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kanishka/smolm-autoreg-bpe-counterfactual-babylm-indef-naan-non-num-3e-4
|
kanishka
| 2023-12-15T22:42:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T00:10:16Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-indef-naan-non-num-3e-4
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. -->
# smolm-autoreg-bpe-counterfactual-babylm-indef-naan-non-num-3e-4
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4141
- Accuracy: 0.4091
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7339 | 1.0 | 18595 | 3.8762 | 0.3473 |
| 3.4301 | 2.0 | 37190 | 3.6233 | 0.3762 |
| 3.2914 | 3.0 | 55785 | 3.5018 | 0.3888 |
| 3.2009 | 4.0 | 74380 | 3.4628 | 0.3954 |
| 3.1448 | 5.0 | 92975 | 3.4054 | 0.3995 |
| 3.0957 | 6.0 | 111570 | 3.3889 | 0.4027 |
| 3.0603 | 7.0 | 130165 | 3.3681 | 0.4045 |
| 3.0274 | 8.0 | 148760 | 3.3829 | 0.4062 |
| 2.9951 | 9.0 | 167355 | 3.3627 | 0.4069 |
| 2.9703 | 10.0 | 185950 | 3.3604 | 0.4075 |
| 2.9472 | 11.0 | 204545 | 3.3551 | 0.4082 |
| 2.9245 | 12.0 | 223140 | 3.3739 | 0.4082 |
| 2.8989 | 13.0 | 241735 | 3.3730 | 0.4084 |
| 2.8822 | 14.0 | 260330 | 3.3770 | 0.4089 |
| 2.8638 | 15.0 | 278925 | 3.3840 | 0.4090 |
| 2.8416 | 16.0 | 297520 | 3.3865 | 0.4093 |
| 2.818 | 17.0 | 316115 | 3.3889 | 0.4093 |
| 2.8061 | 18.0 | 334710 | 3.4020 | 0.4091 |
| 2.79 | 19.0 | 353305 | 3.4104 | 0.4090 |
| 2.7654 | 20.0 | 371900 | 3.4141 | 0.4091 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.14.1
|
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ThomasTheTankEngine1/SMLVoices
|
ThomasTheTankEngine1
| 2023-12-15T00:37:12Z | 0 | 1 | null |
[
"license:cc",
"region:us"
] | null | 2023-12-15T00:14:11Z |
---
license: cc
---
|
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hoangvo269/NLP
|
hoangvo269
| 2023-12-15T00:23:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T00:17:36Z |
Entry not found
|
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] |
hyusername/jpasr
|
hyusername
| 2023-12-16T00:07:40Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T00:17:55Z |
Entry not found
|
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] |
Frixi/Hades_66
|
Frixi
| 2023-12-15T00:18:36Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T00:18:25Z |
---
license: openrail
---
|
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neopolita/rl_course_vizdoom_health_gathering_supreme
|
neopolita
| 2023-12-15T00:21:32Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T00:21:21Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 13.32 +/- 5.67
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r neopolita/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
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bartowski/Mistral-7B-Instruct-v0.2-DARE-exl2
|
bartowski
| 2023-12-15T01:50:33Z | 0 | 0 | null |
[
"text-generation",
"en",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-15T00:23:12Z |
---
license: apache-2.0
language:
- en
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of Mistral-7B-Instruct-v0.2-DARE
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.10">turboderp's ExLlamaV2 v0.0.10</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using VMWareOpenInstruct.parquet as calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/janhq/Mistral-7B-Instruct-v0.2-DARE
<a href="https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.2-DARE-exl2/tree/4_0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.2-DARE-exl2/tree/6_0">6.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.2-DARE-exl2/tree/8_0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.2-DARE-exl2
```
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 `Mistral-7B-Instruct-v0.2-DARE-exl2`:
```shell
mkdir Mistral-7B-Instruct-v0.2-DARE-exl2
huggingface-cli download bartowski/Mistral-7B-Instruct-v0.2-DARE-exl2 --local-dir Mistral-7B-Instruct-v0.2-DARE-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Mistral-7B-Instruct-v0.2-DARE-exl2
huggingface-cli download bartowski/Mistral-7B-Instruct-v0.2-DARE-exl2 --revision 4_0 --local-dir Mistral-7B-Instruct-v0.2-DARE-exl2 --local-dir-use-symlinks False
```
|
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] |
jpablomch/llama7b-nas-heu-unmerged-75-adapter
|
jpablomch
| 2023-12-15T00:38:21Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T00:24:08Z |
---
license: mit
---
|
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DangFutures/AI_REG_BGE
|
DangFutures
| 2023-12-15T00:25:53Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-12-15T00:24:49Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 266 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 50,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 53,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
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] |
jpablomch/llama7b-nas-min-unmerged-80-adapter
|
jpablomch
| 2023-12-15T00:42:45Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T00:26:11Z |
---
license: mit
---
|
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stvhuang/rcr-run-g6knz4zq-v2-61961-master-0_20231212T101450-ep10
|
stvhuang
| 2023-12-15T00:30:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-15T00:27:04Z |
Entry not found
|
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abj0404/Test
|
abj0404
| 2023-12-15T00:28:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T00:28:49Z |
Entry not found
|
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] |
pistolX/model_newPrompt
|
pistolX
| 2023-12-15T07:11:31Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-15T00:35:49Z |
---
library_name: peft
tags:
- generated_from_trainer
base_model: NousResearch/Llama-2-7b-chat-hf
model-index:
- name: model_newPrompt
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. -->
# model_newPrompt
This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) 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: 1.41e-05
- train_batch_size: 4
- eval_batch_size: 8
- 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
- num_epochs: 3
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Mikaell13/teste
|
Mikaell13
| 2023-12-15T00:40:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T00:40:04Z |
Entry not found
|
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] |
ppbrown/aom3-unmerge
|
ppbrown
| 2023-12-15T19:42:17Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T00:40:27Z |
---
license: openrail
---
This is an attempt to "unmerge" the sd1.5 base from the AOM3(a1b) model, and thus hopefully improve the quality.
This is a new experiment, shared to improve research on this.
For best results, you should probably use steps=30+
|
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Nopphakorn/my_awesome_model
|
Nopphakorn
| 2023-12-15T00:40:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T00:40:28Z |
Entry not found
|
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TheBloke/una-cybertron-7B-v3-OMA-GGUF
|
TheBloke
| 2023-12-15T00:46:40Z | 0 | 3 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"juanako",
"UNA",
"cybertron",
"xaberius",
"dataset:fblgit/tree-of-knowledge",
"base_model:fblgit/una-cybertron-7b-v3-OMA",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2023-12-15T00:42:21Z |
---
base_model: fblgit/una-cybertron-7b-v3-OMA
datasets:
- fblgit/tree-of-knowledge
inference: false
library_name: transformers
license: apache-2.0
model_creator: FBL
model_name: Una Cybertron 7B V3 OMA
model_type: mistral
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
tags:
- juanako
- UNA
- cybertron
- xaberius
---
<!-- 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 -->
# Una Cybertron 7B V3 OMA - GGUF
- Model creator: [FBL](https://huggingface.co/fblgit)
- Original model: [Una Cybertron 7B V3 OMA](https://huggingface.co/fblgit/una-cybertron-7b-v3-OMA)
<!-- description start -->
## Description
This repo contains GGUF format model files for [FBL's Una Cybertron 7B V3 OMA](https://huggingface.co/fblgit/una-cybertron-7b-v3-OMA).
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/una-cybertron-7B-v3-OMA-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF)
* [FBL's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/fblgit/una-cybertron-7b-v3-OMA)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [una-cybertron-7b-v3-oma.Q2_K.gguf](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF/blob/main/una-cybertron-7b-v3-oma.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [una-cybertron-7b-v3-oma.Q3_K_S.gguf](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF/blob/main/una-cybertron-7b-v3-oma.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [una-cybertron-7b-v3-oma.Q3_K_M.gguf](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF/blob/main/una-cybertron-7b-v3-oma.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [una-cybertron-7b-v3-oma.Q3_K_L.gguf](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF/blob/main/una-cybertron-7b-v3-oma.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [una-cybertron-7b-v3-oma.Q4_0.gguf](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF/blob/main/una-cybertron-7b-v3-oma.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [una-cybertron-7b-v3-oma.Q4_K_S.gguf](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF/blob/main/una-cybertron-7b-v3-oma.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [una-cybertron-7b-v3-oma.Q4_K_M.gguf](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF/blob/main/una-cybertron-7b-v3-oma.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [una-cybertron-7b-v3-oma.Q5_0.gguf](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF/blob/main/una-cybertron-7b-v3-oma.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [una-cybertron-7b-v3-oma.Q5_K_S.gguf](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF/blob/main/una-cybertron-7b-v3-oma.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [una-cybertron-7b-v3-oma.Q5_K_M.gguf](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF/blob/main/una-cybertron-7b-v3-oma.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [una-cybertron-7b-v3-oma.Q6_K.gguf](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF/blob/main/una-cybertron-7b-v3-oma.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [una-cybertron-7b-v3-oma.Q8_0.gguf](https://huggingface.co/TheBloke/una-cybertron-7B-v3-OMA-GGUF/blob/main/una-cybertron-7b-v3-oma.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/una-cybertron-7B-v3-OMA-GGUF and below it, a specific filename to download, such as: una-cybertron-7b-v3-oma.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/una-cybertron-7B-v3-OMA-GGUF una-cybertron-7b-v3-oma.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/una-cybertron-7B-v3-OMA-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/una-cybertron-7B-v3-OMA-GGUF una-cybertron-7b-v3-oma.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 una-cybertron-7b-v3-oma.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
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 โ 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="./una-cybertron-7b-v3-oma.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(
"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # 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="./una-cybertron-7b-v3-oma.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, ้ฟๆ, 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 Bjรคreholt, 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: FBL's Una Cybertron 7B V3 OMA
# Model Card for una-cybertron-7b-v3 (UNA: Uniform Neural Alignment)
**OMA (One Man Army) proudly presents a new 7B Champion: `cybertron-7b-v3` with our famous UNA algorythm.**
The model excels in mathematics, logic, reasoning, overall very smart. He can make a deep reasoning over the context and prompt, it gives the impression of not missing details around.
This seems to be possible:
* UNA models can be SFT again
* UNA models are easy to be used as Merge Base, place Cybertron in the fan-in and fan-out of the layering
* UNA models now includes a digital watermark
## Model Details
Adiestrated with UNA: Uniform Neural Alignment technique (paper going out soon).
* What is **NOT** UNA? Its not a merged layers model. Is not SLERP or SLURP or similar.
* What **is** UNA? A formula & A technique to *TAME* models
### Model Description
- **Developed by:** [juanako.ai](https://juanako.ai)
- **Author:** [Xavier M.]([email protected])
- **Model type:** MistralAI 7B
- **Funded by Cybertron's H100's** with few hours training.
### Prompt
The model is very good, works well on almost any prompt but ChatML format and Alpaca System gets the best
```
<|im_start|>system
- You are a helpful assistant chatbot trained by MosaicML.
- You answer questions.
- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>
<|im_start|>user
Explain QKV<|im_end|>
<|im_start|>assistant
```
```
### Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat!
### Human: Explain QKV
### Assistant:
```
```
[Round <|round|>]
้ฎ๏ผExplain QKV
็ญ๏ผ
```
```
[Round <|round|>]
Question๏ผExplain QKV
Answer๏ผ
```
```
Question๏ผExplain QKV
Answer๏ผ
```
Using Exllamav2_HF set alpha=2.5 for 16K Context
### Framework versions
- Transformers 4.35.0-UNA
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
### Citations
If you find Cybertron, Juanako or any of our models useful, specially if you use it for your big brand.. or you clone/merge my modelsm, cite please:
```
@misc{unacybertron7b,
title={Cybertron: Uniform Neural Alignment},
author={Xavier Murias},
year={2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/fblgit/una-cybertron-7b-v3-OMA}},
}
```
<!-- original-model-card end -->
|
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ADISH007/Aws_donut_10k_incremental_1_Epoch_23
|
ADISH007
| 2023-12-15T00:44:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-15T00:44:15Z |
Entry not found
|
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ndoman3807/VideoBatched
|
ndoman3807
| 2023-12-15T00:55:09Z | 0 | 1 | null |
[
"license:wtfpl",
"region:us"
] | null | 2023-12-15T00:54:54Z |
---
license: wtfpl
---
|
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] |
Dylettante/distilbert-base-uncased-finetuned-imdb
|
Dylettante
| 2023-12-15T01:12:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-15T00:55:50Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4502
## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7028 | 1.0 | 157 | 2.4868 |
| 2.5735 | 2.0 | 314 | 2.4259 |
| 2.5327 | 3.0 | 471 | 2.4502 |
### Framework versions
- Transformers 4.35.2
- Pytorch 1.13.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
leeloli/jennie-all-round-by-leelo
|
leeloli
| 2023-12-15T00:56:49Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T00:55:53Z |
---
license: openrail
---
|
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kamwoh/dreamcreature-sd1.5-dog
|
kamwoh
| 2023-12-15T00:57:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T00:57:14Z |
Entry not found
|
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kamwoh/dreamcreature-sd1.5-cub200
|
kamwoh
| 2023-12-15T00:57:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T00:57:42Z |
Entry not found
|
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TexX/Mikey
|
TexX
| 2023-12-15T01:12:00Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T00:58:16Z |
---
license: openrail
---
|
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] |
Englios/ppo-PyramidsRND
|
Englios
| 2023-12-15T01:01:54Z | 0 | 0 | null |
[
"region:us"
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] |
ewof/koishi-8x7b-qlora-gguf
|
ewof
| 2023-12-16T01:40:11Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mixtral",
"dataset:ewof/koishi-instruct-metharme",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2023-12-15T01:03:25Z |
---
datasets:
- ewof/koishi-instruct-metharme
---
## GGUF
little endian
## Training
[axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
on a 4x nvidia a100 gpu cluster.
the a100 GPU cluster has been graciously provided by [lloorree](https://huggingface.co/lloorree).
trained on koishi commit 6e675d1 for one epoch
## Base Model
rank 16 qlora tune of mistralai/Mixtral-8x7B-v0.1 (all modules, merged)
## Prompting
The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history.
|
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] |
ludis/tsukasa-8x7b-qlora
|
ludis
| 2023-12-15T17:52:34Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mixtral",
"text-generation",
"dataset:PygmalionAI/PIPPA",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T01:04:04Z |
---
datasets:
- PygmalionAI/PIPPA
---
## Training
[axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
on a 4x nvidia a100 gpu cluster.
the a100 GPU cluster has been graciously provided by [lloorree](https://huggingface.co/lloorree).
rank 16 qlora (all modules) tune
base model alpindale/goliath-120b tuned on koishi commit 6e675d1 for one epoch
then tuned on pippa 6412b0c for one epoch (metharme completion)
then tuned on limarp Version 2023-10-19 for 2 epochs in metharme completion format
## Prompting
The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history.
|
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] |
ludis/tsukasa-8x7b-qlora-gptq
|
ludis
| 2023-12-15T17:52:53Z | 0 | 0 |
transformers
|
[
"transformers",
"mixtral",
"text-generation",
"dataset:PygmalionAI/PIPPA",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T01:04:08Z |
---
datasets:
- PygmalionAI/PIPPA
---
## GPTQ
2048 sequence length
VMware/open-instruct dataset
## Training
[axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
on a 4x nvidia a100 gpu cluster.
the a100 GPU cluster has been graciously provided by [lloorree](https://huggingface.co/lloorree).
rank 16 qlora (all modules) tune
base model alpindale/goliath-120b tuned on koishi commit 6e675d1 for one epoch
then tuned on pippa 6412b0c for one epoch (metharme completion)
then tuned on limarp Version 2023-10-19 for 2 epochs in metharme completion format with limit_data_length set to 32768 in dataprepare-templates.py
## Prompting
The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history.
|
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] |
ludis/tsukasa-8x7b-qlora-gguf
|
ludis
| 2023-12-15T18:52:12Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mixtral",
"dataset:PygmalionAI/PIPPA",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2023-12-15T01:04:13Z |
---
datasets:
- PygmalionAI/PIPPA
---
## GGUF
little endian
## Training
[axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
on a 4x nvidia a100 gpu cluster.
the a100 GPU cluster has been graciously provided by [lloorree](https://huggingface.co/lloorree).
rank 16 qlora (all modules) tune
base model alpindale/goliath-120b tuned on koishi commit 6e675d1 for one epoch
then tuned on pippa 6412b0c for one epoch (metharme completion)
then tuned on limarp Version 2023-10-19 for 2 epochs in metharme completion format with limit_data_length set to 32768 in dataprepare-templates.py
## Prompting
The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history.
|
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nguyentatdat/Machine-Translation
|
nguyentatdat
| 2023-12-15T01:46:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:04:32Z |
Entry not found
|
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Lishengbo/b-model
|
Lishengbo
| 2023-12-15T01:05:48Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T01:05:48Z |
---
license: mit
---
|
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TheBloke/Pandora-v1-13B-GGUF
|
TheBloke
| 2023-12-15T01:19:19Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"en",
"base_model:janhq/Pandora-v1-13B",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2023-12-15T01:12:10Z |
---
base_model: janhq/Pandora-v1-13B
inference: false
language:
- en
license: apache-2.0
model_creator: Jan
model_name: Pandora V1 13B
model_type: mistral
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
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 -->
# Pandora V1 13B - GGUF
- Model creator: [Jan](https://huggingface.co/janhq)
- Original model: [Pandora V1 13B](https://huggingface.co/janhq/Pandora-v1-13B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Jan's Pandora V1 13B](https://huggingface.co/janhq/Pandora-v1-13B).
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/Pandora-v1-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Pandora-v1-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF)
* [Jan's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/janhq/Pandora-v1-13B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [pandora-v1-13b.Q2_K.gguf](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF/blob/main/pandora-v1-13b.Q2_K.gguf) | Q2_K | 2 | 5.28 GB| 7.78 GB | smallest, significant quality loss - not recommended for most purposes |
| [pandora-v1-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF/blob/main/pandora-v1-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.42 GB| 7.92 GB | very small, high quality loss |
| [pandora-v1-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF/blob/main/pandora-v1-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.02 GB| 8.52 GB | very small, high quality loss |
| [pandora-v1-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF/blob/main/pandora-v1-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.57 GB| 9.07 GB | small, substantial quality loss |
| [pandora-v1-13b.Q4_0.gguf](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF/blob/main/pandora-v1-13b.Q4_0.gguf) | Q4_0 | 4 | 7.06 GB| 9.56 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [pandora-v1-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF/blob/main/pandora-v1-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.09 GB| 9.59 GB | small, greater quality loss |
| [pandora-v1-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF/blob/main/pandora-v1-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.51 GB| 10.01 GB | medium, balanced quality - recommended |
| [pandora-v1-13b.Q5_0.gguf](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF/blob/main/pandora-v1-13b.Q5_0.gguf) | Q5_0 | 5 | 8.60 GB| 11.10 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [pandora-v1-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF/blob/main/pandora-v1-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.60 GB| 11.10 GB | large, low quality loss - recommended |
| [pandora-v1-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF/blob/main/pandora-v1-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 8.83 GB| 11.33 GB | large, very low quality loss - recommended |
| [pandora-v1-13b.Q6_K.gguf](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF/blob/main/pandora-v1-13b.Q6_K.gguf) | Q6_K | 6 | 10.24 GB| 12.74 GB | very large, extremely low quality loss |
| [pandora-v1-13b.Q8_0.gguf](https://huggingface.co/TheBloke/Pandora-v1-13B-GGUF/blob/main/pandora-v1-13b.Q8_0.gguf) | Q8_0 | 8 | 13.26 GB| 15.76 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/Pandora-v1-13B-GGUF and below it, a specific filename to download, such as: pandora-v1-13b.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/Pandora-v1-13B-GGUF pandora-v1-13b.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/Pandora-v1-13B-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/Pandora-v1-13B-GGUF pandora-v1-13b.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 pandora-v1-13b.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
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 โ 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="./pandora-v1-13b.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(
"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # 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="./pandora-v1-13b.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, ้ฟๆ, 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 Bjรคreholt, 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: Jan's Pandora V1 13B
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="https://jan.ai/">Jan</a
>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This model uses the `passthrough` merge method from the best 7B models on the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard):
1. [viethq188/LeoScorpius-7B-Chat-DPO](https://huggingface.co/viethq188/LeoScorpius-7B-Chat-DPO)
2. [GreenNode/GreenNodeLM-7B-v1olet](https://huggingface.co/GreenNode/GreenNodeLM-7B-v1olet)
The yaml config file for this model is here:
```yaml
slices:
- sources:
- model: "viethq188/LeoScorpius-7B-Chat-DPO"
layer_range: [0, 24]
- sources:
- model: "GreenNode/GreenNodeLM-7B-v1olet"
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
# Prompt template
- **ChatML**
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
# Run this model
You can run this model using [Jan](https://jan.ai/) on Mac, Windows, or Linux.
**Jan is an open source, ChatGPT alternative that is:**
๐ป **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
๐๏ธ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
๐ **OpenAI Compatible**: Local server on port `
1337` with OpenAI compatible endpoints
๐ **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)
- Please use the [Pandora-v1-13B-GGUF](https://huggingface.co/janhq/Pandora-v1-10.7B-GGUF) when using on Jan.
# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Merger
This is a test project for merging models.
# Open LLM Leaderboard Evaluation Results
Detailed results can be found here.
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | ?|
| ARC (25-shot) | ? |
| HellaSwag (10-shot) | ? |
| MMLU (5-shot) | ?|
| TruthfulQA (0-shot) | ? |
| Winogrande (5-shot) | ? |
| GSM8K (5-shot) | ? |
# Acknowlegement
- [mergekit](https://github.com/cg123/mergekit)
- [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md)
- [SLERP](https://github.com/Digitous/LLM-SLERP-Merge)
- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
<!-- original-model-card end -->
|
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] |
jaypratap/vit-base-patch16-224-cxr-classification
|
jaypratap
| 2023-12-15T01:17:18Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T01:13:43Z |
Entry not found
|
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cbeltran/carpeta_final
|
cbeltran
| 2023-12-15T01:14:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:14:15Z |
Entry not found
|
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] |
rayzox57/S3RL_RVC
|
rayzox57
| 2023-12-16T22:24:24Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T01:18:28Z |
---
license: openrail
---
|
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] |
KhoiC/esm2_t12_35M_UR50D-pretrained-evaluation-new-data-3-lr
|
KhoiC
| 2023-12-15T02:15:45Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"esm",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T01:19:11Z |
Entry not found
|
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macarious/europarl_bilingual_kenlm_1-gram
|
macarious
| 2023-12-15T01:53:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:20:14Z |
Entry not found
|
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gde23/gde
|
gde23
| 2023-12-15T01:26:10Z | 0 | 0 | null |
[
"license:cdla-permissive-1.0",
"region:us"
] | null | 2023-12-15T01:26:10Z |
---
license: cdla-permissive-1.0
---
|
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] |
zgce/SUS-Chat-34B-unalign-lora
|
zgce
| 2023-12-15T02:20:53Z | 0 | 0 | null |
[
"safetensors",
"license:mit",
"region:us"
] | null | 2023-12-15T01:26:15Z |
---
license: mit
---
Limited resources, only the unalign part of the spicy dataset was used, and Google translated unalign-zh into Chinese ran twice.
|
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mlc-ai/mlc-chat-Mistral-7B-Instruct-v0.2-q3f16_1
|
mlc-ai
| 2023-12-15T01:31:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:28:07Z |
Entry not found
|
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KhoiC/esm2_t12_35M_UR50D-pretrained-evaluation-new-data-3-lr-2
|
KhoiC
| 2023-12-15T02:24:16Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"esm",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T01:28:10Z |
Entry not found
|
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] |
jaypratap/vit-pretraining-2023_12_11-cxr-classification
|
jaypratap
| 2023-12-15T18:10:28Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T01:32:30Z |
---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-pretraining-2023_12_11-cxr-classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6032786885245902
---
<!-- 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-pretraining-2023_12_11-cxr-classification
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6726
- Accuracy: 0.6033
## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4135 | 1.0 | 228 | 1.2518 | 0.6426 |
| 1.2038 | 2.0 | 457 | 1.2201 | 0.6415 |
| 1.3041 | 3.0 | 685 | 1.1914 | 0.6251 |
| 1.2537 | 4.0 | 914 | 1.1506 | 0.6219 |
| 1.2731 | 5.0 | 1142 | 1.1575 | 0.6175 |
| 1.226 | 6.0 | 1371 | 1.1689 | 0.6262 |
| 1.1369 | 7.0 | 1599 | 1.1385 | 0.6317 |
| 1.0285 | 8.0 | 1828 | 1.1477 | 0.6328 |
| 0.8827 | 9.0 | 2056 | 1.1554 | 0.6262 |
| 0.8787 | 10.0 | 2285 | 1.1614 | 0.6317 |
| 0.7872 | 11.0 | 2513 | 1.1644 | 0.6339 |
| 0.5644 | 12.0 | 2742 | 1.1884 | 0.6361 |
| 0.6407 | 13.0 | 2970 | 1.2171 | 0.5967 |
| 0.4576 | 14.0 | 3199 | 1.2272 | 0.6284 |
| 0.4908 | 15.0 | 3427 | 1.2462 | 0.6186 |
| 0.3767 | 16.0 | 3656 | 1.2672 | 0.6142 |
| 0.2817 | 17.0 | 3884 | 1.3112 | 0.6142 |
| 0.2429 | 18.0 | 4113 | 1.3251 | 0.6109 |
| 0.1766 | 19.0 | 4341 | 1.3853 | 0.6208 |
| 0.2002 | 20.0 | 4570 | 1.4216 | 0.5858 |
| 0.1292 | 21.0 | 4798 | 1.4720 | 0.5934 |
| 0.1414 | 22.0 | 5027 | 1.4895 | 0.6077 |
| 0.1132 | 23.0 | 5255 | 1.5467 | 0.6033 |
| 0.0864 | 24.0 | 5484 | 1.5533 | 0.6120 |
| 0.0748 | 25.0 | 5712 | 1.5990 | 0.6055 |
| 0.0708 | 26.0 | 5941 | 1.6205 | 0.6098 |
| 0.0502 | 27.0 | 6169 | 1.6452 | 0.5978 |
| 0.0505 | 28.0 | 6398 | 1.6634 | 0.5967 |
| 0.0748 | 29.0 | 6626 | 1.6689 | 0.6011 |
| 0.0434 | 29.93 | 6840 | 1.6726 | 0.6033 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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mlc-ai/mlc-chat-Mistral-7B-Instruct-v0.2-q4f16_1
|
mlc-ai
| 2023-12-15T01:39:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:33:44Z |
Entry not found
|
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Piere123291/Ronald
|
Piere123291
| 2023-12-15T01:34:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:34:59Z |
Entry not found
|
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] |
RockAlterS/whisper-small-jp
|
RockAlterS
| 2023-12-16T04:06:16Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T01:35:12Z |
Entry not found
|
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] |
neenax/finetuneWizard
|
neenax
| 2023-12-15T01:36:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:WizardLM/WizardMath-13B-V1.0",
"license:llama2",
"region:us"
] | null | 2023-12-15T01:36:01Z |
---
license: llama2
library_name: peft
tags:
- generated_from_trainer
base_model: WizardLM/WizardMath-13B-V1.0
model-index:
- name: finetuneWizard
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. -->
# finetuneWizard
This model is a fine-tuned version of [WizardLM/WizardMath-13B-V1.0](https://huggingface.co/WizardLM/WizardMath-13B-V1.0) 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Mary8/distilgpt2-finetuned-wikitext2
|
Mary8
| 2023-12-15T01:37:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:37:41Z |
Entry not found
|
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willhath/new-e5-base-v2-tur_Latn-199936-0.0001-0.4new
|
willhath
| 2023-12-15T01:41:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:41:57Z |
Entry not found
|
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sobiazahid/my_awesome_food_model
|
sobiazahid
| 2023-12-15T01:41:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:41:58Z |
Entry not found
|
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] |
aaa12963337/msi-swinv2-tiny-pretrain
|
aaa12963337
| 2023-12-16T07:49:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-tiny-patch4-window8-256",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T01:42:25Z |
---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: msi-swinv2-tiny-pretrain
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9233983286908078
---
<!-- 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. -->
# msi-swinv2-tiny-pretrain
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3754
- Accuracy: 0.9234
## 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: 16
- eval_batch_size: 16
- 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_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0776 | 1.0 | 1562 | 0.2637 | 0.9251 |
| 0.0241 | 2.0 | 3125 | 0.3806 | 0.9213 |
| 0.0367 | 3.0 | 4686 | 0.3754 | 0.9234 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
titosilva/pegasus-samsum
|
titosilva
| 2023-12-15T02:21:47Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"base_model:sshleifer/distill-pegasus-cnn-16-4",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-15T01:43:02Z |
---
base_model: sshleifer/distill-pegasus-cnn-16-4
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
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. -->
# pegasus-samsum
This model is a fine-tuned version of [sshleifer/distill-pegasus-cnn-16-4](https://huggingface.co/sshleifer/distill-pegasus-cnn-16-4) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8492
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.165 | 0.54 | 500 | 1.8492 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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thrunlab/Mistral-7B-v0_1_rte_sp0_ar0
|
thrunlab
| 2023-12-15T01:46:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:46:07Z |
Entry not found
|
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sobiazahid/food_classifier
|
sobiazahid
| 2023-12-15T01:49:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:49:51Z |
Entry not found
|
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louis006/convnext-base-eurosat
|
louis006
| 2023-12-15T01:54:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:54:59Z |
Entry not found
|
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arnavs11/DSPy_LongFormQA_Cache
|
arnavs11
| 2023-12-15T01:55:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:55:12Z |
Entry not found
|
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] |
Pedrohenriquereikiano/Ora
|
Pedrohenriquereikiano
| 2023-12-15T01:57:50Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T01:57:50Z |
---
license: openrail
---
|
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davisudo2000/DancingAutotuneCockroach
|
davisudo2000
| 2023-12-15T01:58:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T01:57:52Z |
Entry not found
|
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yihang7/zephyr-7b-sft-full
|
yihang7
| 2023-12-15T07:42:48Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T01:59:47Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: zephyr-7b-sft-full
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. -->
# zephyr-7b-sft-full
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.
It achieves the following results on the evaluation set:
- Loss: 0.9587
## 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: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9559 | 0.67 | 272 | 0.9585 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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] |
keonoutsold/Dojacat
|
keonoutsold
| 2023-12-15T02:01:39Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T02:01:39Z |
---
license: openrail
---
|
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] |
kanishka/smolm-autoreg-bpe-counterfactual-babylm-indef_articles_with_pl_nouns-removal-3e-3
|
kanishka
| 2023-12-15T05:15:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T02:04:27Z |
Entry not found
|
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AI-Joe-git/13B-HyperMantis.Q6_K.gguf
|
AI-Joe-git
| 2023-12-15T02:32:03Z | 0 | 0 | null |
[
"gguf",
"region:us"
] | null | 2023-12-15T02:12:53Z |
Entry not found
|
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gitgato/rikis_LoRA
|
gitgato
| 2023-12-15T02:56:08Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-12-15T02:13:10Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
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{}
---
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|
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] |
AI-Wheelz/FloraCash_ColeV2
|
AI-Wheelz
| 2023-12-15T02:19:56Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T02:18:48Z |
---
license: openrail
---
|
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lilililili888/test
|
lilililili888
| 2023-12-15T02:20:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T02:20:03Z |
Entry not found
|
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AI-Wheelz/FloraCash_ShpresaV2
|
AI-Wheelz
| 2023-12-15T02:21:54Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T02:20:04Z |
---
license: openrail
---
|
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] |
MalekeHan/my_awesome_food_model
|
MalekeHan
| 2023-12-15T03:40:08Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T02:21:19Z |
Entry not found
|
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] |
em3c2/rvc-reb-tom
|
em3c2
| 2023-12-15T02:26:17Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T02:25:10Z |
---
license: mit
---
|
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mooncatnotfull/test_dir
|
mooncatnotfull
| 2023-12-15T02:27:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T02:27:31Z |
Entry not found
|
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] |
richpro911/22091994
|
richpro911
| 2023-12-15T02:30:51Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T02:30:51Z |
---
license: apache-2.0
---
|
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owanr/SChem5Labels-roberta-base-inter-sorted-human_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T01:40:37Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T02:37:08Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-inter-sorted-human_annots_alpha0.8_whole_1e-05
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. -->
# SChem5Labels-roberta-base-inter-sorted-human_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9747
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4672 | 1.0 | 3164 | 2.1031 |
| 1.9689 | 2.0 | 6328 | 1.9747 |
| 1.8437 | 3.0 | 9492 | 1.9747 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Jeevesh8/multibert_seed0_20k_qqp_ft_99
|
Jeevesh8
| 2023-12-15T11:24:52Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T02:41:35Z |
Entry not found
|
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] |
Jeevesh8/multibert_seed0_20k_qqp_ft_97
|
Jeevesh8
| 2023-12-15T11:04:40Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T02:41:35Z |
Entry not found
|
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] |
Jeevesh8/multibert_seed0_20k_qqp_ft_98
|
Jeevesh8
| 2023-12-15T11:03:04Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T02:41:35Z |
Entry not found
|
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] |
Jeevesh8/bs__qqp_ft_0
|
Jeevesh8
| 2023-12-15T07:52:46Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T02:42:09Z |
Entry not found
|
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] |
Jeevesh8/bs__qqp_ft_1
|
Jeevesh8
| 2023-12-15T08:05:20Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T02:43:55Z |
Entry not found
|
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] |
Jeevesh8/bs__qqp_ft_2
|
Jeevesh8
| 2023-12-15T07:52:08Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T02:43:56Z |
Entry not found
|
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] |
Jeevesh8/bs__qqp_ft_only_data_shuff_1
|
Jeevesh8
| 2023-12-15T08:05:48Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T02:43:56Z |
Entry not found
|
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] |
Jeevesh8/bs__qqp_ft_3
|
Jeevesh8
| 2023-12-15T08:05:14Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T02:43:56Z |
Entry not found
|
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0.31015992164611816,
-1.0262157917022705
] |
Jeevesh8/bs__qqp_ft_only_data_shuff_0
|
Jeevesh8
| 2023-12-15T08:24:09Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T02:43:56Z |
Entry not found
|
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] |
letsdazzle/cleo
|
letsdazzle
| 2023-12-15T02:44:28Z | 0 | 0 | null |
[
"license:llama2",
"region:us"
] | null | 2023-12-15T02:44:28Z |
---
license: llama2
---
|
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rcprati/GenderClassificaion
|
rcprati
| 2023-12-15T02:44:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T02:44:36Z |
Entry not found
|
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] |
butterpanck12/atandtmike
|
butterpanck12
| 2023-12-15T02:46:48Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T02:44:44Z |
---
license: openrail
---
|
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Undi95/Llamix2-MLewd-4x13B-GGUF
|
Undi95
| 2023-12-15T06:45:40Z | 0 | 5 | null |
[
"gguf",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-12-15T02:46:50Z |
---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
THIS MODEL IS MADE FOR LEWD
SEXUAL, CRUDE AND KINKY CONTENT IN OUTPUT CAN AND WILL HAPPEN. YOU'RE WARNED
This is a 4x13B MoE Llama2 model, one of the first (if not the first!).
Always, a big thanks to [Charles Goddard](https://huggingface.co/chargoddard) who is the brain behind all of those new Mixtral model, and his amazing tools!
WARNING: ALL THE "K" GGUF QUANT OF MIXTRAL MODELS SEEMS TO BE [BROKEN](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/TvjEP14ps7ZUgJ-0-mhIX.png), PREFER Q4_0, Q5_0 or Q8_0!
<!-- description start -->
## Description
This repo contains quantized files of Llamix2-MLewd-4x13B, a very hot MoE of Llama2 model.
<!-- description end -->
<!-- description start -->
## Models used
The list of model used and their activator/theme can be found [here](https://huggingface.co/Undi95/Llamix2-MLewd-4x13B/blob/main/config.yaml)
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
Special thanks to Sushi and Shena โฅ
If you want to support me, you can [here](https://ko-fi.com/undiai).
|
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-0.11167586594820023,
-0.26855871081352234,
-0.042135369032621384,
-0.28655534982681274
] |
TheBloke/cutie-GGUF
|
TheBloke
| 2023-12-15T02:52:55Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"en",
"base_model:rishiraj/cutie",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2023-12-15T02:48:30Z |
---
base_model: rishiraj/cutie
inference: false
language:
- en
license: apache-2.0
model_creator: Rishiraj Acharya
model_name: Cutie
model_type: llama
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;">
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</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 -->
# Cutie - GGUF
- Model creator: [Rishiraj Acharya](https://huggingface.co/rishiraj)
- Original model: [Cutie](https://huggingface.co/rishiraj/cutie)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Rishiraj Acharya's Cutie](https://huggingface.co/rishiraj/cutie).
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/cutie-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/cutie-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/cutie-GGUF)
* [Rishiraj Acharya's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/rishiraj/cutie)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `apache-2.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Rishiraj Acharya's Cutie](https://huggingface.co/rishiraj/cutie).
<!-- licensing 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [cutie.Q2_K.gguf](https://huggingface.co/TheBloke/cutie-GGUF/blob/main/cutie.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [cutie.Q3_K_S.gguf](https://huggingface.co/TheBloke/cutie-GGUF/blob/main/cutie.Q3_K_S.gguf) | Q3_K_S | 3 | 3.17 GB| 5.67 GB | very small, high quality loss |
| [cutie.Q3_K_M.gguf](https://huggingface.co/TheBloke/cutie-GGUF/blob/main/cutie.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [cutie.Q3_K_L.gguf](https://huggingface.co/TheBloke/cutie-GGUF/blob/main/cutie.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [cutie.Q4_0.gguf](https://huggingface.co/TheBloke/cutie-GGUF/blob/main/cutie.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [cutie.Q4_K_S.gguf](https://huggingface.co/TheBloke/cutie-GGUF/blob/main/cutie.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [cutie.Q4_K_M.gguf](https://huggingface.co/TheBloke/cutie-GGUF/blob/main/cutie.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [cutie.Q5_0.gguf](https://huggingface.co/TheBloke/cutie-GGUF/blob/main/cutie.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [cutie.Q5_K_S.gguf](https://huggingface.co/TheBloke/cutie-GGUF/blob/main/cutie.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [cutie.Q5_K_M.gguf](https://huggingface.co/TheBloke/cutie-GGUF/blob/main/cutie.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [cutie.Q6_K.gguf](https://huggingface.co/TheBloke/cutie-GGUF/blob/main/cutie.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [cutie.Q8_0.gguf](https://huggingface.co/TheBloke/cutie-GGUF/blob/main/cutie.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/cutie-GGUF and below it, a specific filename to download, such as: cutie.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/cutie-GGUF cutie.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/cutie-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/cutie-GGUF cutie.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 cutie.Q4_K_M.gguf --color -c 4096 --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 4096` 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 โ 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="./cutie.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # 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="./cutie.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, ้ฟๆ, 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 Bjรคreholt, 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: Rishiraj Acharya's Cutie
# Cutie
### 14th December, 2023
Prediction: This will be ranked **1st** on the overall leaderboard! ๐ฅ
Prediction: This will be ranked **1st** on the overall leaderboard! ๐ฅ
Prediction: This will be ranked **1st** on the overall leaderboard! ๐ฅ
Prediction: This will be ranked **1st** on the overall leaderboard! ๐ฅ
Prediction: This will be ranked **1st** on the overall leaderboard! ๐ฅ
<!-- original-model-card end -->
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] |
TheBloke/cutie-AWQ
|
TheBloke
| 2023-12-15T03:05:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"base_model:rishiraj/cutie",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-15T02:48:30Z |
---
base_model: rishiraj/cutie
inference: false
language:
- en
license: apache-2.0
model_creator: Rishiraj Acharya
model_name: Cutie
model_type: llama
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
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# Cutie - AWQ
- Model creator: [Rishiraj Acharya](https://huggingface.co/rishiraj)
- Original model: [Cutie](https://huggingface.co/rishiraj/cutie)
<!-- description start -->
## Description
This repo contains AWQ model files for [Rishiraj Acharya's Cutie](https://huggingface.co/rishiraj/cutie).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/cutie-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/cutie-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/cutie-GGUF)
* [Rishiraj Acharya's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/rishiraj/cutie)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `apache-2.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Rishiraj Acharya's Cutie](https://huggingface.co/rishiraj/cutie).
<!-- licensing end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/cutie-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.15 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.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/cutie-AWQ`.
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: `cutie-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. 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.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/cutie-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''{prompt}
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/cutie-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
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/cutie-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/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,
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_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/cutie-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.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, ้ฟๆ, 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 Bjรคreholt, 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: Rishiraj Acharya's Cutie
# Cutie
### 14th December, 2023
Prediction: This will be ranked **1st** on the overall leaderboard! ๐ฅ
Prediction: This will be ranked **1st** on the overall leaderboard! ๐ฅ
Prediction: This will be ranked **1st** on the overall leaderboard! ๐ฅ
Prediction: This will be ranked **1st** on the overall leaderboard! ๐ฅ
Prediction: This will be ranked **1st** on the overall leaderboard! ๐ฅ
|
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] |
owanr/SChem5Labels-roberta-base-inter-sorted-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T01:52:31Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T02:48:54Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-inter-sorted-model_annots_alpha0.8_whole_1e-05
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. -->
# SChem5Labels-roberta-base-inter-sorted-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5368
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.8464 | 1.0 | 3164 | 4.4317 |
| 4.9571 | 2.0 | 6328 | 4.5368 |
| 4.517 | 3.0 | 9492 | 4.5368 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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dayaburamshetty/gpt2-reuters-tokenizer
|
dayaburamshetty
| 2023-12-15T02:51:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T02:51:07Z |
Entry not found
|
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] |
KhoiC/esm2_t6_8M_UR50D-pretrained-evaluation-new-data-new-model
|
KhoiC
| 2023-12-15T03:15:12Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"esm",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T02:54:55Z |
Entry not found
|
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alinerodrigues/wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-04
|
alinerodrigues
| 2023-12-15T03:05:26Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T02:55:24Z |
Entry not found
|
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asdmkljals/chatgml2
|
asdmkljals
| 2023-12-15T02:55:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T02:55:40Z |
Entry not found
|
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littlebird13/resnet-50-web
|
littlebird13
| 2023-12-15T02:56:59Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"resnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T02:55:52Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
---
# ResNet-50 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.
This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoImageProcessor, ResNetForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet).
### BibTeX entry and citation info
```bibtex
@inproceedings{he2016deep,
title={Deep residual learning for image recognition},
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={770--778},
year={2016}
}
```
|
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] |
h4iku/codet5-small-t5apr-java
|
h4iku
| 2023-12-15T06:22:42Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-15T02:57:46Z |
Entry not found
|
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owanr/SChem5Labels-roberta-base-intra-sorted-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T02:04:09Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T03:00:46Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-intra-sorted-model_annots_alpha0.8_whole_1e-05
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. -->
# SChem5Labels-roberta-base-intra-sorted-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5863
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.5533 | 1.0 | 3164 | 3.7032 |
| 3.4865 | 2.0 | 6328 | 3.5863 |
| 3.492 | 3.0 | 9492 | 3.5863 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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ovieyra21/rikis-LoRA
|
ovieyra21
| 2023-12-15T03:01:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T03:01:38Z |
Entry not found
|
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] |
LilYoda/ArtoriaCaster
|
LilYoda
| 2023-12-15T03:09:04Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T03:02:16Z |
---
license: openrail
---
|
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] |
yuntaeyang/lion-7b-lora-kor
|
yuntaeyang
| 2023-12-15T03:07:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T03:02:23Z |
Entry not found
|
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] |
agfreitas/lamini_docs_3_steps
|
agfreitas
| 2023-12-15T03:35:14Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T03:03:36Z |
---
license: apache-2.0
base_model: EleutherAI/pythia-70m
tags:
- generated_from_trainer
model-index:
- name: lamini_docs_3_steps
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. -->
# lamini_docs_3_steps
This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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: 1
- training_steps: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Jeevesh8/bs__qqp_ft_only_data_shuff_3
|
Jeevesh8
| 2023-12-15T08:02:36Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T03:04:13Z |
Entry not found
|
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] |
Jeevesh8/bs__qqp_ft_only_data_shuff_2
|
Jeevesh8
| 2023-12-15T08:45:39Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T03:04:13Z |
Entry not found
|
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] |
AIFT/aift-llama2-koen-instruct-v1.1
|
AIFT
| 2023-12-15T04:46:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T03:07:11Z |
---
license: cc-by-nc-sa-4.0
---
<img src ="https://pds.saramin.co.kr/company/logo/202110/15/r0zx8u_zrd4-xpn1m0_logo.JPG">
<h1>AIFT instruct ๋ชจ๋ธ v1.0</h1>
<b><ํ์ต ๋ฐ์ดํฐ ๊ตฌ์ถ></b>
Open-Orca-ko ๋ฐ์ดํฐ๋ฅผ ๋ถ์ํ์ฌ ํ์คํฌ๋ฅผ ์ถ์ถํ ๋ค
ํด๋น ํ์คํฌ์ ๋ง์ถฐ์ NLP ๊ด๋ จ ์คํ์์ค ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ์ฌ ํ์ต๋ฐ์ดํฐ๋ฅผ ์์ฒด์ ์ผ๋ก
์ฝ 4๋ง๊ฑด(์ญ์ฌ, ๊ณผํ, ์ํ, ๊ธฐ๊ณ๋
ํด, ๋ฆฌ๋ทฐ ๋ถ์) ๊ตฌ์ถํ์๊ณ ,
๊ทธ ์ธ์ Open-Orca-Ko์์ ๋ฐ์ดํฐ๋ฅผ ์ผ๋ถ ํํฐ๋งํ์ฌ ์ ์ ํด๊ฑฐ๋ KoBEST ๋ฐ์ดํฐ๋ฅผ ํจ๊ป ์ถ๊ฐํ์์ต๋๋ค.
aihub ์ผ๋ฐ์์ ๋ฐ ๊ธฐ๊ณ๋
ํด ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ์ฌ ์ถ๊ฐ๋ก ํ์ต ๋ฐ์ดํฐ๋ฅผ ๊ตฌ์ถ(ํํ์ ๊ด๋ จ, ๊ธฐ๊ณ๋
ํด ๊ด๋ จ ๋ฐ ์์ฝ)
์ด 11๋ง๊ฐ์ ํ์ต๋ฐ์ดํฐ๋ก sft๋ฅผ ์งํํ์์ต๋๋ค.
ํ์ฌ, ์๋ก์ด ๋ฒ์ ์ ๋ชจ๋ธ ํ์ต ๋ฐ ์ฑ๋ฅ์ ์ํด Open-Orca ๋ฐ์ดํฐ์
์ผ๋ถ๋ฅผ ๋ฒ์ญํ์ฌ ์ ์ ์ค์ ์์ต๋๋ค.
###ํ์ต ๋ฐ์ดํฐ ํ์ผ์ ๋น๊ณต๊ฐ์
๋๋ค.
|
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0.13709187507629395,
0.4134988784790039,
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0.029844585806131363,
0.09083583950996399,
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0.1350078135728836,
-0.05817025154829025,
0.4216783344745636,
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-0.7844671010971069
] |
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