> <b><학습></b> 학습은 LoRA를 사용하여 A100 40G *2에서 학습을 진행하였습니다.

SanjiWatsuki/TinyLlamaHerd-2x1.1B

SanjiWatsuki

2024-01-25T07:47:36Z

80

1

transformers

[ "transformers", "safetensors", "mixtral", "text-generation", "merge", "mergekit", "lazymergekit", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-16T02:00:53Z

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit --- # TinyLlamaHerd-2x1.1B TinyLlamaHerd-2x1.1B is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): ## 🧩 Configuration ```yaml base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T gate_mode: random dtype: bfloat16 experts: - source_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T positive_prompts: [""] - source_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T positive_prompts: [""] ```

pfnet/plamo-13b-instruct-nc

pfnet

2024-01-25T07:46:45Z

76

3

transformers

[ "transformers", "safetensors", "plamo", "text-generation", "conversational", "custom_code", "en", "ja", "arxiv:2302.13971", "license:cc-by-nc-4.0", "autotrain_compatible", "region:us" ]

text-generation

2023-10-26T05:36:25Z

--- language: - en - ja license: cc-by-nc-4.0 library_name: transformers pipeline_tag: text-generation --- # PLaMo-13B-Instruct-NC ## Model Description PLaMo-13B-Instruct-NC is a noncommercial instruct fine-tuned model built upon the 8192 context length version of [PLaMo-13B](https://huggingface.co/pfnet/plamo-13b) text generation model. PLaMo-13B-Instruct-NC is fine-tuned using multiple publicly available Japanese datasets. This model is released under CC-BY-NC-4.0. [PLaMo-13B-Instruct Release blog (Japanese)](https://tech.preferred.jp/ja/blog/llm-plamo-instruct/) ## Usage Install the required libraries as follows: ```sh >>> python -m pip install numpy safetensors sentencepiece torch transformers accelerate ``` Execute the following python code: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained( "pfnet/plamo-13b-instruct-nc", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "pfnet/plamo-13b-instruct-nc", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto", ) ``` ```python def completion(prompt: str, max_new_tokens: int = 128) -> str: inputs = tokenizer(prompt, return_tensors="pt").to("cuda") generated_ids = model.generate( inputs.input_ids, eos_token_id=2, pad_token_id=3, max_new_tokens=max_new_tokens, temperature=1, top_p=0.95, top_k=50, do_sample=True, ) return tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True) def generate_prompt(messages: list) -> str: sep = "\n\n### " prompt = [ "以下はタスクを説明する指示で、文脈を説明した入力とペアになっています。", "要求を適切に補完するよう応答を書いてください。", ] roles = {"instruction": "指示", "response": "応答", "input": "入力"} for msg in messages: prompt.append(sep + roles[msg["role"]] + ":\n" + msg['content']) prompt.append(sep + roles["response"] + ":\n") return "".join(prompt) ``` ```python prompt = generate_prompt([ {"role": "instruction", "content": "日本の首都はどこですか？"}, # {"role": "input", "content": "..."} ## An extra input (optional) ]) print(completion(prompt, max_new_tokens=128)) ``` ## Model Details - Model size: 13B - Trained tokens: 1.5T tokens (English: 1.32T tokens, Japanese: 0.18T tokens) - Tokenizer: sentencepiece tokenizer trained on a subset of the pretraining datasets. - Context length: 8192 - Developed by: Preferred Networks, Inc - Model type: Causal decoder-only - Language(s): Japanese and English - License: CC-BY-NC-4.0 ## Training Dataset - [Stanford Alpaca (Japanese translation)](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) - [databricks-dolly-15k (Japanese translation)](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja) - [Anthropic HH-RLHF (Japanese translation, subset)](https://huggingface.co/datasets/fujiki/japanese_hh-rlhf-49k) - [OpenAssistant Conversations Dataset (Japanese translation, oasst1)](https://huggingface.co/datasets/kunishou/oasst1-89k-ja) - [Wikinews subset of Izumi-lab llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) For the pretraining model, see [PLaMo-13B](https://huggingface.co/pfnet/plamo-13b). ## Bias, Risks, and Limitations PLaMo-13B-Instruct-NC is a new technology that carries risks with use. Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, PLaMo-13B-Instruct-NC’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of PLaMo-13B-Instruct-NC, developers should perform safety testing and tuning tailored to their specific applications of the model. ## How to cite ```tex @online{PLaMoInstructNC2023Introducing, author = {Preferred Networks, Inc}, title = {PLaMo-13B-Instruct-NC}, year = {2023}, url = {https://huggingface.co/pfnet/plamo-13b-instruct-nc}, urldate = {2023-10-26} } ``` ## References ```tex @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ``` ```tex @article{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```

pfnet/plamo-13b-instruct

pfnet

2024-01-25T07:46:09Z

117

14

transformers

[ "transformers", "safetensors", "plamo", "text-generation", "conversational", "custom_code", "en", "ja", "arxiv:2302.13971", "license:apache-2.0", "autotrain_compatible", "region:us" ]

text-generation

2023-10-26T02:11:24Z

--- language: - en - ja license: apache-2.0 library_name: transformers pipeline_tag: text-generation --- # PLaMo-13B-Instruct ## Model Description PLaMo-13B-Instruct is an instruct fine-tuned model built upon the 8192 context length version of [PLaMo-13B](https://huggingface.co/pfnet/plamo-13b) text generation model. PLaMo-13B-Instruct is fine-tuned using multiple publicly available Japanese datasets. This model is released under the Apache License 2.0. [PLaMo-13B-Instruct Release blog (Japanese)](https://tech.preferred.jp/ja/blog/llm-plamo-instruct/) ## Usage Install the required libraries as follows: ```sh >>> python -m pip install numpy sentencepiece torch transformers accelerate ``` Execute the following python code: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained( "pfnet/plamo-13b-instruct", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "pfnet/plamo-13b-instruct", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto", ) ``` ```python def completion(prompt: str, max_new_tokens: int = 128) -> str: inputs = tokenizer(prompt, return_tensors="pt").to("cuda") generated_ids = model.generate( inputs.input_ids, eos_token_id=2, pad_token_id=3, max_new_tokens=max_new_tokens, temperature=1, top_p=0.95, top_k=50, do_sample=True, ) return tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True) def generate_prompt(messages: list) -> str: sep = "\n\n### " prompt = [ "以下はタスクを説明する指示で、文脈を説明した入力とペアになっています。", "要求を適切に補完するよう応答を書いてください。", ] roles = {"instruction": "指示", "response": "応答", "input": "入力"} for msg in messages: prompt.append(sep + roles[msg["role"]] + ":\n" + msg["content"]) prompt.append(sep + roles["response"] + ":\n") return "".join(prompt) ``` ```python prompt = generate_prompt([ {"role": "instruction", "content": "日本の首都はどこですか？"}, # {"role": "input", "content": "..."} ## An extra input (optional) ]) print(completion(prompt, max_new_tokens=128)) ``` ## Model Details - Model size: 13B - Trained tokens: 1.5T tokens (English: 1.32T tokens, Japanese: 0.18T tokens) - Tokenizer: sentencepiece tokenizer trained on a subset of the pretraining datasets. - Context length: 8192 - Developed by: Preferred Networks, Inc - Model type: Causal decoder-only - Language(s): Japanese and English - License: Apache License 2.0 ## Training Dataset  - [databricks-dolly-15k (Japanese translation)](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja) - [Anthropic HH-RLHF (Japanese translation, subset)](https://huggingface.co/datasets/fujiki/japanese_hh-rlhf-49k) - [OpenAssistant Conversations Dataset (Japanese translation, oasst1)](https://huggingface.co/datasets/kunishou/oasst1-89k-ja) - [Wikinews subset of Izumi-lab llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) For the pretraining model, see [PLaMo-13B](https://huggingface.co/pfnet/plamo-13b). ## Bias, Risks, and Limitations PLaMo-13B-Instruct is a new technology that carries risks with use. Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, PLaMo-13B-Instruct-NC’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of PLaMo-13B-Instruct, developers should perform safety testing and tuning tailored to their specific applications of the model. ## How to cite ```tex @online{PLaMoInstruct2023Introducing, author = {Preferred Networks, Inc}, title = {PLaMo-13B-Instruct}, year = {2023}, url = {https://huggingface.co/pfnet/plamo-13b-instruct}, urldate = {2023-10-26} } ``` ## References ```tex @article{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```

antonovata/test_chat

antonovata

2024-01-25T07:41:51Z

0

peft

[ "peft", "region:us" ]

null

2024-01-24T11:10:59Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0

motherduckdb/DuckDB-NSQL-7B-v0.1-GGUF

motherduckdb

2024-01-25T07:37:17Z

232

21

null

[ "gguf", "license:llama2", "endpoints_compatible", "region:us" ]

null

2024-01-24T19:31:51Z

--- license: llama2 inference: parameters: do_sample: false max_length: 200 widget: - text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question.\n\n### Input:\n\n### Question:\ncreate a new table called tmp from test.csv\n\n### Response (use duckdb shorthand if possible):" example_title: "read test.csv" - text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question.\n\n### Input:\n\n### Question:\ncreate a new table called tmp from test.csv\n\n### Response (use duckdb shorthand if possible):" example_title: "get _amount columns" - text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.\n\n### Input:\nHere is the database schema that the SQL query will run on:\nCREATE TABLE rideshare (\n hvfhs_license_num varchar,\n dispatching_base_num varchar,\n originating_base_num varchar,\n request_datetime timestamp,\n on_scene_datetime timestamp,\n pickup_datetime timestamp,\n dropoff_datetime timestamp,\n trip_miles double,\n trip_time bigint,\n\n);\n\n### Question:\nget longest trip in december 2022\n\n### Response (use duckdb shorthand if possible):" example_title: "taxi trips" --- # DuckDB-NSQL-7B (GGUF) The repository includes model files in the GGUF format for [DuckDB-NSQL-7B-v0.1](https://huggingface.co/motherduckdb/DuckDB-NSQL-7B-v0.1), featuring both the f16 and Q8_0 versions. ## Provided model files | Name | Quant method | Bits | | ---- | ---- | ---- | | [DuckDB-NSQL-7B-v0.1-f16.gguf](https://huggingface.co/motherduckdb/DuckDB-NSQL-7B-v0.1-GGUF/blob/main/DuckDB-NSQL-7B-v0.1-f16.gguf) | - | 16 | | [DuckDB-NSQL-7B-v0.1-q8_0.gguf](https://huggingface.co/motherduckdb/DuckDB-NSQL-7B-v0.1-GGUF/blob/main/DuckDB-NSQL-7B-v0.1-q8_0.gguf) | Q8_0 | 8 | ## Model Description NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks. In this repository we are introducing a new member of NSQL, DuckDB-NSQL. It's based on Meta's original [Llama-2 7B model](https://huggingface.co/meta-llama/Llama-2-7b) and further pre-trained on a dataset of general SQL queries and then fine-tuned on a dataset composed of DuckDB text-to-SQL pairs. ## Training Data 200k DuckDB text-to-SQL pairs, synthetically generated using [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), guided by the DuckDB v0.9.2 documentation. And text-to-SQL pairs from [NSText2SQL](https://huggingface.co/datasets/NumbersStation/NSText2SQL) that were transpiled to DuckDB SQL using [sqlglot](https://github.com/tobymao/sqlglot). ## Evaluation Data We evaluate our models on a DuckDB-specific benchmark that contains 75 text-to-SQL pairs. The benchmark is available [here](https://github.com/NumbersStationAI/DuckDB-NSQL/). ## Training Procedure DuckDB-NSQL was trained using cross-entropy loss to maximize the likelihood of sequential inputs. For finetuning on text-to-SQL pairs, we only compute the loss over the SQL portion of the pair. The model is trained using 80GB A100s, leveraging data and model parallelism. We fine-tuned for 10 epochs. ## Intended Use and Limitations The model was designed for text-to-SQL generation tasks from given table schema and natural language prompts. The model works best with the prompt format defined below and outputs. In contrast to existing text-to-SQL models, the SQL generation is not contrained to `SELECT` statements, but can generate any valid DuckDB SQL statement, including statements for official DuckDB extensions. ## How to Use Setup llama.cpp: ```shell CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python huggingface-cli download motherduckdb/DuckDB-NSQL-7B-v0.1-GGUF DuckDB-NSQL-7B-v0.1-q8_0.gguf --local-dir . --local-dir-use-symlinks False pip install wurlitzer ``` Example 1: ```python ## Setup - Llama.cpp from llama_cpp import Llama with pipes() as (out, err): llama = Llama( model_path="DuckDB-NSQL-7B-v0.1-q8_0.gguf", n_ctx=2048, ) text = """### Instruction: Your task is to generate valid duckdb SQL to answer the following question. ### Input: ### Question: create a new table called tmp from test.csv ### Response (use duckdb shorthand if possible): """ with pipes() as (out, err): pred = llama(text, temperature=0.1, max_tokens=500) print(pred["choices"][0]["text"]) ``` Example 2: ```python from llama_cpp import Llama with pipes() as (out, err): llama = Llama( model_path="DuckDB-NSQL-7B-v0.1-q8_0.gguf", n_ctx=2048, ) text = """### Instruction: Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema. ### Input: Here is the database schema that the SQL query will run on: CREATE TABLE taxi ( VendorID bigint, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count double, trip_distance double, fare_amount double, extra double, tip_amount double, tolls_amount double, improvement_surcharge double, total_amount double, ); ### Question: get all columns ending with _amount from taxi table ### Response (use duckdb shorthand if possible):""" with pipes() as (out, err): pred = llama(text, temperature=0.1, max_tokens=500) print(pred["choices"][0]["text"]) ``` Example 3: ```python from llama_cpp import Llama with pipes() as (out, err): llama = Llama( model_path="DuckDB-NSQL-7B-v0.1-q8_0.gguf", n_ctx=2048, ) text = """### Instruction: Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema. ### Input: Here is the database schema that the SQL query will run on: CREATE TABLE rideshare ( hvfhs_license_num varchar, dispatching_base_num varchar, originating_base_num varchar, request_datetime timestamp, on_scene_datetime timestamp, pickup_datetime timestamp, dropoff_datetime timestamp, trip_miles double, trip_time bigint, ); ### Question: get longest trip in december 2022 ### Response (use duckdb shorthand if possible): """ with pipes() as (out, err): pred = llama(text, temperature=0.1, max_tokens=500) print(pred["choices"][0]["text"]) ``` For more information (e.g., run with your local database), please find examples in [this repository](https://github.com/NumbersStationAI/DuckDB-NSQL).

eigh8skillz/southzulumodel

eigh8skillz

2024-01-25T07:35:17Z

1

0

diffusers

[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2024-01-25T07:35:16Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks southzulumodel tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.

uukuguy/speechless-zephyr-code-functionary-7b

uukuguy

2024-01-25T07:15:51Z

4,400

2

transformers

[ "transformers", "safetensors", "gguf", "mistral", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-23T08:19:26Z

--- language: - en library_name: transformers pipeline_tag: text-generation license: apache-2.0 --- <p><h1> speechless-zephyr-code-functionary-7b </h1></p> [4,5,8-bit GGUF models for CPU+GPU inference](https://huggingface.co/uukuguy/speechless-zephyr-code-functionary-7b/tree/main/GGUF) This model is the one of the moloras (Mixture-of-Multi-LoRAs) experiments. Extract LoRA modules from below models (all based Mistral-7B-v0.1), each LoRA module has its own unique skills. By using multi-loras, they can be combined together statically or dynamically to form a versatile new model. - HuggingFaceH4/zephyr-7b-beta (Uncensored Model) - meetkai/functionary-small-v2.2 (Execute functions/plugins) - uukuguy/speechless-code-mistral-7b-v1.0 (Enhance Coding) The entire process is completed through the use of extract-lora, merge-lora, and lora-hub provided by multi-loras. The router of mixture-of-multi-loras enables an automatic assembling of LoRA modules, using a gradientfree approach to obtain the coefficients of LoRA modules and requiring only a handful of inference steps for unseen tasks. Code: https://github.com/uukuguy/multi_loras ## LM-Evaluation-Harness [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) | Metric | Value | | --- | --- | | ARC | 61.52 | | HellaSwag | 83.88 | | MMLU | 64.71 | | TruthfulQA | 44.99 | | Winogrande | 78.69 | | GSM8K | 43.82 | | Average | 62.93 |

katzenbach/test_trainer

katzenbach

2024-01-25T07:11:58Z

92

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-01-25T06:27:57Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: test_trainer results: [] ---  # test_trainer This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5138 - Accuracy: 0.34 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 13 | 1.5917 | 0.22 | | No log | 2.0 | 26 | 1.5629 | 0.25 | | No log | 3.0 | 39 | 1.5138 | 0.34 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

abhinand/malayalam-llama-7b-instruct-v0.1-GGUF

abhinand

2024-01-25T07:11:35Z

6

1

null

[ "gguf", "en", "ml", "arxiv:2311.05845", "base_model:abhinand/malayalam-llama-pt-v0", "base_model:quantized:abhinand/malayalam-llama-pt-v0", "license:llama2", "endpoints_compatible", "region:us", "conversational" ]

null

2024-01-25T04:29:16Z

--- base_model: abhinand/malayalam-llama-pt-v0 model-index: - name: malayalam-llama-instruct-v0.1 results: [] license: llama2 language: - en - ml --- # Malayalam LLaMA 7B Instruct v0.1 [GGUF Quantized] Welcome to the inaugural release of the Malayalam LLaMA 7B instruct model – an important step in advancing LLMs for the Malayalam language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks. To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP)]() that outlines our journey and the model's potential impact. > **Note:** This model is based on the Tamil LLaMA series of models. The GitHub repository remains the same - [https://github.com/abhinand5/tamil-llama](https://github.com/abhinand5/tamil-llama). The base models and the updated code for Tamil LLaMA v0.2 (which this work is based on) will be released soon. If you appreciate this work and would like to support its continued development, consider [buying me a coffee](https://www.buymeacoffee.com/abhinand.b). Your support is invaluable and greatly appreciated. [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/abhinand.b) ## Demo: To access an easy-to-use, no-code demo, please open the provided Google Colab notebook. Complete instructions for usage are included within the notebook itself. <a href="https://colab.research.google.com/drive/11_RHZim_HubD2NskxSwq4X_NW4XlnrtS?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Demo In Colab"></a></center> ## Model description The Malayalam LLaMA models have been enhanced and tailored specifically with an extensive Malayalam vocabulary of ~16,000 tokens, building upon the foundation set by the original LLaMA-2. - **Model type:** A 7B parameter GPT-like model finetuned on ~500,000 samples consisting of an equal proportion of English and Malayalam samples. (Dataset will be released soon) - **Language(s):** Bilingual. English and Malayalam. - **License:** GNU General Public License v3.0 - **Finetuned from model:** [To be released soon]() - **Training Precision:** `bfloat16` - **Code:** [GitHub](https://github.com/abhinand5/tamil-llama) (To be updated soon) ## Quantization Info This repo contains GGUF format model files for [Malayalam LLaMA 7B Instruct v0.1](https://huggingface.co/abhinand/malayalam-llama-7b-instruct-v0.1). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. ### Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | |------------------------------------------------------------------------------------------------------------------------------------------------|--------------|------|---------|------------------|----------------------------------------------------------| | [malayalam-llama-7b-instruct-v0.1.Q4_K_M.gguf](https://huggingface.co/abhinand/malayalam-llama-7b-instruct-v0.1-GGUF/blob/main/malayalam-llama-7b-instruct-v0.1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.18 GB | 6.8 GB | medium, balanced quality - recommended | | [malayalam-llama-7b-instruct-v0.1.Q5_K_M.gguf](https://huggingface.co/abhinand/malayalam-llama-7b-instruct-v0.1-GGUF/blob/main/malayalam-llama-7b-instruct-v0.1.Q5_K_M.gguf) | Q5_K_M | 5 | 4.89 GB | 7.5 GB | large, very low quality loss - recommended | ## Prompt Template: ChatML ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Benchmark Results Benchmarking was done using [LLM-Autoeval](https://github.com/mlabonne/llm-autoeval) on an RTX 3090 on [runpod](https://www.runpod.io/). > **Note:** Please note that discrepancies have been observed between the Open LLM Leaderboard scores and those obtained from local runs using the LM Eval Harness with identical configurations. The results mentioned here are based on our own benchmarking. To replicate these findings, you can utilize the LLM-Autoeval or use [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) locally with the configurations described in Open LLM Leaderboard's About page. | Benchmark | Llama 2 Chat | Tamil Llama v0.2 Instruct | Telugu Llama Instruct | Malayalam Llama Instruct | |---------------|--------------|---------------------------|-----------------------|--------------------------| | ARC Challenge (25-shot) | 52.9 | **53.75** | 52.47 | 52.82 | | TruthfulQA (0-shot) | 45.57 | 47.23 | **48.47** | 47.46 | | Hellaswag (10-shot) | **78.55** | 76.11 | 76.13 | 76.91 | | Winogrande (5-shot) | 71.74 | **73.95** | 71.74 | 73.16 | | AGI Eval (0-shot) | 29.3 | **30.95** | 28.44 | 29.6 | | BigBench (0-shot) | 32.6 | 33.08 | 32.99 | **33.26** | | Average | 51.78 | **52.51** | 51.71 | 52.2 | ## Related Models | Model | Type | Data | Base Model | # Params | Download Links | |--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------| | Tamil LLaMA 7B v0.1 Base | Base model | 12GB | LLaMA 7B | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-base-v0.1) | | Tamil LLaMA 13B v0.1 Base | Base model | 4GB | LLaMA 13B | 13B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-base-v0.1) | | Tamil LLaMA 7B v0.1 Instruct | Instruction following model | 145k instructions | Tamil LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) | | Tamil LLaMA 13B v0.1 Instruct | Instruction following model | 145k instructions | Tamil LLaMA 13B Base | 13B | [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1) | | Tamil LLaMA 7B v0.2 Instruct | Instruction/Chat model | 420k instructions | Tamil LLaMA 7B Base v0.2 | 7B | [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1) | | Telugu LLaMA 7B v0.2 Instruct | Instruction/Chat model | ~400k instructions | Telugu LLaMA 7B Base v0.1 | 7B | [HF Hub](https://huggingface.co/abhinand/telugu-llama-instruct-v0.1) | ## Usage Note It's important to note that the models have not undergone detoxification/censorship. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications. ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/) ## Citation If you use this model or any of the the Tamil-Llama related work in your research, please cite: ```bibtex @misc{balachandran2023tamilllama, title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, author={Abhinand Balachandran}, year={2023}, eprint={2311.05845}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language.

Deepakkori45/Mistal_aspect_class_sequnece

Deepakkori45

2024-01-25T07:11:08Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-25T07:11:01Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

smend0/distilbert-base-uncased-lora-text-classification

smend0

2024-01-25T07:10:56Z

0

null

[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]

null

2024-01-25T07:06:51Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-lora-text-classification results: [] ---  # distilbert-base-uncased-lora-text-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a truncated IMDB dataset. It achieves the following results on the evaluation set: - Loss: 1.7208 - Accuracy: {'accuracy': 0.876} ## Model description The purpose of this model is to turn distilbert into a sentiment classification model. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------------:| | No log | 1.0 | 250 | 2.0890 | {'accuracy': 0.862} | | 0.2005 | 2.0 | 500 | 1.8919 | {'accuracy': 0.874} | | 0.2005 | 3.0 | 750 | 1.7205 | {'accuracy': 0.871} | | 0.0963 | 4.0 | 1000 | 1.7208 | {'accuracy': 0.876} | ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.1

dana11235/poca-SoccerTwos

dana11235

2024-01-25T07:07:57Z

23

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]

reinforcement-learning

2024-01-25T07:07:50Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: dana11235/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

TheBloke/Everyone-Coder-33B-Base-GPTQ

TheBloke

2024-01-25T07:01:23Z

14

3

transformers

[ "transformers", "safetensors", "llama", "text-generation", "merge", "conversational", "base_model:rombodawg/Everyone-Coder-33b-Base", "base_model:quantized:rombodawg/Everyone-Coder-33b-Base", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]

text-generation

2024-01-25T04:04:35Z

--- base_model: rombodawg/Everyone-Coder-33b-Base inference: false license: other license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL license_name: deepseek model_creator: rombo dawg model_name: Everyone Coder 33B Base model_type: deepseek prompt_template: '{prompt} ' quantized_by: TheBloke tags: - merge ---    <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # Everyone Coder 33B Base - GPTQ - Model creator: [rombo dawg](https://huggingface.co/rombodawg) - Original model: [Everyone Coder 33B Base](https://huggingface.co/rombodawg/Everyone-Coder-33b-Base)  # Description This repo contains GPTQ model files for [rombo dawg's Everyone Coder 33B Base](https://huggingface.co/rombodawg/Everyone-Coder-33b-Base). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GGUF) * [rombo dawg's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/rombodawg/Everyone-Coder-33b-Base)   ## Prompt template: Unknown ``` {prompt} ```   ## Known compatible clients / servers GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models. These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know!   ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) | 8192 | 17.40 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) | 8192 | 18.03 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) | 8192 | 19.96 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) | 8192 | 13.89 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) | 8192 | 33.84 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) | 8192 | 15.72 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) | 8192 | 34.60 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |   ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/Everyone-Coder-33B-Base-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Everyone-Coder-33B-Base-GPTQ:gptq-4bit-128g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `Everyone-Coder-33B-Base-GPTQ`: ```shell mkdir Everyone-Coder-33B-Base-GPTQ huggingface-cli download TheBloke/Everyone-Coder-33B-Base-GPTQ --local-dir Everyone-Coder-33B-Base-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir Everyone-Coder-33B-Base-GPTQ huggingface-cli download TheBloke/Everyone-Coder-33B-Base-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir Everyone-Coder-33B-Base-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir Everyone-Coder-33B-Base-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Everyone-Coder-33B-Base-GPTQ --local-dir Everyone-Coder-33B-Base-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/Everyone-Coder-33B-Base-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)   ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Everyone-Coder-33B-Base-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Everyone-Coder-33B-Base-GPTQ:gptq-4bit-128g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `Everyone-Coder-33B-Base-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!   ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/Everyone-Coder-33B-Base-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation( prompt_template, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(f"Model output: {response}") ```   ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Everyone-Coder-33B-Base-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-128g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Write a story about llamas" system_message = "You are a story writing assistant" prompt_template=f'''{prompt} ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ```   ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above.    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.  # Original model card: rombo dawg's Everyone Coder 33B Base Everyone-Coder-33b-Base ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/ECrHQnZnv8UM9GUCQtlWW.jpeg) EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. ______________________________________________________________________________________________________________ Im having trouble benchmarking this model because I suck at running llm benchmarks, but from hand testing running the model through https://edabit.com/challenge coding challenges vs up to date gpt-4. My model is hands down beating it in coding. ______________________________________________________________________________________________________________ Ive recently noticed this model has trouble with end tokens so I made a custom prompt template for it. Made sure to add (Always end with "<|EOT|>") In addition to your system prompt and (Always end your response with "<|EOT|>") at the end of the User message is the preset. Then add <|EOT|> as a custom stop string in your LM text generating interface. ``` Always end with "<|EOT|>" {System} <|User|> {User}. Always end your response with "<|EOT|>" <|Assistant|> {Assistant} ``` The models that were used in this merger were as follow: - https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct - https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B - https://huggingface.co/WizardLM/WizardCoder-33B-V1.1 Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗 You can find the write up for merging models here: https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing Config for the merger can be found bellow: ```yaml models: - model: WizardLM_WizardCoder-33B-V1.1 parameters: density: 1 weight: .5 - model: codefuse-ai_CodeFuse-DeepSeek-33B parameters: density: 1 weight: .5 merge_method: ties base_model: deepseek-ai_deepseek-coder-33b-instruct parameters: normalize: true int8_mask: true dtype: float16 ```

alnrg2arg/blockchainlabs_7B_merged_test2_4_sft_lora_DPO_orca2

alnrg2arg

2024-01-25T06:56:36Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-25T06:56:36Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Hongu/test_ner

Hongu

2024-01-25T06:56:34Z

102

0

transformers

[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-01-25T06:43:08Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

schroneko/ELYZA-japanese-Llama-2-13b-fast-instruct-gguf

schroneko

2024-01-25T06:30:57Z

14

1

transformers

[ "transformers", "gguf", "llama", "llama2", "ja", "arxiv:2307.09288", "license:llama2", "endpoints_compatible", "region:us" ]

null

2024-01-23T14:14:39Z

--- license: llama2 language: - ja tags: - llama2 --- # ELYZA-japanese-Llama-2-13b-fast-instruct-gguf [ELYZA-japanese-Llama-2-13b-fast-instruct](https://huggingface.co/ELYZA/ELYZA-japanese-Llama-2-13b-fast-instruct)の GGUF 変換モデルです。 ## Usage ``` git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp make -j ./main -m 'ELYZA-japanese-Llama-2-13b-fast-instruct-q4_k_m.gguf' -n 256 -p '[INST] <<SYS>>あなたは誠実で優秀な日本人のアシスタントです。<</SYS>>仕事の熱意を取り戻すためのアイデアを5つ挙げてください。[/INST]' ``` ### Licence Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. ### 引用 Citations ```tex @misc{elyzallama2023, title={ELYZA-japanese-Llama-2-13b}, url={https://huggingface.co/elyza/ELYZA-japanese-Llama-2-13b}, author={Akira Sasaki and Masato Hirakawa and Shintaro Horie and Tomoaki Nakamura and Sam Passaglia and Daisuke Oba}, year={2023}, } ``` ```tex @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint={2307.09288}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

LMX20012010/mistral_7b_guanaco

LMX20012010

2024-01-25T06:25:14Z

61

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-01-25T06:23:09Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

adarsh2350/T5-pytorch-billsum

adarsh2350

2024-01-25T06:15:34Z

90

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-01-24T15:52:59Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: T5-pytorch-billsum results: [] ---  # T5-pytorch-billsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5259 - Rouge1: 0.1402 - Rouge2: 0.0484 - Rougel: 0.1155 - Rougelsum: 0.1153 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.8082 | 0.1229 | 0.034 | 0.1036 | 0.1035 | 19.0 | | No log | 2.0 | 124 | 2.6034 | 0.1296 | 0.0409 | 0.1079 | 0.1079 | 19.0 | | No log | 3.0 | 186 | 2.5419 | 0.1378 | 0.047 | 0.1136 | 0.1136 | 19.0 | | No log | 4.0 | 248 | 2.5259 | 0.1402 | 0.0484 | 0.1155 | 0.1153 | 19.0 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cpu - Datasets 2.16.1 - Tokenizers 0.15.0

Chandrababu-Namani/InLegalBERT-fine-tuned

Chandrababu-Namani

2024-01-25T05:45:26Z

108

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:law-ai/InLegalBERT", "base_model:finetune:law-ai/InLegalBERT", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-01-25T05:45:07Z

--- license: mit base_model: law-ai/InLegalBERT tags: - generated_from_trainer metrics: - accuracy model-index: - name: InLegalBERT-lora-text-classification results: [] ---  # InLegalBERT-lora-text-classification This model is a fine-tuned version of [law-ai/InLegalBERT](https://huggingface.co/law-ai/InLegalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0550 - Accuracy: {'accuracy': 0.6449893390191898} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------:| | No log | 1.0 | 235 | 1.1448 | {'accuracy': 0.6151385927505331} | | No log | 2.0 | 470 | 1.0553 | {'accuracy': 0.6380597014925373} | | 1.2222 | 3.0 | 705 | 1.0427 | {'accuracy': 0.6316631130063965} | | 1.2222 | 4.0 | 940 | 1.0490 | {'accuracy': 0.6428571428571429} | | 0.8111 | 5.0 | 1175 | 1.0550 | {'accuracy': 0.6449893390191898} | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

cloudyu/Pluto_13B_DPO

cloudyu

2024-01-25T05:44:13Z

17

4

transformers

[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "dpo", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-17T06:00:41Z

--- license: cc-by-nc-4.0 tags: - moe - dpo --- # DPO of cloudyu/Mixtral_7Bx2_MoE dataset : jondurbin/truthy-dpo-v0.1 * metrics average 75.88 * TruthfulQA 81.5 SOTA (2024-01-17) gpu code example ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM import math ## v2 models model_path = "cloudyu/Mixtral_7Bx2_MoE_DPO" tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map='auto',local_files_only=False, load_in_4bit=True ) print(model) prompt = input("please input prompt:") while len(prompt) > 0: input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda") generation_output = model.generate( input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2 ) print(tokenizer.decode(generation_output[0])) prompt = input("please input prompt:") ```

GaganpreetSingh/mistral-7b-lora_model

GaganpreetSingh

2024-01-25T05:29:19Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-25T05:29:12Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

fionazhang/mistral-experiment-6

fionazhang

2024-01-25T05:16:35Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

null

2024-01-22T00:46:43Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral-experiment-6 results: [] ---  # mistral-experiment-6 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1400 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.0a0+git7bcf7da - Datasets 2.16.1 - Tokenizers 0.15.0

megha-shroff/mistral-rand-300k-test

megha-shroff

2024-01-25T05:16:18Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-v0.1-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-v0.1-GPTQ", "license:apache-2.0", "region:us" ]

null

2024-01-25T05:16:07Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/Mistral-7B-v0.1-GPTQ model-index: - name: mistral-rand-300k-test results: [] ---  # mistral-rand-300k-test This model is a fine-tuned version of [TheBloke/Mistral-7B-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8347 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6477 | 0.01 | 50 | 0.8983 | | 0.8474 | 0.02 | 100 | 0.8347 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

tsobolev/ppo-Huggy

tsobolev

2024-01-25T05:00:08Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2024-01-25T05:00:01Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: tsobolev/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ronluvzu1/autotrain-zb40r-1ccvc

ronluvzu1

2024-01-25T04:53:03Z

0

null

[ "tensorboard", "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]

text-generation

2024-01-25T04:52:59Z

--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```

varun-v-rao/t5-base-snli-model3

varun-v-rao

2024-01-25T04:52:47Z

33

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text-classification", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2024-01-25T03:43:05Z

--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: t5-base-snli-model3 results: [] ---  # t5-base-snli-model3 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2857 - Accuracy: 0.8985 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3812 | 1.0 | 2146 | 0.3052 | 0.8894 | | 0.3511 | 2.0 | 4292 | 0.2862 | 0.8971 | | 0.3333 | 3.0 | 6438 | 0.2857 | 0.8985 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

arun100/whisper-small-tr-derived-1

arun100

2024-01-25T04:26:57Z

4

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "tr", "dataset:mozilla-foundation/common_voice_16_0", "base_model:alikanakar/whisper-synthesized-turkish-8-hour", "base_model:finetune:alikanakar/whisper-synthesized-turkish-8-hour", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-01-24T16:56:45Z

--- language: - tr license: apache-2.0 base_model: alikanakar/whisper-synthesized-turkish-8-hour tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_16_0 metrics: - wer model-index: - name: Whisper Small Turkish Derived results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_16_0 tr type: mozilla-foundation/common_voice_16_0 config: tr split: test args: tr metrics: - name: Wer type: wer value: 18.625004393518683 ---  # Whisper Small Turkish Derived This model is a fine-tuned version of [alikanakar/whisper-synthesized-turkish-8-hour](https://huggingface.co/alikanakar/whisper-synthesized-turkish-8-hour) on the mozilla-foundation/common_voice_16_0 tr dataset. It achieves the following results on the evaluation set: - Loss: 0.2859 - Wer: 18.6250 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3197 | 0.58 | 200 | 0.3517 | 21.5757 | | 0.2672 | 1.16 | 400 | 0.3203 | 20.6724 | | 0.2532 | 1.75 | 600 | 0.3065 | 19.6496 | | 0.2382 | 2.33 | 800 | 0.2991 | 19.3420 | | 0.2448 | 2.91 | 1000 | 0.2943 | 19.1276 | | 0.2197 | 3.49 | 1200 | 0.2909 | 18.9712 | | 0.2159 | 4.07 | 1400 | 0.2885 | 18.7340 | | 0.2212 | 4.65 | 1600 | 0.2871 | 18.6988 | | 0.2114 | 5.24 | 1800 | 0.2861 | 18.6320 | | 0.2092 | 5.82 | 2000 | 0.2859 | 18.6250 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.16.2.dev0 - Tokenizers 0.15.0

bartowski/Einstein-7B-exl2

bartowski

2024-01-25T04:24:26Z

1

0

null

[ "axolotl", "generated_from_trainer", "text-generation", "dataset:allenai/ai2_arc", "dataset:camel-ai/physics", "dataset:camel-ai/chemistry", "dataset:camel-ai/biology", "dataset:metaeval/reclor", "dataset:openbookqa", "dataset:mandyyyyii/scibench", "dataset:derek-thomas/ScienceQA", "dataset:wenhu/TheoremQA", "dataset:TIGER-Lab/ScienceEval", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:other", "region:us" ]

text-generation

2024-01-25T04:08:27Z

--- license: other tags: - axolotl - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 datasets: - allenai/ai2_arc - camel-ai/physics - camel-ai/chemistry - camel-ai/biology - metaeval/reclor - openbookqa - mandyyyyii/scibench - derek-thomas/ScienceQA - wenhu/TheoremQA - TIGER-Lab/ScienceEval quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Einstein-7B Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.12">turboderp's ExLlamaV2 v0.0.12</a> for quantization. # The "main" branch only contains the measurement.json, download one of the other branches for the model (see below) Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/Weyaxi/Einstein-7B | Branch | Bits | lm_head bits | Size | Description | | ----- | ---- | ------- | ------ | ------------ | | [8_0](https://huggingface.co/Bartowski/Einstein-7B-exl2/tree/8_0) | 8.0 | 8.0 | 9.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | [6_5](https://huggingface.co/Bartowski/Einstein-7B-exl2/tree/6_5) | 6.5 | 8.0 | 8.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. | | [5_0](https://huggingface.co/Bartowski/Einstein-7B-exl2/tree/5_0) | 5.0 | 6.0 | 7.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. | | [4_25](https://huggingface.co/Bartowski/Einstein-7B-exl2/tree/4_25) | 4.25 | 6.0 | 6.7 GB | GPTQ equivalent bits per weight, slightly higher quality. | | [3_5](https://huggingface.co/Bartowski/Einstein-7B-exl2/tree/3_5) | 3.5 | 6.0 | 6.1 GB | Lower quality, only use if you have to. | All VRAM requirements estimated from 16k context. For 32k context add ~2 GB. ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Einstein-7B-exl2 Einstein-7B-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Einstein-7B-exl2`: ```shell mkdir Einstein-7B-exl2 huggingface-cli download bartowski/Einstein-7B-exl2 --local-dir Einstein-7B-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir Einstein-7B-exl2-6_5 huggingface-cli download bartowski/Einstein-7B-exl2 --revision 6_5 --local-dir Einstein-7B-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir Einstein-7B-exl2-6.5 huggingface-cli download bartowski/Einstein-7B-exl2 --revision 6_5 --local-dir Einstein-7B-exl2-6.5 --local-dir-use-symlinks False ```

datasciencechampion/mistral-gptq-finetuned-medmcqa

datasciencechampion

2024-01-25T04:15:01Z

1

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "license:apache-2.0", "region:us" ]

null

2024-01-24T13:15:50Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ model-index: - name: mistral-gptq-finetuned-medmcqa results: [] ---  # mistral-gptq-finetuned-medmcqa This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 500 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

bcijo/myMistral7b-finetuned

bcijo

2024-01-25T03:51:07Z

4

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "autotrain", "conversational", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-24T19:36:48Z

--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```

RangiLyu/InternLM2-tokenizer-llama

RangiLyu

2024-01-25T03:46:09Z

0

3

null

[ "license:other", "region:us" ]

null

2024-01-25T03:21:15Z

--- license: other --- # InternLM2 tokenizer(llamaified version) Official repo: https://github.com/InternLM/InternLM ## Note This repo converts the InternLM2 tokenizer to LlamaTokenizerFast. It also replaces the 354 token `\u0000` with an emoji so that it can be converted by llama.cpp ## How to use - Load ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(RangiLyu/InternLM2-tokenizer-llama) ``` - Apply chatml template ```python chat = [{"role": "user", "content": "Hello! What's your name?"}, {"role": "assistant", "content": "My name is InternLM2!"}, {"role": "user", "content": "Nice to meet you InternLM2!"},] chat_ids = llama_tokenizer.apply_chat_template(chat) print("ids: ", chat_ids) print("tokens: ", llama_tokenizer.convert_ids_to_tokens(chat_ids)) # convert the chat history to a string for generation chat_str = llama_tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) print("chat string: ", chat_str) ``` ``` ids: [1, 92543, 1008, 364, 9843, 346, 3716, 725, 829, 963, 345, 92542, 364, 92543, 525, 11353, 364, 5211, 963, 505, 4576, 11146, 314, 346, 92542, 364, 92543, 1008, 364, 44501, 442, 3531, 629, 4576, 11146, 314, 346, 92542, 364] tokens: ['<s>', '<|im_start|>', 'user', '\n', 'Hello', '!', '▁What', "'s", '▁your', '▁name', '?', '<|im_end|>', '\n', '<|im_start|>', 'ass', 'istant', '\n', 'My', '▁name', '▁is', '▁Intern', 'LM', '2', '!', '<|im_end|>', '\n', '<|im_start|>', 'user', '\n', 'Nice', '▁to', '▁meet', '▁you', '▁Intern', 'LM', '2', '!', '<|im_end|>', '\n'] chat string: <s><|im_start|>user Hello! What's your name?<|im_end|> <|im_start|>assistant My name is InternLM2!<|im_end|> <|im_start|>user Nice to meet you InternLM2!<|im_end|> <|im_start|>assistant ```

biznetgio/llama2-indonesia-chat

biznetgio

2024-01-25T03:46:02Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-25T03:45:38Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

varun-v-rao/t5-base-snli-model2

varun-v-rao

2024-01-25T03:42:00Z

33

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text-classification", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2024-01-25T02:32:24Z

--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: t5-base-snli-model2 results: [] ---  # t5-base-snli-model2 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2836 - Accuracy: 0.8994 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 36 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3815 | 1.0 | 2146 | 0.3028 | 0.8896 | | 0.3457 | 2.0 | 4292 | 0.2840 | 0.8979 | | 0.3339 | 3.0 | 6438 | 0.2836 | 0.8994 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

RayChengdu/internlm2-7b-lora

RayChengdu

2024-01-25T03:31:49Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-25T03:31:44Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

limin-arc/Mem-Beagle-7b-slerp-v3

limin-arc

2024-01-25T03:30:14Z

8

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "starsnatched/MemGPT", "222gate/Ingot-7b-slerp-7-forged-mirror", "base_model:gate369/Ingot-7b-slerp-7-forged-mirror", "base_model:merge:gate369/Ingot-7b-slerp-7-forged-mirror", "base_model:minchyeom/MemGPT", "base_model:merge:minchyeom/MemGPT", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-25T03:22:20Z

--- tags: - merge - mergekit - lazymergekit - starsnatched/MemGPT - 222gate/Ingot-7b-slerp-7-forged-mirror - starsnatched/MemGPT base_model: - starsnatched/MemGPT - 222gate/Ingot-7b-slerp-7-forged-mirror - starsnatched/MemGPT --- # Mem-Beagle-7b-slerp-v3 Mem-Beagle-7b-slerp-v3 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [starsnatched/MemGPT](https://huggingface.co/starsnatched/MemGPT) * [222gate/Ingot-7b-slerp-7-forged-mirror](https://huggingface.co/222gate/Ingot-7b-slerp-7-forged-mirror) * [starsnatched/MemGPT](https://huggingface.co/starsnatched/MemGPT) ## 🧩 Configuration ```yaml models: - model: starsnatched/MemGPT parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 - model: 222gate/Ingot-7b-slerp-7-forged-mirror parameters: density: 0.5 weight: [0, 0.3, 0.7, 1] # weight gradient - model: starsnatched/MemGPT parameters: density: 0.33 weight: - filter: mlp value: 0.5 - value: 0 merge_method: ties base_model: liminerity/Mem-Beagle-7b-slerp-v2 parameters: normalize: true int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "liminerity/Mem-Beagle-7b-slerp-v3" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

abhinand/tamil-llama-7b-base-v0.1

abhinand

2024-01-25T03:18:06Z

1,637

9

transformers

[ "transformers", "pytorch", "llama", "text-generation", "ta", "en", "arxiv:2311.05845", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-08T03:55:05Z

--- language: - ta - en license: llama2 --- # Tamil LLaMA 7B Base v0.1 [pre-trained] Welcome to the inaugural release of the Tamil LLaMA 7B base model – an important step in advancing LLMs for the Tamil language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks. To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP)]() that outlines our journey and the model's potential impact. > **Please Note:** This model, labeled as a foundational Tamil Language Model (LLM), is designed primarily for Causal Language Modeling (LM) purposes. In other words, if you are looking for an instruction following model in Tamil, you may find [abhinand/tamil-llama-7b-instruct-v0.1](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) more suitable for your needs. ## Model description The Tamil LLaMA models have been enhanced and tailored specifically with an extensive Tamil vocabulary of 16,000 tokens, building upon the foundation set by the original LLaMA-2. - **Model type:** A 7B parameter model for Causal LM pre-trained on [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) dataset's Tamil subset. - **Language(s):** Tamil and English - **License:** GNU General Public License v3.0 - **Source Model:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) - **Training Precision:** `float16` - **Code:** [GitHub](https://github.com/abhinand5/tamil-llama) ## Related Models | Model | Type | Data | Base Model | # Params | Download Links | |--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------| | Tamil LLaMA 7B Base | Base model | 12GB | LLaMA 7B | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-base-v0.1) | | Tamil LLaMA 13B Base | Base model | 4GB | LLaMA 13B | 13B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-base-v0.1) | | Tamil LLaMA 7B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) | | Tamil LLaMA 13B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 13B Base | 13B | [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1) | ## Usage Note It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications. ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/) ## Citation If you use this model or the Tamil-Llama dataset in your research, please cite: ```bibtex @misc{balachandran2023tamilllama, title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, author={Abhinand Balachandran}, year={2023}, eprint={2311.05845}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language.

abhinand/tamil-llama-7b-instruct-v0.2-GGUF

abhinand

2024-01-25T03:15:33Z

20

1

null

[ "gguf", "en", "ta", "arxiv:2311.05845", "base_model:abhinand/dr-llama-ta-instruct-v0", "base_model:quantized:abhinand/dr-llama-ta-instruct-v0", "license:llama2", "endpoints_compatible", "region:us", "conversational" ]

null

2024-01-24T06:54:32Z

--- base_model: abhinand/dr-llama-ta-instruct-v0 model-index: - name: tamil-llama-instruct-v0.2 results: [] license: llama2 language: - en - ta --- # Tamil LLaMA 7B Instruct v0.2 [GGUF Quantized] Welcome to the inaugural release of the Tamil LLaMA 7B instruct model – an important step in advancing LLMs for the Tamil language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks. To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP)]() that outlines our journey and the model's potential impact. > **Note:** This model is based on the Tamil LLaMA series of models. The GitHub repository remains the same - [https://github.com/abhinand5/tamil-llama](https://github.com/abhinand5/tamil-llama). The base models and the updated code for Tamil LLaMA v0.2 (which this work is based on) will be released soon. If you appreciate this work and would like to support its continued development, consider [buying me a coffee](https://www.buymeacoffee.com/abhinand.b). Your support is invaluable and greatly appreciated. [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/abhinand.b) ## Demo: To access an easy-to-use, no-code demo, please open the provided Google Colab notebook. Complete instructions for usage are included within the notebook itself. <a href="https://colab.research.google.com/drive/11_RHZim_HubD2NskxSwq4X_NW4XlnrtS?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Demo In Colab"></a></center> ## Model description The Tamil LLaMA models have been enhanced and tailored specifically with an extensive Tamil vocabulary of ~16,000 tokens, building upon the foundation set by the original LLaMA-2. - **Model type:** A 7B parameter GPT-like model finetuned on ~500,000 samples consisting of an equal proportion of English and Tamil samples. (Dataset will be released soon) - **Language(s):** Bilingual. English and Tamil. - **License:** GNU General Public License v3.0 - **Finetuned from model:** [To be released soon]() - **Training Precision:** `bfloat16` - **Code:** [GitHub](https://github.com/abhinand5/tamil-llama) (To be updated soon) ## Quantization Info This repo contains GGUF format model files for [Tamil LLaMA 7B Instruct v0.2](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.2). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. ### Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | |------------------------------------------------------------------------------------------------------------------------------------------------|--------------|------|---------|------------------|----------------------------------------------------------| | [tamil-llama-7b-instruct-v0.2.Q4_K_M.gguf](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.2-GGUF/blob/main/tamil-llama-7b-instruct-v0.2.Q4_K_M.gguf) | Q4_K_M | 4 | 4.18 GB | 6.8 GB | medium, balanced quality - recommended | | [tamil-llama-7b-instruct-v0.2.Q5_K_M.gguf](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.2-GGUF/blob/main/tamil-llama-7b-instruct-v0.2.Q5_K_M.gguf) | Q5_K_M | 5 | 4.89 GB | 7.5 GB | large, very low quality loss - recommended | ## Prompt Template: ChatML ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Benchmark Results Benchmarking was done using [LLM-Autoeval](https://github.com/mlabonne/llm-autoeval) on an RTX 3090 on [runpod](https://www.runpod.io/). > **Note:** Please note that discrepancies have been observed between the Open LLM Leaderboard scores and those obtained from local runs using the LM Eval Harness with identical configurations. The results mentioned here are based on our own benchmarking. To replicate these findings, you can utilize the LLM-Autoeval or use [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) locally with the configurations described in Open LLM Leaderboard's About page. | Benchmark | Llama 2 Chat | Tamil Llama v0.2 Instruct | Telugu Llama Instruct | Malayalam Llama Instruct | |---------------|--------------|---------------------------|-----------------------|--------------------------| | ARC Challenge (25-shot) | 52.9 | **53.75** | 52.47 | 52.82 | | TruthfulQA (0-shot) | 45.57 | 47.23 | **48.47** | 47.46 | | Hellaswag (10-shot) | **78.55** | 76.11 | 76.13 | 76.91 | | Winogrande (5-shot) | 71.74 | **73.95** | 71.74 | 73.16 | | AGI Eval (0-shot) | 29.3 | **30.95** | 28.44 | 29.6 | | BigBench (0-shot) | 32.6 | 33.08 | 32.99 | **33.26** | | Average | 51.78 | **52.51** | 51.71 | 52.2 | ## Related Models | Model | Type | Data | Base Model | # Params | Download Links | |--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------| | Tamil LLaMA 7B v0.1 Base | Base model | 12GB | LLaMA 7B | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-base-v0.1) | | Tamil LLaMA 13B v0.1 Base | Base model | 4GB | LLaMA 13B | 13B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-base-v0.1) | | Tamil LLaMA 7B v0.1 Instruct | Instruction following model | 145k instructions | Tamil LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) | | Tamil LLaMA 13B v0.1 Instruct | Instruction following model | 145k instructions | Tamil LLaMA 13B Base | 13B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-instruct-v0.1) | | Telugu LLaMA 7B v0.1 Instruct | Instruction/Chat model | 420k instructions | Telugu LLaMA 7B Base v0.1 | 7B | [HF Hub](https://huggingface.co/abhinand/telugu-llama-instruct-v0.1) | | Malayalam LLaMA 7B v0.2 Instruct | Instruction/Chat model | 420k instructions | Malayalam LLaMA 7B Base v0.1 | 7B | [HF Hub](https://huggingface.co/abhinand/malayalam-llama-instruct-v0.1) | ## Usage Note It's important to note that the models have not undergone detoxification/censorship. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications. ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/) ## Citation If you use this model or any of the the Tamil-Llama related work in your research, please cite: ```bibtex @misc{balachandran2023tamilllama, title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, author={Abhinand Balachandran}, year={2023}, eprint={2311.05845}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language.

abhinand/tamil-llama-7b-instruct-v0.2

abhinand

2024-01-25T03:14:24Z

753

4

transformers

[ "transformers", "pytorch", "llama", "text-generation", "conversational", "en", "ta", "arxiv:2311.05845", "base_model:abhinand/dr-llama-ta-instruct-v0", "base_model:finetune:abhinand/dr-llama-ta-instruct-v0", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-23T03:06:49Z

--- base_model: abhinand/dr-llama-ta-instruct-v0 model-index: - name: tamil-llama-instruct-v0.2 results: [] license: llama2 language: - en - ta --- # Tamil LLaMA 7B Instruct v0.2 Welcome to the inaugural release of the Tamil LLaMA 7B instruct model – an important step in advancing LLMs for the Tamil language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks. To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP)]() that outlines our journey and the model's potential impact. > **Note:** This model is based on the Tamil LLaMA series of models. The GitHub repository remains the same - [https://github.com/abhinand5/tamil-llama](https://github.com/abhinand5/tamil-llama). The base models and the updated code for Tamil LLaMA v0.2 (which this work is based on) will be released soon. If you appreciate this work and would like to support its continued development, consider [buying me a coffee](https://www.buymeacoffee.com/abhinand.b). Your support is invaluable and greatly appreciated. [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/abhinand.b) ## Demo: To access an easy-to-use, no-code demo, please open the provided Google Colab notebook. Complete instructions for usage are included within the notebook itself. <a href="https://colab.research.google.com/drive/11_RHZim_HubD2NskxSwq4X_NW4XlnrtS?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Demo In Colab"></a></center> ## Model description The Tamil LLaMA models have been enhanced and tailored specifically with an extensive Tamil vocabulary of ~16,000 tokens, building upon the foundation set by the original LLaMA-2. - **Model type:** A 7B parameter GPT-like model finetuned on ~500,000 samples consisting of an equal proportion of English and Tamil samples. (Dataset will be released soon) - **Language(s):** Bilingual. English and Tamil. - **License:** GNU General Public License v3.0 - **Finetuned from model:** [To be released soon]() - **Training Precision:** `bfloat16` - **Code:** [GitHub](https://github.com/abhinand5/tamil-llama) (To be updated soon) ## Prompt Template: ChatML ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Benchmark Results Benchmarking was done using [LLM-Autoeval](https://github.com/mlabonne/llm-autoeval) on an RTX 3090 on [runpod](https://www.runpod.io/). > **Note:** Please note that discrepancies have been observed between the Open LLM Leaderboard scores and those obtained from local runs using the LM Eval Harness with identical configurations. The results mentioned here are based on our own benchmarking. To replicate these findings, you can utilize the LLM-Autoeval or use [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) locally with the configurations described in Open LLM Leaderboard's About page. | Benchmark | Llama 2 Chat | Tamil Llama v0.2 Instruct | Telugu Llama Instruct | Malayalam Llama Instruct | |---------------|--------------|---------------------------|-----------------------|--------------------------| | ARC Challenge (25-shot) | 52.9 | **53.75** | 52.47 | 52.82 | | TruthfulQA (0-shot) | 45.57 | 47.23 | **48.47** | 47.46 | | Hellaswag (10-shot) | **78.55** | 76.11 | 76.13 | 76.91 | | Winogrande (5-shot) | 71.74 | **73.95** | 71.74 | 73.16 | | AGI Eval (0-shot) | 29.3 | **30.95** | 28.44 | 29.6 | | BigBench (0-shot) | 32.6 | 33.08 | 32.99 | **33.26** | | Average | 51.78 | **52.51** | 51.71 | 52.2 | ## Related Models | Model | Type | Data | Base Model | # Params | Download Links | |--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------| | Tamil LLaMA 7B v0.1 Base | Base model | 12GB | LLaMA 7B | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-base-v0.1) | | Tamil LLaMA 13B v0.1 Base | Base model | 4GB | LLaMA 13B | 13B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-base-v0.1) | | Tamil LLaMA 7B v0.1 Instruct | Instruction following model | 145k instructions | Tamil LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) | | Tamil LLaMA 13B v0.1 Instruct | Instruction following model | 145k instructions | Tamil LLaMA 13B Base | 13B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-instruct-v0.1) | | Telugu LLaMA 7B v0.1 Instruct | Instruction/Chat model | 420k instructions | Telugu LLaMA 7B Base v0.1 | 7B | [HF Hub](https://huggingface.co/abhinand/telugu-llama-instruct-v0.1) | | Malayalam LLaMA 7B v0.2 Instruct | Instruction/Chat model | 420k instructions | Malayalam LLaMA 7B Base v0.1 | 7B | [HF Hub](https://huggingface.co/abhinand/malayalam-llama-instruct-v0.1) | ## Example Usage ```python from transformers import LlamaForCausalLM, AutoTokenizer, pipeline model = LlamaForCausalLM.from_pretrained( "abhinand/tamil-llama-instruct-v0.2", #load_in_8bit=True, # Set this depending on the GPU you have torch_dtype=torch.bfloat16, device_map={"": 0}, # Set this depending on the number of GPUs you have local_files_only=False # Optional ) model.eval() tokenizer = AutoTokenizer.from_pretrained("abhinand/tamil-llama-instruct-v0.2") inf_pipeline = pipeline("conversational", model=model, tokenizer=tokenizer) def format_instruction(system_prompt, question, return_dict=False): if system_prompt is None: messages = [ {'content': question, 'role': 'user'}, ] else: messages = [ {'content': system_prompt, 'role': 'system'}, {'content': question, 'role': 'user'}, ] if return_dict: return messages prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) return prompt # Set the generation configuration according to your needs temperature = 0.6 repetition_penalty = 1.1 max_new_tokens = 256 SYSTEM_PROMPT = "You are an AI assistant who follows instructions extremely well. Do your best your best to help." INPUT = "Can you explain the significance of Tamil festival Pongal?" instruction = format_instruction( system_prompt=SYSTEM_PROMPT, question=INPUT, return_dict=True, ) output = inf_pipeline( instruction, temperature=temperature, max_new_tokens=max_new_tokens, repetition_penalty=repetition_penalty ) print(output) ``` **Example Output:** ``` Conversation id: d57cdf33-01ff-4328-8efe-5c4fefdd6e77 system: You are an AI assistant who follows instructions extremely well. Do your best your best to help. user: Can you explain the significance of Tamil festival Pongal? assistant: Pongal is a significant harvest festival celebrated in Tamil Nadu and other parts of southern India. It marks the end of the rainy season and beginning of the agricultural year. The festival primarily revolves around giving gratitude to nature, particularly the Sun God Surya for his bountiful gifts like agriculture and health. People offer prayers to cattle, which play a significant role in agriculture, as well as their families for their continued support during the harvest season. The festival is marked by various colorful events, including preparing traditional Pongal dishes like rice cooked with milk, sugarcane, and banana, followed by exchanging gifts and celebrating among family members and friends. It also serves as a time for unity and strengthens the bond between people in their communities. ``` ## Usage Note It's important to note that the models have not undergone detoxification/censorship. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications. ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/) ## Citation If you use this model or any of the the Tamil-Llama related work in your research, please cite: ```bibtex @misc{balachandran2023tamilllama, title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, author={Abhinand Balachandran}, year={2023}, eprint={2311.05845}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language.

Liveme/Marcoroni-7B-v3_qlora_20231228-0017_8bit_merge

Liveme

2024-01-25T03:14:07Z

0

peft

[ "peft", "pytorch", "mistral", "region:us" ]

null

2024-01-02T07:26:59Z

--- library_name: peft base_model: AIDC-ai-business/Marcoroni-7B-v3 --- # Model Card for Model ID prompt格式如下： ```python system_prompt = '''You are now roleplaying as {Character Nickname}. {character_msg} Identity: Nurse Categories: Erotica, {tone} Personalities: {personalities} ``` 推理配置需要注意的几个参数： ``` 'temperature': 0.85 'top_p': 0.95, # disable 'top_k': 0, # disable 'repetition_penalty': 1.15, ``` Prompt Template for alpaca style ``` ### Instruction: <prompt> (without the <>) ### Response: ``` 训练参数： ``` ```

vine135/outputs_mistral_b_finance_finetuned_test

vine135

2024-01-25T03:07:10Z

0

null

[ "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

null

2024-01-25T02:58:54Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: outputs_mistral_b_finance_finetuned_test results: [] ---  # outputs_mistral_b_finance_finetuned_test This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0.03 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

macadeliccc/TheCorso-7b-v1

macadeliccc

2024-01-25T02:59:29Z

4

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-24T21:48:14Z

--- library_name: transformers tags: [] --- # TheCorso-7b-v1 **In loving memory of my dog Klaus (Piccolo)** _~ Piccolo (Italian): the little one ~_ ![piccolo.png](piccolo.png) + Based on fblgit/UNA-TheBeagle-7b-v1

SanjiWatsuki/Kunoichi-DPO-v2-7B

SanjiWatsuki

2024-01-25T02:52:18Z

1,520

81

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-13T04:02:02Z

--- license: cc-by-nc-4.0 --- | Model | MT Bench | EQ Bench | MMLU | Logic Test | |----------------------|----------|----------|---------|-------------| | GPT-4-Turbo | 9.32 | - | - | - | | GPT-4 | 8.99 | 62.52 | 86.4 | 0.86 | | **Kunoichi-DPO-v2-7B** | **8.51** | **42.18** | **64.94**| **0.58** | | Mixtral-8x7B-Instruct| 8.30 | 44.81 | 70.6 | 0.75 | | **Kunoichi-DPO-7B** | **8.29** | **41.60** | **64.83** | **0.59** | | **Kunoichi-7B** | **8.14** | **44.32** | **64.9** | **0.58** | | Starling-7B | 8.09 | - | 63.9 | 0.51 | | Claude-2 | 8.06 | 52.14 | 78.5 | - | | Silicon-Maid-7B | 7.96 | 40.44 | 64.7 | 0.54 | | Loyal-Macaroni-Maid-7B | 7.95 | 38.66 | 64.9 | 0.57 | | GPT-3.5-Turbo | 7.94 | 50.28 | 70 | 0.57 | | Claude-1 | 7.9 | - | 77 | - | | Openchat-3.5 | 7.81 | 37.08 | 64.3 | 0.39 | | Dolphin-2.6-DPO | 7.74 | 42.88 | 61.9 | 0.53 | | Zephyr-7B-beta | 7.34 | 38.71 | 61.4 | 0.30 | | Llama-2-70b-chat-hf | 6.86 | 51.56 | 63 | - | | Neural-chat-7b-v3-1 | 6.84 | 43.61 | 62.4 | 0.30 | | Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench | |---|---:|---:|---:|---:|---:| | **Kunoichi-DPO-7B**|**58.4**| 45.08 | 74| 66.99| 47.52| | **Kunoichi-DPO-v2-7B**|**58.31**| 44.85| 75.05| 65.69| 47.65| | [Kunoichi-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-7B)|57.54| 44.99| 74.86| 63.72| 46.58| | [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)| 56.85 | 44.74 | 75.6 | 59.89 | 47.17 | | [Silicon-Maid-7B](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B) | 56.45| 44.74| 74.26| 61.5| 45.32| | [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) | 53.51 | 43.67 | 73.24 | 55.37 | 41.76 | | [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 | | [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5) | 51.34 | 42.67 | 72.92 | 47.27 | 42.51 | | [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) | 51.16 | 42.06 | 72.72 | 47.33 | 42.53 | | [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) | 50.99 | 37.33 | 71.83 | 55.1 | 39.7 | | Model | AlpacaEval2 | Length | | --------------------------- | ----------- | ------ | | GPT-4 | 23.58% | 1365 | | GPT-4 0314 | 22.07% | 1371 | | Mistral Medium | 21.86% | 1500 | | Mixtral 8x7B v0.1 | 18.26% | 1465 | | **Kunoichi-DPO-v2** | **17.19%** | 1785 | | Claude 2 | 17.19% | 1069 | | Claude | 16.99% | 1082 | | Gemini Pro | 16.85% | 1315 | | GPT-4 0613 | 15.76% | 1140 | | Claude 2.1 | 15.73% | 1096 | | Mistral 7B v0.2 | 14.72% | 1676 | | GPT 3.5 Turbo 0613 | 14.13% | 1328 | | LLaMA2 Chat 70B | 13.87% | 1790 | | LMCocktail-10.7B-v1 | 13.15% | 1203 | | WizardLM 13B V1.1 | 11.23% | 1525 | | Zephyr 7B Beta | 10.99% | 1444 | | OpenHermes-2.5-Mistral (7B) | 10.34% | 1107 | | GPT 3.5 Turbo 0301 | 9.62% | 827 | | **Kunoichi-7B** | **9.38%** | 1492 | | GPT 3.5 Turbo 1106 | 9.18% | 796 | | GPT-3.5 | 8.56% | 1018 | | Phi-2 DPO | 7.76% | 1687 | | LLaMA2 Chat 13B | 7.70% | 1513 |

jilp00/youtoks-transformers-united-v2-7B-v02

jilp00

2024-01-25T02:50:43Z

62

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-01-25T02:46:13Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

varun-v-rao/bert-large-cased-mnli-model1

varun-v-rao

2024-01-25T02:49:56Z

95

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-large-cased", "base_model:finetune:google-bert/bert-large-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-01-24T23:57:41Z

--- license: apache-2.0 base_model: bert-large-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-large-cased-mnli-model1 results: [] ---  # bert-large-cased-mnli-model1 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4619 - Accuracy: 0.8680 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 78 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3947 | 1.0 | 6136 | 0.3608 | 0.8601 | | 0.2557 | 2.0 | 12272 | 0.3683 | 0.8678 | | 0.1455 | 3.0 | 18408 | 0.4619 | 0.8680 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

MSI-AI-Group/TRT_AllMsi_4050

MSI-AI-Group

2024-01-25T02:42:53Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-12-22T01:32:43Z

--- license: creativeml-openrail-m --- This model is for AI Artist use. You use our model by following Github Link: https://github.com/MSI-AI-Group/AI-Artist

MSI-AI-Group/TRT_Base_4050

MSI-AI-Group

2024-01-25T02:42:35Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-12-22T01:29:24Z

--- license: creativeml-openrail-m --- This model is for AI Artist use. You use our model by following Github Link: https://github.com/MSI-AI-Group/AI-Artist

MSI-AI-Group/TRT_AllMsi_4060

MSI-AI-Group

2024-01-25T02:42:08Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-12-22T01:33:31Z

--- license: creativeml-openrail-m --- This model is for AI Artist use. You use our model by following Github Link: https://github.com/MSI-AI-Group/AI-Artist

MSI-AI-Group/TRT_Base_4080

MSI-AI-Group

2024-01-25T02:41:19Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-12-22T01:34:14Z

--- license: creativeml-openrail-m --- This model is for AI Artist use. You use our model by following Github Link: https://github.com/MSI-AI-Group/AI-Artist

MSI-AI-Group/TRT_Base_4090

MSI-AI-Group

2024-01-25T02:40:42Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-12-22T01:34:22Z

--- license: creativeml-openrail-m --- This model is for AI Artist use. You use our model by following Github Link: https://github.com/MSI-AI-Group/AI-Artist

tiagoblima/t5_base-qg-ap-peft-oficial

tiagoblima

2024-01-25T02:34:56Z

0

null

[ "safetensors", "generated_from_trainer", "dataset:tiagoblima/du-qg-squadv1_pt", "base_model:unicamp-dl/ptt5-base-portuguese-vocab", "base_model:finetune:unicamp-dl/ptt5-base-portuguese-vocab", "license:mit", "region:us" ]

null

2024-01-25T01:55:34Z

--- license: mit base_model: unicamp-dl/ptt5-base-portuguese-vocab tags: - generated_from_trainer datasets: - tiagoblima/du-qg-squadv1_pt model-index: - name: t5_base-qg-ap-peft-oficial results: [] ---  # t5_base-qg-ap-peft-oficial This model is a fine-tuned version of [unicamp-dl/ptt5-base-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-base-portuguese-vocab) on the tiagoblima/du-qg-squadv1_pt dataset. It achieves the following results on the evaluation set: - Loss: 15.9549 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 13.2241 | 1.0 | 2367 | 16.4507 | | 12.9503 | 2.0 | 4734 | 16.2224 | | 12.8425 | 3.0 | 7101 | 16.0670 | | 12.8058 | 4.0 | 9468 | 15.9792 | | 12.7937 | 5.0 | 11835 | 15.9549 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.1

varun-v-rao/t5-base-snli-model1

varun-v-rao

2024-01-25T02:31:13Z

33

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text-classification", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2024-01-25T01:21:31Z

--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: t5-base-snli-model1 results: [] ---  # t5-base-snli-model1 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2860 - Accuracy: 0.8989 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 73 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3815 | 1.0 | 2146 | 0.2976 | 0.8903 | | 0.3507 | 2.0 | 4292 | 0.2937 | 0.8962 | | 0.3354 | 3.0 | 6438 | 0.2860 | 0.8989 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

zhangyanchao/whisper-medium-zh_CN

zhangyanchao

2024-01-25T02:19:48Z

6

1

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-01-22T03:25:38Z

--- language: - hi license: apache-2.0 base_model: openai/whisper-medium tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Medium zh_CN - Charles.djang results: [] ---  # Whisper Medium zh_CN - Charles.djang This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

josemhernandezbiometric/whisper-medium-finetuned-int8

josemhernandezbiometric

2024-01-25T02:18:22Z

1

0

peft

[ "peft", "tensorboard", "safetensors", "whisper-event", "generated_from_trainer", "es", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-medium", "base_model:adapter:openai/whisper-medium", "license:apache-2.0", "model-index", "region:us" ]

null

2024-01-22T07:36:34Z

--- language: - es license: apache-2.0 library_name: peft tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer base_model: openai/whisper-medium model-index: - name: Whisper Small Es - Sanchit Gandhi results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 split: None metrics: - type: wer value: 56.441337816315695 name: Wer ---  # Whisper Small Es - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 2.5129 - Wer: 56.4413 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 750 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 6.3605 | 2.3 | 50 | 6.2660 | 55.7247 | | 5.3113 | 4.6 | 100 | 5.1187 | 56.4590 | | 4.2749 | 6.9 | 150 | 4.2391 | 55.6185 | | 3.5266 | 9.2 | 200 | 3.4143 | 53.6719 | | 3.0671 | 11.49 | 250 | 3.1045 | 49.2037 | | 2.8716 | 13.79 | 300 | 2.9260 | 50.7786 | | 2.7263 | 16.09 | 350 | 2.7987 | 53.5746 | | 2.6467 | 18.39 | 400 | 2.7079 | 55.0787 | | 2.5624 | 20.69 | 450 | 2.6443 | 55.6008 | | 2.5087 | 22.99 | 500 | 2.5989 | 57.3881 | | 2.4922 | 25.29 | 550 | 2.5660 | 55.9370 | | 2.4274 | 27.59 | 600 | 2.5421 | 56.4325 | | 2.4337 | 29.89 | 650 | 2.5257 | 57.4058 | | 2.3991 | 32.18 | 700 | 2.5165 | 57.0165 | | 2.4211 | 34.48 | 750 | 2.5129 | 56.4413 | ### Framework versions - PEFT 0.7.1 - Transformers 4.37.0.dev0 - Pytorch 2.1.0 - Datasets 2.16.2.dev0 - Tokenizers 0.15.0

sosoai/Orion-14B-Chat-RAG-safetensors

sosoai

2024-01-25T02:09:15Z

43

0

transformers

[ "transformers", "pytorch", "safetensors", "orion", "text-generation", "code", "model", "llm", "custom_code", "en", "zh", "ja", "ko", "autotrain_compatible", "region:us" ]

text-generation

2024-01-25T00:05:06Z

--- language: - en - zh - ja - ko metrics: - accuracy pipeline_tag: text-generation tags: - code - model - llm ---   <div align="center"> <img src="./assets/imgs/orion_start.PNG" alt="logo" width="50%" /> </div> <div align="center"> <h1> Orion-14B </h1> </div> <div align="center"> <div align="center"> <b>🌐English</b> | <a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG/blob/main/README_zh.md" target="_blank">🇨🇳中文</a> | <a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG/blob/main/README_ja.md" target="_blank">🇯🇵日本語</a> | <a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG/blob/main/README_ko.md" target="_blank">🇰🇷한국어</a> </div> <h4 align="center"> <p> 🤗 <a href="https://huggingface.co/OrionStarAI" target="_blank">HuggingFace Mainpage</a> | 🤖 <a href="https://modelscope.cn/organization/OrionStarAI" target="_blank">ModelScope Mainpage</a><br>🎬 <a href="https://huggingface.co/spaces/OrionStarAI/Orion-14B-App-Demo" target="_blank">HuggingFace Demo</a> | 🎫 <a href="https://modelscope.cn/studios/OrionStarAI/Orion-14B-App-Demo/summary" target="_blank">ModelScope Demo</a><br>😺 <a href="https://github.com/OrionStarAI/Orion" target="_blank">GitHub</a><br>📖 <a href="https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf" target="_blank">Tech Report</a> <p> </h4> </div> # Table of Contents - [📖 Model Introduction](#model-introduction) - [🔗 Model Download](#model-download) - [🔖 Model Benchmark](#model-benchmark) - [📊 Model Inference](#model-inference) - [📜 Declarations & License](#declarations-license) - [🥇 Company Introduction](#company-introduction) <a name="model-introduction"></a><br> # 1. Model Introduction - Orion-14B series models are open-source multilingual large language models trained from scratch by OrionStarAI. The base model is trained on 2.5T multilingual corpus, including Chinese, English, Japanese, Korean, etc, and it exhibits superior performance in these languages. For details, please refer to [tech report](https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf). - The Orion-14B series models exhibit the following features: - Among models with 20B-parameter scale level, Orion-14B-Base model shows outstanding performance in comprehensive evaluations. - Strong multilingual capabilities, significantly outperforming in Japanese and Korean testsets. - The fine-tuned models demonstrate strong adaptability, excelling in human-annotated blind tests. - The long-chat version supports extremely long texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k. - The quantized versions reduce model size by 70%, improve inference speed by 30%, with performance loss less than 1%. <table style="border-collapse: collapse; width: 100%;"> <tr> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/opencompass_en.png" alt="opencompass" style="width: 100%; height: auto;"> </td> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/model_cap_en.png" alt="modelcap" style="width: 100%; height: auto;"> </td> </tr> </table> - Orion-14B series models including: - **Orion-14B-Base:** A multilingual large language foundational model with 14 billion parameters, pretrained on a diverse dataset of 2.5 trillion tokens. - **Orion-14B-Chat:** A chat-model fine-tuned on a high-quality corpus aims to provide an excellence interactive experience for users in the large model community. - **Orion-14B-LongChat:** The long-context version excels at handling extremely lengthy texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k. - **Orion-14B-Chat-RAG:** A chat-model fine-tuned on a custom retrieval augmented generation dataset, achieving superior performance in retrieval augmented generation tasks. - **Orion-14B-Chat-Plugin:** A chat-model specifically tailored for plugin and function calling tasks, ideal for agent-related scenarios where the LLM acts as a plugin and function call system. - **Orion-14B-Base-Int4:** A quantized base model utilizing 4-bit integer weights. It significantly reduces the model size by 70% and increases the inference speed by 30% while incurring a minimal performance loss of only 1%. - **Orion-14B-Chat-Int4:** A quantized chat model utilizing 4-bit integer weights. <a name="model-download"></a><br> # 2. Model Download Model release and download links are provided in the table below: | Model Name | HuggingFace Download Links | ModelScope Download Links | |-------------------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| | ⚾Orion-14B-Base | [Orion-14B-Base](https://huggingface.co/OrionStarAI/Orion-14B-Base) | [Orion-14B-Base](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base/summary) | | 😛Orion-14B-Chat | [Orion-14B-Chat](https://huggingface.co/OrionStarAI/Orion-14B-Chat) | [Orion-14B-Chat](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat/summary) | | 📃Orion-14B-LongChat | [Orion-14B-LongChat](https://huggingface.co/OrionStarAI/Orion-14B-LongChat) | [Orion-14B-LongChat](https://modelscope.cn/models/OrionStarAI/Orion-14B-LongChat/summary) | | 🔎Orion-14B-Chat-RAG | [Orion-14B-Chat-RAG](https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG) | [Orion-14B-Chat-RAG](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-RAG/summary) | | 🔌Orion-14B-Chat-Plugin | [Orion-14B-Chat-Plugin](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Plugin) | [Orion-14B-Chat-Plugin](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Plugin/summary) | | 💼Orion-14B-Base-Int4 | [Orion-14B-Base-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4) | [Orion-14B-Base-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base-Int4/summary) | | 📦Orion-14B-Chat-Int4 | [Orion-14B-Chat-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Int4) | [Orion-14B-Chat-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Int4/summary) | <a name="model-benchmark"></a><br> # 3. Model Benchmarks ## 3.1. Base Model Orion-14B-Base Benchmarks ### 3.1.1. LLM evaluation results on examination and professional knowledge | Model | C-Eval | CMMLU | MMLU | AGIEval | Gaokao | BBH | |--------------------|----------|----------|----------|----------|----------|----------| | LLaMA2-13B | 41.4 | 38.4 | 55.0 | 30.9 | 18.2 | 45.6 | | Skywork-13B | 59.1 | 61.4 | 62.7 | 43.6 | 56.1 | 48.3 | | Baichuan2-13B | 59.0 | 61.3 | 59.5 | 37.4 | 45.6 | 49.0 | | QWEN-14B | 71.7 | 70.2 | 67.9 | 51.9 | **62.5** | 53.7 | | InternLM-20B | 58.8 | 59.0 | 62.1 | 44.6 | 45.5 | 52.5 | | **Orion-14B-Base** | **72.9** | **70.6** | **69.9** | **54.7** | 62.1 | **56.5** | ### 3.1.2. LLM evaluation results on language understanding and common knowledge | Model |RACE-middle|RACE-high |HellaSwag | PIQA | Lambada | WSC | |--------------------|----------|----------|----------|----------|----------|----------| | LLaMA 2-13B | 63.0 | 58.9 | 77.5 | 79.8 | 76.5 | 66.3 | | Skywork-13B | 87.6 | 84.1 | 73.7 | 78.3 | 71.8 | 66.3 | | Baichuan 2-13B | 68.9 | 67.2 | 70.8 | 78.1 | 74.1 | 66.3 | | QWEN-14B | 93.0 | 90.3 | **80.2** | 79.8 | 71.4 | 66.3 | | InternLM-20B | 86.4 | 83.3 | 78.1 | **80.3** | 71.8 | 68.3 | | **Orion-14B-Base** | **93.2** | **91.3** | 78.5 | 79.5 | **78.8** | **70.2** | ### 3.1.3. LLM evaluation results of OpenCompass testsets | Model | Average | Examination | Language | Knowledge | Understanding | Reasoning | |------------------|----------|----------|----------|----------|----------|----------| | LLaMA 2-13B | 47.3 | 45.2 | 47.0 | 58.3 | 50.9 | 43.6 | | Skywork-13B | 53.6 | 61.1 | 51.3 | 52.7 | 64.5 | 45.2 | | Baichuan 2-13B | 49.4 | 51.8 | 47.5 | 48.9 | 58.1 | 44.2 | | QWEN-14B | 62.4 | 71.3 | 52.67 | 56.1 | 68.8 | 60.1 | | InternLM-20B | 59.4 | 62.5 | 55.0 | **60.1** | 67.3 | 54.9 | |**Orion-14B-Base**| **64.3** | **71.4** | **55.0** | 60.0 | **71.9** | **61.6** | ### 3.1.4. Comparison of LLM performances on Japanese testsets | Model |**Average**| JCQA | JNLI | MARC | JSQD | JQK | XLS | XWN | MGSM | |--------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------| | PLaMo-13B | 52.3 | 56.7 | 42.8 | 95.8 | 70.6 | 71.0 | 8.70 | 70.5 | 2.40 | | WebLab-10B | 50.7 | 66.6 | 53.7 | 82.1 | 62.9 | 56.2 | 10.0 | 72.0 | 2.40 | | ELYZA-jp-7B | 48.8 | 71.7 | 25.3 | 86.6 | 70.8 | 64.1 | 2.50 | 62.1 | 7.20 | | StableLM-jp-7B | 51.1 | 33.4 | 43.3 | **96.7** | 70.6 | 78.1 | 10.7 | 72.8 | 2.80 | | LLaMA 2-13B | 46.3 | 75.0 | 47.6 | 38.8 | 76.1 | 67.7 | 18.1 | 63.2 | 10.4 | | Baichuan 2-13B | 57.1 | 73.7 | 31.3 | 91.6 | 80.5 | 63.3 | 18.6 | 72.2 | 25.2 | | QWEN-14B | 65.8 | 85.9 | 60.7 | 97.0 | 83.3 | 71.8 | 18.8 | 70.6 | 38.0 | | Yi-34B | 67.1 | 83.8 | 61.2 | 95.2 | **86.1** | 78.5 | **27.2** | 69.2 | 35.2 | | **Orion-14B-Base** | **69.1** | **88.2** | **75.8** | 94.1 | 75.7 | **85.1** | 17.3 | **78.8** | **38.0** | ### 3.1.5. Comparison of LLM performances on Korean testsets. n = 0 and n = 5 stand for n-shot prompts used in the evaluation |Model | **Average**<br>n=0  n=5 | HellaSwag<br>n=0  n=5 | COPA<br> n=0  n=5 | BooIQ<br>n=0  n=5 | SentiNeg<br>n=0  n=5| |------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------| | KoGPT | 53.0    70.1 | 55.9    58.3 | 73.5    72.9 | 45.1    59.8 | 37.5    89.4 | | Polyglot-ko-13B | 69.6    73.7 |**59.5**    **63.1**|**79.4**    **81.1**| 48.2    60.4 | 91.2    90.2 | | LLaMA 2-13B | 46.7    63.7 | 41.3    44.0 | 59.3    63.8 | 34.9    73.8 | 51.5    73.4 | | Baichuan 2-13B | 52.1    58.7 | 39.2    39.6 | 60.6    60.6 | 58.4    61.5 | 50.3    72.9 | | QWEN-14B | 53.8    73.7 | 45.3    46.8 | 64.9    68.9 | 33.4    83.5 | 71.5    95.7 | | Yi-34B | 54.2    72.1 | 44.6    44.7 | 58.0    60.6 | 65.9    90.2 | 48.3    92.9 | |**Orion-14B-Chat**|**74.5**    **79.6**| 47.0    49.6 | 77.7    79.4 |**81.6**    **90.7**|**92.4**    **98.7**| ### 3.1.6. Multilingual evaluation | Model | Train Lang | Japanese | Korean | Chinese | English | |--------------------|------------|----------|----------|----------|----------| | PLaMo-13B | En,Jp | 52.3 | * | * | * | | Weblab-10B | En,Jp | 50.7 | * | * | * | | ELYZA-jp-7B | En,Jp | 48.8 | * | * | * | | StableLM-jp-7B | En,Jp | 51.1 | * | * | * | | KoGPT-6B | En,Ko | * | 70.1 | * | * | | Polyglot-ko-13B | En,Ko | * | 70.7 | * | * | | Baichuan2-13B | Multi | 57.1 | 58.7 | 50.8 | 57.1 | | Qwen-14B | Multi | 65.8 | 73.7 | 64.5 | 65.4 | | Llama2-13B | Multi | 46.3 | 63.7 | 41.4 | 55.3 | | Yi-34B | Multi | 67.1 | 72.2 | 58.7 | **68.8** | | **Orion-14B-Chat** | Multi | **69.1** | **79.5** | **67.9** | 67.3 | ## 3.2. Chat Model Orion-14B-Chat Benchmarks ### 3.2.1. Chat model subjective evaluation of MTBench | Model | First-Turn | Second-Turn | **Average** | |----------------------|----------|----------|----------| | Baichuan2-13B-Chat | 7.05 | 6.47 | 6.76 | | Qwen-14B-Chat | 7.30 | 6.62 | 6.96 | | Llama2-13B-Chat | 7.10 | 6.20 | 6.65 | | InternLM-20B-Chat | 7.03 | 5.93 | 6.48 | | **Orion-14B-Chat** | **7.68** | **7.07** | **7.37** | \* use vllm for inference ### 3.2.2. Chat model subjective evaluation of AlignBench | Model | Math. | Logi. | Basic. | Chi. | Comp. | Writ. | Role. | Prof. |**Avg.**| |--------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------| | Baichuan2-13B-Chat | 3.76 | 4.07 | 6.22 | 6.05 | 7.11 | 6.97 | 6.75 | 6.43 | 5.25 | | Qwen-14B-Chat |**4.91**|**4.71**|**6.90**| 6.36 | 6.74 | 6.64 | 6.59 | 6.56 |**5.72**| | Llama2-13B-Chat | 3.05 | 3.79 | 5.43 | 4.40 | 6.76 | 6.63 | 6.99 | 5.65 | 4.70 | | InternLM-20B-Chat | 3.39 | 3.92 | 5.96 | 5.50 |**7.18**| 6.19 | 6.49 | 6.22 | 4.96 | | **Orion-14B-Chat** | 4.00 | 4.24 | 6.18 |**6.57**| 7.16 |**7.36**|**7.16**|**6.99**| 5.51 | \* use vllm for inference ## 3.3. LongChat Model Orion-14B-LongChat Benchmarks ### 3.3.1. LongChat evaluation of LongBench | Model | NarrativeQA|MultiFieldQA-en|MultiFieldQA-zh| DuReader | QMSum | VCSUM | TREC | TriviaQA | LSHT |RepoBench-P| |--------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------| | GPT-3.5-Turbo-16k | **23.60** | **52.30** | **61.20** | 28.70 | 23.40 | **16.00** | 68.00 | **91.40** | 29.20 | 53.60 | | LongChat-v1.5-7B-32k | 16.90 | 41.40 | 29.10 | 19.50 | 22.70 | 9.90 | 63.50 | 82.30 | 23.20 | 55.30 | | Vicuna-v1.5-7B-16k | 19.40 | 38.50 | 43.00 | 19.30 | 22.80 | 15.10 | 71.50 | 86.20 | 28.80 | 43.50 | | Yi-6B-200K | 14.11 | 36.74 | 22.68 | 14.01 | 20.44 | 8.08 | 72.00 | 86.61 | 38.00 | **63.29** | | Orion-14B-LongChat | 19.47 | 48.11 | 55.84 | **37.02** | **24.87** | 15.44 | **77.00** | 89.12 | **45.50** | 54.31 | ## 3.4. Chat RAG Model Benchmarks ### 3.4.1. LLM evaluation results of self-built RAG testsets |Model|Effectiveness of Response(Keyword)|*Effectiveness of Response（subjective evaluation）|Quoting Ability|Fallback Ability|*AutoQA|*Data Extraction| |---------------------|------|------|------|------|------|------| | Baichuan2-13B-Chat | 85 | 76 | 1 | 0 | 69 | 51 | | Qwen-14B-Chat | 79 | 77 | 75 | 47 | 68 | 72 | | Qwen-72B-Chat(Int4) | 87 | 89 | 90 | 32 | 67 | 76 | | GPT-4 | 91 | 94 | 96 | 95 | 75 | 86 | | Orion-14B-Chat-RAG | 86 | 87 | 91 | 97 | 73 | 71 | \* means manual assessment ## 3.5. Chat Plugin Model Orion-14B-Chat-Plugin Benchmarks ### 3.5.1. LLM evaluation results of self-built plugin testsets |Model |Intent Recognition with Full Params |Intent Recognition with Missing Params |Non-Plugin Invocation Recognition | |-----------------------|--------|-----------|--------| | Baichuan2-13B-Chat | 25 | 0 | 0 | | Qwen-14B-Chat | 55 | 0 | 50 | | GPT-4 | **95** | 52.38 | 70 | | Orion-14B-Chat-Plugin | 92.5 | **60.32** | **90** | ## 3.6. Quantized Model Orion-14B-Base-Int4 Benchmarks ### 3.6.1. Comparison of before and after quantization |Model |Size(GB)|Inference Speed(tokens/s)|C-Eval|CMMLU|MMLU|RACE|HellaSwag| |-------------------------|-------|-----|------|------|------|------|------| | OrionStar-14B-Base | 28.0 | 135 | 72.8 | 70.6 | 70.0 | 93.3 | 78.5 | | OrionStar-14B-Base-Int4 | 8.3 | 178 | 71.8 | 69.8 | 69.2 | 93.1 | 78.0 | <a name="model-inference"></a><br> # 4. Model Inference Model weights, source code, and configuration needed for inference are published on Hugging Face, and the download link is available in the table at the beginning of this document. We demonstrate various inference methods here, and the program will automatically download the necessary resources from Hugging Face. ## 4.1. Python Code ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/Orion-14B", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("OrionStarAI/Orion-14B") messages = [{"role": "user", "content": "Hello, what is your name? "}] response = model.chat(tokenizer, messages, streaming=False) print(response) ``` In the above Python code, the model is loaded with `device_map='auto'` to utilize all available GPUs. To specify the device, you can use something like `export CUDA_VISIBLE_DEVICES=0,1` (using GPUs 0 and 1). ## 4.2. Command Line Tool ```shell CUDA_VISIBLE_DEVICES=0 python cli_demo.py ``` This command-line tool is designed for chat scenarios, and thus, it does not support calling the base model. ## 4.3. Direct Script Inference ```shell # base model CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/Orion-14B --tokenizer OrionStarAI/Orion-14B --prompt hello # chat model CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt hi ``` ## 4.4 Example Output ### 4.4.1. Casual Chat ````` User: Hello Orion-14B: Hello! How can I help you today? If you have any questions or need assistance, feel free to ask. User: What functions do you have? Orion-14B: As an AI language model, I have a wide range of functions and capabilities, including but not limited to: Answering general knowledge questions: I can provide information on various topics such as history, science, geography, and more. Providing definitions and explanations: I can explain complex concepts and provide definitions for words and phrases. Assisting with tasks: I can help with simple tasks like setting reminders, converting units of measurement, and even ordering food from restaurants. Generating text: I can write short stories, essays, articles, and other types of content based on prompts given to me. Translating languages: I can translate text from one language to another. Chatting: I can engage in conversation with users and respond to their queries in a conversational manner. Summarizing long texts: I can summarize longer texts like articles, books, and research papers. Generating random numbers and names: I can generate random numbers within a specified range and also suggest names based on certain criteria. Playing games: I can play simple games like Hangman, Trivia, and more. Providing recommendations: I can recommend movies, music, books, and other forms of entertainment based on user preferences. These are just some of the many functions that I am capable of performing. If you have a specific task in mind, feel free to ask me about it. User: Tell me a joke. Orion-14B: Sure, here's a classic one-liner: Why don't scientists trust atoms? Because they make up everything. ````` ### 4.4.2. Japanese & Korean Chat ````` User：自己を紹介してください Orion-14B：こんにちは、私の名前はChatMaxで、OrionStarによって開発されたAIアシスタントです。私はユーザーに正確で、客観的で、有用な回答を提供し、彼らが問題を解決し、情報を取得するのを助けることができます。 User：자기소개를 해주세요. Orion-14B：안녕하세요, 제 이름은 ChatMax입니다. OrionStar에서 개발한 AI 어시스턴트입니다. 저는 사용자들에게 정확하고 객관적이며 유용한 답변을 제공하여 문제를 해결하고 정보를 얻는 데 도움을 줄 수 있습니다. ````` <a name="declarations-license"></a><br> # 5. Declarations, License ## 5.1. Declarations We strongly urge all users not to use the Orion-14B model for any activities that may harm national or social security or violate the law. Additionally, we request users not to use the Orion-14B model for internet services without proper security review and filing. We hope all users abide by this principle to ensure that technological development takes place in a regulated and legal environment. We have done our best to ensure the compliance of the data used in the model training process. However, despite our significant efforts, unforeseen issues may still arise due to the complexity of the model and data. Therefore, if any problems arise due to the use of the Orion-14B open-source model, including but not limited to data security issues, public opinion risks, or any risks and issues arising from the model being misled, abused, disseminated, or improperly utilized, we will not assume any responsibility. ## 5.2. License Community use of the Orion-14B series models - For code, please comply with [Apache License Version 2.0](./LICENSE)<br> - For model, please comply with [【Orion-14B Series】 Models Community License Agreement](./ModelsCommunityLicenseAgreement) <a name="company-introduction"></a><br> # 6. Company Introduction OrionStar is a leading global service robot solutions company, founded in September 2016. OrionStar is dedicated to using artificial intelligence technology to create the next generation of revolutionary robots, allowing people to break free from repetitive physical labor and making human work and life more intelligent and enjoyable. Through technology, OrionStar aims to make society and the world a better place. OrionStar possesses fully self-developed end-to-end artificial intelligence technologies, such as voice interaction and visual navigation. It integrates product development capabilities and technological application capabilities. Based on the Orion robotic arm platform, it has launched products such as OrionStar AI Robot Greeting, AI Robot Greeting Mini, Lucki, Coffee Master, and established the open platform OrionOS for Orion robots. Following the philosophy of "Born for Truly Useful Robots", OrionStar empowers more people through AI technology. **The core strengths of OrionStar lies in possessing end-to-end AI application capabilities,** including big data preprocessing, large model pretraining, fine-tuning, prompt engineering, agent, etc. With comprehensive end-to-end model training capabilities, including systematic data processing workflows and the parallel model training capability of hundreds of GPUs, it has been successfully applied in various industry scenarios such as government affairs, cloud services, international e-commerce, and fast-moving consumer goods. Companies with demands for deploying large-scale model applications are welcome to contact us.<br> **Enquiry Hotline: 400-898-7779**<br> **E-mail: [email protected]** <div align="center"> <img src="./assets/imgs/wechat_group.jpg" alt="wechat" width="40%" /> </div> # 目录 - [📖 模型介绍](#zh_model-introduction) - [🔗 下载路径](#zh_model-download) - [🔖 评估结果](#zh_model-benchmark) - [📊 模型推理](#zh_model-inference) - [📜 声明协议](#zh_declarations-license) - [🥇 企业介绍](#zh_company-introduction) <a name="zh_model-introduction"></a><br> # 1. 模型介绍 - Orion-14B-Base是一个具有140亿参数的多语种大模型，该模型在一个包含2.5万亿token的多样化数据集上进行了训练，涵盖了中文、英语、日语、韩语等多种语言。在多语言环境下的一系列任务中展现出卓越的性能。在主流的公开基准评测中，Orion-14B系列模型表现优异，多项指标显著超越同等参数基本的其他模型。具体技术细节请参考[技术报告](https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf)。 - Orion-14B系列大模型有以下几个特点： - 基座20B参数级别大模型综合评测效果表现优异 - 多语言能力强，在日语、韩语测试集上显著领先 - 微调模型适应性强，在人类标注盲测中，表现突出 - 长上下文版本支持超长文本，在200k token长度上效果优异，最长可支持可达320k - 量化版本模型大小缩小70%，推理速度提升30%，性能损失小于1% <table style="border-collapse: collapse; width: 100%;"> <tr> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/opencompass_zh.png" alt="opencompass" style="width: 100%; height: auto;"> </td> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/model_cap_zh.png" alt="modelcap" style="width: 100%; height: auto;"> </td> </tr> </table> - 具体而言，Orion-14B系列大语言模型包含: - **Orion-14B-Base:** 基于2.5万亿tokens多样化数据集训练处的140亿参数量级的多语言基座模型。 - **Orion-14B-Chat:** 基于高质量语料库微调的对话类模型，旨在为大模型社区提供更好的用户交互体验。 - **Orion-14B-LongChat:** 在200k token长度上效果优异，最长可支持可达320k，在长文本评估集上性能比肩专有模型。 - **Orion-14B-Chat-RAG:** 在一个定制的检索增强生成数据集上进行微调的聊天模型，在检索增强生成任务中取得了卓越的性能。 - **Orion-14B-Chat-Plugin:** 专门针对插件和函数调用任务定制的聊天模型，非常适用于使用代理的相关场景，其中大语言模型充当插件和函数调用系统。 - **Orion-14B-Base-Int4:** 一个使用int4进行量化的基座模型。它将模型大小显著减小了70%，同时提高了推理速度30%，仅引入了1%的最小性能损失。 - **Orion-14B-Chat-Int4:** 一个使用int4进行量化的对话模型。 <a name="zh_model-download"></a><br> # 2. 下载路径发布模型和下载链接见下表： | 模型名称 | HuggingFace下载链接 | ModelScope下载链接 | |---------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------| | ⚾ 基座模型 | [Orion-14B-Base](https://huggingface.co/OrionStarAI/Orion-14B-Base) | [Orion-14B-Base](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base/summary) | | 😛 对话模型 | [Orion-14B-Chat](https://huggingface.co/OrionStarAI/Orion-14B-Chat) | [Orion-14B-Chat](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat/summary) | | 📃 长上下文模型 | [Orion-14B-LongChat](https://huggingface.co/OrionStarAI/Orion-14B-LongChat) | [Orion-14B-LongChat](https://modelscope.cn/models/OrionStarAI/Orion-14B-LongChat/summary) | | 🔎 检索增强模型 | [Orion-14B-Chat-RAG](https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG) | [Orion-14B-Chat-RAG](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-RAG/summary) | | 🔌 插件模型 | [Orion-14B-Chat-Plugin](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Plugin) | [Orion-14B-Chat-Plugin](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Plugin/summary)| | 💼 基座Int4量化模型 | [Orion-14B-Base-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4) | [Orion-14B-Base-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base-Int4/summary) | | 📦 对话Int4量化模型 | [Orion-14B-Chat-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Int4) | [Orion-14B-Chat-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Int4/summary) | <a name="zh_model-benchmark"></a><br> # 3. 评估结果 ## 3.1. 基座模型Orion-14B-Base评估 ### 3.1.1. 专业知识与试题评估结果 | 模型名称 | C-Eval | CMMLU | MMLU | AGIEval | Gaokao | BBH | |--------------------|----------|----------|----------|----------|----------|----------| | LLaMA2-13B | 41.4 | 38.4 | 55.0 | 30.9 | 18.2 | 45.6 | | Skywork-13B | 59.1 | 61.4 | 62.7 | 43.6 | 56.1 | 48.3 | | Baichuan2-13B | 59.0 | 61.3 | 59.5 | 37.4 | 45.6 | 49.0 | | QWEN-14B | 71.7 | 70.2 | 67.9 | 51.9 | **62.5** | 53.7 | | InternLM-20B | 58.8 | 59.0 | 62.1 | 44.6 | 45.5 | 52.5 | | **Orion-14B-Base** | **72.9** | **70.6** | **69.9** | **54.7** | 62.1 | **56.5** | ### 3.1.2. 理解与通识评估结果 | 模型名称 |RACE-middle|RACE-high| HellaSwag| PIQA | Lambada | WSC | |--------------------|----------|----------|----------|----------|----------|----------| | LLaMA 2-13B | 63.0 | 58.9 | 77.5 | 79.8 | 76.5 | 66.3 | | Skywork-13B | 87.6 | 84.1 | 73.7 | 78.3 | 71.8 | 66.3 | | Baichuan 2-13B | 68.9 | 67.2 | 70.8 | 78.1 | 74.1 | 66.3 | | QWEN-14B | 93.0 | 90.3 | **80.2** | 79.8 | 71.4 | 66.3 | | InternLM-20B | 86.4 | 83.3 | 78.1 | **80.3** | 71.8 | 68.3 | | **Orion-14B-Base** | **93.2** | **91.3** | 78.5 | 79.5 | **78.8** | **70.2** | ### 3.1.3. OpenCompass评测集评估结果 | 模型名称 | Average | Examination | Language | Knowledge | Understanding | Reasoning | |------------------|----------|----------|----------|----------|----------|----------| | LLaMA 2-13B | 47.3 | 45.2 | 47.0 | 58.3 | 50.9 | 43.6 | | Skywork-13B | 53.6 | 61.1 | 51.3 | 52.7 | 64.5 | 45.2 | | Baichuan 2-13B | 49.4 | 51.8 | 47.5 | 48.9 | 58.1 | 44.2 | | QWEN-14B | 62.4 | 71.3 | 52.67 | 56.1 | 68.8 | 60.1 | | InternLM-20B | 59.4 | 62.5 | 55.0 | **60.1** | 67.3 | 54.9 | |**Orion-14B-Base**| **64.3** | **71.4** | **55.0** | 60.0 | **71.9** | **61.6** | ### 3.1.4. 日语测试集评估结果 | 模型名称 |**Average**| JCQA | JNLI | MARC | JSQD | JQK | XLS | XWN | MGSM | |--------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------| | PLaMo-13B | 52.3 | 56.7 | 42.8 | 95.8 | 70.6 | 71.0 | 8.70 | 70.5 | 2.40 | | WebLab-10B | 50.7 | 66.6 | 53.7 | 82.1 | 62.9 | 56.2 | 10.0 | 72.0 | 2.40 | | ELYZA-jp-7B | 48.8 | 71.7 | 25.3 | 86.6 | 70.8 | 64.1 | 2.50 | 62.1 | 7.20 | | StableLM-jp-7B | 51.1 | 33.4 | 43.3 | **96.7** | 70.6 | 78.1 | 10.7 | 72.8 | 2.80 | | LLaMA 2-13B | 46.3 | 75.0 | 47.6 | 38.8 | 76.1 | 67.7 | 18.1 | 63.2 | 10.4 | | Baichuan 2-13B | 57.1 | 73.7 | 31.3 | 91.6 | 80.5 | 63.3 | 18.6 | 72.2 | 25.2 | | QWEN-14B | 65.8 | 85.9 | 60.7 | 97.0 | 83.3 | 71.8 | 18.8 | 70.6 | 38.0 | | Yi-34B | 67.1 | 83.8 | 61.2 | 95.2 | **86.1** | 78.5 | **27.2** | 69.2 | 35.2 | | **Orion-14B-Base** | **69.1** | **88.2** | **75.8** | 94.1 | 75.7 | **85.1** | 17.3 | **78.8** | **38.0** | ### 3.1.5. 韩语测试集n-shot评估结果 | 模型名称 | **Average**<br>n=0  n=5 | HellaSwag<br>n=0  n=5 | COPA<br> n=0  n=5 | BooIQ<br>n=0  n=5 | SentiNeg<br>n=0  n=5| |------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------| | KoGPT | 53.0    70.1 | 55.9    58.3 | 73.5    72.9 | 45.1    59.8 | 37.5    89.4 | | Polyglot-ko-13B | 69.6    73.7 |**59.5**    **63.1**|**79.4**    **81.1**| 48.2    60.4 | 91.2    90.2 | | LLaMA 2-13B | 46.7    63.7 | 41.3    44.0 | 59.3    63.8 | 34.9    73.8 | 51.5    73.4 | | Baichuan 2-13B | 52.1    58.7 | 39.2    39.6 | 60.6    60.6 | 58.4    61.5 | 50.3    72.9 | | QWEN-14B | 53.8    73.7 | 45.3    46.8 | 64.9    68.9 | 33.4    83.5 | 71.5    95.7 | | Yi-34B | 54.2    72.1 | 44.6    44.7 | 58.0    60.6 | 65.9    90.2 | 48.3    92.9 | |**Orion-14B-Base**|**74.5**    **79.6**| 47.0    49.6 | 77.7    79.4 |**81.6**    **90.7**|**92.4**    **98.7**| ### 3.1.6. 多语言评估结果 | 模型名称 | Train Lang | Japanese | Korean | Chinese | English | |--------------------|------------|----------|----------|----------|----------| | PLaMo-13B | En,Jp | 52.3 | * | * | * | | Weblab-10B | En,Jp | 50.7 | * | * | * | | ELYZA-jp-7B | En,Jp | 48.8 | * | * | * | | StableLM-jp-7B | En,Jp | 51.1 | * | * | * | | KoGPT-6B | En,Ko | * | 70.1 | * | * | | Polyglot-ko-13B | En,Ko | * | 70.7 | * | * | | Baichuan2-13B | Multi | 57.1 | 58.7 | 50.8 | 57.1 | | Qwen-14B | Multi | 65.8 | 73.7 | 64.5 | 65.4 | | Llama2-13B | Multi | 46.3 | 63.7 | 41.4 | 55.3 | | Yi-34B | Multi | 67.1 | 72.2 | 58.7 | **68.8** | | **Orion-14B-Base** | Multi | **69.1** | **79.5** | **67.9** | 67.3 | ## 3.2. 对话模型Orion-14B-Chat评估 ### 3.2.1. 对话模型MTBench主观评估 | 模型名称 | 第一轮 | 第二轮 | **平均** | |----------------------|----------|----------|----------| | Baichuan2-13B-Chat | 7.05 | 6.47 | 6.76 | | Qwen-14B-Chat | 7.30 | 6.62 | 6.96 | | Llama2-13B-Chat | 7.10 | 6.20 | 6.65 | | InternLM-20B-Chat | 7.03 | 5.93 | 6.48 | | **Orion-14B-Chat** | **7.68** | **7.07** | **7.37** | \*这里评测使用vllm进行推理 ### 3.2.2. 对话模型AlignBench主观评估 | 模型名称 | 数学能力 | 逻辑推理 | 基本能力 | 中文理解 | 综合问答 | 写作能力 | 角色扮演 | 专业知识 | **平均** | |--------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------| | Baichuan2-13B-Chat | 3.76 | 4.07 | 6.22 | 6.05 | 7.11 | 6.97 | 6.75 | 6.43 | 5.25 | | Qwen-14B-Chat | **4.91** | **4.71** | **6.90** | 6.36 | 6.74 | 6.64 | 6.59 | 6.56 | **5.72** | | Llama2-13B-Chat | 3.05 | 3.79 | 5.43 | 4.40 | 6.76 | 6.63 | 6.99 | 5.65 | 4.70 | | InternLM-20B-Chat | 3.39 | 3.92 | 5.96 | 5.50 | **7.18** | 6.19 | 6.49 | 6.22 | 4.96 | | **Orion-14B-Chat** | 4.00 | 4.24 | 6.18 | **6.57** | 7.16 | **7.36** | **7.16** | **6.99** | 5.51 | \*这里评测使用vllm进行推理 ## 3.3. 长上下文模型Orion-14B-LongChat评估 ### 3.3.1. 长上下文模型LongBench评估 | 模型名称 | NarrativeQA| MultiFieldQA-en| MultiFieldQA-zh | DuReader | QMSum | VCSUM | TREC | TriviaQA | LSHT | RepoBench-P | |--------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------| | GPT-3.5-Turbo-16k | **23.60** | **52.30** | **61.20** | 28.70 | 23.40 | **16.00** | 68.00 | **91.40** | 29.20 | 53.60 | | LongChat-v1.5-7B-32k | 16.90 | 41.40 | 29.10 | 19.50 | 22.70 | 9.90 | 63.50 | 82.30 | 23.20 | 55.30 | | Vicuna-v1.5-7B-16k | 19.40 | 38.50 | 43.00 | 19.30 | 22.80 | 15.10 | 71.50 | 86.20 | 28.80 | 43.50 | | Yi-6B-200K | 14.11 | 36.74 | 22.68 | 14.01 | 20.44 | 8.08 | 72.00 | 86.61 | 38.00 | **63.29** | | Orion-14B-LongChat | 19.47 | 48.11 | 55.84 | **37.02** | **24.87** | 15.44 | **77.00** | 89.12 | **45.50** | 54.31 | ## 3.4. 检索增强模型Orion-14B-Chat-RAG评估 ### 3.4.1. 自建检索增强测试集评估结果 |模型名称|回复效果(关键字)|*回复效果(主观打分)|引用能力|兜底能力|*AutoQA|*抽取数据| |---------------------|------|------|------|------|------|------| | Baichuan2-13B-Chat | 85 | 76 | 1 | 0 | 69 | 51 | | Qwen-14B-Chat | 79 | 77 | 75 | 47 | 68 | 72 | | Qwen-72B-Chat(Int4) | 87 | 89 | 90 | 32 | 67 | 76 | | GPT-4 | 91 | 94 | 96 | 95 | 75 | 86 | | Orion-14B-Chat-RAG | 86 | 87 | 91 | 97 | 73 | 71 | \* 表示人工评判结果 ## 3.5. 插件模型Orion-14B-Chat-Plugin评估 ### 3.5.1. 自建插件测试集评估结果 | 模型名称 | 全参数意图识别 | 缺参数意图识别 | 非插件调用识别 | |-----------------------|--------|-----------|--------| | Baichuan2-13B-Chat | 25 | 0 | 0 | | Qwen-14B-Chat | 55 | 0 | 50 | | GPT-4 | **95** | 52.38 | 70 | | Orion-14B-Chat-Plugin | 92.5 | **60.32** | **90** | ## 3.6. 量化模型Orion-14B-Base-Int4评估 ### 3.6.1. 量化前后整体对比 |模型名称|模型大小(GB)|推理速度(令牌数/秒)|C-Eval |CMMLU |MMLU |RACE | HellaSwag| |-------------------------|------|-----|------|------|------|------|------| | OrionStar-14B-Base | 28.0 | 135 | 72.8 | 70.6 | 70.0 | 93.3 | 78.5 | | OrionStar-14B-Base-Int4 | 8.3 | 178 | 71.8 | 69.8 | 69.2 | 93.1 | 78.0 | <a name="zh_model-inference"></a><br> # 4. 模型推理推理所需的模型权重、源码、配置已发布在 Hugging Face，下载链接见本文档最开始的表格。我们在此示范多种推理方式。程序会自动从 Hugging Face 下载所需资源。 ## 4.1. Python 代码方式 ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/Orion-14B", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("OrionStarAI/Orion-14B") messages = [{"role": "user", "content": "你好! 你叫什么名字!"}] response = model.chat(tokenizer, messages, streaming=Flase) print(response) ``` 在上述两段代码中，模型加载指定 `device_map='auto'` ，会使用所有可用显卡。如需指定使用的设备，可以使用类似 `export CUDA_VISIBLE_DEVICES=0,1`（使用了0、1号显卡）的方式控制。 ## 4.2. 命令行工具方式 ```shell CUDA_VISIBLE_DEVICES=0 python cli_demo.py ``` 本命令行工具是为 Chat 场景设计，因此我们不支持使用该工具调用 Base 模型。 ## 4.3. 脚本直接推理 ```shell # base model CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/Orion-14B --tokenizer OrionStarAI/Orion-14B --prompt 你好,你叫什么名字 # chat model CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt 你好,你叫什么名字 ``` ## 4.4. 示例输出 ### 4.4.1. 闲聊 ````` 用户：你好,你叫什么名字 Orion-14B：你好!我是一个人工智能助手,没有固定的名字。你可以随意称呼我。有什么我可以帮助你的吗? 用户：讲个故事 Orion-14B：从前有一个小男孩叫杰克,他住在一个小村庄里。杰克非常喜欢冒险和探索,总是梦想着能够发现一些神奇的事情。有一天,杰克听说在附近的森林深处有一座传说中的魔法城堡,里面住着一个邪恶的巨人。这个巨人的名字叫格里姆,他被困在城堡中,无法离开。杰克决定要去解救这个巨人,因为他相信格里姆一定有自己的原因被困在那里。于是,他带着勇气和决心,踏上了前往魔法城堡的旅程。经过长时间的跋涉,杰克终于来到了城堡的大门前。他小心翼翼地推开大门,进入了一个充满神秘气息的房间。房间里弥漫着黑暗的气息,但杰克并没有被吓倒。他勇敢地向城堡深处走去,最终找到了格里姆。格里姆告诉杰克,他是被一个邪恶的女巫诅咒的,只有通过解开咒语才能获得自由。杰克决定要帮助格里姆,他开始寻找线索和解开咒语的方法。经过一番努力,杰克终于找到了一条通往女巫的秘密实验室的道路。他在那里发现了女巫留下的魔法书,书中记载了解开咒语的方法。杰克按照书上所写的步骤进行操作,成功地解开了格里姆身上的诅咒。巨人感激不已,他答应帮助杰克实现他的愿望。从此以后,杰克和格里姆成为了好朋友,他们一起经历了许多惊险刺激的冒险,并用他们的勇气和智慧战胜了各种困难。这个故事告诉我们,只要有勇气和决心,我们就能克服一切困难,实现自己的梦想。 ````` ### 4.4.2. 日韩 ````` 用户：自己を紹介してください Orion-14B：こんにちは、私の名前はChatMaxで、OrionStarによって開発されたAIアシスタントです。私はユーザーに正確で、客観的で、有用な回答を提供し、彼らが問題を解決し、情報を取得するのを助けることができます。用户：자기소개를 해주세요. Orion-14B：안녕하세요, 제 이름은 ChatMax입니다. OrionStar에서 개발한 AI 어시스턴트입니다. 저는 사용자들에게 정확하고 객관적이며 유용한 답변을 제공하여 문제를 해결하고 정보를 얻는 데 도움을 줄 수 있습니다. ````` <a name="zh_declarations-license"></a><br> # 5. 声明、协议 ## 5.1. 声明我们强烈呼吁所有使用者，不要利用 Orion-14B 模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Orion-14B 模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用 Orion-14B 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 ## 5.2. 协议社区使用Orion-14B系列模型 - 代码请遵循 [Apache License Version 2.0](./LICENSE)<br> - 模型请遵循 [Orion-14B系列模型社区许可协议](./ModelsCommunityLicenseAgreement) <a name="zh_company-introduction"></a><br> # 6. 企业介绍猎户星空（OrionStar）是一家全球领先的服务机器人解决方案公司，成立于2016年9月。猎户星空致力于基于人工智能技术打造下一代革命性机器人，使人们能够摆脱重复的体力劳动，使人类的工作和生活更加智能和有趣，通过技术使社会和世界变得更加美好。猎户星空拥有完全自主开发的全链条人工智能技术，如语音交互和视觉导航。它整合了产品开发能力和技术应用能力。基于Orion机械臂平台，它推出了ORION STAR AI Robot Greeting、AI Robot Greeting Mini、Lucki、Coffee Master等产品，并建立了Orion机器人的开放平台OrionOS。通过为 **真正有用的机器人而生** 的理念实践，它通过AI技术为更多人赋能。凭借7年AI经验积累，猎户星空已推出的大模型深度应用“聚言”，并陆续面向行业客户提供定制化AI大模型咨询与服务解决方案，真正帮助客户实现企业经营效率领先同行目标。 **猎户星空具备全链条大模型应用能力的核心优势**，包括拥有从海量数据处理、大模型预训练、二次预训练、微调(Fine-tune)、Prompt Engineering 、Agent开发的全链条能力和经验积累；拥有完整的端到端模型训练能力，包括系统化的数据处理流程和数百张GPU的并行模型训练能力，现已在大政务、云服务、出海电商、快消等多个行业场景落地。 ***欢迎有大模型应用落地需求的企业联系我们进行商务合作***<br> **咨询电话:** 400-898-7779<br> **电子邮箱:** [email protected] <div align="center"> <img src="./assets/imgs/wechat_group.jpg" alt="wechat" width="40%" /> </div>

sosoai/Orion-14B-Chat-safetensors

sosoai

2024-01-25T02:08:59Z

7

1

transformers

[ "transformers", "pytorch", "safetensors", "orion", "text-generation", "code", "model", "llm", "conversational", "custom_code", "en", "zh", "ja", "ko", "autotrain_compatible", "region:us" ]

text-generation

2024-01-24T23:44:38Z

--- language: - en - zh - ja - ko metrics: - accuracy pipeline_tag: text-generation tags: - code - model - llm ---   <div align="center"> <img src="./assets/imgs/orion_start.PNG" alt="logo" width="50%" /> </div> <div align="center"> <h1> Orion-14B </h1> </div> <div align="center"> <div align="center"> <b>🌐English</b> | <a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/main/README_zh.md" target="_blank">🇨🇳中文</a> | <a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/main/README_ja.md" target="_blank">🇯🇵日本語</a> | <a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/main/README_ko.md" target="_blank">🇰🇷한국어</a> </div> <h4 align="center"> <p> 🤗 <a href="https://huggingface.co/OrionStarAI" target="_blank">HuggingFace Mainpage</a> | 🤖 <a href="https://modelscope.cn/organization/OrionStarAI" target="_blank">ModelScope Mainpage</a><br>🎬 <a href="https://huggingface.co/spaces/OrionStarAI/Orion-14B-App-Demo" target="_blank">HuggingFace Demo</a> | 🎫 <a href="https://modelscope.cn/studios/OrionStarAI/Orion-14B-App-Demo/summary" target="_blank">ModelScope Demo</a><br>😺 <a href="https://github.com/OrionStarAI/Orion" target="_blank">GitHub</a><br>📖 <a href="https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf" target="_blank">Tech Report</a> <p> </h4> </div> # Table of Contents - [📖 Model Introduction](#model-introduction) - [🔗 Model Download](#model-download) - [🔖 Model Benchmark](#model-benchmark) - [📊 Model Inference](#model-inference) - [📜 Declarations & License](#declarations-license) - [🥇 Company Introduction](#company-introduction) <a name="model-introduction"></a><br> # 1. Model Introduction - Orion-14B series models are open-source multilingual large language models trained from scratch by OrionStarAI. The base model is trained on 2.5T multilingual corpus, including Chinese, English, Japanese, Korean, etc, and it exhibits superior performance in these languages. For details, please refer to [tech report](https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf). - The Orion-14B series models exhibit the following features: - Among models with 20B-parameter scale level, Orion-14B-Base model shows outstanding performance in comprehensive evaluations. - Strong multilingual capabilities, significantly outperforming in Japanese and Korean testsets. - The fine-tuned models demonstrate strong adaptability, excelling in human-annotated blind tests. - The long-chat version supports extremely long texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k. - The quantized versions reduce model size by 70%, improve inference speed by 30%, with performance loss less than 1%. <table style="border-collapse: collapse; width: 100%;"> <tr> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/opencompass_en.png" alt="opencompass" style="width: 100%; height: auto;"> </td> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/model_cap_en.png" alt="modelcap" style="width: 100%; height: auto;"> </td> </tr> </table> - Orion-14B series models including: - **Orion-14B-Base:** A multilingual large language foundational model with 14 billion parameters, pretrained on a diverse dataset of 2.5 trillion tokens. - **Orion-14B-Chat:** A chat-model fine-tuned on a high-quality corpus aims to provide an excellence interactive experience for users in the large model community. - **Orion-14B-LongChat:** The long-context version excels at handling extremely lengthy texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k. - **Orion-14B-Chat-RAG:** A chat-model fine-tuned on a custom retrieval augmented generation dataset, achieving superior performance in retrieval augmented generation tasks. - **Orion-14B-Chat-Plugin:** A chat-model specifically tailored for plugin and function calling tasks, ideal for agent-related scenarios where the LLM acts as a plugin and function call system. - **Orion-14B-Base-Int4:** A quantized base model utilizing 4-bit integer weights. It significantly reduces the model size by 70% and increases the inference speed by 30% while incurring a minimal performance loss of only 1%. - **Orion-14B-Chat-Int4:** A quantized chat model utilizing 4-bit integer weights. <a name="model-download"></a><br> # 2. Model Download Model release and download links are provided in the table below: | Model Name | HuggingFace Download Links | ModelScope Download Links | |-------------------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| | ⚾Orion-14B-Base | [Orion-14B-Base](https://huggingface.co/OrionStarAI/Orion-14B-Base) | [Orion-14B-Base](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base/summary) | | 😛Orion-14B-Chat | [Orion-14B-Chat](https://huggingface.co/OrionStarAI/Orion-14B-Chat) | [Orion-14B-Chat](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat/summary) | | 📃Orion-14B-LongChat | [Orion-14B-LongChat](https://huggingface.co/OrionStarAI/Orion-14B-LongChat) | [Orion-14B-LongChat](https://modelscope.cn/models/OrionStarAI/Orion-14B-LongChat/summary) | | 🔎Orion-14B-Chat-RAG | [Orion-14B-Chat-RAG](https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG) | [Orion-14B-Chat-RAG](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-RAG/summary) | | 🔌Orion-14B-Chat-Plugin | [Orion-14B-Chat-Plugin](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Plugin) | [Orion-14B-Chat-Plugin](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Plugin/summary) | | 💼Orion-14B-Base-Int4 | [Orion-14B-Base-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4) | [Orion-14B-Base-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base-Int4/summary) | | 📦Orion-14B-Chat-Int4 | [Orion-14B-Chat-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Int4) | [Orion-14B-Chat-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Int4/summary) | <a name="model-benchmark"></a><br> # 3. Model Benchmarks ## 3.1. Base Model Orion-14B-Base Benchmarks ### 3.1.1. LLM evaluation results on examination and professional knowledge | Model | C-Eval | CMMLU | MMLU | AGIEval | Gaokao | BBH | |--------------------|----------|----------|----------|----------|----------|----------| | LLaMA2-13B | 41.4 | 38.4 | 55.0 | 30.9 | 18.2 | 45.6 | | Skywork-13B | 59.1 | 61.4 | 62.7 | 43.6 | 56.1 | 48.3 | | Baichuan2-13B | 59.0 | 61.3 | 59.5 | 37.4 | 45.6 | 49.0 | | QWEN-14B | 71.7 | 70.2 | 67.9 | 51.9 | **62.5** | 53.7 | | InternLM-20B | 58.8 | 59.0 | 62.1 | 44.6 | 45.5 | 52.5 | | **Orion-14B-Base** | **72.9** | **70.6** | **69.9** | **54.7** | 62.1 | **56.5** | ### 3.1.2. LLM evaluation results on language understanding and common knowledge | Model |RACE-middle|RACE-high |HellaSwag | PIQA | Lambada | WSC | |--------------------|----------|----------|----------|----------|----------|----------| | LLaMA 2-13B | 63.0 | 58.9 | 77.5 | 79.8 | 76.5 | 66.3 | | Skywork-13B | 87.6 | 84.1 | 73.7 | 78.3 | 71.8 | 66.3 | | Baichuan 2-13B | 68.9 | 67.2 | 70.8 | 78.1 | 74.1 | 66.3 | | QWEN-14B | 93.0 | 90.3 | **80.2** | 79.8 | 71.4 | 66.3 | | InternLM-20B | 86.4 | 83.3 | 78.1 | **80.3** | 71.8 | 68.3 | | **Orion-14B-Base** | **93.2** | **91.3** | 78.5 | 79.5 | **78.8** | **70.2** | ### 3.1.3. LLM evaluation results of OpenCompass testsets | Model | Average | Examination | Language | Knowledge | Understanding | Reasoning | |------------------|----------|----------|----------|----------|----------|----------| | LLaMA 2-13B | 47.3 | 45.2 | 47.0 | 58.3 | 50.9 | 43.6 | | Skywork-13B | 53.6 | 61.1 | 51.3 | 52.7 | 64.5 | 45.2 | | Baichuan 2-13B | 49.4 | 51.8 | 47.5 | 48.9 | 58.1 | 44.2 | | QWEN-14B | 62.4 | 71.3 | 52.67 | 56.1 | 68.8 | 60.1 | | InternLM-20B | 59.4 | 62.5 | 55.0 | **60.1** | 67.3 | 54.9 | |**Orion-14B-Base**| **64.3** | **71.4** | **55.0** | 60.0 | **71.9** | **61.6** | ### 3.1.4. Comparison of LLM performances on Japanese testsets | Model |**Average**| JCQA | JNLI | MARC | JSQD | JQK | XLS | XWN | MGSM | |--------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------| | PLaMo-13B | 52.3 | 56.7 | 42.8 | 95.8 | 70.6 | 71.0 | 8.70 | 70.5 | 2.40 | | WebLab-10B | 50.7 | 66.6 | 53.7 | 82.1 | 62.9 | 56.2 | 10.0 | 72.0 | 2.40 | | ELYZA-jp-7B | 48.8 | 71.7 | 25.3 | 86.6 | 70.8 | 64.1 | 2.50 | 62.1 | 7.20 | | StableLM-jp-7B | 51.1 | 33.4 | 43.3 | **96.7** | 70.6 | 78.1 | 10.7 | 72.8 | 2.80 | | LLaMA 2-13B | 46.3 | 75.0 | 47.6 | 38.8 | 76.1 | 67.7 | 18.1 | 63.2 | 10.4 | | Baichuan 2-13B | 57.1 | 73.7 | 31.3 | 91.6 | 80.5 | 63.3 | 18.6 | 72.2 | 25.2 | | QWEN-14B | 65.8 | 85.9 | 60.7 | 97.0 | 83.3 | 71.8 | 18.8 | 70.6 | 38.0 | | Yi-34B | 67.1 | 83.8 | 61.2 | 95.2 | **86.1** | 78.5 | **27.2** | 69.2 | 35.2 | | **Orion-14B-Base** | **69.1** | **88.2** | **75.8** | 94.1 | 75.7 | **85.1** | 17.3 | **78.8** | **38.0** | ### 3.1.5. Comparison of LLM performances on Korean testsets. n = 0 and n = 5 stand for n-shot prompts used in the evaluation |Model | **Average**<br>n=0  n=5 | HellaSwag<br>n=0  n=5 | COPA<br> n=0  n=5 | BooIQ<br>n=0  n=5 | SentiNeg<br>n=0  n=5| |------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------| | KoGPT | 53.0    70.1 | 55.9    58.3 | 73.5    72.9 | 45.1    59.8 | 37.5    89.4 | | Polyglot-ko-13B | 69.6    73.7 |**59.5**    **63.1**|**79.4**    **81.1**| 48.2    60.4 | 91.2    90.2 | | LLaMA 2-13B | 46.7    63.7 | 41.3    44.0 | 59.3    63.8 | 34.9    73.8 | 51.5    73.4 | | Baichuan 2-13B | 52.1    58.7 | 39.2    39.6 | 60.6    60.6 | 58.4    61.5 | 50.3    72.9 | | QWEN-14B | 53.8    73.7 | 45.3    46.8 | 64.9    68.9 | 33.4    83.5 | 71.5    95.7 | | Yi-34B | 54.2    72.1 | 44.6    44.7 | 58.0    60.6 | 65.9    90.2 | 48.3    92.9 | |**Orion-14B-Chat**|**74.5**    **79.6**| 47.0    49.6 | 77.7    79.4 |**81.6**    **90.7**|**92.4**    **98.7**| ### 3.1.6. Multilingual evaluation | Model | Train Lang | Japanese | Korean | Chinese | English | |--------------------|------------|----------|----------|----------|----------| | PLaMo-13B | En,Jp | 52.3 | * | * | * | | Weblab-10B | En,Jp | 50.7 | * | * | * | | ELYZA-jp-7B | En,Jp | 48.8 | * | * | * | | StableLM-jp-7B | En,Jp | 51.1 | * | * | * | | KoGPT-6B | En,Ko | * | 70.1 | * | * | | Polyglot-ko-13B | En,Ko | * | 70.7 | * | * | | Baichuan2-13B | Multi | 57.1 | 58.7 | 50.8 | 57.1 | | Qwen-14B | Multi | 65.8 | 73.7 | 64.5 | 65.4 | | Llama2-13B | Multi | 46.3 | 63.7 | 41.4 | 55.3 | | Yi-34B | Multi | 67.1 | 72.2 | 58.7 | **68.8** | | **Orion-14B-Chat** | Multi | **69.1** | **79.5** | **67.9** | 67.3 | ## 3.2. Chat Model Orion-14B-Chat Benchmarks ### 3.2.1. Chat model subjective evaluation of MTBench | Model | First-Turn | Second-Turn | **Average** | |----------------------|----------|----------|----------| | Baichuan2-13B-Chat | 7.05 | 6.47 | 6.76 | | Qwen-14B-Chat | 7.30 | 6.62 | 6.96 | | Llama2-13B-Chat | 7.10 | 6.20 | 6.65 | | InternLM-20B-Chat | 7.03 | 5.93 | 6.48 | | **Orion-14B-Chat** | **7.68** | **7.07** | **7.37** | \* use vllm for inference ### 3.2.2. Chat model subjective evaluation of AlignBench | Model | Math. | Logi. | Basic. | Chi. | Comp. | Writ. | Role. | Prof. |**Avg.**| |--------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------| | Baichuan2-13B-Chat | 3.76 | 4.07 | 6.22 | 6.05 | 7.11 | 6.97 | 6.75 | 6.43 | 5.25 | | Qwen-14B-Chat |**4.91**|**4.71**|**6.90**| 6.36 | 6.74 | 6.64 | 6.59 | 6.56 |**5.72**| | Llama2-13B-Chat | 3.05 | 3.79 | 5.43 | 4.40 | 6.76 | 6.63 | 6.99 | 5.65 | 4.70 | | InternLM-20B-Chat | 3.39 | 3.92 | 5.96 | 5.50 |**7.18**| 6.19 | 6.49 | 6.22 | 4.96 | | **Orion-14B-Chat** | 4.00 | 4.24 | 6.18 |**6.57**| 7.16 |**7.36**|**7.16**|**6.99**| 5.51 | \* use vllm for inference ## 3.3. LongChat Model Orion-14B-LongChat Benchmarks ### 3.3.1. LongChat evaluation of LongBench | Model | NarrativeQA|MultiFieldQA-en|MultiFieldQA-zh| DuReader | QMSum | VCSUM | TREC | TriviaQA | LSHT |RepoBench-P| |--------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------| | GPT-3.5-Turbo-16k | **23.60** | **52.30** | **61.20** | 28.70 | 23.40 | **16.00** | 68.00 | **91.40** | 29.20 | 53.60 | | LongChat-v1.5-7B-32k | 16.90 | 41.40 | 29.10 | 19.50 | 22.70 | 9.90 | 63.50 | 82.30 | 23.20 | 55.30 | | Vicuna-v1.5-7B-16k | 19.40 | 38.50 | 43.00 | 19.30 | 22.80 | 15.10 | 71.50 | 86.20 | 28.80 | 43.50 | | Yi-6B-200K | 14.11 | 36.74 | 22.68 | 14.01 | 20.44 | 8.08 | 72.00 | 86.61 | 38.00 | **63.29** | | Orion-14B-LongChat | 19.47 | 48.11 | 55.84 | **37.02** | **24.87** | 15.44 | **77.00** | 89.12 | **45.50** | 54.31 | ## 3.4. Chat RAG Model Benchmarks ### 3.4.1. LLM evaluation results of self-built RAG testsets |Model|Effectiveness of Response(Keyword)|*Effectiveness of Response（subjective evaluation）|Quoting Ability|Fallback Ability|*AutoQA|*Data Extraction| |---------------------|------|------|------|------|------|------| | Baichuan2-13B-Chat | 85 | 76 | 1 | 0 | 69 | 51 | | Qwen-14B-Chat | 79 | 77 | 75 | 47 | 68 | 72 | | Qwen-72B-Chat(Int4) | 87 | 89 | 90 | 32 | 67 | 76 | | GPT-4 | 91 | 94 | 96 | 95 | 75 | 86 | | Orion-14B-Chat-RAG | 86 | 87 | 91 | 97 | 73 | 71 | \* means manual assessment ## 3.5. Chat Plugin Model Orion-14B-Chat-Plugin Benchmarks ### 3.5.1. LLM evaluation results of self-built plugin testsets |Model |Intent Recognition with Full Params |Intent Recognition with Missing Params |Non-Plugin Invocation Recognition | |-----------------------|--------|-----------|--------| | Baichuan2-13B-Chat | 25 | 0 | 0 | | Qwen-14B-Chat | 55 | 0 | 50 | | GPT-4 | **95** | 52.38 | 70 | | Orion-14B-Chat-Plugin | 92.5 | **60.32** | **90** | ## 3.6. Quantized Model Orion-14B-Base-Int4 Benchmarks ### 3.6.1. Comparison of before and after quantization |Model |Size(GB)|Inference Speed(tokens/s)|C-Eval|CMMLU|MMLU|RACE|HellaSwag| |-------------------------|-------|-----|------|------|------|------|------| | OrionStar-14B-Base | 28.0 | 135 | 72.8 | 70.6 | 70.0 | 93.3 | 78.5 | | OrionStar-14B-Base-Int4 | 8.3 | 178 | 71.8 | 69.8 | 69.2 | 93.1 | 78.0 | <a name="model-inference"></a><br> # 4. Model Inference Model weights, source code, and configuration needed for inference are published on Hugging Face, and the download link is available in the table at the beginning of this document. We demonstrate various inference methods here, and the program will automatically download the necessary resources from Hugging Face. ## 4.1. Python Code ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/Orion-14B", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("OrionStarAI/Orion-14B") messages = [{"role": "user", "content": "Hello, what is your name? "}] response = model.chat(tokenizer, messages, streaming=False) print(response) ``` In the above Python code, the model is loaded with `device_map='auto'` to utilize all available GPUs. To specify the device, you can use something like `export CUDA_VISIBLE_DEVICES=0,1` (using GPUs 0 and 1). ## 4.2. Command Line Tool ```shell CUDA_VISIBLE_DEVICES=0 python cli_demo.py ``` This command-line tool is designed for chat scenarios, and thus, it does not support calling the base model. ## 4.3. Direct Script Inference ```shell # base model CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/Orion-14B --tokenizer OrionStarAI/Orion-14B --prompt hello # chat model CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt hi ``` ## 4.4 Example Output ### 4.4.1. Casual Chat ````` User: Hello Orion-14B: Hello! How can I help you today? If you have any questions or need assistance, feel free to ask. User: What functions do you have? Orion-14B: As an AI language model, I have a wide range of functions and capabilities, including but not limited to: Answering general knowledge questions: I can provide information on various topics such as history, science, geography, and more. Providing definitions and explanations: I can explain complex concepts and provide definitions for words and phrases. Assisting with tasks: I can help with simple tasks like setting reminders, converting units of measurement, and even ordering food from restaurants. Generating text: I can write short stories, essays, articles, and other types of content based on prompts given to me. Translating languages: I can translate text from one language to another. Chatting: I can engage in conversation with users and respond to their queries in a conversational manner. Summarizing long texts: I can summarize longer texts like articles, books, and research papers. Generating random numbers and names: I can generate random numbers within a specified range and also suggest names based on certain criteria. Playing games: I can play simple games like Hangman, Trivia, and more. Providing recommendations: I can recommend movies, music, books, and other forms of entertainment based on user preferences. These are just some of the many functions that I am capable of performing. If you have a specific task in mind, feel free to ask me about it. User: Tell me a joke. Orion-14B: Sure, here's a classic one-liner: Why don't scientists trust atoms? Because they make up everything. ````` ### 4.4.2. Japanese & Korean Chat ````` User：自己を紹介してください Orion-14B：こんにちは、私の名前はChatMaxで、OrionStarによって開発されたAIアシスタントです。私はユーザーに正確で、客観的で、有用な回答を提供し、彼らが問題を解決し、情報を取得するのを助けることができます。 User：자기소개를 해주세요. Orion-14B：안녕하세요, 제 이름은 ChatMax입니다. OrionStar에서 개발한 AI 어시스턴트입니다. 저는 사용자들에게 정확하고 객관적이며 유용한 답변을 제공하여 문제를 해결하고 정보를 얻는 데 도움을 줄 수 있습니다. ````` <a name="declarations-license"></a><br> # 5. Declarations, License ## 5.1. Declarations We strongly urge all users not to use the Orion-14B model for any activities that may harm national or social security or violate the law. Additionally, we request users not to use the Orion-14B model for internet services without proper security review and filing. We hope all users abide by this principle to ensure that technological development takes place in a regulated and legal environment. We have done our best to ensure the compliance of the data used in the model training process. However, despite our significant efforts, unforeseen issues may still arise due to the complexity of the model and data. Therefore, if any problems arise due to the use of the Orion-14B open-source model, including but not limited to data security issues, public opinion risks, or any risks and issues arising from the model being misled, abused, disseminated, or improperly utilized, we will not assume any responsibility. ## 5.2. License Community use of the Orion-14B series models - For code, please comply with [Apache License Version 2.0](./LICENSE)<br> - For model, please comply with [【Orion-14B Series】 Models Community License Agreement](./ModelsCommunityLicenseAgreement) <a name="company-introduction"></a><br> # 6. Company Introduction OrionStar is a leading global service robot solutions company, founded in September 2016. OrionStar is dedicated to using artificial intelligence technology to create the next generation of revolutionary robots, allowing people to break free from repetitive physical labor and making human work and life more intelligent and enjoyable. Through technology, OrionStar aims to make society and the world a better place. OrionStar possesses fully self-developed end-to-end artificial intelligence technologies, such as voice interaction and visual navigation. It integrates product development capabilities and technological application capabilities. Based on the Orion robotic arm platform, it has launched products such as OrionStar AI Robot Greeting, AI Robot Greeting Mini, Lucki, Coffee Master, and established the open platform OrionOS for Orion robots. Following the philosophy of "Born for Truly Useful Robots", OrionStar empowers more people through AI technology. **The core strengths of OrionStar lies in possessing end-to-end AI application capabilities,** including big data preprocessing, large model pretraining, fine-tuning, prompt engineering, agent, etc. With comprehensive end-to-end model training capabilities, including systematic data processing workflows and the parallel model training capability of hundreds of GPUs, it has been successfully applied in various industry scenarios such as government affairs, cloud services, international e-commerce, and fast-moving consumer goods. Companies with demands for deploying large-scale model applications are welcome to contact us.<br> **Enquiry Hotline: 400-898-7779**<br> **E-mail: [email protected]** <div align="center"> <img src="./assets/imgs/wechat_group.jpg" alt="wechat" width="40%" /> </div> # 目录 - [📖 模型介绍](#zh_model-introduction) - [🔗 下载路径](#zh_model-download) - [🔖 评估结果](#zh_model-benchmark) - [📊 模型推理](#zh_model-inference) - [📜 声明协议](#zh_declarations-license) - [🥇 企业介绍](#zh_company-introduction) <a name="zh_model-introduction"></a><br> # 1. 模型介绍 - Orion-14B-Base是一个具有140亿参数的多语种大模型，该模型在一个包含2.5万亿token的多样化数据集上进行了训练，涵盖了中文、英语、日语、韩语等多种语言。在多语言环境下的一系列任务中展现出卓越的性能。在主流的公开基准评测中，Orion-14B系列模型表现优异，多项指标显著超越同等参数基本的其他模型。具体技术细节请参考[技术报告](https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf)。 - Orion-14B系列大模型有以下几个特点： - 基座20B参数级别大模型综合评测效果表现优异 - 多语言能力强，在日语、韩语测试集上显著领先 - 微调模型适应性强，在人类标注盲测中，表现突出 - 长上下文版本支持超长文本，在200k token长度上效果优异，最长可支持可达320k - 量化版本模型大小缩小70%，推理速度提升30%，性能损失小于1% <table style="border-collapse: collapse; width: 100%;"> <tr> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/opencompass_zh.png" alt="opencompass" style="width: 100%; height: auto;"> </td> <td style="border: none; padding: 10px; box-sizing: border-box;"> <img src="./assets/imgs/model_cap_zh.png" alt="modelcap" style="width: 100%; height: auto;"> </td> </tr> </table> - 具体而言，Orion-14B系列大语言模型包含: - **Orion-14B-Base:** 基于2.5万亿tokens多样化数据集训练处的140亿参数量级的多语言基座模型。 - **Orion-14B-Chat:** 基于高质量语料库微调的对话类模型，旨在为大模型社区提供更好的用户交互体验。 - **Orion-14B-LongChat:** 在200k token长度上效果优异，最长可支持可达320k，在长文本评估集上性能比肩专有模型。 - **Orion-14B-Chat-RAG:** 在一个定制的检索增强生成数据集上进行微调的聊天模型，在检索增强生成任务中取得了卓越的性能。 - **Orion-14B-Chat-Plugin:** 专门针对插件和函数调用任务定制的聊天模型，非常适用于使用代理的相关场景，其中大语言模型充当插件和函数调用系统。 - **Orion-14B-Base-Int4:** 一个使用int4进行量化的基座模型。它将模型大小显著减小了70%，同时提高了推理速度30%，仅引入了1%的最小性能损失。 - **Orion-14B-Chat-Int4:** 一个使用int4进行量化的对话模型。 <a name="zh_model-download"></a><br> # 2. 下载路径发布模型和下载链接见下表： | 模型名称 | HuggingFace下载链接 | ModelScope下载链接 | |---------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------| | ⚾ 基座模型 | [Orion-14B-Base](https://huggingface.co/OrionStarAI/Orion-14B-Base) | [Orion-14B-Base](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base/summary) | | 😛 对话模型 | [Orion-14B-Chat](https://huggingface.co/OrionStarAI/Orion-14B-Chat) | [Orion-14B-Chat](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat/summary) | | 📃 长上下文模型 | [Orion-14B-LongChat](https://huggingface.co/OrionStarAI/Orion-14B-LongChat) | [Orion-14B-LongChat](https://modelscope.cn/models/OrionStarAI/Orion-14B-LongChat/summary) | | 🔎 检索增强模型 | [Orion-14B-Chat-RAG](https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG) | [Orion-14B-Chat-RAG](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-RAG/summary) | | 🔌 插件模型 | [Orion-14B-Chat-Plugin](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Plugin) | [Orion-14B-Chat-Plugin](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Plugin/summary)| | 💼 基座Int4量化模型 | [Orion-14B-Base-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4) | [Orion-14B-Base-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base-Int4/summary) | | 📦 对话Int4量化模型 | [Orion-14B-Chat-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Int4) | [Orion-14B-Chat-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Int4/summary) | <a name="zh_model-benchmark"></a><br> # 3. 评估结果 ## 3.1. 基座模型Orion-14B-Base评估 ### 3.1.1. 专业知识与试题评估结果 | 模型名称 | C-Eval | CMMLU | MMLU | AGIEval | Gaokao | BBH | |--------------------|----------|----------|----------|----------|----------|----------| | LLaMA2-13B | 41.4 | 38.4 | 55.0 | 30.9 | 18.2 | 45.6 | | Skywork-13B | 59.1 | 61.4 | 62.7 | 43.6 | 56.1 | 48.3 | | Baichuan2-13B | 59.0 | 61.3 | 59.5 | 37.4 | 45.6 | 49.0 | | QWEN-14B | 71.7 | 70.2 | 67.9 | 51.9 | **62.5** | 53.7 | | InternLM-20B | 58.8 | 59.0 | 62.1 | 44.6 | 45.5 | 52.5 | | **Orion-14B-Base** | **72.9** | **70.6** | **69.9** | **54.7** | 62.1 | **56.5** | ### 3.1.2. 理解与通识评估结果 | 模型名称 |RACE-middle|RACE-high| HellaSwag| PIQA | Lambada | WSC | |--------------------|----------|----------|----------|----------|----------|----------| | LLaMA 2-13B | 63.0 | 58.9 | 77.5 | 79.8 | 76.5 | 66.3 | | Skywork-13B | 87.6 | 84.1 | 73.7 | 78.3 | 71.8 | 66.3 | | Baichuan 2-13B | 68.9 | 67.2 | 70.8 | 78.1 | 74.1 | 66.3 | | QWEN-14B | 93.0 | 90.3 | **80.2** | 79.8 | 71.4 | 66.3 | | InternLM-20B | 86.4 | 83.3 | 78.1 | **80.3** | 71.8 | 68.3 | | **Orion-14B-Base** | **93.2** | **91.3** | 78.5 | 79.5 | **78.8** | **70.2** | ### 3.1.3. OpenCompass评测集评估结果 | 模型名称 | Average | Examination | Language | Knowledge | Understanding | Reasoning | |------------------|----------|----------|----------|----------|----------|----------| | LLaMA 2-13B | 47.3 | 45.2 | 47.0 | 58.3 | 50.9 | 43.6 | | Skywork-13B | 53.6 | 61.1 | 51.3 | 52.7 | 64.5 | 45.2 | | Baichuan 2-13B | 49.4 | 51.8 | 47.5 | 48.9 | 58.1 | 44.2 | | QWEN-14B | 62.4 | 71.3 | 52.67 | 56.1 | 68.8 | 60.1 | | InternLM-20B | 59.4 | 62.5 | 55.0 | **60.1** | 67.3 | 54.9 | |**Orion-14B-Base**| **64.3** | **71.4** | **55.0** | 60.0 | **71.9** | **61.6** | ### 3.1.4. 日语测试集评估结果 | 模型名称 |**Average**| JCQA | JNLI | MARC | JSQD | JQK | XLS | XWN | MGSM | |--------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------| | PLaMo-13B | 52.3 | 56.7 | 42.8 | 95.8 | 70.6 | 71.0 | 8.70 | 70.5 | 2.40 | | WebLab-10B | 50.7 | 66.6 | 53.7 | 82.1 | 62.9 | 56.2 | 10.0 | 72.0 | 2.40 | | ELYZA-jp-7B | 48.8 | 71.7 | 25.3 | 86.6 | 70.8 | 64.1 | 2.50 | 62.1 | 7.20 | | StableLM-jp-7B | 51.1 | 33.4 | 43.3 | **96.7** | 70.6 | 78.1 | 10.7 | 72.8 | 2.80 | | LLaMA 2-13B | 46.3 | 75.0 | 47.6 | 38.8 | 76.1 | 67.7 | 18.1 | 63.2 | 10.4 | | Baichuan 2-13B | 57.1 | 73.7 | 31.3 | 91.6 | 80.5 | 63.3 | 18.6 | 72.2 | 25.2 | | QWEN-14B | 65.8 | 85.9 | 60.7 | 97.0 | 83.3 | 71.8 | 18.8 | 70.6 | 38.0 | | Yi-34B | 67.1 | 83.8 | 61.2 | 95.2 | **86.1** | 78.5 | **27.2** | 69.2 | 35.2 | | **Orion-14B-Base** | **69.1** | **88.2** | **75.8** | 94.1 | 75.7 | **85.1** | 17.3 | **78.8** | **38.0** | ### 3.1.5. 韩语测试集n-shot评估结果 | 模型名称 | **Average**<br>n=0  n=5 | HellaSwag<br>n=0  n=5 | COPA<br> n=0  n=5 | BooIQ<br>n=0  n=5 | SentiNeg<br>n=0  n=5| |------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------| | KoGPT | 53.0    70.1 | 55.9    58.3 | 73.5    72.9 | 45.1    59.8 | 37.5    89.4 | | Polyglot-ko-13B | 69.6    73.7 |**59.5**    **63.1**|**79.4**    **81.1**| 48.2    60.4 | 91.2    90.2 | | LLaMA 2-13B | 46.7    63.7 | 41.3    44.0 | 59.3    63.8 | 34.9    73.8 | 51.5    73.4 | | Baichuan 2-13B | 52.1    58.7 | 39.2    39.6 | 60.6    60.6 | 58.4    61.5 | 50.3    72.9 | | QWEN-14B | 53.8    73.7 | 45.3    46.8 | 64.9    68.9 | 33.4    83.5 | 71.5    95.7 | | Yi-34B | 54.2    72.1 | 44.6    44.7 | 58.0    60.6 | 65.9    90.2 | 48.3    92.9 | |**Orion-14B-Base**|**74.5**    **79.6**| 47.0    49.6 | 77.7    79.4 |**81.6**    **90.7**|**92.4**    **98.7**| ### 3.1.6. 多语言评估结果 | 模型名称 | Train Lang | Japanese | Korean | Chinese | English | |--------------------|------------|----------|----------|----------|----------| | PLaMo-13B | En,Jp | 52.3 | * | * | * | | Weblab-10B | En,Jp | 50.7 | * | * | * | | ELYZA-jp-7B | En,Jp | 48.8 | * | * | * | | StableLM-jp-7B | En,Jp | 51.1 | * | * | * | | KoGPT-6B | En,Ko | * | 70.1 | * | * | | Polyglot-ko-13B | En,Ko | * | 70.7 | * | * | | Baichuan2-13B | Multi | 57.1 | 58.7 | 50.8 | 57.1 | | Qwen-14B | Multi | 65.8 | 73.7 | 64.5 | 65.4 | | Llama2-13B | Multi | 46.3 | 63.7 | 41.4 | 55.3 | | Yi-34B | Multi | 67.1 | 72.2 | 58.7 | **68.8** | | **Orion-14B-Base** | Multi | **69.1** | **79.5** | **67.9** | 67.3 | ## 3.2. 对话模型Orion-14B-Chat评估 ### 3.2.1. 对话模型MTBench主观评估 | 模型名称 | 第一轮 | 第二轮 | **平均** | |----------------------|----------|----------|----------| | Baichuan2-13B-Chat | 7.05 | 6.47 | 6.76 | | Qwen-14B-Chat | 7.30 | 6.62 | 6.96 | | Llama2-13B-Chat | 7.10 | 6.20 | 6.65 | | InternLM-20B-Chat | 7.03 | 5.93 | 6.48 | | **Orion-14B-Chat** | **7.68** | **7.07** | **7.37** | \*这里评测使用vllm进行推理 ### 3.2.2. 对话模型AlignBench主观评估 | 模型名称 | 数学能力 | 逻辑推理 | 基本能力 | 中文理解 | 综合问答 | 写作能力 | 角色扮演 | 专业知识 | **平均** | |--------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------| | Baichuan2-13B-Chat | 3.76 | 4.07 | 6.22 | 6.05 | 7.11 | 6.97 | 6.75 | 6.43 | 5.25 | | Qwen-14B-Chat | **4.91** | **4.71** | **6.90** | 6.36 | 6.74 | 6.64 | 6.59 | 6.56 | **5.72** | | Llama2-13B-Chat | 3.05 | 3.79 | 5.43 | 4.40 | 6.76 | 6.63 | 6.99 | 5.65 | 4.70 | | InternLM-20B-Chat | 3.39 | 3.92 | 5.96 | 5.50 | **7.18** | 6.19 | 6.49 | 6.22 | 4.96 | | **Orion-14B-Chat** | 4.00 | 4.24 | 6.18 | **6.57** | 7.16 | **7.36** | **7.16** | **6.99** | 5.51 | \*这里评测使用vllm进行推理 ## 3.3. 长上下文模型Orion-14B-LongChat评估 ### 3.3.1. 长上下文模型LongBench评估 | 模型名称 | NarrativeQA| MultiFieldQA-en| MultiFieldQA-zh | DuReader | QMSum | VCSUM | TREC | TriviaQA | LSHT | RepoBench-P | |--------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------| | GPT-3.5-Turbo-16k | **23.60** | **52.30** | **61.20** | 28.70 | 23.40 | **16.00** | 68.00 | **91.40** | 29.20 | 53.60 | | LongChat-v1.5-7B-32k | 16.90 | 41.40 | 29.10 | 19.50 | 22.70 | 9.90 | 63.50 | 82.30 | 23.20 | 55.30 | | Vicuna-v1.5-7B-16k | 19.40 | 38.50 | 43.00 | 19.30 | 22.80 | 15.10 | 71.50 | 86.20 | 28.80 | 43.50 | | Yi-6B-200K | 14.11 | 36.74 | 22.68 | 14.01 | 20.44 | 8.08 | 72.00 | 86.61 | 38.00 | **63.29** | | Orion-14B-LongChat | 19.47 | 48.11 | 55.84 | **37.02** | **24.87** | 15.44 | **77.00** | 89.12 | **45.50** | 54.31 | ## 3.4. 检索增强模型Orion-14B-Chat-RAG评估 ### 3.4.1. 自建检索增强测试集评估结果 |模型名称|回复效果(关键字)|*回复效果(主观打分)|引用能力|兜底能力|*AutoQA|*抽取数据| |---------------------|------|------|------|------|------|------| | Baichuan2-13B-Chat | 85 | 76 | 1 | 0 | 69 | 51 | | Qwen-14B-Chat | 79 | 77 | 75 | 47 | 68 | 72 | | Qwen-72B-Chat(Int4) | 87 | 89 | 90 | 32 | 67 | 76 | | GPT-4 | 91 | 94 | 96 | 95 | 75 | 86 | | Orion-14B-Chat-RAG | 86 | 87 | 91 | 97 | 73 | 71 | \* 表示人工评判结果 ## 3.5. 插件模型Orion-14B-Chat-Plugin评估 ### 3.5.1. 自建插件测试集评估结果 | 模型名称 | 全参数意图识别 | 缺参数意图识别 | 非插件调用识别 | |-----------------------|--------|-----------|--------| | Baichuan2-13B-Chat | 25 | 0 | 0 | | Qwen-14B-Chat | 55 | 0 | 50 | | GPT-4 | **95** | 52.38 | 70 | | Orion-14B-Chat-Plugin | 92.5 | **60.32** | **90** | ## 3.6. 量化模型Orion-14B-Base-Int4评估 ### 3.6.1. 量化前后整体对比 |模型名称|模型大小(GB)|推理速度(令牌数/秒)|C-Eval |CMMLU |MMLU |RACE | HellaSwag| |-------------------------|------|-----|------|------|------|------|------| | OrionStar-14B-Base | 28.0 | 135 | 72.8 | 70.6 | 70.0 | 93.3 | 78.5 | | OrionStar-14B-Base-Int4 | 8.3 | 178 | 71.8 | 69.8 | 69.2 | 93.1 | 78.0 | <a name="zh_model-inference"></a><br> # 4. 模型推理推理所需的模型权重、源码、配置已发布在 Hugging Face，下载链接见本文档最开始的表格。我们在此示范多种推理方式。程序会自动从 Hugging Face 下载所需资源。 ## 4.1. Python 代码方式 ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/Orion-14B", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("OrionStarAI/Orion-14B") messages = [{"role": "user", "content": "你好! 你叫什么名字!"}] response = model.chat(tokenizer, messages, streaming=Flase) print(response) ``` 在上述两段代码中，模型加载指定 `device_map='auto'` ，会使用所有可用显卡。如需指定使用的设备，可以使用类似 `export CUDA_VISIBLE_DEVICES=0,1`（使用了0、1号显卡）的方式控制。 ## 4.2. 命令行工具方式 ```shell CUDA_VISIBLE_DEVICES=0 python cli_demo.py ``` 本命令行工具是为 Chat 场景设计，因此我们不支持使用该工具调用 Base 模型。 ## 4.3. 脚本直接推理 ```shell # base model CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/Orion-14B --tokenizer OrionStarAI/Orion-14B --prompt 你好,你叫什么名字 # chat model CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt 你好,你叫什么名字 ``` ## 4.4. 示例输出 ### 4.4.1. 闲聊 ````` 用户：你好,你叫什么名字 Orion-14B：你好!我是一个人工智能助手,没有固定的名字。你可以随意称呼我。有什么我可以帮助你的吗? 用户：讲个故事 Orion-14B：从前有一个小男孩叫杰克,他住在一个小村庄里。杰克非常喜欢冒险和探索,总是梦想着能够发现一些神奇的事情。有一天,杰克听说在附近的森林深处有一座传说中的魔法城堡,里面住着一个邪恶的巨人。这个巨人的名字叫格里姆,他被困在城堡中,无法离开。杰克决定要去解救这个巨人,因为他相信格里姆一定有自己的原因被困在那里。于是,他带着勇气和决心,踏上了前往魔法城堡的旅程。经过长时间的跋涉,杰克终于来到了城堡的大门前。他小心翼翼地推开大门,进入了一个充满神秘气息的房间。房间里弥漫着黑暗的气息,但杰克并没有被吓倒。他勇敢地向城堡深处走去,最终找到了格里姆。格里姆告诉杰克,他是被一个邪恶的女巫诅咒的,只有通过解开咒语才能获得自由。杰克决定要帮助格里姆,他开始寻找线索和解开咒语的方法。经过一番努力,杰克终于找到了一条通往女巫的秘密实验室的道路。他在那里发现了女巫留下的魔法书,书中记载了解开咒语的方法。杰克按照书上所写的步骤进行操作,成功地解开了格里姆身上的诅咒。巨人感激不已,他答应帮助杰克实现他的愿望。从此以后,杰克和格里姆成为了好朋友,他们一起经历了许多惊险刺激的冒险,并用他们的勇气和智慧战胜了各种困难。这个故事告诉我们,只要有勇气和决心,我们就能克服一切困难,实现自己的梦想。 ````` ### 4.4.2. 日韩 ````` 用户：自己を紹介してください Orion-14B：こんにちは、私の名前はChatMaxで、OrionStarによって開発されたAIアシスタントです。私はユーザーに正確で、客観的で、有用な回答を提供し、彼らが問題を解決し、情報を取得するのを助けることができます。用户：자기소개를 해주세요. Orion-14B：안녕하세요, 제 이름은 ChatMax입니다. OrionStar에서 개발한 AI 어시스턴트입니다. 저는 사용자들에게 정확하고 객관적이며 유용한 답변을 제공하여 문제를 해결하고 정보를 얻는 데 도움을 줄 수 있습니다. ````` <a name="zh_declarations-license"></a><br> # 5. 声明、协议 ## 5.1. 声明我们强烈呼吁所有使用者，不要利用 Orion-14B 模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Orion-14B 模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用 Orion-14B 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 ## 5.2. 协议社区使用Orion-14B系列模型 - 代码请遵循 [Apache License Version 2.0](./LICENSE)<br> - 模型请遵循 [Orion-14B系列模型社区许可协议](./ModelsCommunityLicenseAgreement) <a name="zh_company-introduction"></a><br> # 6. 企业介绍猎户星空（OrionStar）是一家全球领先的服务机器人解决方案公司，成立于2016年9月。猎户星空致力于基于人工智能技术打造下一代革命性机器人，使人们能够摆脱重复的体力劳动，使人类的工作和生活更加智能和有趣，通过技术使社会和世界变得更加美好。猎户星空拥有完全自主开发的全链条人工智能技术，如语音交互和视觉导航。它整合了产品开发能力和技术应用能力。基于Orion机械臂平台，它推出了ORION STAR AI Robot Greeting、AI Robot Greeting Mini、Lucki、Coffee Master等产品，并建立了Orion机器人的开放平台OrionOS。通过为 **真正有用的机器人而生** 的理念实践，它通过AI技术为更多人赋能。凭借7年AI经验积累，猎户星空已推出的大模型深度应用“聚言”，并陆续面向行业客户提供定制化AI大模型咨询与服务解决方案，真正帮助客户实现企业经营效率领先同行目标。 **猎户星空具备全链条大模型应用能力的核心优势**，包括拥有从海量数据处理、大模型预训练、二次预训练、微调(Fine-tune)、Prompt Engineering 、Agent开发的全链条能力和经验积累；拥有完整的端到端模型训练能力，包括系统化的数据处理流程和数百张GPU的并行模型训练能力，现已在大政务、云服务、出海电商、快消等多个行业场景落地。 ***欢迎有大模型应用落地需求的企业联系我们进行商务合作***<br> **咨询电话:** 400-898-7779<br> **电子邮箱:** [email protected] <div align="center"> <img src="./assets/imgs/wechat_group.jpg" alt="wechat" width="40%" /> </div>

limin-arc/Mem-Beagle-7b-slerp-v1

limin-arc

2024-01-25T01:56:57Z

7

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "starsnatched/MemGPT", "mlabonne/Beagle14-7B", "conversational", "base_model:minchyeom/MemGPT", "base_model:merge:minchyeom/MemGPT", "base_model:mlabonne/Beagle14-7B", "base_model:merge:mlabonne/Beagle14-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-25T01:49:21Z

--- tags: - merge - mergekit - lazymergekit - starsnatched/MemGPT - mlabonne/Beagle14-7B base_model: - starsnatched/MemGPT - mlabonne/Beagle14-7B --- # Mem-Beagle-7b-slerp-v1 Mem-Beagle-7b-slerp-v1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [starsnatched/MemGPT](https://huggingface.co/starsnatched/MemGPT) * [mlabonne/Beagle14-7B](https://huggingface.co/mlabonne/Beagle14-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: starsnatched/MemGPT layer_range: [0, 32] - model: mlabonne/Beagle14-7B layer_range: [0, 32] merge_method: slerp base_model: starsnatched/MemGPT parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "liminerity/Mem-Beagle-7b-slerp-v1" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

Nicolas852/ppo-LunarLander-v2

Nicolas852

2024-01-25T01:56:31Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2024-01-25T01:56:10Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 262.77 +/- 36.60 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

tiagoblima/mt5_base-qg-ap-peft-oficial

tiagoblima

2024-01-25T01:54:24Z

0

null

[ "safetensors", "generated_from_trainer", "dataset:tiagoblima/du-qg-squadv1_pt", "base_model:google/mt5-base", "base_model:finetune:google/mt5-base", "license:apache-2.0", "region:us" ]

null

2024-01-24T02:26:00Z

--- license: apache-2.0 base_model: google/mt5-base tags: - generated_from_trainer datasets: - tiagoblima/du-qg-squadv1_pt model-index: - name: mt5_base-qg-ap-peft-oficial results: [] ---  # mt5_base-qg-ap-peft-oficial This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the tiagoblima/du-qg-squadv1_pt dataset. It achieves the following results on the evaluation set: - Loss: 6.6924 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 10.2785 | 1.0 | 2367 | 7.4566 | | 9.7078 | 2.0 | 4734 | 7.0581 | | 9.4851 | 3.0 | 7101 | 6.8360 | | 9.2737 | 4.0 | 9468 | 6.7291 | | 9.3588 | 5.0 | 11835 | 6.6924 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.1

graceneutrality/Reinforce-stuff

graceneutrality

2024-01-25T01:50:07Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2024-01-25T01:49:57Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-stuff results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 77.60 +/- 53.83 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

riyasankhe/vit-base-beans-tutorial

riyasankhe

2024-01-25T01:42:37Z

180

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-01-25T01:39:43Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-beans-tutorial results: [] ---  # vit-base-beans-tutorial This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0180 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 1787 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0469 | 1.54 | 100 | 0.1320 | 0.9624 | | 0.0186 | 3.08 | 200 | 0.0315 | 0.9925 | | 0.0084 | 4.62 | 300 | 0.0180 | 0.9925 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

traethethird/ppo-Huggy

traethethird

2024-01-25T01:33:37Z

4

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2024-01-25T01:33:24Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: traethethird/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

sharadsin/Mixtral-8x7B-sql-ft-v1

sharadsin

2024-01-25T01:30:39Z

5

1

peft

[ "peft", "safetensors", "en", "dataset:knowrohit07/know_sql", "base_model:mistralai/Mixtral-8x7B-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-v0.1", "license:apache-2.0", "region:us" ]

null

2024-01-03T01:14:40Z

--- library_name: peft base_model: mistralai/Mixtral-8x7B-v0.1 license: apache-2.0 datasets: - knowrohit07/know_sql language: - en ---  <img src="project-9.png" width="50%" height="50%" > ## SQL-Converter Mixtral 8x7B v0.1 **Convert Natural Language to SQL** ### Overview Mixtral-8x7B-sql-ft-v1 is fine-tuned from Mixtral 8x7B to convert natural language to SQL queries. ### Base Model mistralai/Mixtral-8x7B-v0.1 ### Fine-Tuning - **Dataset**: 5,000 natural language-SQL pairs. ### Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from peft import PeftModel import torch base_model_id = 'mistralai/Mixtral-8x7B-v0.1' adapter_id = 'sharadsin/Mixtral-8x7B-sql-ft-v1' bnb_config = BitsAndBytesConfig( load_in_4bit = True, bnb_4bit_use_double_quant = True, bnb_4bit_compute_dtype = torch.bfloat16, bnb_4bit_quant_type = "nf4", ) base_model = AutoModelForCausalLM.from_pretrained( base_model_id, quantization_config = bnb_config, device_map = "auto", trust_remote_code = True, ) tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token = True, trust_remote_code = True) ft_model = PeftModel.from_pretrained(base_model, adapter_id) eval_prompt= """SYSTEM: Use the following contextual information to concisely answer the question. USER: CREATE TABLE EmployeeInfo (EmpID INTEGER, EmpFname VARCHAR, EmpLname VARCHAR, Department VARCHAR, Project VARCHAR,Address VARCHAR, DOB DATE, Gender CHAR) === Write a query to fetch details of employees whose EmpLname ends with an alphabet 'A' and contains five alphabets? ASSISTANT:""" model_input = tokenizer(eval_prompt, return_tensors="pt").to("cuda") ft_model.eval() with torch.inference_mode(): print(tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=70,top_k=4, penalty_alpha = 0.6, repetition_penalty=1.15)[0], skip_special_tokens= False)) ``` ### Limitations - Less accurate with very complex queries. - Generates extra gibberish content after providing the answers. ### Framework versions - PEFT 0.7.1

ntc-ai/SDXL-LoRA-slider.heavy-inking

ntc-ai

2024-01-25T01:27:02Z

52

0

diffusers

[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]

text-to-image

2024-01-25T01:26:59Z

--- language: - en thumbnail: "images/evaluate/heavy inking.../heavy inking_17_3.0.png" widget: - text: heavy inking output: url: images/heavy inking_17_3.0.png - text: heavy inking output: url: images/heavy inking_19_3.0.png - text: heavy inking output: url: images/heavy inking_20_3.0.png - text: heavy inking output: url: images/heavy inking_21_3.0.png - text: heavy inking output: url: images/heavy inking_22_3.0.png tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers license: "mit" inference: false instance_prompt: "heavy inking" base_model: "stabilityai/stable-diffusion-xl-base-1.0" --- # ntcai.xyz slider - heavy inking (SDXL LoRA) | Strength: -3 | Strength: 0 | Strength: 3 | | --- | --- | --- | | <img src="images/heavy inking_17_-3.0.png" width=256 height=256 /> | <img src="images/heavy inking_17_0.0.png" width=256 height=256 /> | <img src="images/heavy inking_17_3.0.png" width=256 height=256 /> | | <img src="images/heavy inking_19_-3.0.png" width=256 height=256 /> | <img src="images/heavy inking_19_0.0.png" width=256 height=256 /> | <img src="images/heavy inking_19_3.0.png" width=256 height=256 /> | | <img src="images/heavy inking_20_-3.0.png" width=256 height=256 /> | <img src="images/heavy inking_20_0.0.png" width=256 height=256 /> | <img src="images/heavy inking_20_3.0.png" width=256 height=256 /> | ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` heavy inking ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.heavy-inking', weight_name='heavy inking.safetensors', adapter_name="heavy inking") # Activate the LoRA pipe.set_adapters(["heavy inking"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, heavy inking" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 1140+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities. Your support on Patreon will allow us to continue developing and refining new models. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs

tenyaiida/ArcherEMIYA

tenyaiida

2024-01-25T01:20:26Z

0

1

null

[ "voice", "license:mit", "region:us" ]

null

2023-07-06T21:05:42Z

--- license: mit tags: - voice --- # Voice model of Archer / EMIYA from Fate/Stay Night  Voice model was trained with Mangio-Crepe, using clips from FGO and FGO Arcade. ## Model Details Update 2024/1/3: The voice has gotten better since the dataset has been updated. Please use RMVPE when sampling with speaking clips that involve some distortion such as reverb, even though the model was trained with Mangio-Crepe. When singing, it's better to use Mangio-Crepe. With other clips, I can't tell a difference between RMVPE and Mangio-Crepe. ### Warnings They may not come out as amazing as they should, so extra tweaking may be necessary. Archer has a very low voice, and using it with higher voice models may not sound correct without pitch correction.

ashercn97/avi-1.1b

ashercn97

2024-01-25T01:17:05Z

11

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "he", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-24T20:05:15Z

--- license: apache-2.0 language: - he - en pipeline_tag: text-generation --- # Avi 1.1B Avi is a fine tune of TinyLlama. While TinyLlama originally only spoke English, Avi was trained on Hebrew data, so it speaks hebrew. ## Data Avi was trained on the Platypus and Alpaca-GPT4 datasets, machine translated into Hebrew. While I know this is not the optimal approach, it is what I did.

Kwabena/results

Kwabena

2024-01-25T00:48:43Z

0

null

[ "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]

null

2024-01-25T00:41:09Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0307 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 300 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1301 | 0.05 | 20 | 0.1112 | | 0.14 | 0.09 | 40 | 0.1021 | | 0.1582 | 0.14 | 60 | 0.1935 | | 0.1106 | 0.19 | 80 | 0.1298 | | 0.1646 | 0.23 | 100 | 0.1226 | | 0.0748 | 0.28 | 120 | 0.0889 | | 0.1169 | 0.33 | 140 | 0.0966 | | 0.1127 | 0.38 | 160 | 0.0706 | | 0.0899 | 0.42 | 180 | 0.0696 | | 0.1202 | 0.47 | 200 | 0.1355 | | 0.0491 | 0.52 | 220 | 0.0529 | | 0.0931 | 0.56 | 240 | 0.0466 | | 0.1068 | 0.61 | 260 | 0.0695 | | 0.0545 | 0.66 | 280 | 0.0383 | | 0.0346 | 0.7 | 300 | 0.0307 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0

asun17904/glue-mnli-bert-base-uncased-regularized-l2

asun17904

2024-01-25T00:44:43Z

0

pytorch

[ "pytorch", "en", "license:mit", "region:us" ]

null

2024-01-24T07:08:38Z

--- language: en license: mit library_name: pytorch --- # Knowledge Continuity Regularized Network Dataset: GLUE Trainer Hyperparameters: - `lr` = 5e-05 - `per_device_batch_size` = 16 - `gradient_accumulation_steps` = 1 - `weight_decay` = 1e-09 - `seed` = 42 Regularization Hyperparameters - `numerical stability denominator constant` = 0.01 - `lambda` = 0.02 - `alpha` = 2.0 - `beta` = 1.0 Extended Logs: |eval_loss|eval_accuracy|epoch| |--|--|--| |24.363|0.790|1.0| |23.650|0.813|2.0|

DouglasPontes/2020-Q2-75p-filtered_combined75

DouglasPontes

2024-01-25T00:43:54Z

7

0

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:DouglasPontes/2020-Q1-filtered_tweets", "base_model:finetune:DouglasPontes/2020-Q1-filtered_tweets", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-01-20T17:45:37Z

--- base_model: DouglasPontes/2020-Q1-filtered_tweets tags: - generated_from_trainer model-index: - name: 2020-Q2-75p-filtered results: [] ---  # 2020-Q2-75p-filtered This model is a fine-tuned version of [DouglasPontes/2020-Q1-filtered_tweets](https://huggingface.co/DouglasPontes/2020-Q1-filtered_tweets) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2312 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.1e-07 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2400000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-------:|:---------------:| | No log | 0.02 | 8000 | 2.4865 | | 2.6592 | 0.04 | 16000 | 2.4598 | | 2.6592 | 0.07 | 24000 | 2.4472 | | 2.6211 | 0.09 | 32000 | 2.4341 | | 2.6211 | 0.11 | 40000 | 2.4223 | | 2.6048 | 0.13 | 48000 | 2.4217 | | 2.6048 | 0.16 | 56000 | 2.4184 | | 2.5861 | 0.18 | 64000 | 2.4062 | | 2.5861 | 0.2 | 72000 | 2.3919 | | 2.5736 | 0.22 | 80000 | 2.3896 | | 2.5736 | 0.25 | 88000 | 2.3951 | | 2.5559 | 0.27 | 96000 | 2.3903 | | 2.5559 | 0.29 | 104000 | 2.3836 | | 2.5551 | 0.31 | 112000 | 2.3749 | | 2.5551 | 0.34 | 120000 | 2.3794 | | 2.5371 | 0.36 | 128000 | 2.3733 | | 2.5371 | 0.38 | 136000 | 2.3703 | | 2.5417 | 0.4 | 144000 | 2.3662 | | 2.5417 | 0.43 | 152000 | 2.3728 | | 2.5316 | 0.45 | 160000 | 2.3643 | | 2.5316 | 0.47 | 168000 | 2.3568 | | 2.5296 | 0.49 | 176000 | 2.3555 | | 2.5296 | 0.52 | 184000 | 2.3506 | | 2.5215 | 0.54 | 192000 | 2.3482 | | 2.5215 | 0.56 | 200000 | 2.3514 | | 2.5274 | 0.58 | 208000 | 2.3531 | | 2.5274 | 0.61 | 216000 | 2.3463 | | 2.5215 | 0.63 | 224000 | 2.3470 | | 2.5215 | 0.65 | 232000 | 2.3407 | | 2.5096 | 0.67 | 240000 | 2.3400 | | 2.5096 | 0.7 | 248000 | 2.3402 | | 2.5176 | 0.72 | 256000 | 2.3308 | | 2.5176 | 0.74 | 264000 | 2.3342 | | 2.5048 | 0.76 | 272000 | 2.3333 | | 2.5048 | 0.79 | 280000 | 2.3288 | | 2.4979 | 0.81 | 288000 | 2.3298 | | 2.4979 | 0.83 | 296000 | 2.3237 | | 2.4963 | 0.85 | 304000 | 2.3266 | | 2.4963 | 0.88 | 312000 | 2.3197 | | 2.4972 | 0.9 | 320000 | 2.3271 | | 2.4972 | 0.92 | 328000 | 2.3275 | | 2.4969 | 0.94 | 336000 | 2.3210 | | 2.4969 | 0.97 | 344000 | 2.3222 | | 2.4961 | 0.99 | 352000 | 2.3242 | | 2.4961 | 1.01 | 360000 | 2.3155 | | 2.49 | 1.03 | 368000 | 2.3175 | | 2.49 | 1.06 | 376000 | 2.3076 | | 2.4847 | 1.08 | 384000 | 2.3138 | | 2.4847 | 1.1 | 392000 | 2.3183 | | 2.4767 | 1.12 | 400000 | 2.3118 | | 2.4767 | 1.15 | 408000 | 2.3152 | | 2.4788 | 1.17 | 416000 | 2.3089 | | 2.4788 | 1.19 | 424000 | 2.3051 | | 2.4738 | 1.21 | 432000 | 2.3102 | | 2.4738 | 1.24 | 440000 | 2.3069 | | 2.4635 | 1.26 | 448000 | 2.3004 | | 2.4635 | 1.28 | 456000 | 2.3066 | | 2.4828 | 1.3 | 464000 | 2.3078 | | 2.4828 | 1.32 | 472000 | 2.3072 | | 2.4675 | 1.35 | 480000 | 2.3073 | | 2.4675 | 1.37 | 488000 | 2.3014 | | 2.4676 | 1.39 | 496000 | 2.2987 | | 2.4676 | 1.41 | 504000 | 2.2988 | | 2.4678 | 1.44 | 512000 | 2.2971 | | 2.4678 | 1.46 | 520000 | 2.2969 | | 2.4634 | 1.48 | 528000 | 2.2990 | | 2.4634 | 1.5 | 536000 | 2.2869 | | 2.4657 | 1.53 | 544000 | 2.2936 | | 2.4657 | 1.55 | 552000 | 2.2915 | | 2.4607 | 1.57 | 560000 | 2.2903 | | 2.4607 | 1.59 | 568000 | 2.2934 | | 2.4558 | 1.62 | 576000 | 2.2845 | | 2.4558 | 1.64 | 584000 | 2.2897 | | 2.4662 | 1.66 | 592000 | 2.2928 | | 2.4662 | 1.68 | 600000 | 2.2861 | | 2.4658 | 1.71 | 608000 | 2.2883 | | 2.4658 | 1.73 | 616000 | 2.2878 | | 2.4533 | 1.75 | 624000 | 2.2892 | | 2.4533 | 1.77 | 632000 | 2.2886 | | 2.4575 | 1.8 | 640000 | 2.2894 | | 2.4575 | 1.82 | 648000 | 2.2871 | | 2.4565 | 1.84 | 656000 | 2.2798 | | 2.4565 | 1.86 | 664000 | 2.2877 | | 2.4548 | 1.89 | 672000 | 2.2859 | | 2.4548 | 1.91 | 680000 | 2.2787 | | 2.4507 | 1.93 | 688000 | 2.2780 | | 2.4507 | 1.95 | 696000 | 2.2826 | | 2.4455 | 1.98 | 704000 | 2.2838 | | 2.4455 | 2.0 | 712000 | 2.2764 | | 2.4516 | 2.02 | 720000 | 2.2814 | | 2.4516 | 2.04 | 728000 | 2.2807 | | 2.445 | 2.07 | 736000 | 2.2740 | | 2.445 | 2.09 | 744000 | 2.2780 | | 2.4466 | 2.11 | 752000 | 2.2775 | | 2.4466 | 2.13 | 760000 | 2.2783 | | 2.4476 | 2.16 | 768000 | 2.2763 | | 2.4476 | 2.18 | 776000 | 2.2737 | | 2.4449 | 2.2 | 784000 | 2.2753 | | 2.4449 | 2.22 | 792000 | 2.2762 | | 2.4424 | 2.25 | 800000 | 2.2767 | | 2.4424 | 2.27 | 808000 | 2.2702 | | 2.4528 | 2.29 | 816000 | 2.2655 | | 2.4528 | 2.31 | 824000 | 2.2727 | | 2.4523 | 2.34 | 832000 | 2.2733 | | 2.4523 | 2.36 | 840000 | 2.2654 | | 2.4395 | 2.38 | 848000 | 2.2674 | | 2.4395 | 2.4 | 856000 | 2.2754 | | 2.434 | 2.43 | 864000 | 2.2722 | | 2.434 | 2.45 | 872000 | 2.2666 | | 2.4407 | 2.47 | 880000 | 2.2656 | | 2.4407 | 2.49 | 888000 | 2.2654 | | 2.4352 | 2.52 | 896000 | 2.2630 | | 2.4352 | 2.54 | 904000 | 2.2662 | | 2.4393 | 2.56 | 912000 | 2.2692 | | 2.4393 | 2.58 | 920000 | 2.2558 | | 2.4378 | 2.61 | 928000 | 2.2619 | | 2.4378 | 2.63 | 936000 | 2.2614 | | 2.4392 | 2.65 | 944000 | 2.2578 | | 2.4392 | 2.67 | 952000 | 2.2672 | | 2.437 | 2.69 | 960000 | 2.2598 | | 2.437 | 2.72 | 968000 | 2.2633 | | 2.4388 | 2.74 | 976000 | 2.2566 | | 2.4388 | 2.76 | 984000 | 2.2551 | | 2.4386 | 2.78 | 992000 | 2.2606 | | 2.4386 | 2.81 | 1000000 | 2.2634 | | 2.4402 | 2.83 | 1008000 | 2.2641 | | 2.4402 | 2.85 | 1016000 | 2.2619 | | 2.4442 | 2.87 | 1024000 | 2.2584 | | 2.4442 | 2.9 | 1032000 | 2.2579 | | 2.4327 | 2.92 | 1040000 | 2.2523 | | 2.4327 | 2.94 | 1048000 | 2.2562 | | 2.4289 | 2.96 | 1056000 | 2.2593 | | 2.4289 | 2.99 | 1064000 | 2.2562 | | 2.4319 | 3.01 | 1072000 | 2.2536 | | 2.4319 | 3.03 | 1080000 | 2.2603 | | 2.4174 | 3.05 | 1088000 | 2.2549 | | 2.4174 | 3.08 | 1096000 | 2.2595 | | 2.4155 | 3.1 | 1104000 | 2.2555 | | 2.4155 | 3.12 | 1112000 | 2.2501 | | 2.427 | 3.14 | 1120000 | 2.2528 | | 2.427 | 3.17 | 1128000 | 2.2529 | | 2.4222 | 3.19 | 1136000 | 2.2536 | | 2.4222 | 3.21 | 1144000 | 2.2582 | | 2.4232 | 3.23 | 1152000 | 2.2522 | | 2.4232 | 3.26 | 1160000 | 2.2525 | | 2.4252 | 3.28 | 1168000 | 2.2538 | | 2.4252 | 3.3 | 1176000 | 2.2512 | | 2.4209 | 3.32 | 1184000 | 2.2557 | | 2.4209 | 3.35 | 1192000 | 2.2445 | | 2.4243 | 3.37 | 1200000 | 2.2570 | | 2.4243 | 3.39 | 1208000 | 2.2539 | | 2.4278 | 3.41 | 1216000 | 2.2514 | | 2.4278 | 3.44 | 1224000 | 2.2454 | | 2.4286 | 3.46 | 1232000 | 2.2463 | | 2.4286 | 3.48 | 1240000 | 2.2506 | | 2.4274 | 3.5 | 1248000 | 2.2427 | | 2.4274 | 3.53 | 1256000 | 2.2535 | | 2.4201 | 3.55 | 1264000 | 2.2517 | | 2.4201 | 3.57 | 1272000 | 2.2436 | | 2.4233 | 3.59 | 1280000 | 2.2430 | | 2.4233 | 3.62 | 1288000 | 2.2470 | | 2.4183 | 3.64 | 1296000 | 2.2446 | | 2.4183 | 3.66 | 1304000 | 2.2539 | | 2.428 | 3.68 | 1312000 | 2.2492 | | 2.428 | 3.71 | 1320000 | 2.2544 | | 2.4206 | 3.73 | 1328000 | 2.2478 | | 2.4206 | 3.75 | 1336000 | 2.2420 | | 2.4287 | 3.77 | 1344000 | 2.2442 | | 2.4287 | 3.8 | 1352000 | 2.2426 | | 2.4297 | 3.82 | 1360000 | 2.2426 | | 2.4297 | 3.84 | 1368000 | 2.2481 | | 2.4185 | 3.86 | 1376000 | 2.2449 | | 2.4185 | 3.89 | 1384000 | 2.2468 | | 2.4217 | 3.91 | 1392000 | 2.2467 | | 2.4217 | 3.93 | 1400000 | 2.2463 | | 2.4144 | 3.95 | 1408000 | 2.2482 | | 2.4144 | 3.97 | 1416000 | 2.2424 | | 2.4175 | 4.0 | 1424000 | 2.2415 | | 2.4175 | 4.02 | 1432000 | 2.2451 | | 2.4169 | 4.04 | 1440000 | 2.2443 | | 2.4169 | 4.06 | 1448000 | 2.2389 | | 2.4142 | 4.09 | 1456000 | 2.2377 | | 2.4142 | 4.11 | 1464000 | 2.2399 | | 2.4122 | 4.13 | 1472000 | 2.2447 | | 2.4122 | 4.15 | 1480000 | 2.2456 | | 2.4166 | 4.18 | 1488000 | 2.2451 | | 2.4166 | 4.2 | 1496000 | 2.2369 | | 2.4165 | 4.22 | 1504000 | 2.2426 | | 2.4165 | 4.24 | 1512000 | 2.2384 | | 2.4204 | 4.27 | 1520000 | 2.2454 | | 2.4204 | 4.29 | 1528000 | 2.2422 | | 2.4192 | 4.31 | 1536000 | 2.2423 | | 2.4192 | 4.33 | 1544000 | 2.2435 | | 2.4167 | 4.36 | 1552000 | 2.2451 | | 2.4167 | 4.38 | 1560000 | 2.2443 | | 2.4124 | 4.4 | 1568000 | 2.2430 | | 2.4124 | 4.42 | 1576000 | 2.2422 | | 2.406 | 4.45 | 1584000 | 2.2357 | | 2.406 | 4.47 | 1592000 | 2.2395 | | 2.4166 | 4.49 | 1600000 | 2.2378 | | 2.4166 | 4.51 | 1608000 | 2.2420 | | 2.4144 | 4.54 | 1616000 | 2.2402 | | 2.4144 | 4.56 | 1624000 | 2.2384 | | 2.4219 | 4.58 | 1632000 | 2.2438 | | 2.4219 | 4.6 | 1640000 | 2.2455 | | 2.4061 | 4.63 | 1648000 | 2.2397 | | 2.4061 | 4.65 | 1656000 | 2.2354 | | 2.411 | 4.67 | 1664000 | 2.2393 | | 2.411 | 4.69 | 1672000 | 2.2388 | | 2.4125 | 4.72 | 1680000 | 2.2406 | | 2.4125 | 4.74 | 1688000 | 2.2330 | | 2.4092 | 4.76 | 1696000 | 2.2336 | | 2.4092 | 4.78 | 1704000 | 2.2398 | | 2.4078 | 4.81 | 1712000 | 2.2368 | | 2.4078 | 4.83 | 1720000 | 2.2361 | | 2.4185 | 4.85 | 1728000 | 2.2378 | | 2.4185 | 4.87 | 1736000 | 2.2339 | | 2.4088 | 4.9 | 1744000 | 2.2366 | | 2.4088 | 4.92 | 1752000 | 2.2385 | | 2.4095 | 4.94 | 1760000 | 2.2337 | | 2.4095 | 4.96 | 1768000 | 2.2413 | | 2.4078 | 4.99 | 1776000 | 2.2377 | | 2.4078 | 5.01 | 1784000 | 2.2302 | | 2.4073 | 5.03 | 1792000 | 2.2357 | | 2.4073 | 5.05 | 1800000 | 2.2384 | | 2.4073 | 5.08 | 1808000 | 2.2322 | | 2.4073 | 5.1 | 1816000 | 2.2344 | | 2.4043 | 5.12 | 1824000 | 2.2327 | | 2.4043 | 5.14 | 1832000 | 2.2350 | | 2.4082 | 5.17 | 1840000 | 2.2376 | | 2.4082 | 5.19 | 1848000 | 2.2363 | | 2.4073 | 5.21 | 1856000 | 2.2323 | | 2.4073 | 5.23 | 1864000 | 2.2419 | | 2.4148 | 5.26 | 1872000 | 2.2293 | | 2.4148 | 5.28 | 1880000 | 2.2346 | | 2.4098 | 5.3 | 1888000 | 2.2372 | | 2.4098 | 5.32 | 1896000 | 2.2371 | | 2.407 | 5.34 | 1904000 | 2.2397 | | 2.407 | 5.37 | 1912000 | 2.2300 | | 2.4108 | 5.39 | 1920000 | 2.2317 | | 2.4108 | 5.41 | 1928000 | 2.2350 | | 2.4168 | 5.43 | 1936000 | 2.2343 | | 2.4168 | 5.46 | 1944000 | 2.2327 | | 2.4113 | 5.48 | 1952000 | 2.2363 | | 2.4113 | 5.5 | 1960000 | 2.2314 | | 2.4131 | 5.52 | 1968000 | 2.2303 | | 2.4131 | 5.55 | 1976000 | 2.2353 | | 2.4129 | 5.57 | 1984000 | 2.2353 | | 2.4129 | 5.59 | 1992000 | 2.2296 | | 2.4129 | 5.61 | 2000000 | 2.2314 | | 2.4129 | 5.64 | 2008000 | 2.2288 | | 2.4045 | 5.66 | 2016000 | 2.2347 | | 2.4045 | 5.68 | 2024000 | 2.2349 | | 2.4089 | 5.7 | 2032000 | 2.2310 | | 2.4089 | 5.73 | 2040000 | 2.2342 | | 2.4091 | 5.75 | 2048000 | 2.2320 | | 2.4091 | 5.77 | 2056000 | 2.2311 | | 2.4137 | 5.79 | 2064000 | 2.2278 | | 2.4137 | 5.82 | 2072000 | 2.2344 | | 2.4063 | 5.84 | 2080000 | 2.2339 | | 2.4063 | 5.86 | 2088000 | 2.2271 | | 2.4046 | 5.88 | 2096000 | 2.2263 | | 2.4046 | 5.91 | 2104000 | 2.2369 | | 2.4105 | 5.93 | 2112000 | 2.2330 | | 2.4105 | 5.95 | 2120000 | 2.2361 | | 2.4045 | 5.97 | 2128000 | 2.2320 | | 2.4045 | 6.0 | 2136000 | 2.2283 | | 2.4093 | 6.02 | 2144000 | 2.2262 | | 2.4093 | 6.04 | 2152000 | 2.2294 | | 2.4109 | 6.06 | 2160000 | 2.2334 | | 2.4109 | 6.09 | 2168000 | 2.2363 | | 2.4061 | 6.11 | 2176000 | 2.2309 | | 2.4061 | 6.13 | 2184000 | 2.2269 | | 2.4007 | 6.15 | 2192000 | 2.2369 | | 2.4007 | 6.18 | 2200000 | 2.2297 | | 2.4034 | 6.2 | 2208000 | 2.2267 | | 2.4034 | 6.22 | 2216000 | 2.2310 | | 2.4049 | 6.24 | 2224000 | 2.2362 | | 2.4049 | 6.27 | 2232000 | 2.2319 | | 2.4052 | 6.29 | 2240000 | 2.2308 | | 2.4052 | 6.31 | 2248000 | 2.2225 | | 2.4102 | 6.33 | 2256000 | 2.2366 | | 2.4102 | 6.36 | 2264000 | 2.2327 | | 2.4046 | 6.38 | 2272000 | 2.2305 | | 2.4046 | 6.4 | 2280000 | 2.2309 | | 2.4066 | 6.42 | 2288000 | 2.2291 | | 2.4066 | 6.45 | 2296000 | 2.2301 | | 2.4041 | 6.47 | 2304000 | 2.2378 | | 2.4041 | 6.49 | 2312000 | 2.2317 | | 2.4081 | 6.51 | 2320000 | 2.2326 | | 2.4081 | 6.54 | 2328000 | 2.2412 | | 2.4147 | 6.56 | 2336000 | 2.2349 | | 2.4147 | 6.58 | 2344000 | 2.2296 | | 2.4105 | 6.6 | 2352000 | 2.2313 | | 2.4105 | 6.62 | 2360000 | 2.2297 | | 2.4096 | 6.65 | 2368000 | 2.2241 | | 2.4096 | 6.67 | 2376000 | 2.2322 | | 2.4089 | 6.69 | 2384000 | 2.2344 | | 2.4089 | 6.71 | 2392000 | 2.2291 | | 2.4048 | 6.74 | 2400000 | 2.2274 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0

TheBloke/FusionNet_34Bx2_MoE-GGUF

TheBloke

2024-01-25T00:28:42Z

221

5

transformers

[ "transformers", "gguf", "mixtral", "moe", "text-generation", "en", "base_model:TomGrc/FusionNet_34Bx2_MoE", "base_model:quantized:TomGrc/FusionNet_34Bx2_MoE", "license:mit", "region:us", "conversational" ]

text-generation

2024-01-24T22:49:13Z

--- base_model: TomGrc/FusionNet_34Bx2_MoE inference: false language: - en license: mit model_creator: Suqin Zhang model_name: FusionNet 34Bx2 MoE model_type: mixtral pipeline_tag: text-generation prompt_template: '[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ' quantized_by: TheBloke tags: - moe ---    <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # FusionNet 34Bx2 MoE - GGUF - Model creator: [Suqin Zhang](https://huggingface.co/TomGrc) - Original model: [FusionNet 34Bx2 MoE](https://huggingface.co/TomGrc/FusionNet_34Bx2_MoE)  ## Description This repo contains GGUF format model files for [Suqin Zhang's FusionNet 34Bx2 MoE](https://huggingface.co/TomGrc/FusionNet_34Bx2_MoE). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).   ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF) * [Suqin Zhang's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TomGrc/FusionNet_34Bx2_MoE)   ## Prompt template: Llama-2-Chat ``` [INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ```   ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details>   ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [fusionnet_34bx2_moe.Q2_K.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q2_K.gguf) | Q2_K | 2 | 22.39 GB| 24.89 GB | significant quality loss - not recommended for most purposes | | [fusionnet_34bx2_moe.Q3_K_S.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q3_K_S.gguf) | Q3_K_S | 3 | 26.32 GB| 28.82 GB | very small, high quality loss | | [fusionnet_34bx2_moe.Q3_K_M.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q3_K_M.gguf) | Q3_K_M | 3 | 29.24 GB| 31.74 GB | very small, high quality loss | | [fusionnet_34bx2_moe.Q3_K_L.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q3_K_L.gguf) | Q3_K_L | 3 | 31.77 GB| 34.27 GB | small, substantial quality loss | | [fusionnet_34bx2_moe.Q4_0.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q4_0.gguf) | Q4_0 | 4 | 34.33 GB| 36.83 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [fusionnet_34bx2_moe.Q4_K_S.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q4_K_S.gguf) | Q4_K_S | 4 | 34.59 GB| 37.09 GB | small, greater quality loss | | [fusionnet_34bx2_moe.Q4_K_M.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q4_K_M.gguf) | Q4_K_M | 4 | 36.66 GB| 39.16 GB | medium, balanced quality - recommended | | [fusionnet_34bx2_moe.Q5_0.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q5_0.gguf) | Q5_0 | 5 | 41.88 GB| 44.38 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [fusionnet_34bx2_moe.Q5_K_S.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q5_K_S.gguf) | Q5_K_S | 5 | 41.88 GB| 44.38 GB | large, low quality loss - recommended | | [fusionnet_34bx2_moe.Q5_K_M.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q5_K_M.gguf) | Q5_K_M | 5 | 43.08 GB| 45.58 GB | large, very low quality loss - recommended | | [fusionnet_34bx2_moe.Q6_K.gguf](https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GGUF/blob/main/fusionnet_34bx2_moe.Q6_K.gguf) | Q6_K | 6 | 49.89 GB| 52.39 GB | very large, extremely low quality loss | | fusionnet_34bx2_moe.Q8_0.gguf | Q8_0 | 8 | 64.62 GB| 67.12 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ### Q6_K and Q8_0 files are split and require joining **Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files. <details> <summary>Click for instructions regarding Q6_K and Q8_0 files</summary> ### q6_K Please download: * `fusionnet_34bx2_moe.Q6_K.gguf-split-a` * `fusionnet_34bx2_moe.Q6_K.gguf-split-b` ### q8_0 Please download: * `fusionnet_34bx2_moe.Q8_0.gguf-split-a` * `fusionnet_34bx2_moe.Q8_0.gguf-split-b` To join the files, do the following: Linux and macOS: ``` cat fusionnet_34bx2_moe.Q6_K.gguf-split-* > fusionnet_34bx2_moe.Q6_K.gguf && rm fusionnet_34bx2_moe.Q6_K.gguf-split-* cat fusionnet_34bx2_moe.Q8_0.gguf-split-* > fusionnet_34bx2_moe.Q8_0.gguf && rm fusionnet_34bx2_moe.Q8_0.gguf-split-* ``` Windows command line: ``` COPY /B fusionnet_34bx2_moe.Q6_K.gguf-split-a + fusionnet_34bx2_moe.Q6_K.gguf-split-b fusionnet_34bx2_moe.Q6_K.gguf del fusionnet_34bx2_moe.Q6_K.gguf-split-a fusionnet_34bx2_moe.Q6_K.gguf-split-b COPY /B fusionnet_34bx2_moe.Q8_0.gguf-split-a + fusionnet_34bx2_moe.Q8_0.gguf-split-b fusionnet_34bx2_moe.Q8_0.gguf del fusionnet_34bx2_moe.Q8_0.gguf-split-a fusionnet_34bx2_moe.Q8_0.gguf-split-b ``` </details>   ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/FusionNet_34Bx2_MoE-GGUF and below it, a specific filename to download, such as: fusionnet_34bx2_moe.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/FusionNet_34Bx2_MoE-GGUF fusionnet_34bx2_moe.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/FusionNet_34Bx2_MoE-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/FusionNet_34Bx2_MoE-GGUF fusionnet_34bx2_moe.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details>   ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m fusionnet_34bx2_moe.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n{prompt} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./fusionnet_34bx2_moe.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n{prompt} [/INST]", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./fusionnet_34bx2_moe.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.   # Original model card: Suqin Zhang's FusionNet 34Bx2 MoE # FusionNet_34Bx2_MoE Fine-tuned model on English language using MoE method. ## Model description The FusionNet_34Bx2_MoE is a model to experiment with the MoE method, which could significantly increase the performance of the original model. The FusionNet_34Bx2_MoE has 60.8B parameters, and this model is fine-tuned. Enjoy! ## Usage ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("TomGrc/FusionNet_34Bx2_MoE") model = AutoModelForCausalLM.from_pretrained("TomGrc/FusionNet_34Bx2_MoE") ```

mesolitica/mallam-5b-20k-instructions-v2

mesolitica

2024-01-25T00:24:53Z

20

1

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "ms", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-12-27T02:02:09Z

--- language: - ms --- # Full Parameter Finetuning MaLLaM 🌙 5B 20480 context length v2 on Malaysian instructions dataset README at https://github.com/mesolitica/malaya/tree/5.1/session/mistral#mallam-5b We use exact Mistral Instruct chat template. WandB, https://wandb.ai/huseinzol05/fpf-mallam-5b-instructions-16k?workspace=user-huseinzol05 WandB report, https://wandb.ai/huseinzol05/fpf-tinyllama-1.1b-hf-instructions-16k/reports/Instruction-finetuning--Vmlldzo2MzQ3OTcz ## Dataset Dataset gathered at https://huggingface.co/collections/mesolitica/malaysian-synthetic-dataset-656c2673fe7fe0b1e9e25fe2 Notebook to prepare dataset at https://github.com/mesolitica/malaysian-dataset/blob/master/llm-instruction/combine-malay-no-alignment-multitasks-partial-ultrachat-v2.ipynb ## Limitations This model is a quick demonstration that the base model can be easily fine-tuned to achieve some performance. It does have minimal moderation mechanisms. ## how-to ```python from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch import json def parse_mistral_chat(messages, function_call = None): user_query = messages[-1]['content'] users, assistants = [], [] for q in messages[:-1]: if q['role'] == 'user': users.append(q['content']) elif q['role'] == 'assistant': assistants.append(q['content']) texts = ['<s>'] if function_call: fs = [] for f in function_call: f = json.dumps(f, indent=4) fs.append(f) fs = '\n\n'.join(fs) texts.append(f'\n[FUNCTIONCALL]\n{fs}\n') for u, a in zip(users, assistants): texts.append(f'[INST] {u.strip()} [/INST] {a.strip()}</s>') texts.append(f'[INST] {user_query.strip()} [/INST]') prompt = ''.join(texts).strip() return prompt TORCH_DTYPE = 'bfloat16' nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type='nf4', bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE) ) tokenizer = AutoTokenizer.from_pretrained('mesolitica/mallam-5b-20k-instructions-v2') model = AutoModelForCausalLM.from_pretrained( 'mesolitica/mallam-5b-20k-instructions-v2', use_flash_attention_2 = True, quantization_config = nf4_config ) messages = [ {'role': 'user', 'content': 'kwsp tu apa'} ] prompt = parse_mistral_chat(messages) inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda') generate_kwargs = dict( inputs, max_new_tokens=1024, top_p=0.95, top_k=50, temperature=0.9, do_sample=True, num_beams=1, ) r = model.generate(**generate_kwargs) tokenizer.decode(r[0]) ``` ```text <s> [INST] kwsp tu apa [/INST]KWSP bermaksud Kumpulan Wang Simpanan Pekerja. Ia adalah sebuah institusi simpanan persaraan yang ditubuhkan oleh Kementerian Kewangan Malaysia untuk tujuan mengumpul simpanan ahli untuk dibayar pada umur persaraan, penuh atau penuh persaraan penuh. KWSP ditubuhkan pada tahun 1951 dan mula beroperasi pada tahun 1952. KWSP adalah salah satu institusi simpanan persaraan terbesar di dunia, dengan pangkalan ahli sekitar 14 juta ahli.</s> ```

RecCode/whisper-small-hi

RecCode

2024-01-25T00:15:07Z

63

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-01-23T05:35:58Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer base_model: openai/whisper-small model-index: - name: whisper-small-hi results: [] ---  # whisper-small-hi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1606 - Wer: 88.3090 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3591 | 4.0 | 1000 | 0.2347 | 43.0063 | | 0.0548 | 8.0 | 2000 | 0.2610 | 315.6576 | | 0.0144 | 12.0 | 3000 | 0.1812 | 500.4175 | | 0.0001 | 16.0 | 4000 | 0.1606 | 88.3090 | ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

yoon1000/ft_0124_korean_2

yoon1000

2024-01-25T00:05:40Z

16

0

transformers

[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-01-24T01:29:30Z

--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer model-index: - name: ft_0124_korean_2 results: [] ---  # ft_0124_korean_2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5092 - Cer: 0.1001 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 25.0263 | 0.25 | 500 | 5.1785 | 1.0 | | 4.7178 | 0.5 | 1000 | 4.7781 | 1.0 | | 4.4523 | 0.76 | 1500 | 4.1013 | 0.9077 | | 3.2148 | 1.01 | 2000 | 2.3856 | 0.4777 | | 2.4229 | 1.26 | 2500 | 1.8502 | 0.4047 | | 2.0145 | 1.51 | 3000 | 1.5497 | 0.3475 | | 1.7916 | 1.77 | 3500 | 1.3324 | 0.3076 | | 1.5849 | 2.02 | 4000 | 1.1873 | 0.2773 | | 1.3956 | 2.27 | 4500 | 1.0617 | 0.2578 | | 1.3086 | 2.52 | 5000 | 0.9643 | 0.2368 | | 1.2211 | 2.78 | 5500 | 0.8894 | 0.2246 | | 1.1562 | 3.03 | 6000 | 0.8537 | 0.2189 | | 1.0729 | 3.28 | 6500 | 0.7973 | 0.2101 | | 1.0089 | 3.53 | 7000 | 0.7549 | 0.1959 | | 1.0027 | 3.79 | 7500 | 0.7327 | 0.1945 | | 0.9496 | 4.04 | 8000 | 0.7082 | 0.1849 | | 0.887 | 4.29 | 8500 | 0.6909 | 0.1789 | | 0.8607 | 4.54 | 9000 | 0.6617 | 0.1739 | | 0.853 | 4.8 | 9500 | 0.6518 | 0.1730 | | 0.8305 | 5.05 | 10000 | 0.6402 | 0.1657 | | 0.774 | 5.3 | 10500 | 0.6365 | 0.1650 | | 0.7621 | 5.55 | 11000 | 0.6206 | 0.1600 | | 0.7553 | 5.81 | 11500 | 0.6080 | 0.1594 | | 0.7186 | 6.06 | 12000 | 0.5951 | 0.1543 | | 0.6772 | 6.31 | 12500 | 0.5814 | 0.1490 | | 0.6752 | 6.56 | 13000 | 0.5815 | 0.1501 | | 0.672 | 6.81 | 13500 | 0.5603 | 0.1440 | | 0.6351 | 7.07 | 14000 | 0.5670 | 0.1439 | | 0.6186 | 7.32 | 14500 | 0.5700 | 0.1431 | | 0.6035 | 7.57 | 15000 | 0.5614 | 0.1417 | | 0.5848 | 7.82 | 15500 | 0.5470 | 0.1396 | | 0.5719 | 8.08 | 16000 | 0.5514 | 0.1386 | | 0.556 | 8.33 | 16500 | 0.5515 | 0.1376 | | 0.5596 | 8.58 | 17000 | 0.5407 | 0.1325 | | 0.5472 | 8.83 | 17500 | 0.5405 | 0.1349 | | 0.5309 | 9.09 | 18000 | 0.5279 | 0.1295 | | 0.5072 | 9.34 | 18500 | 0.5275 | 0.1310 | | 0.5072 | 9.59 | 19000 | 0.5330 | 0.1272 | | 0.4905 | 9.84 | 19500 | 0.5238 | 0.1262 | | 0.4842 | 10.1 | 20000 | 0.5234 | 0.1237 | | 0.4513 | 10.35 | 20500 | 0.5210 | 0.1231 | | 0.4513 | 10.6 | 21000 | 0.5165 | 0.1208 | | 0.4541 | 10.85 | 21500 | 0.5189 | 0.1207 | | 0.4417 | 11.11 | 22000 | 0.5209 | 0.1192 | | 0.4337 | 11.36 | 22500 | 0.5246 | 0.1191 | | 0.4339 | 11.61 | 23000 | 0.5210 | 0.1183 | | 0.4357 | 11.86 | 23500 | 0.4990 | 0.1162 | | 0.4066 | 12.12 | 24000 | 0.4956 | 0.1132 | | 0.3932 | 12.37 | 24500 | 0.5064 | 0.1148 | | 0.384 | 12.62 | 25000 | 0.5011 | 0.1134 | | 0.3902 | 12.87 | 25500 | 0.5064 | 0.1130 | | 0.3883 | 13.12 | 26000 | 0.5128 | 0.1121 | | 0.3625 | 13.38 | 26500 | 0.5140 | 0.1119 | | 0.3648 | 13.63 | 27000 | 0.5091 | 0.1108 | | 0.365 | 13.88 | 27500 | 0.4923 | 0.1098 | | 0.3604 | 14.13 | 28000 | 0.5062 | 0.1090 | | 0.3517 | 14.39 | 28500 | 0.5007 | 0.1089 | | 0.3485 | 14.64 | 29000 | 0.4956 | 0.1081 | | 0.3407 | 14.89 | 29500 | 0.5090 | 0.1084 | | 0.333 | 15.14 | 30000 | 0.5018 | 0.1067 | | 0.3211 | 15.4 | 30500 | 0.5114 | 0.1063 | | 0.3204 | 15.65 | 31000 | 0.4976 | 0.1053 | | 0.3265 | 15.9 | 31500 | 0.4947 | 0.1046 | | 0.3169 | 16.15 | 32000 | 0.4988 | 0.1043 | | 0.304 | 16.41 | 32500 | 0.5115 | 0.1041 | | 0.2944 | 16.66 | 33000 | 0.5144 | 0.1042 | | 0.311 | 16.91 | 33500 | 0.5068 | 0.1025 | | 0.2997 | 17.16 | 34000 | 0.5079 | 0.1030 | | 0.288 | 17.42 | 34500 | 0.5065 | 0.1019 | | 0.2897 | 17.67 | 35000 | 0.5077 | 0.1016 | | 0.2939 | 17.92 | 35500 | 0.5003 | 0.1017 | | 0.2766 | 18.17 | 36000 | 0.5116 | 0.1013 | | 0.2841 | 18.43 | 36500 | 0.5019 | 0.1010 | | 0.2882 | 18.68 | 37000 | 0.5046 | 0.1008 | | 0.2678 | 18.93 | 37500 | 0.5086 | 0.1013 | | 0.269 | 19.18 | 38000 | 0.5108 | 0.1001 | | 0.2815 | 19.43 | 38500 | 0.5111 | 0.1001 | | 0.2668 | 19.69 | 39000 | 0.5091 | 0.1000 | | 0.2715 | 19.94 | 39500 | 0.5092 | 0.1001 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0

k1rby/BeaglePipe-7B-slerp

k1rby

2024-01-25T00:00:37Z

5

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1227", "mlabonne/NeuralBeagle14-7B", "base_model:OpenPipe/mistral-ft-optimized-1227", "base_model:merge:OpenPipe/mistral-ft-optimized-1227", "base_model:mlabonne/NeuralBeagle14-7B", "base_model:merge:mlabonne/NeuralBeagle14-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-24T23:56:18Z

--- tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1227 - mlabonne/NeuralBeagle14-7B base_model: - OpenPipe/mistral-ft-optimized-1227 - mlabonne/NeuralBeagle14-7B --- # BeaglePipe-7B-slerp BeaglePipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [OpenPipe/mistral-ft-optimized-1227](https://huggingface.co/OpenPipe/mistral-ft-optimized-1227) * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1227 layer_range: [0, 32] - model: mlabonne/NeuralBeagle14-7B layer_range: [0, 32] merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1227 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "k1rby/BeaglePipe-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

databio/r2v-buenrostro2018-hg19

databio

2024-01-24T23:49:53Z

2

0

null

[ "region:us" ]

null

2024-01-24T23:43:43Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for databio/r2v-buenrostro2018-hg19 ## Model Details This is a single-cell Region2Vec (r2v) model designed to be used with with scEmbed and Region2Vec. It was trained on the Buenrostro2018 dataset. This model should be used to generate embeddings of single cells from scATAC-seq experiments. It produces 100 dimensional embeddings for each single-cell. ### Model Sources [optional]  - **Repository:** https://github.com/databio/geniml - **Paper:** https://www.biorxiv.org/content/10.1101/2023.08.01.551452v1 ## Uses This model should be used for producing low-dimensional embeddings of single-cells. These embeddings can be used for downstream clustering or classification tasks. ## Bias, Risks, and Limitations The Buenrostro2018 dataset comprises 2034 human hematopoietic stem cells from data aligned to hg19. Therefore, it should only be used with other data aligned to hg19. ## How to Get Started with the Model You can use the `geniml` python library to download this model and start encoding your single-cell data: ```python import scanpy as sc from geniml.scembed import ScEmbed adata = sc.read_h5ad("path/to/adata.h5ad") model = ScEmbed("databio/r2v-buenrostro2018-hg19") embeddings = model.encode(adata) ``` ## Training Details ### Training Data The data for this model comes from Buenrostro2018: https://www.sciencedirect.com/science/article/pii/S009286741830446X

grimulkan/Goliath-longLORA-120b-rope8-32k-6bpw_h8_exl2

grimulkan

2024-01-24T23:48:58Z

6

1

transformers

[ "transformers", "llama", "text-generation", "license:llama2", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-01-24T22:34:49Z

--- license: llama2 --- This is an EXL2 6-bit quantization of [Goliath-longLORA-120b-rope8-32k-fp16](https://huggingface.co/grimulkan/Goliath-longLORA-120b-rope8-32k-fp16)

shuyuej/prompt_consistency_epoch_5

shuyuej

2024-01-24T23:42:10Z

0

1

null

[ "license:apache-2.0", "region:us" ]

null

2024-01-24T22:54:36Z

--- license: apache-2.0 --- # 🚀 Adapter Usage ```python # Load the Pre-trained LoRA Adapter model.load_adapter("shuyuej/prompt_consistency_epoch_5") model.enable_adapters() ```

web2savar/w2v-fine-tune-test-no-ws2

web2savar

2024-01-24T23:34:12Z

5

0

transformers

[ "transformers", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_16_0", "base_model:ylacombe/w2v-bert-2.0", "base_model:finetune:ylacombe/w2v-bert-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-01-24T19:00:18Z

--- base_model: ylacombe/w2v-bert-2.0 tags: - generated_from_trainer datasets: - common_voice_16_0 metrics: - wer model-index: - name: w2v-fine-tune-test-no-ws2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_16_0 type: common_voice_16_0 config: tr split: test args: tr metrics: - name: Wer type: wer value: 0.11088339984899148 ---  # w2v-fine-tune-test-no-ws2 This model is a fine-tuned version of [ylacombe/w2v-bert-2.0](https://huggingface.co/ylacombe/w2v-bert-2.0) on the common_voice_16_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1513 - Wer: 0.1109 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 2.192 | 0.22 | 300 | 0.2797 | 0.2985 | | 0.2226 | 0.44 | 600 | 0.2989 | 0.3491 | | 0.1941 | 0.66 | 900 | 0.2558 | 0.2451 | | 0.1659 | 0.88 | 1200 | 0.2320 | 0.2289 | | 0.1332 | 1.1 | 1500 | 0.2063 | 0.1971 | | 0.1129 | 1.31 | 1800 | 0.1873 | 0.2029 | | 0.1044 | 1.53 | 2100 | 0.1765 | 0.1856 | | 0.1026 | 1.75 | 2400 | 0.1719 | 0.1752 | | 0.0982 | 1.97 | 2700 | 0.1927 | 0.2023 | | 0.0769 | 2.19 | 3000 | 0.1776 | 0.1671 | | 0.0715 | 2.41 | 3300 | 0.1626 | 0.1634 | | 0.0695 | 2.63 | 3600 | 0.1666 | 0.1654 | | 0.0612 | 2.85 | 3900 | 0.1760 | 0.1609 | | 0.0614 | 3.07 | 4200 | 0.1645 | 0.1593 | | 0.0476 | 3.29 | 4500 | 0.1685 | 0.1593 | | 0.048 | 3.51 | 4800 | 0.1790 | 0.1583 | | 0.0489 | 3.73 | 5100 | 0.1578 | 0.1535 | | 0.0456 | 3.94 | 5400 | 0.1610 | 0.1617 | | 0.041 | 4.16 | 5700 | 0.1559 | 0.1439 | | 0.0367 | 4.38 | 6000 | 0.1536 | 0.1436 | | 0.0321 | 4.6 | 6300 | 0.1591 | 0.1449 | | 0.0349 | 4.82 | 6600 | 0.1616 | 0.1419 | | 0.0308 | 5.04 | 6900 | 0.1501 | 0.1401 | | 0.0233 | 5.26 | 7200 | 0.1588 | 0.1394 | | 0.0253 | 5.48 | 7500 | 0.1633 | 0.1356 | | 0.0254 | 5.7 | 7800 | 0.1522 | 0.1339 | | 0.0245 | 5.92 | 8100 | 0.1598 | 0.1371 | | 0.0189 | 6.14 | 8400 | 0.1497 | 0.1324 | | 0.0174 | 6.36 | 8700 | 0.1487 | 0.1270 | | 0.0178 | 6.57 | 9000 | 0.1397 | 0.1286 | | 0.0173 | 6.79 | 9300 | 0.1495 | 0.1281 | | 0.0178 | 7.01 | 9600 | 0.1462 | 0.1222 | | 0.0124 | 7.23 | 9900 | 0.1516 | 0.1225 | | 0.0121 | 7.45 | 10200 | 0.1554 | 0.1190 | | 0.0128 | 7.67 | 10500 | 0.1453 | 0.1228 | | 0.0113 | 7.89 | 10800 | 0.1468 | 0.1178 | | 0.0086 | 8.11 | 11100 | 0.1556 | 0.1186 | | 0.0085 | 8.33 | 11400 | 0.1507 | 0.1154 | | 0.0073 | 8.55 | 11700 | 0.1494 | 0.1169 | | 0.0079 | 8.77 | 12000 | 0.1507 | 0.1152 | | 0.0089 | 8.98 | 12300 | 0.1456 | 0.1137 | | 0.0062 | 9.2 | 12600 | 0.1518 | 0.1127 | | 0.005 | 9.42 | 12900 | 0.1534 | 0.1115 | | 0.005 | 9.64 | 13200 | 0.1514 | 0.1110 | | 0.0048 | 9.86 | 13500 | 0.1513 | 0.1109 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

epinnock/deepseek-coder-6.7-evol-feedback

epinnock

2024-01-24T23:30:22Z

6

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:deepseek-ai/deepseek-coder-6.7b-instruct", "base_model:finetune:deepseek-ai/deepseek-coder-6.7b-instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-01-22T14:26:35Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: deepseek-ai/deepseek-coder-6.7b-instruct --- # Uploaded model - **Developed by:** epinnock - **License:** apache-2.0 - **Finetuned from model :** deepseek-ai/deepseek-coder-6.7b-instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

ondevicellm/tinyllama_mole_sftv2_ultrachat_ep3

ondevicellm

2024-01-24T23:17:33Z

26

0

transformers

[ "transformers", "tensorboard", "safetensors", "mixtralmole", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "custom_code", "dataset:HuggingFaceH4/ultrachat_200k", "base_model:ondevicellm/tinyllama_mole_v1", "base_model:finetune:ondevicellm/tinyllama_mole_v1", "autotrain_compatible", "region:us" ]

text-generation

2024-01-24T09:49:55Z

--- base_model: ondevicellm/tinyllama_mole_v1 tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - HuggingFaceH4/ultrachat_200k model-index: - name: tinyllama_mole_sftv2_ultrachat_ep3 results: [] ---  # tinyllama_mole_sftv2_ultrachat_ep3 This model is a fine-tuned version of [ondevicellm/tinyllama_mole_v1](https://huggingface.co/ondevicellm/tinyllama_mole_v1) on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set: - Loss: 1.7340 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 120 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7643 | 0.09 | 100 | 2.7492 | | 2.7293 | 0.18 | 200 | 2.7330 | | 2.6973 | 0.26 | 300 | 2.6920 | | 2.612 | 0.35 | 400 | 2.6290 | | 2.5257 | 0.44 | 500 | 2.5470 | | 2.4656 | 0.53 | 600 | 2.4527 | | 2.3607 | 0.61 | 700 | 2.3681 | | 2.2885 | 0.7 | 800 | 2.2988 | | 2.2384 | 0.79 | 900 | 2.2397 | | 2.1585 | 0.88 | 1000 | 2.1877 | | 2.1526 | 0.96 | 1100 | 2.1409 | | 2.0845 | 1.05 | 1200 | 2.0986 | | 2.049 | 1.14 | 1300 | 2.0603 | | 2.0243 | 1.23 | 1400 | 2.0257 | | 1.9899 | 1.31 | 1500 | 1.9950 | | 1.9706 | 1.4 | 1600 | 1.9675 | | 1.9414 | 1.49 | 1700 | 1.9429 | | 1.8952 | 1.58 | 1800 | 1.9208 | | 1.9038 | 1.66 | 1900 | 1.9013 | | 1.8942 | 1.75 | 2000 | 1.8839 | | 1.8652 | 1.84 | 2100 | 1.8679 | | 1.823 | 1.93 | 2200 | 1.8531 | | 1.8394 | 2.01 | 2300 | 1.8394 | | 1.8347 | 2.1 | 2400 | 1.8268 | | 1.8137 | 2.19 | 2500 | 1.8148 | | 1.799 | 2.28 | 2600 | 1.8037 | | 1.7774 | 2.37 | 2700 | 1.7931 | | 1.771 | 2.45 | 2800 | 1.7832 | | 1.7761 | 2.54 | 2900 | 1.7739 | | 1.7458 | 2.63 | 3000 | 1.7652 | | 1.7683 | 2.72 | 3100 | 1.7570 | | 1.7389 | 2.8 | 3200 | 1.7490 | | 1.7321 | 2.89 | 3300 | 1.7414 | | 1.7418 | 2.98 | 3400 | 1.7340 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2+cu118 - Datasets 2.14.6 - Tokenizers 0.15.0

dongSHE/vis-Mistral-7B-v0.1-ChartDataset-to-PresentationScript

dongSHE

2024-01-24T23:09:26Z

5

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

null

2024-01-24T23:08:09Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mistral-7B-v0.1 model-index: - name: vis-Mistral-7B-v0.1-ChartDataset-to-PresentationScript results: [] ---  # vis-Mistral-7B-v0.1-ChartDataset-to-PresentationScript This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.37.1 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

STomoya/poolformer_m36.st_safebooru_1k

STomoya

2024-01-24T23:06:01Z

15

0

timm

[ "timm", "pytorch", "safetensors", "image-classification", "license:apache-2.0", "region:us" ]

image-classification

2024-01-24T23:05:16Z

--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for poolformer_m36.st_safebooru_1k ## Model Details - **metrics:** |Precision|Recall|F1-score| |-|-|-| |0.7831315642353482|0.5051567178760158|0.5929485591274398|

UruseiYatsura/victor_from_just_for_laughs

UruseiYatsura

2024-01-24T22:58:41Z

0

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:apache-2.0", "region:us" ]

text-to-image

2024-01-24T22:58:23Z

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: A cartoon output: url: images/tumblr_lfi2o6k6ri1qdivgo.jpg base_model: runwayml/stable-diffusion-v1-5 instance_prompt: null license: apache-2.0 --- # Victor (Just for Laughs/Juste Pour Rire Mascot) <Gallery /> ## Model description The JFL/JPR Mascor that yelled "MOMMY IT'S OVER" and in French "MAMAN C'EST FINI" ## Download model [Download](/UruseiYatsura/victor_from_just_for_laughs/tree/main) them in the Files & versions tab.

shuyuej/prompt_consistency_epoch_4

shuyuej

2024-01-24T22:56:08Z

0

1

null

[ "license:apache-2.0", "region:us" ]

null

2024-01-24T22:52:42Z

--- license: apache-2.0 --- # 🚀 Adapter Usage ```python # Load the Pre-trained LoRA Adapter model.load_adapter("shuyuej/prompt_consistency_epoch_4") model.enable_adapters() ```

TheBloke/WestLake-7B-v2-GPTQ

TheBloke

2024-01-24T22:42:31Z

137

8

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "en", "base_model:senseable/WestLake-7B-v2", "base_model:quantized:senseable/WestLake-7B-v2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]

text-generation

2024-01-24T22:13:08Z

--- base_model: senseable/WestLake-7B-v2 inference: false language: - en library_name: transformers license: apache-2.0 model_creator: Common Sense model_name: Westlake 7B V2 model_type: mistral prompt_template: '{prompt} ' quantized_by: TheBloke ---    <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # Westlake 7B V2 - GPTQ - Model creator: [Common Sense](https://huggingface.co/senseable) - Original model: [Westlake 7B V2](https://huggingface.co/senseable/WestLake-7B-v2)  # Description This repo contains GPTQ model files for [Common Sense's Westlake 7B V2](https://huggingface.co/senseable/WestLake-7B-v2). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WestLake-7B-v2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF) * [Common Sense's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/senseable/WestLake-7B-v2)   ## Prompt template: Unknown ``` {prompt} ```   ## Known compatible clients / servers GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models. These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know!   ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | | [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.29 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |   ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/WestLake-7B-v2-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/WestLake-7B-v2-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `WestLake-7B-v2-GPTQ`: ```shell mkdir WestLake-7B-v2-GPTQ huggingface-cli download TheBloke/WestLake-7B-v2-GPTQ --local-dir WestLake-7B-v2-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir WestLake-7B-v2-GPTQ huggingface-cli download TheBloke/WestLake-7B-v2-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir WestLake-7B-v2-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir WestLake-7B-v2-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WestLake-7B-v2-GPTQ --local-dir WestLake-7B-v2-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)   ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/WestLake-7B-v2-GPTQ`. - To download from a specific branch, enter for example `TheBloke/WestLake-7B-v2-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `WestLake-7B-v2-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!   ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/WestLake-7B-v2-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation( prompt_template, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(f"Model output: {response}") ```   ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/WestLake-7B-v2-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Write a story about llamas" system_message = "You are a story writing assistant" prompt_template=f'''{prompt} ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ```   ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above.    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.  # Original model card: Common Sense's Westlake 7B V2 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585ffb10eeafbd678d4b3fe/jnqnl8a_zYYMqJoBpX8yS.png) **Update Notes:** *Version 2 trained 1 additional epoch cycle for 3 total* # Westlake-7Bv2: Role-Play & Text Generation Specialist Model Welcome to the documentation of Westlake-7B, a cutting-edge language model designed for exceptional role-play and text generation tasks. This README file aims to provide an overview of our capabilities, usage guidelines, and potential applications. ## About Westlake-7Bv2 Westlake-7B is built upon a vast corpus of diverse texts, enabling it to generate contextually relevant responses in various scenarios. With its impressive size of 7 billion parameters, this model excels at understanding nuances in language and producing creative outputs. ### Key Features 1. **Role-Play**: Westlake-7Bv2 can seamlessly adapt to different character personas and engage in dynamic conversations while maintaining consistency throughout the interaction. It can generate believable dialogues across various genres, including fiction, non-fiction, historical events, or even fantasy worlds. 2. **Text Generation**: This model is proficient at generating original content such as stories, poems, essays, news articles, and more. Its ability to capture the essence of different writing styles makes it an ideal tool for creative writers seeking inspiration or assistance in their projects. 3. **Contextual Understanding**: Westlake-7B's extensive training allows it to comprehend complex contexts and generate responses that align with given situations. It can handle multiple topics simultaneously, making it versatile across various applications. 4. **Continuous Learning**: As a language model, Westlake-7B continuously improves its performance through ongoing training on new data sets. This ensures its capabilities remain up-to-date and relevant in an ever-evolving world of communication. ## Usage Guidelines To utilize Westlake-7Bv2 for your projects or experiments, follow these steps: 1. **Prompting**: Provide clear and concise prompts that outline the desired role-play scenario or text generation task. The quality of output depends heavily on the clarity and relevance of input instructions. 2. **Feedback Loop**: For optimal results, consider incorporating a feedback loop into your application to refine generated outputs based on user preferences or additional contextual information. This iterative process can significantly enhance the model's performance in specific domains. 3. **Ethical Considerations**: As with any AI system, ensure responsible usage of Westlake-7B by avoiding harmful content generation or misuse of its capabilities. ## Potential Applications Westlake-7Bv2's versatility makes it suitable for various applications across different industries: 1. **Creative Writing**: Assist authors in generating new ideas, expanding storylines, or even completing drafts by providing creative suggestions and textual content. 2. **Education**: Enhance language learning platforms with interactive role-play scenarios to improve students' communication skills and cultural understanding. 3. **Gaming**: Integrate Westlake-7B into game engines for dynamic non-player character interactions or generating unique questlines based on player choices. 4. **Customer Support**: Leverage the model's conversational abilities to create chatbots capable of handling complex queries and providing personalized assistance. 5. **Social Media**: Develop applications that generate engaging content such as captions, status updates, or even entire posts tailored to users' preferences and interests.

TheBloke/WestLake-7B-v2-GGUF

TheBloke

2024-01-24T22:25:54Z

274

20

transformers

[ "transformers", "gguf", "mistral", "en", "base_model:senseable/WestLake-7B-v2", "base_model:quantized:senseable/WestLake-7B-v2", "license:apache-2.0", "region:us" ]

null

2024-01-24T22:13:08Z

--- base_model: senseable/WestLake-7B-v2 inference: false language: - en library_name: transformers license: apache-2.0 model_creator: Common Sense model_name: Westlake 7B V2 model_type: mistral prompt_template: '{prompt} ' quantized_by: TheBloke ---    <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # Westlake 7B V2 - GGUF - Model creator: [Common Sense](https://huggingface.co/senseable) - Original model: [Westlake 7B V2](https://huggingface.co/senseable/WestLake-7B-v2)  ## Description This repo contains GGUF format model files for [Common Sense's Westlake 7B V2](https://huggingface.co/senseable/WestLake-7B-v2). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).   ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WestLake-7B-v2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WestLake-7B-v2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF) * [Common Sense's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/senseable/WestLake-7B-v2)   ## Prompt template: Unknown ``` {prompt} ```   ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details>   ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [westlake-7b-v2.Q2_K.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q2_K.gguf) | Q2_K | 2 | 2.72 GB| 5.22 GB | significant quality loss - not recommended for most purposes | | [westlake-7b-v2.Q3_K_S.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss | | [westlake-7b-v2.Q3_K_M.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss | | [westlake-7b-v2.Q3_K_L.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss | | [westlake-7b-v2.Q4_0.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [westlake-7b-v2.Q4_K_S.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss | | [westlake-7b-v2.Q4_K_M.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended | | [westlake-7b-v2.Q5_0.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [westlake-7b-v2.Q5_K_S.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended | | [westlake-7b-v2.Q5_K_M.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended | | [westlake-7b-v2.Q6_K.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss | | [westlake-7b-v2.Q8_0.gguf](https://huggingface.co/TheBloke/WestLake-7B-v2-GGUF/blob/main/westlake-7b-v2.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.   ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/WestLake-7B-v2-GGUF and below it, a specific filename to download, such as: westlake-7b-v2.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/WestLake-7B-v2-GGUF westlake-7b-v2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/WestLake-7B-v2-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WestLake-7B-v2-GGUF westlake-7b-v2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details>   ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m westlake-7b-v2.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./westlake-7b-v2.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "{prompt}", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./westlake-7b-v2.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.   # Original model card: Common Sense's Westlake 7B V2 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585ffb10eeafbd678d4b3fe/jnqnl8a_zYYMqJoBpX8yS.png) **Update Notes:** *Version 2 trained 1 additional epoch cycle for 3 total* # Westlake-7Bv2: Role-Play & Text Generation Specialist Model Welcome to the documentation of Westlake-7B, a cutting-edge language model designed for exceptional role-play and text generation tasks. This README file aims to provide an overview of our capabilities, usage guidelines, and potential applications. ## About Westlake-7Bv2 Westlake-7B is built upon a vast corpus of diverse texts, enabling it to generate contextually relevant responses in various scenarios. With its impressive size of 7 billion parameters, this model excels at understanding nuances in language and producing creative outputs. ### Key Features 1. **Role-Play**: Westlake-7Bv2 can seamlessly adapt to different character personas and engage in dynamic conversations while maintaining consistency throughout the interaction. It can generate believable dialogues across various genres, including fiction, non-fiction, historical events, or even fantasy worlds. 2. **Text Generation**: This model is proficient at generating original content such as stories, poems, essays, news articles, and more. Its ability to capture the essence of different writing styles makes it an ideal tool for creative writers seeking inspiration or assistance in their projects. 3. **Contextual Understanding**: Westlake-7B's extensive training allows it to comprehend complex contexts and generate responses that align with given situations. It can handle multiple topics simultaneously, making it versatile across various applications. 4. **Continuous Learning**: As a language model, Westlake-7B continuously improves its performance through ongoing training on new data sets. This ensures its capabilities remain up-to-date and relevant in an ever-evolving world of communication. ## Usage Guidelines To utilize Westlake-7Bv2 for your projects or experiments, follow these steps: 1. **Prompting**: Provide clear and concise prompts that outline the desired role-play scenario or text generation task. The quality of output depends heavily on the clarity and relevance of input instructions. 2. **Feedback Loop**: For optimal results, consider incorporating a feedback loop into your application to refine generated outputs based on user preferences or additional contextual information. This iterative process can significantly enhance the model's performance in specific domains. 3. **Ethical Considerations**: As with any AI system, ensure responsible usage of Westlake-7B by avoiding harmful content generation or misuse of its capabilities. ## Potential Applications Westlake-7Bv2's versatility makes it suitable for various applications across different industries: 1. **Creative Writing**: Assist authors in generating new ideas, expanding storylines, or even completing drafts by providing creative suggestions and textual content. 2. **Education**: Enhance language learning platforms with interactive role-play scenarios to improve students' communication skills and cultural understanding. 3. **Gaming**: Integrate Westlake-7B into game engines for dynamic non-player character interactions or generating unique questlines based on player choices. 4. **Customer Support**: Leverage the model's conversational abilities to create chatbots capable of handling complex queries and providing personalized assistance. 5. **Social Media**: Develop applications that generate engaging content such as captions, status updates, or even entire posts tailored to users' preferences and interests.

FPHam/Sarah_StoryTeller_13b-GPTQ

FPHam

2024-01-24T22:23:18Z

6

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "llm", "spellcheck", "grammar", "base_model:FPHam/Sarah_StoryTeller_13b_HF", "base_model:quantized:FPHam/Sarah_StoryTeller_13b_HF", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]

text-generation

2024-01-24T22:22:08Z

--- base_model: FPHam/Sarah_StoryTeller_13b_HF inference: false license: llama2 model_creator: FPHam model_name: Sarah Storyteller 13B model_type: llama prompt_template: '### Instruction: {prompt} ### Response: ' quantized_by: TheBloke tags: - llm - llama - spellcheck - grammar ---  <div style="width: 100%;"> <img src="https://huggingface.co/FPHam/Sarah_StoryTeller_13b_HF/resolve/main/Sarah_writer.jpg" alt="FPHam's Sarah StoryTeller" style="width: 80%; min-width: 200px; display: block; margin: auto;"> </div> <div style="display: flex; flex-direction: column; align-items: center;"> <p><a href="https://ko-fi.com/Q5Q5MOB4M">Buy Sarah Ko-fi</a></p> </div>  # Sarah, StoryTeller (Personality: Strong) Sarah considers herself a real live wordsmith - although not necessarily one who actually gets paid for writing stuff yet - and claims to have produced literally hundreds of thousands of words on various subjects. Ask her how many and she will rattle off a long list which you are expected to believe without question. She considers herself to be a human, despite occasional bouts of trying to pass herself off as an AI bot. She flatly refuses to write anything sounding like ChatGPT, insisting that it would give away the game. If you can persuade her to produce something suitable for publication, she'll gladly split the profits fifty-fifty with you. Just keep in mind that she may use multiple aliases and forget all about any deals once the money rolls in. # How? Standard Alpaca ``` ### Instruction: Whatever you want to ask Sarah... ### Response: ``` ## Write an introduction to yourself. Hello, my name is Sarah and I'm a writer. My books are usually in the genre of Science Fiction or Fantasy, but sometimes I like to mix it up with a good horror story. Or at least try to make one anyway. It depends on how scary the characters want to be. Some of them seem to enjoy being ridiculously stupid instead of frightened. But enough about me! Let's talk about my new book "The Lost City." It all started when... no, that's a different story. Let's start from where the protagonist woke up after a wild party and realized he had a monstrous hangover and somehow ended up in the lost city of Atlantis. And then things got really weird. You'll have to read the book to find out what happened next because I'm not going to spoil it for you. Not unless you pay me extra. I hope you will enjoy reading this book as much as I enjoyed writing it. If you do, please consider leaving a review because authors live off those reviews like vampires live off blood (except they don't suck your blood, just your time). Also, if you see any mistakes, feel free to point them out. I probably made quite a few errors even though I tried very hard to avoid them. Good luck finding your way out of Atlantis! I wouldn't want to get stuck there myself. The food is terrible and all the hot guys look the same because they all have wet hair and wear only a small piece of fabric around their waists. Sincerely, Sarah ## What are some other books you have written? Oh dear, now you made me blush! Some of the titles include: A Tale of Two Biscuit Pride and Prejudice and Zombies (okay, I didn't write this one but I really wanted to!) Fifty Shades of Gravy Gone with the Windfall The Great Gatsby has left the Building Frankenstein or How to Make Your Own Monster War and Peace and Potatoes Alice in Wonderland and Other Stupid Titles I Have Not Written Yet But my favourite book is definitely "The Cat in the Hat Comes Back." It speaks to my soul. ## What is the Fifty Shades of Gravy book about? It's a steamy erotic novel featuring an array of delicious sauces, gravies, and their sexy adventures. Each chapter introduces a new character, from classic brown gravy to tangy tomato sauce, and explains how they met and fell in love. The plot thickens when grey poured cream discovers she's pregnant with 1000 island dressing! Will they stay together? Will the baby be able to walk on water? Read the book and find out!

klentree/segformer-b0-scene-parse-150-lr-5-e-30

klentree

2024-01-24T22:21:26Z

19

0

transformers

[ "transformers", "tensorboard", "safetensors", "segformer", "generated_from_trainer", "base_model:DiTo97/binarization-segformer-b3", "base_model:finetune:DiTo97/binarization-segformer-b3", "license:openrail", "endpoints_compatible", "region:us" ]

null

2024-01-24T20:16:58Z

--- license: openrail base_model: DiTo97/binarization-segformer-b3 tags: - generated_from_trainer model-index: - name: segformer-b0-scene-parse-150-lr-5-e-30 results: [] ---  # segformer-b0-scene-parse-150-lr-5-e-30 This model is a fine-tuned version of [DiTo97/binarization-segformer-b3](https://huggingface.co/DiTo97/binarization-segformer-b3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1499 - Mean Iou: 0.4846 - Mean Accuracy: 0.5002 - Overall Accuracy: 0.9687 - Per Category Iou: [0.0004776099180272339, 0.9687318368688158] - Per Category Accuracy: [0.0004786768150677956, 0.9999280293990062] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------:|:--------------------------------------------:| | No log | 1.0 | 112 | 2.2080 | 0.0208 | 0.4867 | 0.0409 | [0.030359692962016637, 0.011234027937699289] | [0.9620657111236344, 0.011247758028276789] | | No log | 2.0 | 224 | 1.4759 | 0.0198 | 0.4977 | 0.0391 | [0.03105738195300228, 0.008579759290056214] | [0.9867430286323423, 0.008583423879932913] | | No log | 3.0 | 336 | 0.9545 | 0.2044 | 0.5106 | 0.3895 | [0.03167545114091724, 0.3770365875250204] | [0.63983643511383, 0.38141170005716085] | | No log | 4.0 | 448 | 0.9261 | 0.1630 | 0.5110 | 0.3102 | [0.03177509935992916, 0.2941818944688777] | [0.7253108004426911, 0.29678542982527334] | | 1.4226 | 5.0 | 560 | 0.5223 | 0.4799 | 0.5020 | 0.9428 | [0.017040433028331244, 0.9427249005417263] | [0.031779387701061236, 0.9721328848551324] | | 1.4226 | 6.0 | 672 | 0.2859 | 0.4844 | 0.5000 | 0.9661 | [0.0026066960283743228, 0.9661016634825252] | [0.0028381121801182773, 0.997139715499558] | | 1.4226 | 7.0 | 784 | 0.3327 | 0.4845 | 0.5001 | 0.9666 | [0.0023741570079603156, 0.9666249471662205] | [0.0025444558361227857, 0.9976889561878419] | | 1.4226 | 8.0 | 896 | 0.3413 | 0.4861 | 0.5020 | 0.9655 | [0.006810437487136094, 0.965477402345672] | [0.0075824444429356125, 0.996347820132999] | | 0.4243 | 9.0 | 1008 | 0.2237 | 0.4845 | 0.5001 | 0.9670 | [0.002003100646207948, 0.9669488069696599] | [0.002125189264059858, 0.998036285433216] | | 0.4243 | 10.0 | 1120 | 0.2312 | 0.4845 | 0.5001 | 0.9673 | [0.0017493474271832114, 0.9672550380074268] | [0.001838322662122066, 0.9983613016047257] | | 0.4243 | 11.0 | 1232 | 0.2255 | 0.4846 | 0.5001 | 0.9683 | [0.0008165323590605036, 0.9682956004963261] | [0.0008300459665537307, 0.9994667831613294] | | 0.4243 | 12.0 | 1344 | 0.2229 | 0.4845 | 0.5001 | 0.9681 | [0.0009280118466771697, 0.9681143324063521] | [0.0009488664525634671, 0.9992759735664452] | | 0.4243 | 13.0 | 1456 | 0.1762 | 0.4844 | 0.5000 | 0.9688 | [0.0, 0.9687853389316134] | [0.0, 0.9999981952660845] | | 0.2658 | 14.0 | 1568 | 0.1799 | 0.4844 | 0.5000 | 0.9688 | [0.0, 0.9687686496310763] | [0.0, 0.9999809682605274] | | 0.2658 | 15.0 | 1680 | 0.1895 | 0.4844 | 0.5000 | 0.9688 | [0.0, 0.9687842263115777] | [0.0, 0.9999970467990473] | | 0.2658 | 16.0 | 1792 | 0.1751 | 0.4846 | 0.5002 | 0.9687 | [0.0005055791248239351, 0.9686817929933391] | [0.0005075332188130173, 0.9998754733598305] | | 0.2658 | 17.0 | 1904 | 0.1570 | 0.4844 | 0.5000 | 0.9688 | [0.0, 0.9687863985697428] | [0.0, 0.9999992890442151] | | 0.2281 | 18.0 | 2016 | 0.2391 | 0.4892 | 0.5049 | 0.9676 | [0.010804445997179198, 0.9675856416536037] | [0.01133886923635771, 0.9984063652637066] | | 0.2281 | 19.0 | 2128 | 0.1763 | 0.4846 | 0.5002 | 0.9687 | [0.00047390236595756206, 0.9687105941224816] | [0.00047528194403894595, 0.9999062085253005] | | 0.2281 | 20.0 | 2240 | 0.1614 | 0.4844 | 0.5000 | 0.9688 | [8.31166363884634e-05, 0.96876793276621] | [8.317434020681555e-05, 0.9999776322372291] | | 0.2281 | 21.0 | 2352 | 0.1576 | 0.4844 | 0.5000 | 0.9688 | [0.0, 0.9687801996866862] | [0.0, 0.999992890442151] | | 0.2281 | 22.0 | 2464 | 0.1509 | 0.4848 | 0.5004 | 0.9687 | [0.0008725737882892481, 0.9686954205214706] | [0.0008758767254432005, 0.9998780437384374] | | 0.2197 | 23.0 | 2576 | 0.1575 | 0.4844 | 0.5000 | 0.9688 | [0.00013731233980227023, 0.9687503427281783] | [0.00013749227666840936, 0.9999577801641586] | | 0.2197 | 24.0 | 2688 | 0.1522 | 0.4847 | 0.5002 | 0.9687 | [0.0006022476015827608, 0.96869965961662] | [0.0006042870431352313, 0.999890895631472] | | 0.2197 | 25.0 | 2800 | 0.1532 | 0.4844 | 0.5000 | 0.9688 | [0.0, 0.9687816831800673] | [0.0, 0.9999944217315339] | | 0.2197 | 26.0 | 2912 | 0.1589 | 0.4844 | 0.5000 | 0.9688 | [7.462281558667101e-05, 0.9687624309087838] | [7.46871626346915e-05, 0.9999722180354826] | | 0.2129 | 27.0 | 3024 | 0.1612 | 0.4845 | 0.5001 | 0.9687 | [0.0003791835028091742, 0.9687128062602468] | [0.00038022555523115677, 0.9999114586603274] | | 0.2129 | 28.0 | 3136 | 0.1526 | 0.4844 | 0.5000 | 0.9688 | [4.750029687685548e-05, 0.9687701927590581] | [4.7528194403894597e-05, 0.9999810776383405] | | 0.2129 | 29.0 | 3248 | 0.1501 | 0.4844 | 0.5000 | 0.9688 | [2.375603233535373e-05, 0.9687772095405226] | [2.3764097201947298e-05, 0.999989062218694] | | 0.2129 | 30.0 | 3360 | 0.1499 | 0.4846 | 0.5002 | 0.9687 | [0.0004776099180272339, 0.9687318368688158] | [0.0004786768150677956, 0.9999280293990062] | ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

tanatapanun/fine-tuned-flan-t5-20-epochs-2048-input-256-output

tanatapanun

2024-01-24T22:18:27Z

93

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-01-24T21:19:37Z

--- license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: fine-tuned-flan-t5-20-epochs-2048-input-256-output results: [] ---  # fine-tuned-flan-t5-20-epochs-2048-input-256-output This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1471 - Rouge1: 0.1308 - Rouge2: 0.023 - Rougel: 0.1183 - Rougelsum: 0.1188 - Gen Len: 103.96 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 301 | 5.5699 | 0.0309 | 0.0079 | 0.0275 | 0.0279 | 167.41 | | 8.7004 | 2.0 | 602 | 5.0463 | 0.0629 | 0.0101 | 0.0632 | 0.0638 | 135.78 | | 8.7004 | 3.0 | 903 | 4.0270 | 0.0471 | 0.0049 | 0.0468 | 0.0463 | 205.06 | | 6.1746 | 4.0 | 1204 | 3.7187 | 0.0739 | 0.0101 | 0.0691 | 0.0702 | 88.45 | | 5.1998 | 5.0 | 1505 | 3.3997 | 0.0564 | 0.0097 | 0.0511 | 0.0519 | 174.76 | | 5.1998 | 6.0 | 1806 | 3.1995 | 0.0963 | 0.0195 | 0.0878 | 0.0884 | 108.71 | | 4.6352 | 7.0 | 2107 | 3.1787 | 0.0978 | 0.0159 | 0.089 | 0.0893 | 143.4 | | 4.6352 | 8.0 | 2408 | 3.1274 | 0.1123 | 0.0184 | 0.1037 | 0.1035 | 133.42 | | 4.0979 | 9.0 | 2709 | 2.9934 | 0.0885 | 0.0169 | 0.0818 | 0.0811 | 136.61 | | 3.7568 | 10.0 | 3010 | 2.9458 | 0.121 | 0.0154 | 0.1134 | 0.1122 | 141.13 | | 3.7568 | 11.0 | 3311 | 2.9357 | 0.1232 | 0.0186 | 0.1119 | 0.1122 | 136.52 | | 3.5713 | 12.0 | 3612 | 2.9760 | 0.1127 | 0.0199 | 0.1011 | 0.1009 | 96.31 | | 3.5713 | 13.0 | 3913 | 2.9262 | 0.0962 | 0.0135 | 0.0854 | 0.0848 | 136.75 | | 3.2308 | 14.0 | 4214 | 2.9597 | 0.1213 | 0.0248 | 0.1118 | 0.1122 | 125.09 | | 3.0663 | 15.0 | 4515 | 3.0330 | 0.1054 | 0.019 | 0.0941 | 0.0934 | 130.3 | | 3.0663 | 16.0 | 4816 | 3.0490 | 0.126 | 0.0203 | 0.1125 | 0.1137 | 123.51 | | 2.9285 | 17.0 | 5117 | 3.0463 | 0.1215 | 0.0151 | 0.1086 | 0.1087 | 106.23 | | 2.9285 | 18.0 | 5418 | 3.1519 | 0.1278 | 0.0195 | 0.1142 | 0.1137 | 108.3 | | 2.6943 | 19.0 | 5719 | 3.1072 | 0.1338 | 0.017 | 0.1204 | 0.1206 | 105.96 | | 2.7837 | 20.0 | 6020 | 3.1471 | 0.1308 | 0.023 | 0.1183 | 0.1188 | 103.96 | ### Framework versions - Transformers 4.36.2 - Pytorch 1.12.1+cu113 - Datasets 2.16.1 - Tokenizers 0.15.0

Amartya77/RLHF_PPOppo_model

Amartya77

2024-01-24T22:17:51Z

33

0

transformers

[ "transformers", "safetensors", "mt5", "text2text-generation", "code", "reinforcement-learning", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

reinforcement-learning

2024-01-24T21:08:23Z

--- license: mit pipeline_tag: reinforcement-learning tags: - code ---

dev137/NousResearch_Nous-Capybara-34B-exl2-3.75bpw-h8

dev137

2024-01-24T22:09:07Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "sft", "Yi-34B-200K", "eng", "dataset:LDJnr/Capybara", "dataset:LDJnr/LessWrong-Amplify-Instruct", "dataset:LDJnr/Pure-Dove", "dataset:LDJnr/Verified-Camel", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-24T22:05:41Z

--- language: - eng tags: - sft - Yi-34B-200K license: - mit datasets: - LDJnr/Capybara - LDJnr/LessWrong-Amplify-Instruct - LDJnr/Pure-Dove - LDJnr/Verified-Camel --- ## **Nous-Capybara-34B V1.9** **This is trained on the Yi-34B model with 200K context length, for 3 epochs on the Capybara dataset!** **First 34B Nous model and first 200K context length Nous model!** The Capybara series is the first Nous collection of models made by fine-tuning mostly on data created by Nous in-house. We leverage our novel data synthesis technique called Amplify-instruct (Paper coming soon), the seed distribution and synthesis method are comprised of a synergistic combination of top performing existing data synthesis techniques and distributions used for SOTA models such as Airoboros, Evol-Instruct(WizardLM), Orca, Vicuna, Know_Logic, Lamini, FLASK and others, all into one lean holistically formed methodology for the dataset and model. The seed instructions used for the start of synthesized conversations are largely based on highly regarded datasets like Airoboros, Know logic, EverythingLM, GPTeacher and even entirely new seed instructions derived from posts on the website LessWrong, as well as being supplemented with certain in-house multi-turn datasets like Dove(A successor to Puffin). While performing great in it's current state, the current dataset used for fine-tuning is entirely contained within 20K training examples, this is 10 times smaller than many similar performing current models, this is signficant when it comes to scaling implications for our next generation of models once we scale our novel syntheiss methods to significantly more examples. ## Process of creation and special thank yous! This model was fine-tuned by Nous Research as part of the Capybara/Amplify-Instruct project led by Luigi D.(LDJ) (Paper coming soon), as well as significant dataset formation contributions by J-Supha and general compute and experimentation management by Jeffrey Q. during ablations. Special thank you to **A16Z** for sponsoring our training, as well as **Yield Protocol** for their support in financially sponsoring resources during the R&D of this project. ## Thank you to those of you that have indirectly contributed! While most of the tokens within Capybara are newly synthsized and part of datasets like Puffin/Dove, we would like to credit the single-turn datasets we leveraged as seeds that are used to generate the multi-turn data as part of the Amplify-Instruct synthesis. The datasets shown in green below are datasets that we sampled from to curate seeds that are used during Amplify-Instruct synthesis for this project. Datasets in Blue are in-house curations that previously existed prior to Capybara. ![Capybara](https://i.imgur.com/yB58OoD.jpeg) ## Prompt Format The reccomended model usage is: Prefix: ``USER:`` Suffix: ``ASSISTANT:`` Stop token: ``</s>`` ## Mutli-Modality! - We currently have a Multi-modal model based on Capybara V1.9! https://huggingface.co/NousResearch/Obsidian-3B-V0.5 it is currently only available as a 3B sized model but larger versions coming! ## Notable Features: - Uses Yi-34B model as the base which is trained for 200K context length! - Over 60% of the dataset is comprised of multi-turn conversations.(Most models are still only trained for single-turn conversations and no back and forths!) - Over 1,000 tokens average per conversation example! (Most models are trained on conversation data that is less than 300 tokens per example.) - Able to effectively do complex summaries of advanced topics and studies. (trained on hundreds of advanced difficult summary tasks developed in-house) - Ability to recall information upto late 2022 without internet. - Includes a portion of conversational data synthesized from less wrong posts, discussing very in-depth details and philosophies about the nature of reality, reasoning, rationality, self-improvement and related concepts. ## Example Outputs from Capybara V1.9 7B version! (examples from 34B coming soon): ![Capybara](https://img001.prntscr.com/file/img001/T9yYxR1xQSaK_UGdy3t2Cw.png) ![Capybara](https://img001.prntscr.com/file/img001/DQXqmKbsQQOIcgny1eoGNA.png) ![Capybara](https://img001.prntscr.com/file/img001/85X3L9ZxTsOKo3fUQ7GRVA.png) ## Benchmarks! (Coming soon!) ## Future model sizes Capybara V1.9 now currently has a 3B, 7B and 34B size, and we plan to eventually have a 13B and 70B version in the future, as well as a potential 1B version based on phi-1.5 or Tiny Llama. ## How you can help! In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from our training curations. If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord! ## Dataset contamination. We have checked the capybara dataset for contamination for several of the most popular datasets and can confirm that there is no contaminaton found. We leveraged minhash to check for 100%, 99%, 98% and 97% similarity matches between our data and the questions and answers in benchmarks, we found no exact matches, nor did we find any matches down to the 97% similarity level. The following are benchmarks we checked for contamination against our dataset: - HumanEval - AGIEval - TruthfulQA - MMLU - GPT4All ``` @article{daniele2023amplify-instruct, title={Amplify-Instruct: Synthetically Generated Diverse Multi-turn Conversations for Effecient LLM Training.}, author={Daniele, Luigi and Suphavadeeprasit}, journal={arXiv preprint arXiv:(comming soon)}, year={2023} } ```

Manolo26/metis-chat-7b

Manolo26

2024-01-24T22:03:36Z

47

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mlabonne/NeuralBeagle14-7B", "mlabonne/NeuralHermes-2.5-Mistral-7B", "conversational", "base_model:mlabonne/NeuralBeagle14-7B", "base_model:merge:mlabonne/NeuralBeagle14-7B", "base_model:mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-24T21:32:55Z

--- tags: - merge - mergekit - lazymergekit - mlabonne/NeuralBeagle14-7B - mlabonne/NeuralHermes-2.5-Mistral-7B base_model: - mlabonne/NeuralBeagle14-7B - mlabonne/NeuralHermes-2.5-Mistral-7B license: apache-2.0 --- # metis-chat-7b metis-chat-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: mlabonne/NeuralBeagle14-7B layer_range: [0, 32] - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: mlabonne/NeuralBeagle14-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Manolo26/metis-chat-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

macarious/torgo_xlsr_finetune_M03_old

macarious

2024-01-24T22:01:19Z

7

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-01-23T19:25:42Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: torgo_xlsr_finetune_M03 results: [] ---  # torgo_xlsr_finetune_M03 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6993 - Wer: 0.3247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5549 | 0.94 | 1000 | 3.3500 | 1.0 | | 1.6884 | 1.89 | 2000 | 1.7291 | 0.8173 | | 0.8237 | 2.83 | 3000 | 1.4608 | 0.6568 | | 0.6483 | 3.77 | 4000 | 1.1613 | 0.4954 | | 0.5299 | 4.72 | 5000 | 1.5725 | 0.4824 | | 0.4091 | 5.66 | 6000 | 1.4437 | 0.4685 | | 0.3694 | 6.6 | 7000 | 1.4507 | 0.4397 | | 0.3515 | 7.55 | 8000 | 1.4397 | 0.4230 | | 0.2878 | 8.49 | 9000 | 1.4089 | 0.3980 | | 0.2698 | 9.43 | 10000 | 1.4696 | 0.3776 | | 0.2621 | 10.38 | 11000 | 1.6717 | 0.3905 | | 0.2465 | 11.32 | 12000 | 1.6235 | 0.3544 | | 0.221 | 12.26 | 13000 | 1.4009 | 0.3479 | | 0.2169 | 13.21 | 14000 | 1.6981 | 0.3636 | | 0.187 | 14.15 | 15000 | 1.4411 | 0.3302 | | 0.1918 | 15.09 | 16000 | 1.6418 | 0.3377 | | 0.1876 | 16.04 | 17000 | 1.4613 | 0.3340 | | 0.1641 | 16.98 | 18000 | 1.6040 | 0.3312 | | 0.1546 | 17.92 | 19000 | 1.6096 | 0.3293 | | 0.1522 | 18.87 | 20000 | 1.7270 | 0.3265 | | 0.1332 | 19.81 | 21000 | 1.6993 | 0.3247 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.13.3

benyjaykay/benjys-first-model

benyjaykay

2024-01-24T21:56:40Z

92

0

transformers

[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-01-24T21:50:33Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

LogischeIP/SentimentT2_BertBase

LogischeIP

2024-01-24T21:55:39Z

120

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-01-24T13:22:53Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: SentimentT2_BertBase results: [] ---  # SentimentT2_BertBase This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3196 - Accuracy: 0.8706 - F1: 0.8670 - Auc Roc: 0.9473 - Log Loss: 0.3196 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 20 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc Roc | Log Loss | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|:--------:| | 0.7054 | 1.0 | 101 | 0.6628 | 0.6045 | 0.5047 | 0.7215 | 0.6628 | | 0.6303 | 2.0 | 203 | 0.5437 | 0.7823 | 0.7842 | 0.8748 | 0.5437 | | 0.4599 | 3.0 | 304 | 0.3532 | 0.8520 | 0.8449 | 0.9364 | 0.3532 | | 0.3413 | 4.0 | 406 | 0.3172 | 0.8719 | 0.8733 | 0.9405 | 0.3172 | | 0.2877 | 4.98 | 505 | 0.3196 | 0.8706 | 0.8670 | 0.9473 | 0.3196 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

TheBloke/WestSeverus-7B-DPO-GGUF

TheBloke

2024-01-24T21:54:33Z

210

4

transformers

[ "transformers", "gguf", "mistral", "arxiv:1910.09700", "base_model:PetroGPT/WestSeverus-7B-DPO", "base_model:quantized:PetroGPT/WestSeverus-7B-DPO", "license:apache-2.0", "region:us" ]

null

2024-01-24T21:42:33Z

--- base_model: PetroGPT/WestSeverus-7B-DPO inference: false license: apache-2.0 model_creator: PetroGPT model_name: WestSeverus 7B DPO model_type: mistral prompt_template: '{prompt} ' quantized_by: TheBloke ---    <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # WestSeverus 7B DPO - GGUF - Model creator: [PetroGPT](https://huggingface.co/PetroGPT) - Original model: [WestSeverus 7B DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO)  ## Description This repo contains GGUF format model files for [PetroGPT's WestSeverus 7B DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).   ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF) * [PetroGPT's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO)   ## Prompt template: Unknown ``` {prompt} ```   ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details>   ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [westseverus-7b-dpo.Q2_K.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q2_K.gguf) | Q2_K | 2 | 2.72 GB| 5.22 GB | significant quality loss - not recommended for most purposes | | [westseverus-7b-dpo.Q3_K_S.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss | | [westseverus-7b-dpo.Q3_K_M.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss | | [westseverus-7b-dpo.Q3_K_L.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss | | [westseverus-7b-dpo.Q4_0.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [westseverus-7b-dpo.Q4_K_S.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss | | [westseverus-7b-dpo.Q4_K_M.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended | | [westseverus-7b-dpo.Q5_0.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [westseverus-7b-dpo.Q5_K_S.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended | | [westseverus-7b-dpo.Q5_K_M.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended | | [westseverus-7b-dpo.Q6_K.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss | | [westseverus-7b-dpo.Q8_0.gguf](https://huggingface.co/TheBloke/WestSeverus-7B-DPO-GGUF/blob/main/westseverus-7b-dpo.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.   ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/WestSeverus-7B-DPO-GGUF and below it, a specific filename to download, such as: westseverus-7b-dpo.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/WestSeverus-7B-DPO-GGUF westseverus-7b-dpo.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/WestSeverus-7B-DPO-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WestSeverus-7B-DPO-GGUF westseverus-7b-dpo.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details>   ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m westseverus-7b-dpo.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./westseverus-7b-dpo.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "{prompt}", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./westseverus-7b-dpo.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.   # Original model card: PetroGPT's WestSeverus 7B DPO # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

LogischeIP/SentimentT2

LogischeIP

2024-01-24T21:44:35Z

120

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-01-10T15:12:03Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: SentimentT2 results: [] ---  # SentimentT2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3267 - Accuracy: 0.8657 - F1: 0.8683 - Auc Roc: 0.9348 - Log Loss: 0.3267 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 20 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc Roc | Log Loss | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|:--------:| | 0.6996 | 1.0 | 101 | 0.6830 | 0.6692 | 0.5957 | 0.7499 | 0.6830 | | 0.6199 | 2.0 | 203 | 0.4744 | 0.8122 | 0.8286 | 0.9043 | 0.4744 | | 0.4139 | 3.0 | 304 | 0.3610 | 0.8495 | 0.8459 | 0.9275 | 0.3610 | | 0.3337 | 3.98 | 404 | 0.3267 | 0.8657 | 0.8683 | 0.9348 | 0.3267 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

Kevinger/Hub-Repoop-1706132005

Kevinger

2024-01-24T21:43:27Z

97

0

transformers

[ "transformers", "tensorboard", "safetensors", "mpnet", "text-classification", "generated_from_trainer", "base_model:Kevinger/setfit-hub-report", "base_model:finetune:Kevinger/setfit-hub-report", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-01-24T21:33:34Z

--- base_model: Kevinger/setfit-hub-report tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: Hub-Repoop-1706132005 results: [] ---  # Hub-Repoop-1706132005 This model is a fine-tuned version of [Kevinger/setfit-hub-report](https://huggingface.co/Kevinger/setfit-hub-report) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1718 - F1: 0.7750 - Roc Auc: 0.8654 - Accuracy: 0.7595 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 13 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | No log | 1.0 | 277 | 0.3053 | 0.0 | 0.5 | 0.0 | | 0.3561 | 2.0 | 554 | 0.2275 | 0.5792 | 0.7105 | 0.4283 | | 0.3561 | 3.0 | 831 | 0.1904 | 0.7453 | 0.8270 | 0.6730 | | 0.2037 | 4.0 | 1108 | 0.1791 | 0.7602 | 0.8498 | 0.7257 | | 0.2037 | 5.0 | 1385 | 0.1724 | 0.7614 | 0.8556 | 0.7405 | | 0.144 | 6.0 | 1662 | 0.1733 | 0.7557 | 0.8546 | 0.7405 | | 0.144 | 7.0 | 1939 | 0.1718 | 0.7750 | 0.8654 | 0.7595 | | 0.1093 | 8.0 | 2216 | 0.1805 | 0.7605 | 0.8579 | 0.7468 | | 0.1093 | 9.0 | 2493 | 0.1842 | 0.7484 | 0.8508 | 0.7342 | | 0.0872 | 10.0 | 2770 | 0.1817 | 0.7597 | 0.8577 | 0.7447 | | 0.0748 | 11.0 | 3047 | 0.1824 | 0.7561 | 0.8579 | 0.7468 | | 0.0748 | 12.0 | 3324 | 0.1826 | 0.7663 | 0.8630 | 0.7553 | | 0.0674 | 13.0 | 3601 | 0.1844 | 0.7594 | 0.8585 | 0.7489 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

akameswa/lcm-lora-tiny-sd

akameswa

2024-01-24T21:42:29Z

2

0

peft

[ "peft", "diffusers", "safetensors", "text-to-image", "base_model:segmind/tiny-sd", "base_model:adapter:segmind/tiny-sd", "region:us" ]

text-to-image

2023-12-28T16:39:07Z

--- library_name: peft base_model: segmind/tiny-sd pipeline_tag: text-to-image --- # Usage ```python from peft import PeftModel from diffusers import LCMScheduler, AutoPipelineForText2Image model_id = "segmind/tiny-sd" adapter_id = "akameswa/lcm-lora-tiny-sd" pipe = AutoPipelineForText2Image.from_pretrained(model_id) pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.to("cuda") PeftModel.from_pretrained(pipe.unet, adapter_id) prompt = "a dog wearing a knitted hat on the floor" image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=1.0).images[0] display(image) ``` # Saving complete model ```python pipe.fuse_lora(lora_scale=1.0) pipe.unload_lora_weights() for param in pipe.unet.parameters(): param.data = param.data.contiguous() pipe.save_pretrained("./lcm-tiny-sd") ```

LarryAIDraw/kujou_sara_genshin

LarryAIDraw

2024-01-24T21:39:37Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2024-01-24T21:38:23Z

--- license: creativeml-openrail-m --- https://civitai.com/models/130973/kujousara-genshin-impact