librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

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NTIS/hf_gemma3_2-checkpoint-101000	NTIS	2025-06-25T01:46:29Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "pytorch", "causal-lm", "ko", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T05:06:57Z	--- license: apache-2.0 language: - ko - en tags: - text-generation - pytorch - causal-lm library_name: transformers --- # hf_gemma3_2-checkpoint-101000 이 모델은 파인튜닝된 언어 모델 체크포인트입니다. ## 모델 정보 - 베이스 모델: hf_gemma3_2 - 체크포인트: checkpoint-101000 - 타입: Causal Language Model - 라이선스: Apache 2.0 ## 사용 방법 ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name = "NTIS/hf_gemma3_2-checkpoint-101000" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto" ) # 텍스트 생성 text = "안녕하세요" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` ## 주의사항 - 이 모델은 연구/실험 목적으로 제공됩니다 - 상업적 사용 전에 라이선스를 확인하세요
NTIS/hf_gemma3_2-checkpoint-100000	NTIS	2025-06-25T01:44:05Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "pytorch", "causal-lm", "ko", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T05:04:40Z	--- license: apache-2.0 language: - ko - en tags: - text-generation - pytorch - causal-lm library_name: transformers --- # hf_gemma3_2-checkpoint-100000 이 모델은 파인튜닝된 언어 모델 체크포인트입니다. ## 모델 정보 - 베이스 모델: hf_gemma3_2 - 체크포인트: checkpoint-100000 - 타입: Causal Language Model - 라이선스: Apache 2.0 ## 사용 방법 ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name = "NTIS/hf_gemma3_2-checkpoint-100000" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto" ) # 텍스트 생성 text = "안녕하세요" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` ## 주의사항 - 이 모델은 연구/실험 목적으로 제공됩니다 - 상업적 사용 전에 라이선스를 확인하세요
microsoft/elem2design	microsoft	2025-06-25T01:39:39Z	22	4	null	[ "safetensors", "llava_llama", "arxiv:2412.19712", "arxiv:2311.16974", "arxiv:2303.15937", "arxiv:2404.00995", "arxiv:2303.18248", "arxiv:1910.09700", "license:mit", "region:us" ]	null	2025-05-27T10:31:19Z	--- license: mit --- ## Model Details ### Model Description This model aims to compose user-provided graphic elements into a pleasing graphical design. It takes graphic elements (i.e., the images and texts) from users as input and generates the position, color and font information of each element as output. - Developed by: Jiawei Lin, Shizhao Sun, Danqing Huang, Ting Liu, Ji Li and Jiang Bian - Model type: Large Language Models - Language(s): Python - License: MIT - Finetuned from model: Llama-3.1-8B ### Model Sources <!-- Provide the basic links for the model. --> - Repository: https://github.com/microsoft/elem2design - Paper: https://arxiv.org/abs/2412.19712 ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> Compose user-provided graphic elements (i.e., images and texts) into a pleasing graphic design. Elem2Design is being shared with the research community to facilitate reproduction of our results and foster further research in this area. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> We do not recommend using Elem2Design in commercial or real-world applications without further testing and development. It is being released for research purposes. Use in any manner that violates applicable laws or regulations. ## Risks and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Elem2Design inherits any biases, errors, or omissions produced by its base model. Developers are advised to choose an appropriate base LLM/MLLM carefully, depending on the intended use case.  Elem2Design uses the Llama model. See https://huggingface.co/meta-llama/Llama-3.1-8B to understand the capabilities and limitations of this model.   As the model is fine-tuned on very specific data about design composition, it is unlikely to generate information other than position, color and font. However, this is possible. It is more likely to happen when instructions unrelated to graphic design composition, e.g., how has the social media influenced our daily life, are fed into the model. Graphic designs generated by Elem2Design may not be technically accurate or meet user specifications in all cases. Users are responsible for assessing the acceptability of generated content for each intended use case. Elem2Design was developed for research and experimental purposes. Further testing and validation are needed before considering its application in commercial or real-world scenarios. ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Please only provide the images and texts that you want to show on the graphic design to the model. Users are responsible for sourcing their content legally and ethically. This could include securing appropriate copy rights, ensuring consent for use of images of people, and/or the anonymization of data prior to use in research.   ## How to Get Started with the Model ``` python llava/infer/infer.py \ --model_name_or_path /path/to/model/checkpoint-xxxx \ --data_path /path/to/data/test.json \ --image_folder /path/to/crello_images \ --output_dir /path/to/output_dir \ --start_layer_index 0 \ --end_layer_index 4 ``` For more information, please visit our GitHub repo: https://github.com/microsoft/elem2design. ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> The training data is from an open-source dataset (https://huggingface.co/datasets/cyberagent/crello). ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing The training samples with more than 25 design elements are filtered out to maintain a limited sequence length and thereby improve training efficiency. #### Training Hyperparameters - Learning rate: 2e-4 - Global batch size: 128 - Number of training steps: 7000 - Rank and alpha of LoRA: 32 and 64 #### Speeds, Sizes, Times <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> - Llama-3.1-8B: 8B parameters - CLIP ViT-Large-Patch14: 428M parameters ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> #### Testing Data <!-- This should link to a Dataset Card if possible. --> The testing data is from an open-source dataset (https://huggingface.co/datasets/cyberagent/crello). #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> - Overall metrics. We use a robust proxy model (https://huggingface.co/llava-hf/llava-onevision-qwen2-7b-ov-hf) for comprehensive evaluation from five aspects: (i) design and layout, (ii) content relevance, (iii) typography and color, (iv) graphics and images, and (v) innovation and originality. We use the same prompts as presented in COLE [[1](https://arxiv.org/abs/2311.16974)]. - Geometry-related metrics. These metrics focus purely on the geometric attributes of elements without considering their content, including element validity (Val), Overlap (Ove), Alignment (Ali) and underlay effectiveness (Undl, Unds) [[2](https://arxiv.org/abs/2303.15937 )][[3](https://arxiv.org/pdf/2404.00995 )]. ### Results We use prior work FlexDM [[1](https://arxiv.org/pdf/2303.18248)] and prompting GPT-4o [[2](https://platform.openai.com/docs/models#gpt-4o)] as baselines. In comparison, Elem2Design demonstrates superior performance across nearly all metrics. For example, on overall metrics, Elem2Design achieves 8.08, 7.92, 8.00, 7.82 and 6.98 on the evaluated five aspects. For another example, regarding geometry-related metrics, Elem2Design and FlexDM achieve Ove score of 0.0865 and 0.3242 respectively, indicting that Elem2Design effectively addresses the overlap issue whereas FlexDM encounters difficulties in this area. See Table 1 for the complete evaluation in our paper (https://arxiv.org/pdf/2412.19712) ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> ``` @InProceedings{lin2024elements, title={From Elements to Design: A Layered Approach for Automatic Graphic Design Composition}, author={Lin, Jiawei and Sun, Shizhao and Huang, Danqing and Liu, Ting and Li, Ji and Bian, Jiang}, booktitle={CVPR}, year={2025} } ``` ## Model Card Contact We welcome feedback and collaboration from our audience. If you have suggestions, questions, or observe unexpected/offensive behavior in our technology, please contact us at Shizhao Sun, [email protected]. If the team receives reports of undesired behavior or identifies issues independently, we will update this repository with appropriate mitigations.
stablediffusionapi/intorealismxl-v31	stablediffusionapi	2025-06-25T01:35:58Z	0	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-06-25T01:25:13Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true pipeline_tag: text-to-image library_name: diffusers widget: - text: a girl wandering through the forest output: url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/39cc9d7d-1e27-4727-a0b2-8511224e7f32/width=1344/84048230.jpeg --- # IntoRealism XL - v3.1 API Inference <Gallery /> ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "intorealismxl-v31" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/intorealismxl-v31) Model link: [View model](https://modelslab.com/models/intorealismxl-v31) View all models: [View Models](https://modelslab.com/models) ```python import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "intorealismxl-v31", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "", "lora": "", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) ``` > Use this coupon code to get 25% off DMGG0RBN
New-videos-Gungun-Gupta-viral-video-Clips/FULL.VIDEO.Gungun.Gupta.Viral.Video.Tutorial.Official	New-videos-Gungun-Gupta-viral-video-Clips	2025-06-25T01:26:55Z	0	0	null	[ "region:us" ]	null	2025-06-25T01:26:42Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
zhiqing/Qwen3-Reranker-0.6B-ONNX	zhiqing	2025-06-25T01:26:30Z	383	3	transformers	[ "transformers", "onnx", "qwen3", "text-generation", "conversational", "base_model:Qwen/Qwen3-Reranker-0.6B", "base_model:quantized:Qwen/Qwen3-Reranker-0.6B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-09T02:32:51Z	--- license: apache-2.0 base_model: - Qwen/Qwen3-Reranker-0.6B library_name: transformers pipeline_tag: text-generation --- # Qwen3-Reranker-0.6B-ONNX How to use ```python from transformers import AutoTokenizer import onnxruntime as ort import numpy as np import torch from typing import List class Qwen3RerankerONNX: def __init__( self, model_path: str = "zhiqing/Qwen3-Reranker-0.6B-ONNX/model.onnx", tokenizer_dir: str = "zhiqing/Qwen3-Reranker-0.6B-ONNX", providers: List[str] = ("CUDAExecutionProvider", "CPUExecutionProvider"), max_length: int = 2048, ): self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir, padding_side="left") self.prefix = "<\|im_start\|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".'<\|im_end\|>\n<\|im_start\|>user\n" self.suffix = "<\|im_end\|>\n<\|im_start\|>assistant\n<think>\n\n</think>\n\n" self.prefix_tokens = self.tokenizer.encode(self.prefix, add_special_tokens=False) self.suffix_tokens = self.tokenizer.encode(self.suffix, add_special_tokens=False) self.default_instruction = ( "Given a web search query, retrieve relevant passages that answer the query" ) self.max_length = max_length self.token_false_id = self.tokenizer.convert_tokens_to_ids("no") self.token_true_id = self.tokenizer.convert_tokens_to_ids("yes") self.session = ort.InferenceSession(model_path, providers=list(providers)) self.output_name = "logits" def _format_instruction(self, instruction: str, query: str, doc: str) -> str: inst = instruction if instruction is not None else self.default_instruction return f"<Instruct>: {inst}\n<Query>: {query}\n<Document>: {doc}" def _tokenize(self, pairs: List[str]): encoded = self.tokenizer( [self.prefix + s + self.suffix for s in pairs], padding=True, truncation="longest_first", max_length=self.max_length - len(self.prefix_tokens) - len(self.suffix_tokens), add_special_tokens=False, return_tensors="np", ) input_ids = encoded["input_ids"].astype(np.int64) attention_mask = encoded["attention_mask"].astype(np.int64) seq_len = input_ids.shape[1] position_ids = ( np.arange(seq_len, dtype=np.int64)[None, :].repeat(input_ids.shape[0], axis=0) ) return input_ids, attention_mask, position_ids def infer( self, queries: List[str], documents: List[str], instruction: str = None, ): if len(queries) == 1 and len(documents) > 1: queries = [queries[0]] * len(documents) elif len(queries) != len(documents): raise ValueError("The number of queries must be 1 or equal to the number of documents.") pairs = [ self._format_instruction(instruction, q, d) for q, d in zip(queries, documents) ] input_ids, attention_mask, position_ids = self._tokenize(pairs) ort_inputs = { "input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids, } logits_np = self.session.run([self.output_name], ort_inputs)[0] last_token_logits = torch.from_numpy(logits_np[:, -1, :]).float() false_logits = last_token_logits[:, self.token_false_id] true_logits = last_token_logits[:, self.token_true_id] probs = torch.softmax(torch.stack([false_logits, true_logits], dim=1), dim=1) scores_no = probs[:, 0].tolist() scores_yes = probs[:, 1].tolist() return scores_yes, scores_no ``` <p align="center"> <img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen3.png" width="400"/> <p> ## Highlights The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. Exceptional Versatility: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks No.1 in the MTEB multilingual leaderboard (as of June 5, 2025, score 70.58), while the reranking model excels in various text retrieval scenarios. Comprehensive Flexibility: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios. Multilingual Capability: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities. ## Model Overview Qwen3-Reranker-0.6B has the following features: - Model Type: Text Reranking - Supported Languages: 100+ Languages - Number of Paramaters: 0.6B - Context Length: 32k For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-embedding/), [GitHub](https://github.com/QwenLM/Qwen3-Embedding). ## Qwen3 Embedding Series Model list \| Model Type \| Models \| Size \| Layers \| Sequence Length \| Embedding Dimension \| MRL Support \| Instruction Aware \| \|------------------\|----------------------\|------\|--------\|-----------------\|---------------------\|-------------\|----------------\| \| Text Embedding \| [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) \| 0.6B \| 28 \| 32K \| 1024 \| Yes \| Yes \| \| Text Embedding \| [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) \| 4B \| 36 \| 32K \| 2560 \| Yes \| Yes \| \| Text Embedding \| [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) \| 8B \| 36 \| 32K \| 4096 \| Yes \| Yes \| \| Text Reranking \| [Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) \| 0.6B \| 28 \| 32K \| - \| - \| Yes \| \| Text Reranking \| [Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) \| 4B \| 36 \| 32K \| - \| - \| Yes \| \| Text Reranking \| [Qwen3-Reranker-8B](https://huggingface.co/Qwen/Qwen3-Reranker-8B) \| 8B \| 36 \| 32K \| - \| - \| Yes \| > Note: > - `MRL Support` indicates whether the embedding model supports custom dimensions for the final embedding. > - `Instruction Aware` notes whether the embedding or reranking model supports customizing the input instruction according to different tasks. > - Our evaluation indicates that, for most downstream tasks, using instructions (instruct) typically yields an improvement of 1% to 5% compared to not using them. Therefore, we recommend that developers create tailored instructions specific to their tasks and scenarios. In multilingual contexts, we also advise users to write their instructions in English, as most instructions utilized during the model training process were originally written in English. ## Usage With Transformers versions earlier than 4.51.0, you may encounter the following error: ``` KeyError: 'qwen3' ``` 📌 Tip: We recommend that developers customize the `instruct` according to their specific scenarios, tasks, and languages. Our tests have shown that in most retrieval scenarios, not using an `instruct` on the query side can lead to a drop in retrieval performance by approximately 1% to 5%. ## Evaluation \| Model \| Param \| MTEB-R \| CMTEB-R \| MMTEB-R \| MLDR \| MTEB-Code \| FollowIR \| \|------------------------------------\|--------\|---------\|---------\|---------\|--------\|-----------\|----------\| \| Qwen3-Embedding-0.6B \| 0.6B \| 61.82 \| 71.02 \| 64.64 \| 50.26 \| 75.41 \| 5.09 \| \| Jina-multilingual-reranker-v2-base \| 0.3B \| 58.22 \| 63.37 \| 63.73 \| 39.66 \| 58.98 \| -0.68 \| \| gte-multilingual-reranker-base \| 0.3B \| 59.51 \| 74.08 \| 59.44 \| 66.33 \| 54.18 \| -1.64 \| \| BGE-reranker-v2-m3 \| 0.6B \| 57.03 \| 72.16 \| 58.36 \| 59.51 \| 41.38 \| -0.01 \| \| Qwen3-Reranker-0.6B \| 0.6B \| 65.80 \| 71.31 \| 66.36 \| 67.28 \| 73.42 \| 5.41 \| \| Qwen3-Reranker-4B \| 1.7B \| 69.76 \| 75.94 \| 72.74 \| 69.97 \| 81.20 \| 14.84 \| \| Qwen3-Reranker-8B \| 8B \| 69.02 \| 77.45 \| 72.94 \| 70.19 \| 81.22 \| 8.05 \| > Note: > - Evaluation results for reranking models. We use the retrieval subsets of MTEB(eng, v2), MTEB(cmn, v1), MMTEB and MTEB (Code), which are MTEB-R, CMTEB-R, MMTEB-R and MTEB-Code. > - All scores are our runs based on the top-100 candidates retrieved by dense embedding model [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3-embedding, title = {Qwen3-Embedding}, url = {https://qwenlm.github.io/blog/qwen3/}, author = {Qwen Team}, month = {May}, year = {2025} } ```
fangcaotank/task-10-Qwen-Qwen2.5-7B-Instruct-optuna	fangcaotank	2025-06-25T01:22:43Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]	null	2025-06-24T10:17:38Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.12.0 - PEFT 0.13.2
Rojosan/classificador	Rojosan	2025-06-25T01:22:30Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-25T01:16:37Z	--- library_name: transformers tags: - autotrain - text-classification base_model: google-bert/bert-base-uncased widget: - text: "I love AutoTrain" --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.04549918696284294 f1_macro: 1.0 f1_micro: 1.0 f1_weighted: 1.0 precision_macro: 1.0 precision_micro: 1.0 precision_weighted: 1.0 recall_macro: 1.0 recall_micro: 1.0 recall_weighted: 1.0 accuracy: 1.0
tanny2109/llamaToxic05	tanny2109	2025-06-25T01:22:11Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]	null	2025-06-25T01:21:02Z	--- base_model: meta-llama/Meta-Llama-3.1-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
donoway/l0nupcvl_20250623_233420	donoway	2025-06-25T01:22:08Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:adapter:meta-llama/Llama-3.2-1B", "license:llama3.2", "region:us" ]	null	2025-06-25T01:22:05Z	--- library_name: peft license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: l0nupcvl_20250623_233420 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. --> # l0nupcvl_20250623_233420 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8677 - Model Preparation Time: 0.0121 - Move Accuracy: 0.1838 - Token Accuracy: 0.6621 - Accuracy: 0.1838 ## 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: 0.003 - train_batch_size: 128 - eval_batch_size: 256 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Move Accuracy \| Token Accuracy \| Accuracy \| \|:-------------:\|:------:\|:------:\|:---------------:\|:----------------------:\|:-------------:\|:--------------:\|:--------:\| \| No log \| 0 \| 0 \| 6.4123 \| 0.0121 \| 0.0 \| 0.1049 \| 0.0 \| \| 1.8134 \| 0.0098 \| 100 \| 1.8386 \| 0.0121 \| 0.0024 \| 0.2653 \| 0.0024 \| \| 1.7692 \| 0.0196 \| 200 \| 1.7239 \| 0.0121 \| 0.0047 \| 0.3117 \| 0.0047 \| \| 1.6779 \| 0.0295 \| 300 \| 1.6996 \| 0.0121 \| 0.0090 \| 0.3341 \| 0.0090 \| \| 1.626 \| 0.0393 \| 400 \| 1.6646 \| 0.0121 \| 0.0155 \| 0.3515 \| 0.0155 \| \| 1.6473 \| 0.0491 \| 500 \| 1.6401 \| 0.0121 \| 0.0177 \| 0.3677 \| 0.0177 \| \| 1.5746 \| 0.0589 \| 600 \| 1.6117 \| 0.0121 \| 0.0213 \| 0.3739 \| 0.0213 \| \| 1.6295 \| 0.0687 \| 700 \| 1.6012 \| 0.0121 \| 0.0213 \| 0.3784 \| 0.0213 \| \| 1.6309 \| 0.0785 \| 800 \| 1.6188 \| 0.0121 \| 0.0213 \| 0.3714 \| 0.0213 \| \| 1.6113 \| 0.0884 \| 900 \| 1.5729 \| 0.0121 \| 0.0264 \| 0.3849 \| 0.0264 \| \| 1.5866 \| 0.0982 \| 1000 \| 1.5754 \| 0.0121 \| 0.0268 \| 0.3817 \| 0.0268 \| \| 1.4901 \| 0.1080 \| 1100 \| 1.5613 \| 0.0121 \| 0.0335 \| 0.3907 \| 0.0335 \| \| 1.5038 \| 0.1178 \| 1200 \| 1.5597 \| 0.0121 \| 0.0307 \| 0.3920 \| 0.0307 \| \| 1.5518 \| 0.1276 \| 1300 \| 1.5826 \| 0.0121 \| 0.0246 \| 0.3877 \| 0.0246 \| \| 1.5581 \| 0.1374 \| 1400 \| 1.5264 \| 0.0121 \| 0.0353 \| 0.4067 \| 0.0353 \| \| 1.4864 \| 0.1473 \| 1500 \| 1.5148 \| 0.0121 \| 0.0371 \| 0.4159 \| 0.0371 \| \| 1.4902 \| 0.1571 \| 1600 \| 1.4866 \| 0.0121 \| 0.0414 \| 0.4229 \| 0.0414 \| \| 1.4227 \| 0.1669 \| 1700 \| 1.4785 \| 0.0121 \| 0.0402 \| 0.4306 \| 0.0402 \| \| 1.4888 \| 0.1767 \| 1800 \| 1.4807 \| 0.0121 \| 0.0362 \| 0.4273 \| 0.0362 \| \| 1.4003 \| 0.1865 \| 1900 \| 1.4640 \| 0.0121 \| 0.0431 \| 0.4345 \| 0.0431 \| \| 1.3846 \| 0.1963 \| 2000 \| 1.4368 \| 0.0121 \| 0.0429 \| 0.4426 \| 0.0429 \| \| 1.4298 \| 0.2062 \| 2100 \| 1.4343 \| 0.0121 \| 0.0477 \| 0.4420 \| 0.0477 \| \| 1.4368 \| 0.2160 \| 2200 \| 1.4206 \| 0.0121 \| 0.0533 \| 0.4530 \| 0.0533 \| \| 1.432 \| 0.2258 \| 2300 \| 1.4048 \| 0.0121 \| 0.0489 \| 0.4569 \| 0.0489 \| \| 1.3462 \| 0.2356 \| 2400 \| 1.4218 \| 0.0121 \| 0.0434 \| 0.4477 \| 0.0434 \| \| 1.3158 \| 0.2454 \| 2500 \| 1.4085 \| 0.0121 \| 0.0479 \| 0.4554 \| 0.0479 \| \| 1.3674 \| 0.2553 \| 2600 \| 1.3735 \| 0.0121 \| 0.0558 \| 0.4709 \| 0.0558 \| \| 1.3556 \| 0.2651 \| 2700 \| 1.3663 \| 0.0121 \| 0.0576 \| 0.4759 \| 0.0576 \| \| 1.3193 \| 0.2749 \| 2800 \| 1.3417 \| 0.0121 \| 0.0595 \| 0.4813 \| 0.0595 \| \| 1.3479 \| 0.2847 \| 2900 \| 1.3414 \| 0.0121 \| 0.0602 \| 0.4799 \| 0.0602 \| \| 1.3024 \| 0.2945 \| 3000 \| 1.3114 \| 0.0121 \| 0.0705 \| 0.4954 \| 0.0705 \| \| 1.3576 \| 0.3043 \| 3100 \| 1.3098 \| 0.0121 \| 0.0639 \| 0.4942 \| 0.0639 \| \| 1.3043 \| 0.3142 \| 3200 \| 1.2850 \| 0.0121 \| 0.0730 \| 0.5077 \| 0.0730 \| \| 1.2639 \| 0.3240 \| 3300 \| 1.2703 \| 0.0121 \| 0.0727 \| 0.5114 \| 0.0727 \| \| 1.2676 \| 0.3338 \| 3400 \| 1.2552 \| 0.0121 \| 0.0759 \| 0.5170 \| 0.0759 \| \| 1.2058 \| 0.3436 \| 3500 \| 1.2532 \| 0.0121 \| 0.0743 \| 0.5149 \| 0.0743 \| \| 1.2568 \| 0.3534 \| 3600 \| 1.2460 \| 0.0121 \| 0.0775 \| 0.5223 \| 0.0775 \| \| 1.1544 \| 0.3632 \| 3700 \| 1.2190 \| 0.0121 \| 0.0804 \| 0.5309 \| 0.0804 \| \| 1.1985 \| 0.3731 \| 3800 \| 1.2199 \| 0.0121 \| 0.0832 \| 0.5322 \| 0.0832 \| \| 1.1214 \| 0.3829 \| 3900 \| 1.1944 \| 0.0121 \| 0.0896 \| 0.5432 \| 0.0896 \| \| 1.2423 \| 0.3927 \| 4000 \| 1.2111 \| 0.0121 \| 0.0803 \| 0.5363 \| 0.0803 \| \| 1.1277 \| 0.4025 \| 4100 \| 1.1957 \| 0.0121 \| 0.0867 \| 0.5406 \| 0.0867 \| \| 1.1588 \| 0.4123 \| 4200 \| 1.2106 \| 0.0121 \| 0.0821 \| 0.5381 \| 0.0821 \| \| 1.1443 \| 0.4221 \| 4300 \| 1.1801 \| 0.0121 \| 0.0833 \| 0.5478 \| 0.0833 \| \| 1.215 \| 0.4320 \| 4400 \| 1.1494 \| 0.0121 \| 0.0955 \| 0.5557 \| 0.0955 \| \| 1.1168 \| 0.4418 \| 4500 \| 1.1449 \| 0.0121 \| 0.1079 \| 0.5616 \| 0.1079 \| \| 1.1143 \| 0.4516 \| 4600 \| 1.1396 \| 0.0121 \| 0.1049 \| 0.5636 \| 0.1049 \| \| 1.1495 \| 0.4614 \| 4700 \| 1.1196 \| 0.0121 \| 0.1021 \| 0.5709 \| 0.1021 \| \| 1.1388 \| 0.4712 \| 4800 \| 1.1307 \| 0.0121 \| 0.1017 \| 0.5660 \| 0.1017 \| \| 1.1701 \| 0.4811 \| 4900 \| 1.1317 \| 0.0121 \| 0.1029 \| 0.5663 \| 0.1029 \| \| 1.038 \| 0.4909 \| 5000 \| 1.1348 \| 0.0121 \| 0.1086 \| 0.5656 \| 0.1086 \| \| 1.176 \| 0.5007 \| 5100 \| 1.1275 \| 0.0121 \| 0.1006 \| 0.5676 \| 0.1006 \| \| 1.1175 \| 0.5105 \| 5200 \| 1.1255 \| 0.0121 \| 0.1009 \| 0.5697 \| 0.1009 \| \| 1.101 \| 0.5203 \| 5300 \| 1.1134 \| 0.0121 \| 0.1024 \| 0.5725 \| 0.1024 \| \| 1.0745 \| 0.5301 \| 5400 \| 1.1158 \| 0.0121 \| 0.1079 \| 0.5739 \| 0.1079 \| \| 1.1114 \| 0.5400 \| 5500 \| 1.1041 \| 0.0121 \| 0.1125 \| 0.5784 \| 0.1125 \| \| 1.1863 \| 0.5498 \| 5600 \| 1.1140 \| 0.0121 \| 0.1081 \| 0.5753 \| 0.1081 \| \| 1.0536 \| 0.5596 \| 5700 \| 1.0964 \| 0.0121 \| 0.1096 \| 0.5799 \| 0.1096 \| \| 1.1317 \| 0.5694 \| 5800 \| 1.0854 \| 0.0121 \| 0.1160 \| 0.5854 \| 0.1160 \| \| 1.1592 \| 0.5792 \| 5900 \| 1.0925 \| 0.0121 \| 0.1056 \| 0.5792 \| 0.1056 \| \| 1.0494 \| 0.5890 \| 6000 \| 1.0837 \| 0.0121 \| 0.1138 \| 0.5852 \| 0.1138 \| \| 1.1148 \| 0.5989 \| 6100 \| 1.0965 \| 0.0121 \| 0.1039 \| 0.5828 \| 0.1039 \| \| 1.1007 \| 0.6087 \| 6200 \| 1.0683 \| 0.0121 \| 0.1138 \| 0.5858 \| 0.1138 \| \| 1.073 \| 0.6185 \| 6300 \| 1.1008 \| 0.0121 \| 0.1043 \| 0.5776 \| 0.1043 \| \| 1.0281 \| 0.6283 \| 6400 \| 1.0673 \| 0.0121 \| 0.1125 \| 0.5895 \| 0.1125 \| \| 1.1181 \| 0.6381 \| 6500 \| 1.0736 \| 0.0121 \| 0.1197 \| 0.5951 \| 0.1197 \| \| 1.1005 \| 0.6479 \| 6600 \| 1.0828 \| 0.0121 \| 0.1134 \| 0.5847 \| 0.1134 \| \| 1.008 \| 0.6578 \| 6700 \| 1.0594 \| 0.0121 \| 0.1176 \| 0.5935 \| 0.1176 \| \| 1.0802 \| 0.6676 \| 6800 \| 1.0700 \| 0.0121 \| 0.1160 \| 0.5873 \| 0.1160 \| \| 1.057 \| 0.6774 \| 6900 \| 1.0603 \| 0.0121 \| 0.1154 \| 0.5924 \| 0.1154 \| \| 1.0362 \| 0.6872 \| 7000 \| 1.0496 \| 0.0121 \| 0.1204 \| 0.5974 \| 0.1204 \| \| 1.1061 \| 0.6970 \| 7100 \| 1.0542 \| 0.0121 \| 0.1140 \| 0.5903 \| 0.1140 \| \| 0.9967 \| 0.7069 \| 7200 \| 1.0405 \| 0.0121 \| 0.1292 \| 0.6027 \| 0.1292 \| \| 0.9899 \| 0.7167 \| 7300 \| 1.0427 \| 0.0121 \| 0.1300 \| 0.6013 \| 0.1300 \| \| 1.0461 \| 0.7265 \| 7400 \| 1.0622 \| 0.0121 \| 0.1195 \| 0.5928 \| 0.1195 \| \| 1.0 \| 0.7363 \| 7500 \| 1.0679 \| 0.0121 \| 0.1198 \| 0.5913 \| 0.1198 \| \| 1.0434 \| 0.7461 \| 7600 \| 1.0331 \| 0.0121 \| 0.1249 \| 0.6000 \| 0.1249 \| \| 1.1196 \| 0.7559 \| 7700 \| 1.0513 \| 0.0121 \| 0.1225 \| 0.5969 \| 0.1225 \| \| 1.0365 \| 0.7658 \| 7800 \| 1.0457 \| 0.0121 \| 0.1163 \| 0.5982 \| 0.1163 \| \| 1.0007 \| 0.7756 \| 7900 \| 1.0389 \| 0.0121 \| 0.1249 \| 0.6003 \| 0.1249 \| \| 1.0079 \| 0.7854 \| 8000 \| 1.0427 \| 0.0121 \| 0.1220 \| 0.6008 \| 0.1220 \| \| 1.0649 \| 0.7952 \| 8100 \| 1.0577 \| 0.0121 \| 0.1201 \| 0.5949 \| 0.1201 \| \| 1.0393 \| 0.8050 \| 8200 \| 1.0503 \| 0.0121 \| 0.1239 \| 0.5942 \| 0.1239 \| \| 1.0037 \| 0.8148 \| 8300 \| 1.0440 \| 0.0121 \| 0.1223 \| 0.5970 \| 0.1223 \| \| 1.0126 \| 0.8247 \| 8400 \| 1.0230 \| 0.0121 \| 0.1252 \| 0.6057 \| 0.1252 \| \| 0.9743 \| 0.8345 \| 8500 \| 1.0123 \| 0.0121 \| 0.1339 \| 0.6117 \| 0.1339 \| \| 1.0477 \| 0.8443 \| 8600 \| 1.0315 \| 0.0121 \| 0.1240 \| 0.6062 \| 0.1240 \| \| 1.0048 \| 0.8541 \| 8700 \| 1.0204 \| 0.0121 \| 0.1296 \| 0.6055 \| 0.1296 \| \| 1.0247 \| 0.8639 \| 8800 \| 1.0482 \| 0.0121 \| 0.1210 \| 0.5993 \| 0.1210 \| \| 1.0746 \| 0.8737 \| 8900 \| 1.0367 \| 0.0121 \| 0.1231 \| 0.6024 \| 0.1231 \| \| 1.0519 \| 0.8836 \| 9000 \| 1.0341 \| 0.0121 \| 0.1264 \| 0.6026 \| 0.1264 \| \| 0.9807 \| 0.8934 \| 9100 \| 1.0475 \| 0.0121 \| 0.1215 \| 0.5967 \| 0.1215 \| \| 1.0206 \| 0.9032 \| 9200 \| 1.0202 \| 0.0121 \| 0.1361 \| 0.6079 \| 0.1361 \| \| 1.0404 \| 0.9130 \| 9300 \| 1.0219 \| 0.0121 \| 0.1305 \| 0.6105 \| 0.1305 \| \| 0.9316 \| 0.9228 \| 9400 \| 1.0256 \| 0.0121 \| 0.1281 \| 0.6045 \| 0.1281 \| \| 1.0496 \| 0.9327 \| 9500 \| 1.0093 \| 0.0121 \| 0.1351 \| 0.6138 \| 0.1351 \| \| 1.048 \| 0.9425 \| 9600 \| 1.0273 \| 0.0121 \| 0.1315 \| 0.6058 \| 0.1315 \| \| 1.0517 \| 0.9523 \| 9700 \| 1.0251 \| 0.0121 \| 0.1283 \| 0.6040 \| 0.1283 \| \| 0.9922 \| 0.9621 \| 9800 \| 1.0176 \| 0.0121 \| 0.1330 \| 0.6091 \| 0.1330 \| \| 1.0489 \| 0.9719 \| 9900 \| 1.0010 \| 0.0121 \| 0.1374 \| 0.6174 \| 0.1374 \| \| 1.001 \| 0.9817 \| 10000 \| 1.0232 \| 0.0121 \| 0.1191 \| 0.6050 \| 0.1191 \| \| 1.0379 \| 0.9916 \| 10100 \| 1.0070 \| 0.0121 \| 0.1314 \| 0.6116 \| 0.1314 \| \| 0.9638 \| 1.0014 \| 10200 \| 1.0107 \| 0.0121 \| 0.1379 \| 0.6134 \| 0.1379 \| \| 1.0171 \| 1.0112 \| 10300 \| 1.0011 \| 0.0121 \| 0.1374 \| 0.6132 \| 0.1374 \| \| 1.0563 \| 1.0210 \| 10400 \| 1.0131 \| 0.0121 \| 0.1354 \| 0.6099 \| 0.1354 \| \| 0.9535 \| 1.0308 \| 10500 \| 1.0031 \| 0.0121 \| 0.1426 \| 0.6157 \| 0.1426 \| \| 1.0147 \| 1.0406 \| 10600 \| 0.9979 \| 0.0121 \| 0.1428 \| 0.6192 \| 0.1428 \| \| 0.9887 \| 1.0505 \| 10700 \| 0.9860 \| 0.0121 \| 0.1468 \| 0.6231 \| 0.1468 \| \| 0.96 \| 1.0603 \| 10800 \| 0.9923 \| 0.0121 \| 0.1414 \| 0.6188 \| 0.1414 \| \| 1.019 \| 1.0701 \| 10900 \| 0.9954 \| 0.0121 \| 0.1402 \| 0.6199 \| 0.1402 \| \| 1.0251 \| 1.0799 \| 11000 \| 1.0069 \| 0.0121 \| 0.1338 \| 0.6098 \| 0.1338 \| \| 1.0115 \| 1.0897 \| 11100 \| 1.0059 \| 0.0121 \| 0.1314 \| 0.6142 \| 0.1314 \| \| 1.117 \| 1.0995 \| 11200 \| 1.0123 \| 0.0121 \| 0.1337 \| 0.6132 \| 0.1337 \| \| 1.0679 \| 1.1094 \| 11300 \| 0.9997 \| 0.0121 \| 0.1284 \| 0.6158 \| 0.1284 \| \| 0.9931 \| 1.1192 \| 11400 \| 1.0009 \| 0.0121 \| 0.1399 \| 0.6153 \| 0.1399 \| \| 0.9517 \| 1.1290 \| 11500 \| 0.9963 \| 0.0121 \| 0.1386 \| 0.6183 \| 0.1386 \| \| 1.0001 \| 1.1388 \| 11600 \| 0.9802 \| 0.0121 \| 0.1403 \| 0.6220 \| 0.1403 \| \| 1.0511 \| 1.1486 \| 11700 \| 0.9988 \| 0.0121 \| 0.1293 \| 0.6131 \| 0.1293 \| \| 0.9338 \| 1.1585 \| 11800 \| 0.9964 \| 0.0121 \| 0.1368 \| 0.6152 \| 0.1368 \| \| 1.0301 \| 1.1683 \| 11900 \| 0.9983 \| 0.0121 \| 0.1347 \| 0.6168 \| 0.1347 \| \| 0.9509 \| 1.1781 \| 12000 \| 0.9915 \| 0.0121 \| 0.1410 \| 0.6199 \| 0.1410 \| \| 1.0277 \| 1.1879 \| 12100 \| 0.9802 \| 0.0121 \| 0.1450 \| 0.6224 \| 0.1450 \| \| 0.8848 \| 1.1977 \| 12200 \| 0.9875 \| 0.0121 \| 0.1401 \| 0.6213 \| 0.1401 \| \| 0.9449 \| 1.2075 \| 12300 \| 0.9951 \| 0.0121 \| 0.1410 \| 0.6181 \| 0.1410 \| \| 0.9625 \| 1.2174 \| 12400 \| 0.9794 \| 0.0121 \| 0.1434 \| 0.6243 \| 0.1434 \| \| 1.0681 \| 1.2272 \| 12500 \| 0.9966 \| 0.0121 \| 0.1361 \| 0.6185 \| 0.1361 \| \| 0.9168 \| 1.2370 \| 12600 \| 0.9991 \| 0.0121 \| 0.1346 \| 0.6184 \| 0.1346 \| \| 0.967 \| 1.2468 \| 12700 \| 0.9908 \| 0.0121 \| 0.1353 \| 0.6200 \| 0.1353 \| \| 1.0241 \| 1.2566 \| 12800 \| 0.9971 \| 0.0121 \| 0.1462 \| 0.6214 \| 0.1462 \| \| 0.993 \| 1.2664 \| 12900 \| 0.9854 \| 0.0121 \| 0.1417 \| 0.6244 \| 0.1417 \| \| 0.9463 \| 1.2763 \| 13000 \| 0.9922 \| 0.0121 \| 0.1321 \| 0.6154 \| 0.1321 \| \| 1.0137 \| 1.2861 \| 13100 \| 1.0065 \| 0.0121 \| 0.1323 \| 0.6179 \| 0.1323 \| \| 1.0213 \| 1.2959 \| 13200 \| 0.9849 \| 0.0121 \| 0.1358 \| 0.6204 \| 0.1358 \| \| 0.9703 \| 1.3057 \| 13300 \| 0.9956 \| 0.0121 \| 0.1368 \| 0.6178 \| 0.1368 \| \| 0.9907 \| 1.3155 \| 13400 \| 0.9831 \| 0.0121 \| 0.1434 \| 0.6218 \| 0.1434 \| \| 0.9598 \| 1.3253 \| 13500 \| 0.9821 \| 0.0121 \| 0.1403 \| 0.6233 \| 0.1403 \| \| 0.9971 \| 1.3352 \| 13600 \| 0.9948 \| 0.0121 \| 0.1385 \| 0.6155 \| 0.1385 \| \| 0.9906 \| 1.3450 \| 13700 \| 0.9914 \| 0.0121 \| 0.1327 \| 0.6210 \| 0.1327 \| \| 1.0052 \| 1.3548 \| 13800 \| 0.9698 \| 0.0121 \| 0.1428 \| 0.6295 \| 0.1428 \| \| 0.9364 \| 1.3646 \| 13900 \| 0.9682 \| 0.0121 \| 0.1494 \| 0.6284 \| 0.1494 \| \| 0.9855 \| 1.3744 \| 14000 \| 0.9829 \| 0.0121 \| 0.1354 \| 0.6193 \| 0.1354 \| \| 1.0504 \| 1.3843 \| 14100 \| 1.0161 \| 0.0121 \| 0.1326 \| 0.6110 \| 0.1326 \| \| 0.9568 \| 1.3941 \| 14200 \| 0.9870 \| 0.0121 \| 0.1469 \| 0.6204 \| 0.1469 \| \| 0.9925 \| 1.4039 \| 14300 \| 0.9874 \| 0.0121 \| 0.1435 \| 0.6187 \| 0.1435 \| \| 0.9385 \| 1.4137 \| 14400 \| 0.9771 \| 0.0121 \| 0.1435 \| 0.6235 \| 0.1435 \| \| 1.0083 \| 1.4235 \| 14500 \| 0.9787 \| 0.0121 \| 0.1370 \| 0.6225 \| 0.1370 \| \| 1.0438 \| 1.4333 \| 14600 \| 0.9856 \| 0.0121 \| 0.1295 \| 0.6157 \| 0.1295 \| \| 0.9362 \| 1.4432 \| 14700 \| 0.9799 \| 0.0121 \| 0.1456 \| 0.6255 \| 0.1456 \| \| 0.97 \| 1.4530 \| 14800 \| 0.9902 \| 0.0121 \| 0.1388 \| 0.6190 \| 0.1388 \| \| 1.0245 \| 1.4628 \| 14900 \| 1.0147 \| 0.0121 \| 0.1354 \| 0.6148 \| 0.1354 \| \| 0.9025 \| 1.4726 \| 15000 \| 0.9620 \| 0.0121 \| 0.1465 \| 0.6282 \| 0.1465 \| \| 0.981 \| 1.4824 \| 15100 \| 0.9800 \| 0.0121 \| 0.1432 \| 0.6198 \| 0.1432 \| \| 0.9617 \| 1.4922 \| 15200 \| 0.9749 \| 0.0121 \| 0.1438 \| 0.6253 \| 0.1438 \| \| 0.9422 \| 1.5021 \| 15300 \| 0.9753 \| 0.0121 \| 0.1438 \| 0.6235 \| 0.1438 \| \| 1.0265 \| 1.5119 \| 15400 \| 0.9643 \| 0.0121 \| 0.1471 \| 0.6279 \| 0.1471 \| \| 1.0726 \| 1.5217 \| 15500 \| 0.9658 \| 0.0121 \| 0.1452 \| 0.6290 \| 0.1452 \| \| 0.9245 \| 1.5315 \| 15600 \| 0.9633 \| 0.0121 \| 0.1534 \| 0.6294 \| 0.1534 \| \| 1.016 \| 1.5413 \| 15700 \| 0.9809 \| 0.0121 \| 0.1421 \| 0.6215 \| 0.1421 \| \| 0.9818 \| 1.5511 \| 15800 \| 0.9862 \| 0.0121 \| 0.1377 \| 0.6172 \| 0.1377 \| \| 0.9433 \| 1.5610 \| 15900 \| 0.9748 \| 0.0121 \| 0.1408 \| 0.6218 \| 0.1408 \| \| 0.9866 \| 1.5708 \| 16000 \| 0.9823 \| 0.0121 \| 0.1495 \| 0.6269 \| 0.1495 \| \| 0.9928 \| 1.5806 \| 16100 \| 0.9679 \| 0.0121 \| 0.1510 \| 0.6298 \| 0.1510 \| \| 0.97 \| 1.5904 \| 16200 \| 0.9827 \| 0.0121 \| 0.1355 \| 0.6177 \| 0.1355 \| \| 0.9551 \| 1.6002 \| 16300 \| 0.9803 \| 0.0121 \| 0.1394 \| 0.6240 \| 0.1394 \| \| 1.0441 \| 1.6101 \| 16400 \| 0.9890 \| 0.0121 \| 0.1356 \| 0.6198 \| 0.1356 \| \| 1.0044 \| 1.6199 \| 16500 \| 0.9757 \| 0.0121 \| 0.1393 \| 0.6208 \| 0.1393 \| \| 0.9966 \| 1.6297 \| 16600 \| 0.9595 \| 0.0121 \| 0.1484 \| 0.6307 \| 0.1484 \| \| 1.0272 \| 1.6395 \| 16700 \| 0.9787 \| 0.0121 \| 0.1463 \| 0.6230 \| 0.1463 \| \| 0.9615 \| 1.6493 \| 16800 \| 0.9805 \| 0.0121 \| 0.1370 \| 0.6180 \| 0.1370 \| \| 0.8994 \| 1.6591 \| 16900 \| 0.9773 \| 0.0121 \| 0.1526 \| 0.6230 \| 0.1526 \| \| 0.9124 \| 1.6690 \| 17000 \| 0.9852 \| 0.0121 \| 0.1455 \| 0.6219 \| 0.1455 \| \| 0.9764 \| 1.6788 \| 17100 \| 0.9638 \| 0.0121 \| 0.1475 \| 0.6296 \| 0.1475 \| \| 0.9907 \| 1.6886 \| 17200 \| 0.9952 \| 0.0121 \| 0.1381 \| 0.6190 \| 0.1381 \| \| 0.939 \| 1.6984 \| 17300 \| 0.9960 \| 0.0121 \| 0.1305 \| 0.6135 \| 0.1305 \| \| 0.8737 \| 1.7082 \| 17400 \| 0.9657 \| 0.0121 \| 0.1461 \| 0.6229 \| 0.1461 \| \| 0.9767 \| 1.7180 \| 17500 \| 0.9627 \| 0.0121 \| 0.1400 \| 0.6245 \| 0.1400 \| \| 0.958 \| 1.7279 \| 17600 \| 0.9632 \| 0.0121 \| 0.1461 \| 0.6273 \| 0.1461 \| \| 1.0461 \| 1.7377 \| 17700 \| 0.9866 \| 0.0121 \| 0.1463 \| 0.6216 \| 0.1463 \| \| 1.0028 \| 1.7475 \| 17800 \| 0.9834 \| 0.0121 \| 0.1375 \| 0.6227 \| 0.1375 \| \| 1.0034 \| 1.7573 \| 17900 \| 0.9781 \| 0.0121 \| 0.1390 \| 0.6218 \| 0.1390 \| \| 0.9821 \| 1.7671 \| 18000 \| 0.9592 \| 0.0121 \| 0.1484 \| 0.6289 \| 0.1484 \| \| 1.0287 \| 1.7769 \| 18100 \| 0.9725 \| 0.0121 \| 0.1470 \| 0.6254 \| 0.1470 \| \| 0.9723 \| 1.7868 \| 18200 \| 0.9728 \| 0.0121 \| 0.1477 \| 0.6240 \| 0.1477 \| \| 1.0263 \| 1.7966 \| 18300 \| 0.9656 \| 0.0121 \| 0.1412 \| 0.6257 \| 0.1412 \| \| 0.9584 \| 1.8064 \| 18400 \| 0.9598 \| 0.0121 \| 0.1470 \| 0.6322 \| 0.1470 \| \| 0.9164 \| 1.8162 \| 18500 \| 0.9788 \| 0.0121 \| 0.1453 \| 0.6255 \| 0.1453 \| \| 0.9565 \| 1.8260 \| 18600 \| 0.9697 \| 0.0121 \| 0.1504 \| 0.6283 \| 0.1504 \| \| 0.9243 \| 1.8359 \| 18700 \| 0.9401 \| 0.0121 \| 0.1516 \| 0.6342 \| 0.1516 \| \| 0.8968 \| 1.8457 \| 18800 \| 0.9777 \| 0.0121 \| 0.1437 \| 0.6219 \| 0.1437 \| \| 0.9723 \| 1.8555 \| 18900 \| 0.9620 \| 0.0121 \| 0.1444 \| 0.6256 \| 0.1444 \| \| 0.9894 \| 1.8653 \| 19000 \| 0.9724 \| 0.0121 \| 0.1409 \| 0.6211 \| 0.1409 \| \| 0.9166 \| 1.8751 \| 19100 \| 0.9666 \| 0.0121 \| 0.1453 \| 0.6270 \| 0.1453 \| \| 0.9474 \| 1.8849 \| 19200 \| 0.9608 \| 0.0121 \| 0.1485 \| 0.6305 \| 0.1485 \| \| 1.0545 \| 1.8948 \| 19300 \| 0.9621 \| 0.0121 \| 0.1486 \| 0.6277 \| 0.1486 \| \| 0.9987 \| 1.9046 \| 19400 \| 0.9767 \| 0.0121 \| 0.1481 \| 0.6262 \| 0.1481 \| \| 0.9155 \| 1.9144 \| 19500 \| 0.9611 \| 0.0121 \| 0.1478 \| 0.6287 \| 0.1478 \| \| 0.9585 \| 1.9242 \| 19600 \| 0.9675 \| 0.0121 \| 0.1488 \| 0.6263 \| 0.1488 \| \| 1.0011 \| 1.9340 \| 19700 \| 0.9921 \| 0.0121 \| 0.1326 \| 0.6175 \| 0.1326 \| \| 0.9271 \| 1.9438 \| 19800 \| 0.9526 \| 0.0121 \| 0.1526 \| 0.6305 \| 0.1526 \| \| 0.9035 \| 1.9537 \| 19900 \| 0.9507 \| 0.0121 \| 0.1556 \| 0.6322 \| 0.1556 \| \| 0.8893 \| 1.9635 \| 20000 \| 0.9448 \| 0.0121 \| 0.1499 \| 0.6318 \| 0.1499 \| \| 0.915 \| 1.9733 \| 20100 \| 0.9794 \| 0.0121 \| 0.1490 \| 0.6225 \| 0.1490 \| \| 0.9985 \| 1.9831 \| 20200 \| 0.9754 \| 0.0121 \| 0.1388 \| 0.6213 \| 0.1388 \| \| 0.8801 \| 1.9929 \| 20300 \| 0.9533 \| 0.0121 \| 0.1501 \| 0.6296 \| 0.1501 \| \| 0.9559 \| 2.0027 \| 20400 \| 0.9712 \| 0.0121 \| 0.1423 \| 0.6289 \| 0.1423 \| \| 0.9222 \| 2.0126 \| 20500 \| 0.9513 \| 0.0121 \| 0.1461 \| 0.6321 \| 0.1461 \| \| 0.9745 \| 2.0224 \| 20600 \| 0.9815 \| 0.0121 \| 0.1490 \| 0.6290 \| 0.1490 \| \| 0.8946 \| 2.0322 \| 20700 \| 0.9743 \| 0.0121 \| 0.1403 \| 0.6209 \| 0.1403 \| \| 0.9683 \| 2.0420 \| 20800 \| 0.9473 \| 0.0121 \| 0.1461 \| 0.6330 \| 0.1461 \| \| 0.9867 \| 2.0518 \| 20900 \| 0.9475 \| 0.0121 \| 0.1463 \| 0.6332 \| 0.1463 \| \| 0.8907 \| 2.0617 \| 21000 \| 0.9526 \| 0.0121 \| 0.1528 \| 0.6348 \| 0.1528 \| \| 0.9788 \| 2.0715 \| 21100 \| 0.9630 \| 0.0121 \| 0.1477 \| 0.6275 \| 0.1477 \| \| 1.0092 \| 2.0813 \| 21200 \| 0.9736 \| 0.0121 \| 0.1510 \| 0.6248 \| 0.1510 \| \| 1.0112 \| 2.0911 \| 21300 \| 0.9526 \| 0.0121 \| 0.1514 \| 0.6311 \| 0.1514 \| \| 0.9428 \| 2.1009 \| 21400 \| 0.9563 \| 0.0121 \| 0.1537 \| 0.6324 \| 0.1537 \| \| 0.9599 \| 2.1107 \| 21500 \| 0.9540 \| 0.0121 \| 0.1490 \| 0.6273 \| 0.1490 \| \| 0.8925 \| 2.1206 \| 21600 \| 0.9555 \| 0.0121 \| 0.1442 \| 0.6281 \| 0.1442 \| \| 1.0038 \| 2.1304 \| 21700 \| 0.9669 \| 0.0121 \| 0.1448 \| 0.6274 \| 0.1448 \| \| 0.9397 \| 2.1402 \| 21800 \| 0.9381 \| 0.0121 \| 0.1552 \| 0.6359 \| 0.1552 \| \| 0.9569 \| 2.1500 \| 21900 \| 0.9469 \| 0.0121 \| 0.1505 \| 0.6325 \| 0.1505 \| \| 0.9395 \| 2.1598 \| 22000 \| 0.9608 \| 0.0121 \| 0.1470 \| 0.6275 \| 0.1470 \| \| 0.9211 \| 2.1696 \| 22100 \| 0.9482 \| 0.0121 \| 0.1528 \| 0.6315 \| 0.1528 \| \| 0.8696 \| 2.1795 \| 22200 \| 0.9493 \| 0.0121 \| 0.1527 \| 0.6325 \| 0.1527 \| \| 0.9608 \| 2.1893 \| 22300 \| 0.9597 \| 0.0121 \| 0.1480 \| 0.6302 \| 0.1480 \| \| 1.02 \| 2.1991 \| 22400 \| 0.9853 \| 0.0121 \| 0.1378 \| 0.6196 \| 0.1378 \| \| 0.9805 \| 2.2089 \| 22500 \| 0.9694 \| 0.0121 \| 0.1372 \| 0.6228 \| 0.1372 \| \| 0.9496 \| 2.2187 \| 22600 \| 0.9381 \| 0.0121 \| 0.1544 \| 0.6351 \| 0.1544 \| \| 0.9892 \| 2.2285 \| 22700 \| 0.9810 \| 0.0121 \| 0.1429 \| 0.6232 \| 0.1429 \| \| 0.9565 \| 2.2384 \| 22800 \| 0.9567 \| 0.0121 \| 0.1454 \| 0.6310 \| 0.1454 \| \| 0.9254 \| 2.2482 \| 22900 \| 0.9529 \| 0.0121 \| 0.1510 \| 0.6291 \| 0.1510 \| \| 0.9032 \| 2.2580 \| 23000 \| 0.9515 \| 0.0121 \| 0.1479 \| 0.6335 \| 0.1479 \| \| 0.9845 \| 2.2678 \| 23100 \| 0.9451 \| 0.0121 \| 0.1549 \| 0.6383 \| 0.1549 \| \| 0.9407 \| 2.2776 \| 23200 \| 0.9483 \| 0.0121 \| 0.1472 \| 0.6336 \| 0.1472 \| \| 1.032 \| 2.2875 \| 23300 \| 0.9775 \| 0.0121 \| 0.1359 \| 0.6237 \| 0.1359 \| \| 0.9924 \| 2.2973 \| 23400 \| 0.9406 \| 0.0121 \| 0.1495 \| 0.6336 \| 0.1495 \| \| 0.9701 \| 2.3071 \| 23500 \| 0.9443 \| 0.0121 \| 0.1499 \| 0.6353 \| 0.1499 \| \| 1.0408 \| 2.3169 \| 23600 \| 0.9481 \| 0.0121 \| 0.1498 \| 0.6313 \| 0.1498 \| \| 0.9238 \| 2.3267 \| 23700 \| 0.9537 \| 0.0121 \| 0.1548 \| 0.6313 \| 0.1548 \| \| 0.9766 \| 2.3365 \| 23800 \| 0.9444 \| 0.0121 \| 0.1537 \| 0.6363 \| 0.1537 \| \| 0.9323 \| 2.3464 \| 23900 \| 0.9402 \| 0.0121 \| 0.1505 \| 0.6321 \| 0.1505 \| \| 0.9244 \| 2.3562 \| 24000 \| 0.9423 \| 0.0121 \| 0.1504 \| 0.6372 \| 0.1504 \| \| 0.936 \| 2.3660 \| 24100 \| 0.9427 \| 0.0121 \| 0.1544 \| 0.6351 \| 0.1544 \| \| 1.0031 \| 2.3758 \| 24200 \| 0.9476 \| 0.0121 \| 0.1572 \| 0.6383 \| 0.1572 \| \| 0.9001 \| 2.3856 \| 24300 \| 0.9370 \| 0.0121 \| 0.1438 \| 0.6322 \| 0.1438 \| \| 0.9784 \| 2.3954 \| 24400 \| 0.9549 \| 0.0121 \| 0.1479 \| 0.6304 \| 0.1479 \| \| 0.8829 \| 2.4053 \| 24500 \| 0.9265 \| 0.0121 \| 0.1594 \| 0.6419 \| 0.1594 \| \| 0.923 \| 2.4151 \| 24600 \| 0.9429 \| 0.0121 \| 0.1504 \| 0.6353 \| 0.1504 \| \| 0.9826 \| 2.4249 \| 24700 \| 0.9405 \| 0.0121 \| 0.1546 \| 0.6338 \| 0.1546 \| \| 0.9287 \| 2.4347 \| 24800 \| 0.9376 \| 0.0121 \| 0.1558 \| 0.6381 \| 0.1558 \| \| 0.9229 \| 2.4445 \| 24900 \| 0.9317 \| 0.0121 \| 0.1618 \| 0.6399 \| 0.1618 \| \| 0.9156 \| 2.4543 \| 25000 \| 0.9436 \| 0.0121 \| 0.1506 \| 0.6331 \| 0.1506 \| \| 0.9068 \| 2.4642 \| 25100 \| 0.9839 \| 0.0121 \| 0.1399 \| 0.6214 \| 0.1399 \| \| 0.9131 \| 2.4740 \| 25200 \| 0.9196 \| 0.0121 \| 0.1576 \| 0.6414 \| 0.1576 \| \| 0.8854 \| 2.4838 \| 25300 \| 0.9454 \| 0.0121 \| 0.1521 \| 0.6343 \| 0.1521 \| \| 0.9291 \| 2.4936 \| 25400 \| 0.9533 \| 0.0121 \| 0.1402 \| 0.6274 \| 0.1402 \| \| 0.9474 \| 2.5034 \| 25500 \| 0.9558 \| 0.0121 \| 0.1526 \| 0.6311 \| 0.1526 \| \| 0.9155 \| 2.5133 \| 25600 \| 0.9469 \| 0.0121 \| 0.1607 \| 0.6360 \| 0.1607 \| \| 0.9235 \| 2.5231 \| 25700 \| 0.9403 \| 0.0121 \| 0.1492 \| 0.6339 \| 0.1492 \| \| 0.9101 \| 2.5329 \| 25800 \| 0.9544 \| 0.0121 \| 0.1438 \| 0.6291 \| 0.1438 \| \| 0.9359 \| 2.5427 \| 25900 \| 0.9438 \| 0.0121 \| 0.1539 \| 0.6382 \| 0.1539 \| \| 0.8916 \| 2.5525 \| 26000 \| 0.9364 \| 0.0121 \| 0.1507 \| 0.6349 \| 0.1507 \| \| 0.8944 \| 2.5623 \| 26100 \| 0.9561 \| 0.0121 \| 0.1404 \| 0.6297 \| 0.1404 \| \| 0.9263 \| 2.5722 \| 26200 \| 0.9661 \| 0.0121 \| 0.1534 \| 0.6310 \| 0.1534 \| \| 0.9523 \| 2.5820 \| 26300 \| 0.9413 \| 0.0121 \| 0.1470 \| 0.6315 \| 0.1470 \| \| 0.9576 \| 2.5918 \| 26400 \| 0.9706 \| 0.0121 \| 0.1462 \| 0.6226 \| 0.1462 \| \| 0.9309 \| 2.6016 \| 26500 \| 0.9487 \| 0.0121 \| 0.1564 \| 0.6350 \| 0.1564 \| \| 0.9006 \| 2.6114 \| 26600 \| 0.9385 \| 0.0121 \| 0.1535 \| 0.6320 \| 0.1535 \| \| 0.8887 \| 2.6212 \| 26700 \| 0.9554 \| 0.0121 \| 0.1512 \| 0.6343 \| 0.1512 \| \| 0.9398 \| 2.6311 \| 26800 \| 0.9373 \| 0.0121 \| 0.1539 \| 0.6373 \| 0.1539 \| \| 0.9373 \| 2.6409 \| 26900 \| 0.9550 \| 0.0121 \| 0.1459 \| 0.6270 \| 0.1459 \| \| 0.9648 \| 2.6507 \| 27000 \| 0.9423 \| 0.0121 \| 0.1508 \| 0.6334 \| 0.1508 \| \| 0.8716 \| 2.6605 \| 27100 \| 0.9331 \| 0.0121 \| 0.1546 \| 0.6360 \| 0.1546 \| \| 0.914 \| 2.6703 \| 27200 \| 0.9313 \| 0.0121 \| 0.1572 \| 0.6392 \| 0.1572 \| \| 0.8962 \| 2.6801 \| 27300 \| 0.9311 \| 0.0121 \| 0.1584 \| 0.6380 \| 0.1584 \| \| 0.9496 \| 2.6900 \| 27400 \| 0.9347 \| 0.0121 \| 0.1561 \| 0.6393 \| 0.1561 \| \| 0.9796 \| 2.6998 \| 27500 \| 0.9494 \| 0.0121 \| 0.1428 \| 0.6282 \| 0.1428 \| \| 0.954 \| 2.7096 \| 27600 \| 0.9220 \| 0.0121 \| 0.1602 \| 0.6412 \| 0.1602 \| \| 0.9002 \| 2.7194 \| 27700 \| 0.9286 \| 0.0121 \| 0.1546 \| 0.6390 \| 0.1546 \| \| 0.9716 \| 2.7292 \| 27800 \| 0.9388 \| 0.0121 \| 0.1562 \| 0.6380 \| 0.1562 \| \| 0.877 \| 2.7391 \| 27900 \| 0.9416 \| 0.0121 \| 0.1494 \| 0.6331 \| 0.1494 \| \| 0.9771 \| 2.7489 \| 28000 \| 0.9317 \| 0.0121 \| 0.1544 \| 0.6374 \| 0.1544 \| \| 0.8698 \| 2.7587 \| 28100 \| 0.9433 \| 0.0121 \| 0.1430 \| 0.6325 \| 0.1430 \| \| 0.8791 \| 2.7685 \| 28200 \| 0.9317 \| 0.0121 \| 0.1483 \| 0.6352 \| 0.1483 \| \| 0.8812 \| 2.7783 \| 28300 \| 0.9563 \| 0.0121 \| 0.1488 \| 0.6286 \| 0.1488 \| \| 0.9155 \| 2.7881 \| 28400 \| 0.9240 \| 0.0121 \| 0.1506 \| 0.6374 \| 0.1506 \| \| 0.9326 \| 2.7980 \| 28500 \| 0.9373 \| 0.0121 \| 0.1508 \| 0.6327 \| 0.1508 \| \| 0.9114 \| 2.8078 \| 28600 \| 0.9188 \| 0.0121 \| 0.1610 \| 0.6399 \| 0.1610 \| \| 0.9256 \| 2.8176 \| 28700 \| 0.9220 \| 0.0121 \| 0.1655 \| 0.6426 \| 0.1655 \| \| 0.9301 \| 2.8274 \| 28800 \| 0.9313 \| 0.0121 \| 0.1506 \| 0.6396 \| 0.1506 \| \| 0.9878 \| 2.8372 \| 28900 \| 0.9335 \| 0.0121 \| 0.1517 \| 0.6351 \| 0.1517 \| \| 0.8843 \| 2.8470 \| 29000 \| 0.9219 \| 0.0121 \| 0.1582 \| 0.6426 \| 0.1582 \| \| 0.8614 \| 2.8569 \| 29100 \| 0.9298 \| 0.0121 \| 0.1566 \| 0.6390 \| 0.1566 \| \| 0.9368 \| 2.8667 \| 29200 \| 0.9302 \| 0.0121 \| 0.1501 \| 0.6394 \| 0.1501 \| \| 0.9222 \| 2.8765 \| 29300 \| 0.9263 \| 0.0121 \| 0.1513 \| 0.6389 \| 0.1513 \| \| 0.8301 \| 2.8863 \| 29400 \| 0.9288 \| 0.0121 \| 0.1523 \| 0.6391 \| 0.1523 \| \| 0.8956 \| 2.8961 \| 29500 \| 0.9437 \| 0.0121 \| 0.1512 \| 0.6318 \| 0.1512 \| \| 0.8816 \| 2.9059 \| 29600 \| 0.9209 \| 0.0121 \| 0.1539 \| 0.6405 \| 0.1539 \| \| 0.9365 \| 2.9158 \| 29700 \| 0.9408 \| 0.0121 \| 0.1511 \| 0.6358 \| 0.1511 \| \| 0.9062 \| 2.9256 \| 29800 \| 0.9419 \| 0.0121 \| 0.1447 \| 0.6334 \| 0.1447 \| \| 0.9414 \| 2.9354 \| 29900 \| 0.9470 \| 0.0121 \| 0.1459 \| 0.6344 \| 0.1459 \| \| 1.0127 \| 2.9452 \| 30000 \| 0.9288 \| 0.0121 \| 0.1646 \| 0.6422 \| 0.1646 \| \| 0.9034 \| 2.9550 \| 30100 \| 0.9362 \| 0.0121 \| 0.1586 \| 0.6366 \| 0.1586 \| \| 0.9152 \| 2.9649 \| 30200 \| 0.9244 \| 0.0121 \| 0.1577 \| 0.6372 \| 0.1577 \| \| 0.9419 \| 2.9747 \| 30300 \| 0.9267 \| 0.0121 \| 0.1562 \| 0.6386 \| 0.1562 \| \| 0.9524 \| 2.9845 \| 30400 \| 0.9200 \| 0.0121 \| 0.1539 \| 0.6378 \| 0.1539 \| \| 1.002 \| 2.9943 \| 30500 \| 0.9388 \| 0.0121 \| 0.1501 \| 0.6345 \| 0.1501 \| \| 1.0183 \| 3.0041 \| 30600 \| 0.9252 \| 0.0121 \| 0.1542 \| 0.6376 \| 0.1542 \| \| 0.9304 \| 3.0139 \| 30700 \| 0.9251 \| 0.0121 \| 0.1566 \| 0.6404 \| 0.1566 \| \| 0.9888 \| 3.0238 \| 30800 \| 0.9469 \| 0.0121 \| 0.1483 \| 0.6302 \| 0.1483 \| \| 1.0105 \| 3.0336 \| 30900 \| 0.9161 \| 0.0121 \| 0.1588 \| 0.6423 \| 0.1588 \| \| 0.8535 \| 3.0434 \| 31000 \| 0.9255 \| 0.0121 \| 0.1588 \| 0.6418 \| 0.1588 \| \| 0.9747 \| 3.0532 \| 31100 \| 0.9314 \| 0.0121 \| 0.1579 \| 0.6385 \| 0.1579 \| \| 0.9358 \| 3.0630 \| 31200 \| 0.9202 \| 0.0121 \| 0.1541 \| 0.6407 \| 0.1541 \| \| 0.9373 \| 3.0728 \| 31300 \| 0.9098 \| 0.0121 \| 0.1622 \| 0.6454 \| 0.1622 \| \| 0.9595 \| 3.0827 \| 31400 \| 0.9125 \| 0.0121 \| 0.1675 \| 0.6450 \| 0.1675 \| \| 0.9259 \| 3.0925 \| 31500 \| 0.9296 \| 0.0121 \| 0.1528 \| 0.6378 \| 0.1528 \| \| 0.907 \| 3.1023 \| 31600 \| 0.9319 \| 0.0121 \| 0.1604 \| 0.6367 \| 0.1604 \| \| 0.9075 \| 3.1121 \| 31700 \| 0.9384 \| 0.0121 \| 0.1566 \| 0.6375 \| 0.1566 \| \| 0.9477 \| 3.1219 \| 31800 \| 0.9312 \| 0.0121 \| 0.1491 \| 0.6361 \| 0.1491 \| \| 0.9008 \| 3.1317 \| 31900 \| 0.9270 \| 0.0121 \| 0.1573 \| 0.6387 \| 0.1573 \| \| 0.8841 \| 3.1416 \| 32000 \| 0.9197 \| 0.0121 \| 0.1574 \| 0.6417 \| 0.1574 \| \| 0.9321 \| 3.1514 \| 32100 \| 0.9275 \| 0.0121 \| 0.1518 \| 0.6385 \| 0.1518 \| \| 0.975 \| 3.1612 \| 32200 \| 0.9208 \| 0.0121 \| 0.1526 \| 0.6429 \| 0.1526 \| \| 0.9376 \| 3.1710 \| 32300 \| 0.9322 \| 0.0121 \| 0.1555 \| 0.6395 \| 0.1555 \| \| 0.9798 \| 3.1808 \| 32400 \| 0.9341 \| 0.0121 \| 0.1505 \| 0.6336 \| 0.1505 \| \| 0.9683 \| 3.1907 \| 32500 \| 0.9319 \| 0.0121 \| 0.1446 \| 0.6342 \| 0.1446 \| \| 0.9052 \| 3.2005 \| 32600 \| 0.9151 \| 0.0121 \| 0.1602 \| 0.6426 \| 0.1602 \| \| 0.9259 \| 3.2103 \| 32700 \| 0.9253 \| 0.0121 \| 0.1510 \| 0.6389 \| 0.1510 \| \| 0.8892 \| 3.2201 \| 32800 \| 0.9094 \| 0.0121 \| 0.1595 \| 0.6447 \| 0.1595 \| \| 0.9092 \| 3.2299 \| 32900 \| 0.9113 \| 0.0121 \| 0.1606 \| 0.6431 \| 0.1606 \| \| 0.9226 \| 3.2397 \| 33000 \| 0.9349 \| 0.0121 \| 0.1568 \| 0.6371 \| 0.1568 \| \| 0.9054 \| 3.2496 \| 33100 \| 0.9350 \| 0.0121 \| 0.1559 \| 0.6360 \| 0.1559 \| \| 0.8966 \| 3.2594 \| 33200 \| 0.9317 \| 0.0121 \| 0.1475 \| 0.6367 \| 0.1475 \| \| 0.9313 \| 3.2692 \| 33300 \| 0.9153 \| 0.0121 \| 0.1628 \| 0.6434 \| 0.1628 \| \| 0.9739 \| 3.2790 \| 33400 \| 0.9286 \| 0.0121 \| 0.1539 \| 0.6368 \| 0.1539 \| \| 0.917 \| 3.2888 \| 33500 \| 0.9192 \| 0.0121 \| 0.1572 \| 0.6390 \| 0.1572 \| \| 0.9198 \| 3.2986 \| 33600 \| 0.9014 \| 0.0121 \| 0.1684 \| 0.6486 \| 0.1684 \| \| 0.8973 \| 3.3085 \| 33700 \| 0.9252 \| 0.0121 \| 0.1617 \| 0.6419 \| 0.1617 \| \| 0.9723 \| 3.3183 \| 33800 \| 0.9220 \| 0.0121 \| 0.1584 \| 0.6423 \| 0.1584 \| \| 0.9152 \| 3.3281 \| 33900 \| 0.9562 \| 0.0121 \| 0.1345 \| 0.6292 \| 0.1345 \| \| 0.9201 \| 3.3379 \| 34000 \| 0.9187 \| 0.0121 \| 0.1563 \| 0.6411 \| 0.1563 \| \| 0.8978 \| 3.3477 \| 34100 \| 0.9338 \| 0.0121 \| 0.1598 \| 0.6381 \| 0.1598 \| \| 0.886 \| 3.3575 \| 34200 \| 0.9305 \| 0.0121 \| 0.1597 \| 0.6390 \| 0.1597 \| \| 0.9052 \| 3.3674 \| 34300 \| 0.9304 \| 0.0121 \| 0.1573 \| 0.6402 \| 0.1573 \| \| 0.9958 \| 3.3772 \| 34400 \| 0.9293 \| 0.0121 \| 0.1573 \| 0.6406 \| 0.1573 \| \| 0.9306 \| 3.3870 \| 34500 \| 0.9252 \| 0.0121 \| 0.1651 \| 0.6403 \| 0.1651 \| \| 0.8415 \| 3.3968 \| 34600 \| 0.9252 \| 0.0121 \| 0.1568 \| 0.6394 \| 0.1568 \| \| 0.9207 \| 3.4066 \| 34700 \| 0.9251 \| 0.0121 \| 0.1546 \| 0.6394 \| 0.1546 \| \| 0.9702 \| 3.4165 \| 34800 \| 0.9296 \| 0.0121 \| 0.1609 \| 0.6383 \| 0.1609 \| \| 1.0098 \| 3.4263 \| 34900 \| 0.9228 \| 0.0121 \| 0.1560 \| 0.6407 \| 0.1560 \| \| 0.9492 \| 3.4361 \| 35000 \| 0.9290 \| 0.0121 \| 0.1572 \| 0.6383 \| 0.1572 \| \| 0.9288 \| 3.4459 \| 35100 \| 0.9511 \| 0.0121 \| 0.1497 \| 0.6310 \| 0.1497 \| \| 0.8967 \| 3.4557 \| 35200 \| 0.9255 \| 0.0121 \| 0.1579 \| 0.6395 \| 0.1579 \| \| 0.9164 \| 3.4655 \| 35300 \| 0.9464 \| 0.0121 \| 0.1541 \| 0.6335 \| 0.1541 \| \| 0.8837 \| 3.4754 \| 35400 \| 0.9199 \| 0.0121 \| 0.1446 \| 0.6367 \| 0.1446 \| \| 0.9106 \| 3.4852 \| 35500 \| 0.9277 \| 0.0121 \| 0.1560 \| 0.6401 \| 0.1560 \| \| 0.9453 \| 3.4950 \| 35600 \| 0.9446 \| 0.0121 \| 0.1459 \| 0.6302 \| 0.1459 \| \| 0.8714 \| 3.5048 \| 35700 \| 0.9027 \| 0.0121 \| 0.1653 \| 0.6483 \| 0.1653 \| \| 0.9315 \| 3.5146 \| 35800 \| 0.9343 \| 0.0121 \| 0.1435 \| 0.6347 \| 0.1435 \| \| 0.9 \| 3.5244 \| 35900 \| 0.9182 \| 0.0121 \| 0.1539 \| 0.6415 \| 0.1539 \| \| 0.9719 \| 3.5343 \| 36000 \| 0.9792 \| 0.0121 \| 0.1390 \| 0.6188 \| 0.1390 \| \| 0.8949 \| 3.5441 \| 36100 \| 0.9056 \| 0.0121 \| 0.1644 \| 0.6482 \| 0.1644 \| \| 0.9956 \| 3.5539 \| 36200 \| 0.9319 \| 0.0121 \| 0.1512 \| 0.6358 \| 0.1512 \| \| 0.885 \| 3.5637 \| 36300 \| 0.9130 \| 0.0121 \| 0.1649 \| 0.6457 \| 0.1649 \| \| 0.9 \| 3.5735 \| 36400 \| 0.9204 \| 0.0121 \| 0.1555 \| 0.6383 \| 0.1555 \| \| 0.8741 \| 3.5833 \| 36500 \| 0.9177 \| 0.0121 \| 0.1567 \| 0.6416 \| 0.1567 \| \| 0.8922 \| 3.5932 \| 36600 \| 0.9259 \| 0.0121 \| 0.1521 \| 0.6368 \| 0.1521 \| \| 0.9051 \| 3.6030 \| 36700 \| 0.9211 \| 0.0121 \| 0.1553 \| 0.6398 \| 0.1553 \| \| 1.0028 \| 3.6128 \| 36800 \| 0.9315 \| 0.0121 \| 0.1573 \| 0.6389 \| 0.1573 \| \| 0.8824 \| 3.6226 \| 36900 \| 0.9306 \| 0.0121 \| 0.1620 \| 0.6402 \| 0.1620 \| \| 0.839 \| 3.6324 \| 37000 \| 0.9052 \| 0.0121 \| 0.1654 \| 0.6472 \| 0.1654 \| \| 0.9111 \| 3.6423 \| 37100 \| 0.9097 \| 0.0121 \| 0.1552 \| 0.6444 \| 0.1552 \| \| 0.8494 \| 3.6521 \| 37200 \| 0.9137 \| 0.0121 \| 0.1566 \| 0.6418 \| 0.1566 \| \| 0.8726 \| 3.6619 \| 37300 \| 0.8948 \| 0.0121 \| 0.1622 \| 0.6473 \| 0.1622 \| \| 0.9294 \| 3.6717 \| 37400 \| 0.9186 \| 0.0121 \| 0.1553 \| 0.6425 \| 0.1553 \| \| 0.9176 \| 3.6815 \| 37500 \| 0.9126 \| 0.0121 \| 0.1651 \| 0.6443 \| 0.1651 \| \| 0.8867 \| 3.6913 \| 37600 \| 0.9143 \| 0.0121 \| 0.1583 \| 0.6433 \| 0.1583 \| \| 0.9085 \| 3.7012 \| 37700 \| 0.9157 \| 0.0121 \| 0.1528 \| 0.6409 \| 0.1528 \| \| 0.944 \| 3.7110 \| 37800 \| 0.9161 \| 0.0121 \| 0.1613 \| 0.6429 \| 0.1613 \| \| 0.819 \| 3.7208 \| 37900 \| 0.9066 \| 0.0121 \| 0.1591 \| 0.6434 \| 0.1591 \| \| 0.8937 \| 3.7306 \| 38000 \| 0.9160 \| 0.0121 \| 0.1661 \| 0.6441 \| 0.1661 \| \| 0.9581 \| 3.7404 \| 38100 \| 0.9193 \| 0.0121 \| 0.1571 \| 0.6414 \| 0.1571 \| \| 0.958 \| 3.7502 \| 38200 \| 0.9243 \| 0.0121 \| 0.1554 \| 0.6374 \| 0.1554 \| \| 0.9196 \| 3.7601 \| 38300 \| 0.9255 \| 0.0121 \| 0.1524 \| 0.6414 \| 0.1524 \| \| 1.0096 \| 3.7699 \| 38400 \| 0.9228 \| 0.0121 \| 0.1519 \| 0.6393 \| 0.1519 \| \| 0.9651 \| 3.7797 \| 38500 \| 0.9224 \| 0.0121 \| 0.1539 \| 0.6417 \| 0.1539 \| \| 0.9387 \| 3.7895 \| 38600 \| 0.9258 \| 0.0121 \| 0.1546 \| 0.6371 \| 0.1546 \| \| 0.9042 \| 3.7993 \| 38700 \| 0.9389 \| 0.0121 \| 0.1530 \| 0.6356 \| 0.1530 \| \| 0.9249 \| 3.8091 \| 38800 \| 0.9316 \| 0.0121 \| 0.1480 \| 0.6358 \| 0.1480 \| \| 0.9778 \| 3.8190 \| 38900 \| 0.9109 \| 0.0121 \| 0.1597 \| 0.6426 \| 0.1597 \| \| 0.9166 \| 3.8288 \| 39000 \| 0.9112 \| 0.0121 \| 0.1596 \| 0.6423 \| 0.1596 \| \| 0.949 \| 3.8386 \| 39100 \| 0.9153 \| 0.0121 \| 0.1579 \| 0.6423 \| 0.1579 \| \| 0.982 \| 3.8484 \| 39200 \| 0.9246 \| 0.0121 \| 0.1504 \| 0.6398 \| 0.1504 \| \| 0.9135 \| 3.8582 \| 39300 \| 0.9201 \| 0.0121 \| 0.1577 \| 0.6407 \| 0.1577 \| \| 0.9437 \| 3.8681 \| 39400 \| 0.9165 \| 0.0121 \| 0.1550 \| 0.6394 \| 0.1550 \| \| 0.9937 \| 3.8779 \| 39500 \| 0.8967 \| 0.0121 \| 0.1657 \| 0.6482 \| 0.1657 \| \| 0.8937 \| 3.8877 \| 39600 \| 0.9084 \| 0.0121 \| 0.1686 \| 0.6486 \| 0.1686 \| \| 0.901 \| 3.8975 \| 39700 \| 0.9164 \| 0.0121 \| 0.1579 \| 0.6393 \| 0.1579 \| \| 0.9045 \| 3.9073 \| 39800 \| 0.9013 \| 0.0121 \| 0.1612 \| 0.6472 \| 0.1612 \| \| 1.0313 \| 3.9171 \| 39900 \| 0.9325 \| 0.0121 \| 0.1548 \| 0.6349 \| 0.1548 \| \| 1.0183 \| 3.9270 \| 40000 \| 0.9256 \| 0.0121 \| 0.1557 \| 0.6380 \| 0.1557 \| \| 0.9019 \| 3.9368 \| 40100 \| 0.9097 \| 0.0121 \| 0.1556 \| 0.6420 \| 0.1556 \| \| 0.9219 \| 3.9466 \| 40200 \| 0.9354 \| 0.0121 \| 0.1573 \| 0.6370 \| 0.1573 \| \| 0.9101 \| 3.9564 \| 40300 \| 0.9286 \| 0.0121 \| 0.1589 \| 0.6416 \| 0.1589 \| \| 0.914 \| 3.9662 \| 40400 \| 0.9033 \| 0.0121 \| 0.1658 \| 0.6473 \| 0.1658 \| \| 0.8996 \| 3.9760 \| 40500 \| 0.9187 \| 0.0121 \| 0.1578 \| 0.6400 \| 0.1578 \| \| 0.9002 \| 3.9859 \| 40600 \| 0.9055 \| 0.0121 \| 0.1612 \| 0.6459 \| 0.1612 \| \| 1.0091 \| 3.9957 \| 40700 \| 0.8948 \| 0.0121 \| 0.1673 \| 0.6523 \| 0.1673 \| \| 0.8874 \| 4.0055 \| 40800 \| 0.9226 \| 0.0121 \| 0.1566 \| 0.6420 \| 0.1566 \| \| 0.9004 \| 4.0153 \| 40900 \| 0.9081 \| 0.0121 \| 0.1594 \| 0.6424 \| 0.1594 \| \| 0.962 \| 4.0251 \| 41000 \| 0.9222 \| 0.0121 \| 0.1615 \| 0.6436 \| 0.1615 \| \| 0.9959 \| 4.0349 \| 41100 \| 0.9125 \| 0.0121 \| 0.1566 \| 0.6438 \| 0.1566 \| \| 0.8541 \| 4.0448 \| 41200 \| 0.9039 \| 0.0121 \| 0.1675 \| 0.6464 \| 0.1675 \| \| 0.8316 \| 4.0546 \| 41300 \| 0.9225 \| 0.0121 \| 0.1498 \| 0.6367 \| 0.1498 \| \| 0.8869 \| 4.0644 \| 41400 \| 0.9282 \| 0.0121 \| 0.1478 \| 0.6346 \| 0.1478 \| \| 0.9328 \| 4.0742 \| 41500 \| 0.9237 \| 0.0121 \| 0.1632 \| 0.6409 \| 0.1632 \| \| 0.8534 \| 4.0840 \| 41600 \| 0.8998 \| 0.0121 \| 0.1693 \| 0.6488 \| 0.1693 \| \| 1.002 \| 4.0939 \| 41700 \| 0.9398 \| 0.0121 \| 0.1501 \| 0.6356 \| 0.1501 \| \| 0.8411 \| 4.1037 \| 41800 \| 0.9140 \| 0.0121 \| 0.1604 \| 0.6425 \| 0.1604 \| \| 0.8828 \| 4.1135 \| 41900 \| 0.8929 \| 0.0121 \| 0.1653 \| 0.6490 \| 0.1653 \| \| 0.8654 \| 4.1233 \| 42000 \| 0.9065 \| 0.0121 \| 0.1702 \| 0.6482 \| 0.1702 \| \| 0.8602 \| 4.1331 \| 42100 \| 0.9147 \| 0.0121 \| 0.1546 \| 0.6404 \| 0.1546 \| \| 0.8268 \| 4.1429 \| 42200 \| 0.9238 \| 0.0121 \| 0.1540 \| 0.6402 \| 0.1540 \| \| 0.9743 \| 4.1528 \| 42300 \| 0.9081 \| 0.0121 \| 0.1629 \| 0.6435 \| 0.1629 \| \| 0.9224 \| 4.1626 \| 42400 \| 0.9007 \| 0.0121 \| 0.1673 \| 0.6473 \| 0.1673 \| \| 0.9082 \| 4.1724 \| 42500 \| 0.9077 \| 0.0121 \| 0.1610 \| 0.6457 \| 0.1610 \| \| 0.9227 \| 4.1822 \| 42600 \| 0.9160 \| 0.0121 \| 0.1595 \| 0.6404 \| 0.1595 \| \| 0.925 \| 4.1920 \| 42700 \| 0.9133 \| 0.0121 \| 0.1625 \| 0.6450 \| 0.1625 \| \| 0.9158 \| 4.2018 \| 42800 \| 0.8978 \| 0.0121 \| 0.1539 \| 0.6438 \| 0.1539 \| \| 0.96 \| 4.2117 \| 42900 \| 0.9021 \| 0.0121 \| 0.1567 \| 0.6459 \| 0.1567 \| \| 0.8507 \| 4.2215 \| 43000 \| 0.8975 \| 0.0121 \| 0.1681 \| 0.6522 \| 0.1681 \| \| 0.9323 \| 4.2313 \| 43100 \| 0.8988 \| 0.0121 \| 0.1686 \| 0.6495 \| 0.1686 \| \| 0.9649 \| 4.2411 \| 43200 \| 0.9099 \| 0.0121 \| 0.1672 \| 0.6460 \| 0.1672 \| \| 0.826 \| 4.2509 \| 43300 \| 0.9070 \| 0.0121 \| 0.1641 \| 0.6456 \| 0.1641 \| \| 0.8683 \| 4.2608 \| 43400 \| 0.9204 \| 0.0121 \| 0.1648 \| 0.6445 \| 0.1648 \| \| 0.9506 \| 4.2706 \| 43500 \| 0.8995 \| 0.0121 \| 0.1723 \| 0.6487 \| 0.1723 \| \| 0.9039 \| 4.2804 \| 43600 \| 0.9058 \| 0.0121 \| 0.1627 \| 0.6459 \| 0.1627 \| \| 0.9048 \| 4.2902 \| 43700 \| 0.8986 \| 0.0121 \| 0.1596 \| 0.6452 \| 0.1596 \| \| 0.9044 \| 4.3000 \| 43800 \| 0.9013 \| 0.0121 \| 0.1676 \| 0.6490 \| 0.1676 \| \| 0.9858 \| 4.3098 \| 43900 \| 0.9035 \| 0.0121 \| 0.1615 \| 0.6445 \| 0.1615 \| \| 0.8395 \| 4.3197 \| 44000 \| 0.9093 \| 0.0121 \| 0.1624 \| 0.6480 \| 0.1624 \| \| 0.8611 \| 4.3295 \| 44100 \| 0.9118 \| 0.0121 \| 0.1598 \| 0.6417 \| 0.1598 \| \| 0.8509 \| 4.3393 \| 44200 \| 0.9076 \| 0.0121 \| 0.1645 \| 0.6467 \| 0.1645 \| \| 0.9728 \| 4.3491 \| 44300 \| 0.9200 \| 0.0121 \| 0.1575 \| 0.6418 \| 0.1575 \| \| 0.9003 \| 4.3589 \| 44400 \| 0.9100 \| 0.0121 \| 0.1624 \| 0.6437 \| 0.1624 \| \| 0.9193 \| 4.3687 \| 44500 \| 0.9107 \| 0.0121 \| 0.1599 \| 0.6457 \| 0.1599 \| \| 0.9026 \| 4.3786 \| 44600 \| 0.9110 \| 0.0121 \| 0.1590 \| 0.6444 \| 0.1590 \| \| 0.9528 \| 4.3884 \| 44700 \| 0.9107 \| 0.0121 \| 0.1622 \| 0.6426 \| 0.1622 \| \| 0.9313 \| 4.3982 \| 44800 \| 0.9001 \| 0.0121 \| 0.1637 \| 0.6475 \| 0.1637 \| \| 0.9124 \| 4.4080 \| 44900 \| 0.9170 \| 0.0121 \| 0.1636 \| 0.6408 \| 0.1636 \| \| 0.8478 \| 4.4178 \| 45000 \| 0.9140 \| 0.0121 \| 0.1650 \| 0.6415 \| 0.1650 \| \| 0.9162 \| 4.4276 \| 45100 \| 0.9020 \| 0.0121 \| 0.1633 \| 0.6450 \| 0.1633 \| \| 0.8447 \| 4.4375 \| 45200 \| 0.9037 \| 0.0121 \| 0.1642 \| 0.6461 \| 0.1642 \| \| 0.9101 \| 4.4473 \| 45300 \| 0.8941 \| 0.0121 \| 0.1657 \| 0.6455 \| 0.1657 \| \| 0.9524 \| 4.4571 \| 45400 \| 0.9030 \| 0.0121 \| 0.1695 \| 0.6466 \| 0.1695 \| \| 0.903 \| 4.4669 \| 45500 \| 0.9293 \| 0.0121 \| 0.1484 \| 0.6388 \| 0.1484 \| \| 0.889 \| 4.4767 \| 45600 \| 0.9204 \| 0.0121 \| 0.1528 \| 0.6379 \| 0.1528 \| \| 0.8872 \| 4.4866 \| 45700 \| 0.9075 \| 0.0121 \| 0.1577 \| 0.6472 \| 0.1577 \| \| 0.929 \| 4.4964 \| 45800 \| 0.9106 \| 0.0121 \| 0.1572 \| 0.6418 \| 0.1572 \| \| 0.9765 \| 4.5062 \| 45900 \| 0.9218 \| 0.0121 \| 0.1510 \| 0.6383 \| 0.1510 \| \| 0.9421 \| 4.5160 \| 46000 \| 0.9214 \| 0.0121 \| 0.1549 \| 0.6389 \| 0.1549 \| \| 0.926 \| 4.5258 \| 46100 \| 0.9083 \| 0.0121 \| 0.1651 \| 0.6468 \| 0.1651 \| \| 0.875 \| 4.5356 \| 46200 \| 0.9061 \| 0.0121 \| 0.1631 \| 0.6459 \| 0.1631 \| \| 0.9123 \| 4.5455 \| 46300 \| 0.9016 \| 0.0121 \| 0.1577 \| 0.6463 \| 0.1577 \| \| 0.8782 \| 4.5553 \| 46400 \| 0.8928 \| 0.0121 \| 0.1677 \| 0.6507 \| 0.1677 \| \| 0.9383 \| 4.5651 \| 46500 \| 0.9129 \| 0.0121 \| 0.1659 \| 0.6448 \| 0.1659 \| \| 0.9099 \| 4.5749 \| 46600 \| 0.9179 \| 0.0121 \| 0.1541 \| 0.6394 \| 0.1541 \| \| 0.8764 \| 4.5847 \| 46700 \| 0.9009 \| 0.0121 \| 0.1708 \| 0.6477 \| 0.1708 \| \| 0.8533 \| 4.5945 \| 46800 \| 0.9146 \| 0.0121 \| 0.1622 \| 0.6450 \| 0.1622 \| \| 0.9333 \| 4.6044 \| 46900 \| 0.8864 \| 0.0121 \| 0.1672 \| 0.6512 \| 0.1672 \| \| 0.8858 \| 4.6142 \| 47000 \| 0.9083 \| 0.0121 \| 0.1586 \| 0.6458 \| 0.1586 \| \| 0.9595 \| 4.6240 \| 47100 \| 0.9188 \| 0.0121 \| 0.1544 \| 0.6387 \| 0.1544 \| \| 0.8651 \| 4.6338 \| 47200 \| 0.9047 \| 0.0121 \| 0.1660 \| 0.6489 \| 0.1660 \| \| 0.9615 \| 4.6436 \| 47300 \| 0.8948 \| 0.0121 \| 0.1675 \| 0.6491 \| 0.1675 \| \| 0.8732 \| 4.6534 \| 47400 \| 0.9062 \| 0.0121 \| 0.1603 \| 0.6438 \| 0.1603 \| \| 0.8855 \| 4.6633 \| 47500 \| 0.9121 \| 0.0121 \| 0.1647 \| 0.6429 \| 0.1647 \| \| 0.9367 \| 4.6731 \| 47600 \| 0.9005 \| 0.0121 \| 0.1595 \| 0.6475 \| 0.1595 \| \| 0.8297 \| 4.6829 \| 47700 \| 0.9032 \| 0.0121 \| 0.1616 \| 0.6448 \| 0.1616 \| \| 0.7984 \| 4.6927 \| 47800 \| 0.9010 \| 0.0121 \| 0.1673 \| 0.6477 \| 0.1673 \| \| 0.8957 \| 4.7025 \| 47900 \| 0.8924 \| 0.0121 \| 0.1605 \| 0.6480 \| 0.1605 \| \| 0.9367 \| 4.7124 \| 48000 \| 0.8977 \| 0.0121 \| 0.1657 \| 0.6475 \| 0.1657 \| \| 0.8833 \| 4.7222 \| 48100 \| 0.9226 \| 0.0121 \| 0.1606 \| 0.6431 \| 0.1606 \| \| 0.9096 \| 4.7320 \| 48200 \| 0.9154 \| 0.0121 \| 0.1603 \| 0.6420 \| 0.1603 \| \| 0.9061 \| 4.7418 \| 48300 \| 0.8940 \| 0.0121 \| 0.1672 \| 0.6503 \| 0.1672 \| \| 0.9153 \| 4.7516 \| 48400 \| 0.9089 \| 0.0121 \| 0.1591 \| 0.6443 \| 0.1591 \| \| 0.9201 \| 4.7614 \| 48500 \| 0.9072 \| 0.0121 \| 0.1597 \| 0.6431 \| 0.1597 \| \| 0.8633 \| 4.7713 \| 48600 \| 0.9093 \| 0.0121 \| 0.1554 \| 0.6425 \| 0.1554 \| \| 0.825 \| 4.7811 \| 48700 \| 0.8990 \| 0.0121 \| 0.1708 \| 0.6501 \| 0.1708 \| \| 0.9 \| 4.7909 \| 48800 \| 0.9117 \| 0.0121 \| 0.1616 \| 0.6446 \| 0.1616 \| \| 0.8629 \| 4.8007 \| 48900 \| 0.8871 \| 0.0121 \| 0.1724 \| 0.6538 \| 0.1724 \| \| 0.9299 \| 4.8105 \| 49000 \| 0.9120 \| 0.0121 \| 0.1512 \| 0.6406 \| 0.1512 \| \| 0.9157 \| 4.8203 \| 49100 \| 0.9028 \| 0.0121 \| 0.1703 \| 0.6484 \| 0.1703 \| \| 0.8361 \| 4.8302 \| 49200 \| 0.9110 \| 0.0121 \| 0.1613 \| 0.6455 \| 0.1613 \| \| 0.9751 \| 4.8400 \| 49300 \| 0.9383 \| 0.0121 \| 0.1497 \| 0.6332 \| 0.1497 \| \| 0.8713 \| 4.8498 \| 49400 \| 0.9014 \| 0.0121 \| 0.1687 \| 0.6472 \| 0.1687 \| \| 0.9002 \| 4.8596 \| 49500 \| 0.9264 \| 0.0121 \| 0.1531 \| 0.6391 \| 0.1531 \| \| 0.8755 \| 4.8694 \| 49600 \| 0.9032 \| 0.0121 \| 0.1627 \| 0.6473 \| 0.1627 \| \| 0.9272 \| 4.8792 \| 49700 \| 0.9072 \| 0.0121 \| 0.1623 \| 0.6450 \| 0.1623 \| \| 0.884 \| 4.8891 \| 49800 \| 0.9052 \| 0.0121 \| 0.1593 \| 0.6451 \| 0.1593 \| \| 0.8862 \| 4.8989 \| 49900 \| 0.9035 \| 0.0121 \| 0.1635 \| 0.6501 \| 0.1635 \| \| 0.9846 \| 4.9087 \| 50000 \| 0.8958 \| 0.0121 \| 0.1740 \| 0.6529 \| 0.1740 \| \| 0.8923 \| 4.9185 \| 50100 \| 0.8934 \| 0.0121 \| 0.1716 \| 0.6501 \| 0.1716 \| \| 0.8942 \| 4.9283 \| 50200 \| 0.8920 \| 0.0121 \| 0.1627 \| 0.6473 \| 0.1627 \| \| 0.91 \| 4.9382 \| 50300 \| 0.9024 \| 0.0121 \| 0.1579 \| 0.6461 \| 0.1579 \| \| 0.9646 \| 4.9480 \| 50400 \| 0.8986 \| 0.0121 \| 0.1604 \| 0.6477 \| 0.1604 \| \| 0.8794 \| 4.9578 \| 50500 \| 0.9007 \| 0.0121 \| 0.1663 \| 0.6498 \| 0.1663 \| \| 0.8963 \| 4.9676 \| 50600 \| 0.9015 \| 0.0121 \| 0.1631 \| 0.6477 \| 0.1631 \| \| 0.8735 \| 4.9774 \| 50700 \| 0.9166 \| 0.0121 \| 0.1577 \| 0.6401 \| 0.1577 \| \| 0.86 \| 4.9872 \| 50800 \| 0.8967 \| 0.0121 \| 0.1637 \| 0.6486 \| 0.1637 \| \| 0.8749 \| 4.9971 \| 50900 \| 0.9178 \| 0.0121 \| 0.1564 \| 0.6424 \| 0.1564 \| \| 0.8572 \| 5.0069 \| 51000 \| 0.9051 \| 0.0121 \| 0.1619 \| 0.6443 \| 0.1619 \| \| 0.8619 \| 5.0167 \| 51100 \| 0.9050 \| 0.0121 \| 0.1655 \| 0.6450 \| 0.1655 \| \| 0.9452 \| 5.0265 \| 51200 \| 0.9003 \| 0.0121 \| 0.1661 \| 0.6489 \| 0.1661 \| \| 0.9752 \| 5.0363 \| 51300 \| 0.9262 \| 0.0121 \| 0.1496 \| 0.6386 \| 0.1496 \| \| 0.9512 \| 5.0461 \| 51400 \| 0.9059 \| 0.0121 \| 0.1627 \| 0.6450 \| 0.1627 \| \| 0.9348 \| 5.0560 \| 51500 \| 0.8991 \| 0.0121 \| 0.1683 \| 0.6502 \| 0.1683 \| \| 0.8832 \| 5.0658 \| 51600 \| 0.9097 \| 0.0121 \| 0.1703 \| 0.6475 \| 0.1703 \| \| 0.859 \| 5.0756 \| 51700 \| 0.8906 \| 0.0121 \| 0.1663 \| 0.6481 \| 0.1663 \| \| 0.9556 \| 5.0854 \| 51800 \| 0.8922 \| 0.0121 \| 0.1673 \| 0.6503 \| 0.1673 \| \| 0.8665 \| 5.0952 \| 51900 \| 0.8930 \| 0.0121 \| 0.1617 \| 0.6467 \| 0.1617 \| \| 0.8041 \| 5.1050 \| 52000 \| 0.9068 \| 0.0121 \| 0.1624 \| 0.6480 \| 0.1624 \| \| 0.9299 \| 5.1149 \| 52100 \| 0.9135 \| 0.0121 \| 0.1596 \| 0.6399 \| 0.1596 \| \| 0.8976 \| 5.1247 \| 52200 \| 0.9117 \| 0.0121 \| 0.1616 \| 0.6433 \| 0.1616 \| \| 0.8793 \| 5.1345 \| 52300 \| 0.8878 \| 0.0121 \| 0.1677 \| 0.6520 \| 0.1677 \| \| 0.9637 \| 5.1443 \| 52400 \| 0.9007 \| 0.0121 \| 0.1606 \| 0.6467 \| 0.1606 \| \| 0.8663 \| 5.1541 \| 52500 \| 0.9148 \| 0.0121 \| 0.1628 \| 0.6436 \| 0.1628 \| \| 0.9043 \| 5.1640 \| 52600 \| 0.8960 \| 0.0121 \| 0.1733 \| 0.6521 \| 0.1733 \| \| 0.9171 \| 5.1738 \| 52700 \| 0.9052 \| 0.0121 \| 0.1614 \| 0.6452 \| 0.1614 \| \| 0.9416 \| 5.1836 \| 52800 \| 0.8995 \| 0.0121 \| 0.1696 \| 0.6510 \| 0.1696 \| \| 0.8467 \| 5.1934 \| 52900 \| 0.8946 \| 0.0121 \| 0.1689 \| 0.6497 \| 0.1689 \| \| 0.94 \| 5.2032 \| 53000 \| 0.9179 \| 0.0121 \| 0.1648 \| 0.6414 \| 0.1648 \| \| 0.917 \| 5.2130 \| 53100 \| 0.8858 \| 0.0121 \| 0.1670 \| 0.6532 \| 0.1670 \| \| 0.891 \| 5.2229 \| 53200 \| 0.9270 \| 0.0121 \| 0.1528 \| 0.6374 \| 0.1528 \| \| 0.9123 \| 5.2327 \| 53300 \| 0.9126 \| 0.0121 \| 0.1534 \| 0.6422 \| 0.1534 \| \| 0.8681 \| 5.2425 \| 53400 \| 0.9152 \| 0.0121 \| 0.1572 \| 0.6426 \| 0.1572 \| \| 0.8683 \| 5.2523 \| 53500 \| 0.9346 \| 0.0121 \| 0.1537 \| 0.6355 \| 0.1537 \| \| 0.9497 \| 5.2621 \| 53600 \| 0.9204 \| 0.0121 \| 0.1525 \| 0.6362 \| 0.1525 \| \| 0.8694 \| 5.2719 \| 53700 \| 0.9116 \| 0.0121 \| 0.1568 \| 0.6435 \| 0.1568 \| \| 0.9946 \| 5.2818 \| 53800 \| 0.8966 \| 0.0121 \| 0.1611 \| 0.6457 \| 0.1611 \| \| 0.8512 \| 5.2916 \| 53900 \| 0.8941 \| 0.0121 \| 0.1626 \| 0.6463 \| 0.1626 \| \| 0.8805 \| 5.3014 \| 54000 \| 0.9016 \| 0.0121 \| 0.1675 \| 0.6466 \| 0.1675 \| \| 0.8873 \| 5.3112 \| 54100 \| 0.8944 \| 0.0121 \| 0.1684 \| 0.6478 \| 0.1684 \| \| 0.973 \| 5.3210 \| 54200 \| 0.8996 \| 0.0121 \| 0.1655 \| 0.6483 \| 0.1655 \| \| 0.8152 \| 5.3308 \| 54300 \| 0.9010 \| 0.0121 \| 0.1714 \| 0.6501 \| 0.1714 \| \| 0.9256 \| 5.3407 \| 54400 \| 0.8908 \| 0.0121 \| 0.1646 \| 0.6506 \| 0.1646 \| \| 0.9047 \| 5.3505 \| 54500 \| 0.9029 \| 0.0121 \| 0.1606 \| 0.6422 \| 0.1606 \| \| 0.8359 \| 5.3603 \| 54600 \| 0.9361 \| 0.0121 \| 0.1551 \| 0.6387 \| 0.1551 \| \| 0.967 \| 5.3701 \| 54700 \| 0.9100 \| 0.0121 \| 0.1651 \| 0.6453 \| 0.1651 \| \| 0.9209 \| 5.3799 \| 54800 \| 0.8941 \| 0.0121 \| 0.1653 \| 0.6501 \| 0.1653 \| \| 0.8872 \| 5.3898 \| 54900 \| 0.8784 \| 0.0121 \| 0.1683 \| 0.6546 \| 0.1683 \| \| 0.8653 \| 5.3996 \| 55000 \| 0.9149 \| 0.0121 \| 0.1584 \| 0.6417 \| 0.1584 \| \| 0.9349 \| 5.4094 \| 55100 \| 0.8910 \| 0.0121 \| 0.1704 \| 0.6518 \| 0.1704 \| \| 0.8506 \| 5.4192 \| 55200 \| 0.8923 \| 0.0121 \| 0.1713 \| 0.6518 \| 0.1713 \| \| 0.9151 \| 5.4290 \| 55300 \| 0.9275 \| 0.0121 \| 0.1561 \| 0.6383 \| 0.1561 \| \| 0.8983 \| 5.4388 \| 55400 \| 0.8984 \| 0.0121 \| 0.1677 \| 0.6493 \| 0.1677 \| \| 0.9229 \| 5.4487 \| 55500 \| 0.8860 \| 0.0121 \| 0.1708 \| 0.6523 \| 0.1708 \| \| 0.9612 \| 5.4585 \| 55600 \| 0.8972 \| 0.0121 \| 0.1700 \| 0.6480 \| 0.1700 \| \| 0.9427 \| 5.4683 \| 55700 \| 0.9043 \| 0.0121 \| 0.1622 \| 0.6479 \| 0.1622 \| \| 0.9168 \| 5.4781 \| 55800 \| 0.9019 \| 0.0121 \| 0.1667 \| 0.6470 \| 0.1667 \| \| 0.878 \| 5.4879 \| 55900 \| 0.9124 \| 0.0121 \| 0.1611 \| 0.6463 \| 0.1611 \| \| 0.9137 \| 5.4977 \| 56000 \| 0.9074 \| 0.0121 \| 0.1610 \| 0.6458 \| 0.1610 \| \| 0.8934 \| 5.5076 \| 56100 \| 0.9205 \| 0.0121 \| 0.1574 \| 0.6446 \| 0.1574 \| \| 0.8924 \| 5.5174 \| 56200 \| 0.9048 \| 0.0121 \| 0.1652 \| 0.6462 \| 0.1652 \| \| 0.8633 \| 5.5272 \| 56300 \| 0.8943 \| 0.0121 \| 0.1682 \| 0.6476 \| 0.1682 \| \| 0.8871 \| 5.5370 \| 56400 \| 0.8909 \| 0.0121 \| 0.1684 \| 0.6520 \| 0.1684 \| \| 0.8729 \| 5.5468 \| 56500 \| 0.8900 \| 0.0121 \| 0.1708 \| 0.6502 \| 0.1708 \| \| 0.9497 \| 5.5566 \| 56600 \| 0.9064 \| 0.0121 \| 0.1584 \| 0.6438 \| 0.1584 \| \| 0.8594 \| 5.5665 \| 56700 \| 0.8851 \| 0.0121 \| 0.1692 \| 0.6524 \| 0.1692 \| \| 0.9684 \| 5.5763 \| 56800 \| 0.8993 \| 0.0121 \| 0.1583 \| 0.6465 \| 0.1583 \| \| 0.8726 \| 5.5861 \| 56900 \| 0.8977 \| 0.0121 \| 0.1657 \| 0.6488 \| 0.1657 \| \| 0.8668 \| 5.5959 \| 57000 \| 0.8910 \| 0.0121 \| 0.1669 \| 0.6498 \| 0.1669 \| \| 0.8763 \| 5.6057 \| 57100 \| 0.8903 \| 0.0121 \| 0.1644 \| 0.6519 \| 0.1644 \| \| 0.8591 \| 5.6156 \| 57200 \| 0.8960 \| 0.0121 \| 0.1682 \| 0.6497 \| 0.1682 \| \| 0.9064 \| 5.6254 \| 57300 \| 0.9095 \| 0.0121 \| 0.1657 \| 0.6467 \| 0.1657 \| \| 0.9166 \| 5.6352 \| 57400 \| 0.9030 \| 0.0121 \| 0.1637 \| 0.6473 \| 0.1637 \| \| 0.8296 \| 5.6450 \| 57500 \| 0.8968 \| 0.0121 \| 0.1619 \| 0.6486 \| 0.1619 \| \| 0.8507 \| 5.6548 \| 57600 \| 0.9010 \| 0.0121 \| 0.1597 \| 0.6465 \| 0.1597 \| \| 0.9312 \| 5.6646 \| 57700 \| 0.8972 \| 0.0121 \| 0.1596 \| 0.6445 \| 0.1596 \| \| 0.8648 \| 5.6745 \| 57800 \| 0.8814 \| 0.0121 \| 0.1690 \| 0.6539 \| 0.1690 \| \| 0.8798 \| 5.6843 \| 57900 \| 0.8859 \| 0.0121 \| 0.1638 \| 0.6538 \| 0.1638 \| \| 0.8728 \| 5.6941 \| 58000 \| 0.8916 \| 0.0121 \| 0.1705 \| 0.6527 \| 0.1705 \| \| 0.899 \| 5.7039 \| 58100 \| 0.8994 \| 0.0121 \| 0.1676 \| 0.6479 \| 0.1676 \| \| 0.8982 \| 5.7137 \| 58200 \| 0.8926 \| 0.0121 \| 0.1727 \| 0.6530 \| 0.1727 \| \| 0.928 \| 5.7235 \| 58300 \| 0.9125 \| 0.0121 \| 0.1718 \| 0.6457 \| 0.1718 \| \| 0.9265 \| 5.7334 \| 58400 \| 0.9196 \| 0.0121 \| 0.1556 \| 0.6383 \| 0.1556 \| \| 0.8782 \| 5.7432 \| 58500 \| 0.8883 \| 0.0121 \| 0.1695 \| 0.6519 \| 0.1695 \| \| 0.9272 \| 5.7530 \| 58600 \| 0.9011 \| 0.0121 \| 0.1653 \| 0.6492 \| 0.1653 \| \| 0.8898 \| 5.7628 \| 58700 \| 0.9017 \| 0.0121 \| 0.1722 \| 0.6504 \| 0.1722 \| \| 0.8858 \| 5.7726 \| 58800 \| 0.8997 \| 0.0121 \| 0.1616 \| 0.6476 \| 0.1616 \| \| 0.9103 \| 5.7824 \| 58900 \| 0.9088 \| 0.0121 \| 0.1587 \| 0.6456 \| 0.1587 \| \| 0.8883 \| 5.7923 \| 59000 \| 0.8969 \| 0.0121 \| 0.1670 \| 0.6484 \| 0.1670 \| \| 0.8947 \| 5.8021 \| 59100 \| 0.9202 \| 0.0121 \| 0.1597 \| 0.6420 \| 0.1597 \| \| 0.9053 \| 5.8119 \| 59200 \| 0.8866 \| 0.0121 \| 0.1706 \| 0.6516 \| 0.1706 \| \| 0.8692 \| 5.8217 \| 59300 \| 0.8748 \| 0.0121 \| 0.1667 \| 0.6529 \| 0.1667 \| \| 0.8223 \| 5.8315 \| 59400 \| 0.8913 \| 0.0121 \| 0.1698 \| 0.6524 \| 0.1698 \| \| 0.9592 \| 5.8414 \| 59500 \| 0.8905 \| 0.0121 \| 0.1681 \| 0.6524 \| 0.1681 \| \| 0.8031 \| 5.8512 \| 59600 \| 0.9016 \| 0.0121 \| 0.1663 \| 0.6488 \| 0.1663 \| \| 0.9475 \| 5.8610 \| 59700 \| 0.9167 \| 0.0121 \| 0.1592 \| 0.6442 \| 0.1592 \| \| 0.9078 \| 5.8708 \| 59800 \| 0.9012 \| 0.0121 \| 0.1726 \| 0.6480 \| 0.1726 \| \| 0.9446 \| 5.8806 \| 59900 \| 0.8916 \| 0.0121 \| 0.1646 \| 0.6524 \| 0.1646 \| \| 0.8724 \| 5.8904 \| 60000 \| 0.8824 \| 0.0121 \| 0.1662 \| 0.6545 \| 0.1662 \| \| 0.9384 \| 5.9003 \| 60100 \| 0.9172 \| 0.0121 \| 0.1527 \| 0.6384 \| 0.1527 \| \| 0.9091 \| 5.9101 \| 60200 \| 0.8846 \| 0.0121 \| 0.1711 \| 0.6554 \| 0.1711 \| \| 0.8407 \| 5.9199 \| 60300 \| 0.9147 \| 0.0121 \| 0.1617 \| 0.6450 \| 0.1617 \| \| 0.9015 \| 5.9297 \| 60400 \| 0.8986 \| 0.0121 \| 0.1642 \| 0.6510 \| 0.1642 \| \| 0.8919 \| 5.9395 \| 60500 \| 0.8881 \| 0.0121 \| 0.1722 \| 0.6528 \| 0.1722 \| \| 0.9051 \| 5.9493 \| 60600 \| 0.9188 \| 0.0121 \| 0.1533 \| 0.6386 \| 0.1533 \| \| 0.9186 \| 5.9592 \| 60700 \| 0.8916 \| 0.0121 \| 0.1648 \| 0.6498 \| 0.1648 \| \| 0.8539 \| 5.9690 \| 60800 \| 0.8972 \| 0.0121 \| 0.1654 \| 0.6504 \| 0.1654 \| \| 0.9481 \| 5.9788 \| 60900 \| 0.8835 \| 0.0121 \| 0.1684 \| 0.6534 \| 0.1684 \| \| 0.8576 \| 5.9886 \| 61000 \| 0.8902 \| 0.0121 \| 0.1642 \| 0.6499 \| 0.1642 \| \| 0.9714 \| 5.9984 \| 61100 \| 0.8893 \| 0.0121 \| 0.1660 \| 0.6517 \| 0.1660 \| \| 0.8351 \| 6.0082 \| 61200 \| 0.9071 \| 0.0121 \| 0.1677 \| 0.6478 \| 0.1677 \| \| 0.7786 \| 6.0181 \| 61300 \| 0.9043 \| 0.0121 \| 0.1700 \| 0.6483 \| 0.1700 \| \| 0.823 \| 6.0279 \| 61400 \| 0.9296 \| 0.0121 \| 0.1618 \| 0.6405 \| 0.1618 \| \| 0.9533 \| 6.0377 \| 61500 \| 0.8938 \| 0.0121 \| 0.1657 \| 0.6495 \| 0.1657 \| \| 0.8797 \| 6.0475 \| 61600 \| 0.8994 \| 0.0121 \| 0.1642 \| 0.6498 \| 0.1642 \| \| 0.9222 \| 6.0573 \| 61700 \| 0.9007 \| 0.0121 \| 0.1719 \| 0.6535 \| 0.1719 \| \| 0.7826 \| 6.0672 \| 61800 \| 0.8861 \| 0.0121 \| 0.1749 \| 0.6539 \| 0.1749 \| \| 0.9418 \| 6.0770 \| 61900 \| 0.8965 \| 0.0121 \| 0.1626 \| 0.6475 \| 0.1626 \| \| 0.9099 \| 6.0868 \| 62000 \| 0.9134 \| 0.0121 \| 0.1564 \| 0.6417 \| 0.1564 \| \| 0.8789 \| 6.0966 \| 62100 \| 0.8873 \| 0.0121 \| 0.1643 \| 0.6523 \| 0.1643 \| \| 0.91 \| 6.1064 \| 62200 \| 0.8909 \| 0.0121 \| 0.1707 \| 0.6511 \| 0.1707 \| \| 0.8766 \| 6.1162 \| 62300 \| 0.8964 \| 0.0121 \| 0.1619 \| 0.6493 \| 0.1619 \| \| 0.8721 \| 6.1261 \| 62400 \| 0.8966 \| 0.0121 \| 0.1740 \| 0.6512 \| 0.1740 \| \| 0.8653 \| 6.1359 \| 62500 \| 0.8980 \| 0.0121 \| 0.1653 \| 0.6514 \| 0.1653 \| \| 0.9314 \| 6.1457 \| 62600 \| 0.9137 \| 0.0121 \| 0.1627 \| 0.6460 \| 0.1627 \| \| 0.8731 \| 6.1555 \| 62700 \| 0.8799 \| 0.0121 \| 0.1684 \| 0.6551 \| 0.1684 \| \| 0.9052 \| 6.1653 \| 62800 \| 0.8863 \| 0.0121 \| 0.1667 \| 0.6509 \| 0.1667 \| \| 0.8165 \| 6.1751 \| 62900 \| 0.9010 \| 0.0121 \| 0.1633 \| 0.6483 \| 0.1633 \| \| 0.8366 \| 6.1850 \| 63000 \| 0.8970 \| 0.0121 \| 0.1651 \| 0.6496 \| 0.1651 \| \| 0.9723 \| 6.1948 \| 63100 \| 0.8961 \| 0.0121 \| 0.1668 \| 0.6486 \| 0.1668 \| \| 0.9703 \| 6.2046 \| 63200 \| 0.9108 \| 0.0121 \| 0.1612 \| 0.6446 \| 0.1612 \| \| 0.8922 \| 6.2144 \| 63300 \| 0.8987 \| 0.0121 \| 0.1645 \| 0.6480 \| 0.1645 \| \| 0.8852 \| 6.2242 \| 63400 \| 0.9052 \| 0.0121 \| 0.1626 \| 0.6433 \| 0.1626 \| \| 0.8619 \| 6.2340 \| 63500 \| 0.9053 \| 0.0121 \| 0.1679 \| 0.6471 \| 0.1679 \| \| 0.898 \| 6.2439 \| 63600 \| 0.8993 \| 0.0121 \| 0.1582 \| 0.6470 \| 0.1582 \| \| 0.87 \| 6.2537 \| 63700 \| 0.8971 \| 0.0121 \| 0.1662 \| 0.6504 \| 0.1662 \| \| 0.911 \| 6.2635 \| 63800 \| 0.8920 \| 0.0121 \| 0.1675 \| 0.6509 \| 0.1675 \| \| 0.8969 \| 6.2733 \| 63900 \| 0.8862 \| 0.0121 \| 0.1651 \| 0.6520 \| 0.1651 \| \| 0.9455 \| 6.2831 \| 64000 \| 0.8855 \| 0.0121 \| 0.1700 \| 0.6533 \| 0.1700 \| \| 0.866 \| 6.2930 \| 64100 \| 0.9047 \| 0.0121 \| 0.1628 \| 0.6458 \| 0.1628 \| \| 0.8634 \| 6.3028 \| 64200 \| 0.8756 \| 0.0121 \| 0.1696 \| 0.6544 \| 0.1696 \| \| 0.9044 \| 6.3126 \| 64300 \| 0.8910 \| 0.0121 \| 0.1709 \| 0.6531 \| 0.1709 \| \| 0.9051 \| 6.3224 \| 64400 \| 0.8884 \| 0.0121 \| 0.1680 \| 0.6546 \| 0.1680 \| \| 0.8775 \| 6.3322 \| 64500 \| 0.9103 \| 0.0121 \| 0.1570 \| 0.6431 \| 0.1570 \| \| 0.9304 \| 6.3420 \| 64600 \| 0.8946 \| 0.0121 \| 0.1631 \| 0.6519 \| 0.1631 \| \| 0.8777 \| 6.3519 \| 64700 \| 0.8930 \| 0.0121 \| 0.1647 \| 0.6493 \| 0.1647 \| \| 0.8525 \| 6.3617 \| 64800 \| 0.9022 \| 0.0121 \| 0.1663 \| 0.6477 \| 0.1663 \| \| 0.8442 \| 6.3715 \| 64900 \| 0.8868 \| 0.0121 \| 0.1699 \| 0.6520 \| 0.1699 \| \| 0.8797 \| 6.3813 \| 65000 \| 0.8790 \| 0.0121 \| 0.1724 \| 0.6544 \| 0.1724 \| \| 0.8941 \| 6.3911 \| 65100 \| 0.8873 \| 0.0121 \| 0.1769 \| 0.6536 \| 0.1769 \| \| 0.9563 \| 6.4009 \| 65200 \| 0.8859 \| 0.0121 \| 0.1707 \| 0.6523 \| 0.1707 \| \| 0.9128 \| 6.4108 \| 65300 \| 0.9141 \| 0.0121 \| 0.1564 \| 0.6401 \| 0.1564 \| \| 0.8711 \| 6.4206 \| 65400 \| 0.8837 \| 0.0121 \| 0.1682 \| 0.6514 \| 0.1682 \| \| 0.956 \| 6.4304 \| 65500 \| 0.8755 \| 0.0121 \| 0.1747 \| 0.6565 \| 0.1747 \| \| 0.9201 \| 6.4402 \| 65600 \| 0.8956 \| 0.0121 \| 0.1689 \| 0.6499 \| 0.1689 \| \| 0.953 \| 6.4500 \| 65700 \| 0.8916 \| 0.0121 \| 0.1662 \| 0.6510 \| 0.1662 \| \| 0.8706 \| 6.4598 \| 65800 \| 0.8956 \| 0.0121 \| 0.1605 \| 0.6458 \| 0.1605 \| \| 0.9539 \| 6.4697 \| 65900 \| 0.9331 \| 0.0121 \| 0.1551 \| 0.6393 \| 0.1551 \| \| 0.8699 \| 6.4795 \| 66000 \| 0.8920 \| 0.0121 \| 0.1689 \| 0.6513 \| 0.1689 \| \| 0.9043 \| 6.4893 \| 66100 \| 0.9007 \| 0.0121 \| 0.1563 \| 0.6450 \| 0.1563 \| \| 0.88 \| 6.4991 \| 66200 \| 0.8906 \| 0.0121 \| 0.1746 \| 0.6507 \| 0.1746 \| \| 0.9186 \| 6.5089 \| 66300 \| 0.9041 \| 0.0121 \| 0.1573 \| 0.6465 \| 0.1573 \| \| 0.8728 \| 6.5188 \| 66400 \| 0.8923 \| 0.0121 \| 0.1605 \| 0.6471 \| 0.1605 \| \| 0.9381 \| 6.5286 \| 66500 \| 0.8779 \| 0.0121 \| 0.1731 \| 0.6594 \| 0.1731 \| \| 0.8294 \| 6.5384 \| 66600 \| 0.9043 \| 0.0121 \| 0.1570 \| 0.6442 \| 0.1570 \| \| 0.9222 \| 6.5482 \| 66700 \| 0.9021 \| 0.0121 \| 0.1624 \| 0.6488 \| 0.1624 \| \| 0.8413 \| 6.5580 \| 66800 \| 0.9031 \| 0.0121 \| 0.1619 \| 0.6489 \| 0.1619 \| \| 0.9295 \| 6.5678 \| 66900 \| 0.8977 \| 0.0121 \| 0.1595 \| 0.6482 \| 0.1595 \| \| 0.8932 \| 6.5777 \| 67000 \| 0.8765 \| 0.0121 \| 0.1776 \| 0.6584 \| 0.1776 \| \| 0.9345 \| 6.5875 \| 67100 \| 0.8952 \| 0.0121 \| 0.1643 \| 0.6517 \| 0.1643 \| \| 0.8763 \| 6.5973 \| 67200 \| 0.8965 \| 0.0121 \| 0.1670 \| 0.6519 \| 0.1670 \| \| 0.9188 \| 6.6071 \| 67300 \| 0.9477 \| 0.0121 \| 0.1480 \| 0.6334 \| 0.1480 \| \| 0.8674 \| 6.6169 \| 67400 \| 0.9052 \| 0.0121 \| 0.1640 \| 0.6449 \| 0.1640 \| \| 0.9324 \| 6.6267 \| 67500 \| 0.8874 \| 0.0121 \| 0.1631 \| 0.6515 \| 0.1631 \| \| 0.8973 \| 6.6366 \| 67600 \| 0.9005 \| 0.0121 \| 0.1682 \| 0.6465 \| 0.1682 \| \| 0.9835 \| 6.6464 \| 67700 \| 0.8900 \| 0.0121 \| 0.1650 \| 0.6498 \| 0.1650 \| \| 0.8465 \| 6.6562 \| 67800 \| 0.8835 \| 0.0121 \| 0.1741 \| 0.6527 \| 0.1741 \| \| 0.8645 \| 6.6660 \| 67900 \| 0.8923 \| 0.0121 \| 0.1731 \| 0.6512 \| 0.1731 \| \| 0.8838 \| 6.6758 \| 68000 \| 0.8957 \| 0.0121 \| 0.1645 \| 0.6486 \| 0.1645 \| \| 0.8578 \| 6.6856 \| 68100 \| 0.9084 \| 0.0121 \| 0.1644 \| 0.6441 \| 0.1644 \| \| 0.8734 \| 6.6955 \| 68200 \| 0.8908 \| 0.0121 \| 0.1694 \| 0.6515 \| 0.1694 \| \| 0.8643 \| 6.7053 \| 68300 \| 0.8887 \| 0.0121 \| 0.1691 \| 0.6531 \| 0.1691 \| \| 0.7971 \| 6.7151 \| 68400 \| 0.8909 \| 0.0121 \| 0.1660 \| 0.6516 \| 0.1660 \| \| 0.8904 \| 6.7249 \| 68500 \| 0.8926 \| 0.0121 \| 0.1665 \| 0.6508 \| 0.1665 \| \| 0.8864 \| 6.7347 \| 68600 \| 0.8771 \| 0.0121 \| 0.1735 \| 0.6570 \| 0.1735 \| \| 0.8464 \| 6.7446 \| 68700 \| 0.9023 \| 0.0121 \| 0.1628 \| 0.6463 \| 0.1628 \| \| 0.9121 \| 6.7544 \| 68800 \| 0.8985 \| 0.0121 \| 0.1600 \| 0.6477 \| 0.1600 \| \| 0.9137 \| 6.7642 \| 68900 \| 0.9187 \| 0.0121 \| 0.1537 \| 0.6423 \| 0.1537 \| \| 0.9691 \| 6.7740 \| 69000 \| 0.9116 \| 0.0121 \| 0.1583 \| 0.6423 \| 0.1583 \| \| 0.925 \| 6.7838 \| 69100 \| 0.9123 \| 0.0121 \| 0.1696 \| 0.6470 \| 0.1696 \| \| 0.984 \| 6.7936 \| 69200 \| 0.9114 \| 0.0121 \| 0.1613 \| 0.6447 \| 0.1613 \| \| 0.7877 \| 6.8035 \| 69300 \| 0.8902 \| 0.0121 \| 0.1700 \| 0.6523 \| 0.1700 \| \| 0.9316 \| 6.8133 \| 69400 \| 0.8867 \| 0.0121 \| 0.1729 \| 0.6549 \| 0.1729 \| \| 0.9002 \| 6.8231 \| 69500 \| 0.9049 \| 0.0121 \| 0.1661 \| 0.6476 \| 0.1661 \| \| 0.9117 \| 6.8329 \| 69600 \| 0.9036 \| 0.0121 \| 0.1571 \| 0.6483 \| 0.1571 \| \| 0.8923 \| 6.8427 \| 69700 \| 0.9154 \| 0.0121 \| 0.1553 \| 0.6440 \| 0.1553 \| \| 0.8687 \| 6.8525 \| 69800 \| 0.8993 \| 0.0121 \| 0.1596 \| 0.6499 \| 0.1596 \| \| 0.8335 \| 6.8624 \| 69900 \| 0.8929 \| 0.0121 \| 0.1734 \| 0.6522 \| 0.1734 \| \| 0.9734 \| 6.8722 \| 70000 \| 0.8966 \| 0.0121 \| 0.1693 \| 0.6496 \| 0.1693 \| \| 0.8941 \| 6.8820 \| 70100 \| 0.9150 \| 0.0121 \| 0.1639 \| 0.6440 \| 0.1639 \| \| 0.9068 \| 6.8918 \| 70200 \| 0.9009 \| 0.0121 \| 0.1635 \| 0.6469 \| 0.1635 \| \| 0.8599 \| 6.9016 \| 70300 \| 0.8984 \| 0.0121 \| 0.1674 \| 0.6487 \| 0.1674 \| \| 0.8525 \| 6.9114 \| 70400 \| 0.8959 \| 0.0121 \| 0.1668 \| 0.6488 \| 0.1668 \| \| 0.9187 \| 6.9213 \| 70500 \| 0.9066 \| 0.0121 \| 0.1675 \| 0.6446 \| 0.1675 \| \| 0.8898 \| 6.9311 \| 70600 \| 0.9043 \| 0.0121 \| 0.1655 \| 0.6481 \| 0.1655 \| \| 0.8829 \| 6.9409 \| 70700 \| 0.9234 \| 0.0121 \| 0.1574 \| 0.6422 \| 0.1574 \| \| 0.8977 \| 6.9507 \| 70800 \| 0.8913 \| 0.0121 \| 0.1665 \| 0.6503 \| 0.1665 \| \| 0.8974 \| 6.9605 \| 70900 \| 0.9152 \| 0.0121 \| 0.1594 \| 0.6419 \| 0.1594 \| \| 0.8644 \| 6.9704 \| 71000 \| 0.8937 \| 0.0121 \| 0.1690 \| 0.6506 \| 0.1690 \| \| 0.9495 \| 6.9802 \| 71100 \| 0.8918 \| 0.0121 \| 0.1700 \| 0.6485 \| 0.1700 \| \| 0.9866 \| 6.9900 \| 71200 \| 0.9175 \| 0.0121 \| 0.1516 \| 0.6396 \| 0.1516 \| \| 0.9188 \| 6.9998 \| 71300 \| 0.9003 \| 0.0121 \| 0.1624 \| 0.6480 \| 0.1624 \| \| 0.8109 \| 7.0096 \| 71400 \| 0.8966 \| 0.0121 \| 0.1656 \| 0.6508 \| 0.1656 \| \| 0.8681 \| 7.0194 \| 71500 \| 0.8798 \| 0.0121 \| 0.1735 \| 0.6573 \| 0.1735 \| \| 0.8698 \| 7.0293 \| 71600 \| 0.9139 \| 0.0121 \| 0.1588 \| 0.6407 \| 0.1588 \| \| 0.9085 \| 7.0391 \| 71700 \| 0.8874 \| 0.0121 \| 0.1640 \| 0.6530 \| 0.1640 \| \| 0.8601 \| 7.0489 \| 71800 \| 0.9003 \| 0.0121 \| 0.1615 \| 0.6481 \| 0.1615 \| \| 0.8931 \| 7.0587 \| 71900 \| 0.8947 \| 0.0121 \| 0.1612 \| 0.6486 \| 0.1612 \| \| 0.8785 \| 7.0685 \| 72000 \| 0.8977 \| 0.0121 \| 0.1689 \| 0.6522 \| 0.1689 \| \| 0.9064 \| 7.0783 \| 72100 \| 0.9052 \| 0.0121 \| 0.1632 \| 0.6462 \| 0.1632 \| \| 0.8819 \| 7.0882 \| 72200 \| 0.9062 \| 0.0121 \| 0.1599 \| 0.6433 \| 0.1599 \| \| 0.8778 \| 7.0980 \| 72300 \| 0.8969 \| 0.0121 \| 0.1705 \| 0.6507 \| 0.1705 \| \| 0.9143 \| 7.1078 \| 72400 \| 0.9161 \| 0.0121 \| 0.1538 \| 0.6411 \| 0.1538 \| \| 0.8533 \| 7.1176 \| 72500 \| 0.8996 \| 0.0121 \| 0.1610 \| 0.6468 \| 0.1610 \| \| 0.9242 \| 7.1274 \| 72600 \| 0.8970 \| 0.0121 \| 0.1686 \| 0.6510 \| 0.1686 \| \| 0.8492 \| 7.1372 \| 72700 \| 0.8880 \| 0.0121 \| 0.1634 \| 0.6498 \| 0.1634 \| \| 0.9335 \| 7.1471 \| 72800 \| 0.9017 \| 0.0121 \| 0.1626 \| 0.6479 \| 0.1626 \| \| 0.8984 \| 7.1569 \| 72900 \| 0.8902 \| 0.0121 \| 0.1639 \| 0.6507 \| 0.1639 \| \| 0.8653 \| 7.1667 \| 73000 \| 0.8906 \| 0.0121 \| 0.1678 \| 0.6518 \| 0.1678 \| \| 0.8975 \| 7.1765 \| 73100 \| 0.8762 \| 0.0121 \| 0.1744 \| 0.6573 \| 0.1744 \| \| 0.899 \| 7.1863 \| 73200 \| 0.8923 \| 0.0121 \| 0.1673 \| 0.6513 \| 0.1673 \| \| 0.9245 \| 7.1962 \| 73300 \| 0.8920 \| 0.0121 \| 0.1626 \| 0.6499 \| 0.1626 \| \| 0.8859 \| 7.2060 \| 73400 \| 0.8925 \| 0.0121 \| 0.1675 \| 0.6500 \| 0.1675 \| \| 0.8344 \| 7.2158 \| 73500 \| 0.8864 \| 0.0121 \| 0.1657 \| 0.6496 \| 0.1657 \| \| 0.8866 \| 7.2256 \| 73600 \| 0.8803 \| 0.0121 \| 0.1758 \| 0.6567 \| 0.1758 \| \| 0.8842 \| 7.2354 \| 73700 \| 0.8880 \| 0.0121 \| 0.1677 \| 0.6536 \| 0.1677 \| \| 0.9165 \| 7.2452 \| 73800 \| 0.8877 \| 0.0121 \| 0.1716 \| 0.6528 \| 0.1716 \| \| 0.8621 \| 7.2551 \| 73900 \| 0.8900 \| 0.0121 \| 0.1756 \| 0.6525 \| 0.1756 \| \| 0.8732 \| 7.2649 \| 74000 \| 0.8912 \| 0.0121 \| 0.1713 \| 0.6517 \| 0.1713 \| \| 0.895 \| 7.2747 \| 74100 \| 0.8889 \| 0.0121 \| 0.1702 \| 0.6521 \| 0.1702 \| \| 0.9558 \| 7.2845 \| 74200 \| 0.8893 \| 0.0121 \| 0.1675 \| 0.6537 \| 0.1675 \| \| 0.8515 \| 7.2943 \| 74300 \| 0.8876 \| 0.0121 \| 0.1613 \| 0.6503 \| 0.1613 \| \| 0.833 \| 7.3041 \| 74400 \| 0.8864 \| 0.0121 \| 0.1773 \| 0.6521 \| 0.1773 \| \| 0.8715 \| 7.3140 \| 74500 \| 0.9271 \| 0.0121 \| 0.1455 \| 0.6365 \| 0.1455 \| \| 0.8638 \| 7.3238 \| 74600 \| 0.8798 \| 0.0121 \| 0.1743 \| 0.6558 \| 0.1743 \| \| 0.8635 \| 7.3336 \| 74700 \| 0.8945 \| 0.0121 \| 0.1620 \| 0.6476 \| 0.1620 \| \| 0.7944 \| 7.3434 \| 74800 \| 0.8882 \| 0.0121 \| 0.1730 \| 0.6516 \| 0.1730 \| \| 0.9072 \| 7.3532 \| 74900 \| 0.9084 \| 0.0121 \| 0.1566 \| 0.6444 \| 0.1566 \| \| 0.9102 \| 7.3630 \| 75000 \| 0.8870 \| 0.0121 \| 0.1720 \| 0.6509 \| 0.1720 \| \| 0.9261 \| 7.3729 \| 75100 \| 0.9056 \| 0.0121 \| 0.1742 \| 0.6501 \| 0.1742 \| \| 0.9421 \| 7.3827 \| 75200 \| 0.8887 \| 0.0121 \| 0.1683 \| 0.6511 \| 0.1683 \| \| 0.9074 \| 7.3925 \| 75300 \| 0.8886 \| 0.0121 \| 0.1682 \| 0.6510 \| 0.1682 \| \| 0.9349 \| 7.4023 \| 75400 \| 0.8970 \| 0.0121 \| 0.1692 \| 0.6498 \| 0.1692 \| \| 0.9081 \| 7.4121 \| 75500 \| 0.8855 \| 0.0121 \| 0.1690 \| 0.6525 \| 0.1690 \| \| 0.8903 \| 7.4220 \| 75600 \| 0.9003 \| 0.0121 \| 0.1605 \| 0.6460 \| 0.1605 \| \| 0.7998 \| 7.4318 \| 75700 \| 0.8866 \| 0.0121 \| 0.1611 \| 0.6524 \| 0.1611 \| \| 0.9273 \| 7.4416 \| 75800 \| 0.9242 \| 0.0121 \| 0.1550 \| 0.6418 \| 0.1550 \| \| 0.9488 \| 7.4514 \| 75900 \| 0.8843 \| 0.0121 \| 0.1681 \| 0.6528 \| 0.1681 \| \| 0.9358 \| 7.4612 \| 76000 \| 0.9096 \| 0.0121 \| 0.1612 \| 0.6482 \| 0.1612 \| \| 0.9421 \| 7.4710 \| 76100 \| 0.8894 \| 0.0121 \| 0.1711 \| 0.6539 \| 0.1711 \| \| 0.8566 \| 7.4809 \| 76200 \| 0.8851 \| 0.0121 \| 0.1764 \| 0.6535 \| 0.1764 \| \| 0.8836 \| 7.4907 \| 76300 \| 0.8883 \| 0.0121 \| 0.1758 \| 0.6524 \| 0.1758 \| \| 0.9199 \| 7.5005 \| 76400 \| 0.9105 \| 0.0121 \| 0.1589 \| 0.6472 \| 0.1589 \| \| 0.8648 \| 7.5103 \| 76500 \| 0.8708 \| 0.0121 \| 0.1773 \| 0.6584 \| 0.1773 \| \| 0.8728 \| 7.5201 \| 76600 \| 0.8804 \| 0.0121 \| 0.1719 \| 0.6557 \| 0.1719 \| \| 0.8438 \| 7.5299 \| 76700 \| 0.8894 \| 0.0121 \| 0.1675 \| 0.6523 \| 0.1675 \| \| 0.9103 \| 7.5398 \| 76800 \| 0.9067 \| 0.0121 \| 0.1701 \| 0.6485 \| 0.1701 \| \| 0.9266 \| 7.5496 \| 76900 \| 0.8889 \| 0.0121 \| 0.1622 \| 0.6497 \| 0.1622 \| \| 0.9135 \| 7.5594 \| 77000 \| 0.9084 \| 0.0121 \| 0.1642 \| 0.6457 \| 0.1642 \| \| 0.905 \| 7.5692 \| 77100 \| 0.8855 \| 0.0121 \| 0.1720 \| 0.6552 \| 0.1720 \| \| 0.8316 \| 7.5790 \| 77200 \| 0.9042 \| 0.0121 \| 0.1643 \| 0.6474 \| 0.1643 \| \| 0.9467 \| 7.5888 \| 77300 \| 0.8937 \| 0.0121 \| 0.1678 \| 0.6497 \| 0.1678 \| \| 0.8321 \| 7.5987 \| 77400 \| 0.8800 \| 0.0121 \| 0.1575 \| 0.6520 \| 0.1575 \| \| 0.9027 \| 7.6085 \| 77500 \| 0.8975 \| 0.0121 \| 0.1731 \| 0.6500 \| 0.1731 \| \| 0.9398 \| 7.6183 \| 77600 \| 0.9001 \| 0.0121 \| 0.1628 \| 0.6472 \| 0.1628 \| \| 0.8569 \| 7.6281 \| 77700 \| 0.8941 \| 0.0121 \| 0.1599 \| 0.6478 \| 0.1599 \| \| 0.8378 \| 7.6379 \| 77800 \| 0.8791 \| 0.0121 \| 0.1709 \| 0.6539 \| 0.1709 \| \| 0.889 \| 7.6478 \| 77900 \| 0.9053 \| 0.0121 \| 0.1631 \| 0.6445 \| 0.1631 \| \| 0.8715 \| 7.6576 \| 78000 \| 0.9165 \| 0.0121 \| 0.1637 \| 0.6423 \| 0.1637 \| \| 0.8828 \| 7.6674 \| 78100 \| 0.8772 \| 0.0121 \| 0.1801 \| 0.6570 \| 0.1801 \| \| 0.8339 \| 7.6772 \| 78200 \| 0.8880 \| 0.0121 \| 0.1644 \| 0.6505 \| 0.1644 \| \| 0.8239 \| 7.6870 \| 78300 \| 0.8844 \| 0.0121 \| 0.1738 \| 0.6554 \| 0.1738 \| \| 0.861 \| 7.6968 \| 78400 \| 0.8858 \| 0.0121 \| 0.1726 \| 0.6551 \| 0.1726 \| \| 0.8917 \| 7.7067 \| 78500 \| 0.8873 \| 0.0121 \| 0.1650 \| 0.6513 \| 0.1650 \| \| 0.8643 \| 7.7165 \| 78600 \| 0.8824 \| 0.0121 \| 0.1778 \| 0.6583 \| 0.1778 \| \| 0.8449 \| 7.7263 \| 78700 \| 0.8747 \| 0.0121 \| 0.1695 \| 0.6555 \| 0.1695 \| \| 0.9109 \| 7.7361 \| 78800 \| 0.8924 \| 0.0121 \| 0.1660 \| 0.6511 \| 0.1660 \| \| 0.8526 \| 7.7459 \| 78900 \| 0.8828 \| 0.0121 \| 0.1693 \| 0.6539 \| 0.1693 \| \| 0.8111 \| 7.7557 \| 79000 \| 0.8904 \| 0.0121 \| 0.1683 \| 0.6542 \| 0.1683 \| \| 0.8652 \| 7.7656 \| 79100 \| 0.8681 \| 0.0121 \| 0.1791 \| 0.6613 \| 0.1791 \| \| 0.8739 \| 7.7754 \| 79200 \| 0.8777 \| 0.0121 \| 0.1703 \| 0.6577 \| 0.1703 \| \| 0.8648 \| 7.7852 \| 79300 \| 0.8916 \| 0.0121 \| 0.1635 \| 0.6461 \| 0.1635 \| \| 0.8935 \| 7.7950 \| 79400 \| 0.9094 \| 0.0121 \| 0.1621 \| 0.6456 \| 0.1621 \| \| 0.8423 \| 7.8048 \| 79500 \| 0.8895 \| 0.0121 \| 0.1744 \| 0.6534 \| 0.1744 \| \| 0.9597 \| 7.8146 \| 79600 \| 0.8942 \| 0.0121 \| 0.1706 \| 0.6518 \| 0.1706 \| \| 0.8766 \| 7.8245 \| 79700 \| 0.9027 \| 0.0121 \| 0.1702 \| 0.6493 \| 0.1702 \| \| 0.8886 \| 7.8343 \| 79800 \| 0.8931 \| 0.0121 \| 0.1693 \| 0.6501 \| 0.1693 \| \| 0.9142 \| 7.8441 \| 79900 \| 0.8899 \| 0.0121 \| 0.1737 \| 0.6528 \| 0.1737 \| \| 0.8584 \| 7.8539 \| 80000 \| 0.8916 \| 0.0121 \| 0.1663 \| 0.6502 \| 0.1663 \| \| 0.8833 \| 7.8637 \| 80100 \| 0.8787 \| 0.0121 \| 0.1683 \| 0.6554 \| 0.1683 \| \| 0.7781 \| 7.8736 \| 80200 \| 0.8710 \| 0.0121 \| 0.1743 \| 0.6591 \| 0.1743 \| \| 0.9024 \| 7.8834 \| 80300 \| 0.8760 \| 0.0121 \| 0.1696 \| 0.6546 \| 0.1696 \| \| 0.9207 \| 7.8932 \| 80400 \| 0.8953 \| 0.0121 \| 0.1707 \| 0.6503 \| 0.1707 \| \| 0.8648 \| 7.9030 \| 80500 \| 0.8848 \| 0.0121 \| 0.1684 \| 0.6512 \| 0.1684 \| \| 0.8437 \| 7.9128 \| 80600 \| 0.8763 \| 0.0121 \| 0.1661 \| 0.6549 \| 0.1661 \| \| 0.8794 \| 7.9226 \| 80700 \| 0.8653 \| 0.0121 \| 0.1733 \| 0.6594 \| 0.1733 \| \| 0.8541 \| 7.9325 \| 80800 \| 0.8885 \| 0.0121 \| 0.1667 \| 0.6526 \| 0.1667 \| \| 0.9453 \| 7.9423 \| 80900 \| 0.8888 \| 0.0121 \| 0.1679 \| 0.6522 \| 0.1679 \| \| 0.9241 \| 7.9521 \| 81000 \| 0.8943 \| 0.0121 \| 0.1604 \| 0.6498 \| 0.1604 \| \| 0.8594 \| 7.9619 \| 81100 \| 0.8747 \| 0.0121 \| 0.1783 \| 0.6583 \| 0.1783 \| \| 0.8906 \| 7.9717 \| 81200 \| 0.8913 \| 0.0121 \| 0.1731 \| 0.6521 \| 0.1731 \| \| 0.8827 \| 7.9815 \| 81300 \| 0.8862 \| 0.0121 \| 0.1686 \| 0.6527 \| 0.1686 \| \| 0.8428 \| 7.9914 \| 81400 \| 0.8754 \| 0.0121 \| 0.1733 \| 0.6578 \| 0.1733 \| \| 0.8502 \| 8.0012 \| 81500 \| 0.8785 \| 0.0121 \| 0.1716 \| 0.6549 \| 0.1716 \| \| 0.8665 \| 8.0110 \| 81600 \| 0.8976 \| 0.0121 \| 0.1543 \| 0.6499 \| 0.1543 \| \| 0.8266 \| 8.0208 \| 81700 \| 0.8728 \| 0.0121 \| 0.1725 \| 0.6588 \| 0.1725 \| \| 0.8515 \| 8.0306 \| 81800 \| 0.8866 \| 0.0121 \| 0.1689 \| 0.6522 \| 0.1689 \| \| 0.8596 \| 8.0404 \| 81900 \| 0.8747 \| 0.0121 \| 0.1711 \| 0.6569 \| 0.1711 \| \| 0.933 \| 8.0503 \| 82000 \| 0.8958 \| 0.0121 \| 0.1679 \| 0.6524 \| 0.1679 \| \| 0.8987 \| 8.0601 \| 82100 \| 0.8788 \| 0.0121 \| 0.1664 \| 0.6553 \| 0.1664 \| \| 0.9081 \| 8.0699 \| 82200 \| 0.8943 \| 0.0121 \| 0.1714 \| 0.6487 \| 0.1714 \| \| 0.8583 \| 8.0797 \| 82300 \| 1.0938 \| 0.0121 \| 0.1248 \| 0.5901 \| 0.1248 \| \| 0.9374 \| 8.0895 \| 82400 \| 0.8959 \| 0.0121 \| 0.1687 \| 0.6499 \| 0.1687 \| \| 0.8707 \| 8.0994 \| 82500 \| 0.8787 \| 0.0121 \| 0.1724 \| 0.6535 \| 0.1724 \| \| 0.8492 \| 8.1092 \| 82600 \| 0.8869 \| 0.0121 \| 0.1686 \| 0.6569 \| 0.1686 \| \| 0.9135 \| 8.1190 \| 82700 \| 0.9008 \| 0.0121 \| 0.1577 \| 0.6485 \| 0.1577 \| \| 0.9245 \| 8.1288 \| 82800 \| 0.8776 \| 0.0121 \| 0.1707 \| 0.6550 \| 0.1707 \| \| 0.8326 \| 8.1386 \| 82900 \| 0.8856 \| 0.0121 \| 0.1641 \| 0.6495 \| 0.1641 \| \| 0.9092 \| 8.1484 \| 83000 \| 0.8960 \| 0.0121 \| 0.1657 \| 0.6498 \| 0.1657 \| \| 0.9163 \| 8.1583 \| 83100 \| 0.8778 \| 0.0121 \| 0.1736 \| 0.6549 \| 0.1736 \| \| 0.8896 \| 8.1681 \| 83200 \| 0.8936 \| 0.0121 \| 0.1597 \| 0.6499 \| 0.1597 \| \| 0.8872 \| 8.1779 \| 83300 \| 0.8760 \| 0.0121 \| 0.1693 \| 0.6554 \| 0.1693 \| \| 0.8451 \| 8.1877 \| 83400 \| 0.8668 \| 0.0121 \| 0.1776 \| 0.6617 \| 0.1776 \| \| 0.8352 \| 8.1975 \| 83500 \| 0.8719 \| 0.0121 \| 0.1798 \| 0.6570 \| 0.1798 \| \| 0.9612 \| 8.2073 \| 83600 \| 0.8775 \| 0.0121 \| 0.1726 \| 0.6546 \| 0.1726 \| \| 0.8528 \| 8.2172 \| 83700 \| 0.8798 \| 0.0121 \| 0.1773 \| 0.6579 \| 0.1773 \| \| 0.8939 \| 8.2270 \| 83800 \| 0.9222 \| 0.0121 \| 0.1631 \| 0.6410 \| 0.1631 \| \| 0.8205 \| 8.2368 \| 83900 \| 0.8907 \| 0.0121 \| 0.1723 \| 0.6511 \| 0.1723 \| \| 0.9358 \| 8.2466 \| 84000 \| 0.8756 \| 0.0121 \| 0.1760 \| 0.6592 \| 0.1760 \| \| 0.8687 \| 8.2564 \| 84100 \| 0.8841 \| 0.0121 \| 0.1714 \| 0.6565 \| 0.1714 \| \| 0.9093 \| 8.2662 \| 84200 \| 0.9097 \| 0.0121 \| 0.1587 \| 0.6445 \| 0.1587 \| \| 0.9625 \| 8.2761 \| 84300 \| 0.9028 \| 0.0121 \| 0.1605 \| 0.6459 \| 0.1605 \| \| 0.9625 \| 8.2859 \| 84400 \| 0.9074 \| 0.0121 \| 0.1628 \| 0.6451 \| 0.1628 \| \| 0.8268 \| 8.2957 \| 84500 \| 0.8868 \| 0.0121 \| 0.1682 \| 0.6510 \| 0.1682 \| \| 0.9055 \| 8.3055 \| 84600 \| 0.8898 \| 0.0121 \| 0.1693 \| 0.6514 \| 0.1693 \| \| 0.878 \| 8.3153 \| 84700 \| 0.9080 \| 0.0121 \| 0.1660 \| 0.6472 \| 0.1660 \| \| 0.8911 \| 8.3252 \| 84800 \| 0.8842 \| 0.0121 \| 0.1744 \| 0.6533 \| 0.1744 \| \| 0.7994 \| 8.3350 \| 84900 \| 0.8865 \| 0.0121 \| 0.1653 \| 0.6518 \| 0.1653 \| \| 0.8959 \| 8.3448 \| 85000 \| 0.8986 \| 0.0121 \| 0.1706 \| 0.6522 \| 0.1706 \| \| 0.7867 \| 8.3546 \| 85100 \| 0.8807 \| 0.0121 \| 0.1724 \| 0.6567 \| 0.1724 \| \| 0.9298 \| 8.3644 \| 85200 \| 0.8962 \| 0.0121 \| 0.1630 \| 0.6483 \| 0.1630 \| \| 0.8122 \| 8.3742 \| 85300 \| 0.8741 \| 0.0121 \| 0.1724 \| 0.6575 \| 0.1724 \| \| 0.8754 \| 8.3841 \| 85400 \| 0.8894 \| 0.0121 \| 0.1663 \| 0.6517 \| 0.1663 \| \| 0.8224 \| 8.3939 \| 85500 \| 0.8961 \| 0.0121 \| 0.1643 \| 0.6468 \| 0.1643 \| \| 0.879 \| 8.4037 \| 85600 \| 0.8843 \| 0.0121 \| 0.1756 \| 0.6550 \| 0.1756 \| \| 0.9025 \| 8.4135 \| 85700 \| 0.8946 \| 0.0121 \| 0.1636 \| 0.6513 \| 0.1636 \| \| 0.8163 \| 8.4233 \| 85800 \| 0.8884 \| 0.0121 \| 0.1621 \| 0.6508 \| 0.1621 \| \| 0.8935 \| 8.4331 \| 85900 \| 0.8889 \| 0.0121 \| 0.1675 \| 0.6520 \| 0.1675 \| \| 0.9107 \| 8.4430 \| 86000 \| 0.8672 \| 0.0121 \| 0.1811 \| 0.6608 \| 0.1811 \| \| 0.9779 \| 8.4528 \| 86100 \| 0.8804 \| 0.0121 \| 0.1725 \| 0.6568 \| 0.1725 \| \| 0.8305 \| 8.4626 \| 86200 \| 0.9006 \| 0.0121 \| 0.1624 \| 0.6471 \| 0.1624 \| \| 0.8844 \| 8.4724 \| 86300 \| 0.8945 \| 0.0121 \| 0.1631 \| 0.6512 \| 0.1631 \| \| 0.8139 \| 8.4822 \| 86400 \| 0.8809 \| 0.0121 \| 0.1773 \| 0.6555 \| 0.1773 \| \| 0.8526 \| 8.4920 \| 86500 \| 0.8811 \| 0.0121 \| 0.1614 \| 0.6521 \| 0.1614 \| \| 0.8673 \| 8.5019 \| 86600 \| 0.8800 \| 0.0121 \| 0.1715 \| 0.6550 \| 0.1715 \| \| 0.9264 \| 8.5117 \| 86700 \| 0.8915 \| 0.0121 \| 0.1763 \| 0.6525 \| 0.1763 \| \| 0.8396 \| 8.5215 \| 86800 \| 0.9141 \| 0.0121 \| 0.1674 \| 0.6444 \| 0.1674 \| \| 0.8332 \| 8.5313 \| 86900 \| 0.8931 \| 0.0121 \| 0.1620 \| 0.6514 \| 0.1620 \| \| 0.924 \| 8.5411 \| 87000 \| 0.8912 \| 0.0121 \| 0.1622 \| 0.6511 \| 0.1622 \| \| 0.8566 \| 8.5510 \| 87100 \| 0.8906 \| 0.0121 \| 0.1613 \| 0.6518 \| 0.1613 \| \| 0.8294 \| 8.5608 \| 87200 \| 0.9110 \| 0.0121 \| 0.1633 \| 0.6455 \| 0.1633 \| \| 0.9732 \| 8.5706 \| 87300 \| 0.8983 \| 0.0121 \| 0.1679 \| 0.6487 \| 0.1679 \| \| 0.87 \| 8.5804 \| 87400 \| 0.8930 \| 0.0121 \| 0.1562 \| 0.6488 \| 0.1562 \| \| 0.8377 \| 8.5902 \| 87500 \| 0.8701 \| 0.0121 \| 0.1743 \| 0.6586 \| 0.1743 \| \| 0.857 \| 8.6000 \| 87600 \| 0.8985 \| 0.0121 \| 0.1610 \| 0.6496 \| 0.1610 \| \| 0.8647 \| 8.6099 \| 87700 \| 0.8798 \| 0.0121 \| 0.1784 \| 0.6579 \| 0.1784 \| \| 0.8771 \| 8.6197 \| 87800 \| 0.8793 \| 0.0121 \| 0.1686 \| 0.6559 \| 0.1686 \| \| 0.9198 \| 8.6295 \| 87900 \| 0.8892 \| 0.0121 \| 0.1657 \| 0.6532 \| 0.1657 \| \| 0.9074 \| 8.6393 \| 88000 \| 0.9027 \| 0.0121 \| 0.1631 \| 0.6482 \| 0.1631 \| \| 0.9175 \| 8.6491 \| 88100 \| 0.8917 \| 0.0121 \| 0.1647 \| 0.6498 \| 0.1647 \| \| 0.8699 \| 8.6589 \| 88200 \| 0.8791 \| 0.0121 \| 0.1692 \| 0.6567 \| 0.1692 \| \| 0.8949 \| 8.6688 \| 88300 \| 0.8906 \| 0.0121 \| 0.1653 \| 0.6481 \| 0.1653 \| \| 0.8686 \| 8.6786 \| 88400 \| 0.8984 \| 0.0121 \| 0.1607 \| 0.6481 \| 0.1607 \| \| 1.035 \| 8.6884 \| 88500 \| 0.9014 \| 0.0121 \| 0.1642 \| 0.6487 \| 0.1642 \| \| 0.8353 \| 8.6982 \| 88600 \| 0.8832 \| 0.0121 \| 0.1724 \| 0.6540 \| 0.1724 \| \| 0.8507 \| 8.7080 \| 88700 \| 0.9010 \| 0.0121 \| 0.1680 \| 0.6498 \| 0.1680 \| \| 0.9186 \| 8.7178 \| 88800 \| 0.8872 \| 0.0121 \| 0.1735 \| 0.6534 \| 0.1735 \| \| 0.8306 \| 8.7277 \| 88900 \| 0.8810 \| 0.0121 \| 0.1671 \| 0.6556 \| 0.1671 \| \| 0.8596 \| 8.7375 \| 89000 \| 0.9024 \| 0.0121 \| 0.1653 \| 0.6488 \| 0.1653 \| \| 0.8422 \| 8.7473 \| 89100 \| 0.8811 \| 0.0121 \| 0.1751 \| 0.6529 \| 0.1751 \| \| 0.9112 \| 8.7571 \| 89200 \| 0.8775 \| 0.0121 \| 0.1675 \| 0.6540 \| 0.1675 \| \| 0.835 \| 8.7669 \| 89300 \| 0.8832 \| 0.0121 \| 0.1753 \| 0.6556 \| 0.1753 \| \| 0.8434 \| 8.7768 \| 89400 \| 0.8837 \| 0.0121 \| 0.1767 \| 0.6568 \| 0.1767 \| \| 0.933 \| 8.7866 \| 89500 \| 0.8876 \| 0.0121 \| 0.1715 \| 0.6529 \| 0.1715 \| \| 0.8242 \| 8.7964 \| 89600 \| 0.8777 \| 0.0121 \| 0.1693 \| 0.6542 \| 0.1693 \| \| 0.8515 \| 8.8062 \| 89700 \| 0.8861 \| 0.0121 \| 0.1744 \| 0.6541 \| 0.1744 \| \| 0.8414 \| 8.8160 \| 89800 \| 0.8788 \| 0.0121 \| 0.1719 \| 0.6539 \| 0.1719 \| \| 0.8493 \| 8.8258 \| 89900 \| 0.8801 \| 0.0121 \| 0.1643 \| 0.6533 \| 0.1643 \| \| 0.8985 \| 8.8357 \| 90000 \| 0.8813 \| 0.0121 \| 0.1728 \| 0.6557 \| 0.1728 \| \| 0.9069 \| 8.8455 \| 90100 \| 0.8783 \| 0.0121 \| 0.1664 \| 0.6541 \| 0.1664 \| \| 0.8162 \| 8.8553 \| 90200 \| 0.8989 \| 0.0121 \| 0.1639 \| 0.6484 \| 0.1639 \| \| 0.8368 \| 8.8651 \| 90300 \| 0.8738 \| 0.0121 \| 0.1725 \| 0.6568 \| 0.1725 \| \| 0.8865 \| 8.8749 \| 90400 \| 0.8841 \| 0.0121 \| 0.1649 \| 0.6544 \| 0.1649 \| \| 0.8651 \| 8.8847 \| 90500 \| 0.8779 \| 0.0121 \| 0.1733 \| 0.6550 \| 0.1733 \| \| 0.8775 \| 8.8946 \| 90600 \| 0.8850 \| 0.0121 \| 0.1608 \| 0.6524 \| 0.1608 \| \| 0.8091 \| 8.9044 \| 90700 \| 0.8816 \| 0.0121 \| 0.1668 \| 0.6561 \| 0.1668 \| \| 0.8726 \| 8.9142 \| 90800 \| 0.8871 \| 0.0121 \| 0.1651 \| 0.6512 \| 0.1651 \| \| 0.9415 \| 8.9240 \| 90900 \| 0.8769 \| 0.0121 \| 0.1741 \| 0.6572 \| 0.1741 \| \| 0.846 \| 8.9338 \| 91000 \| 0.8900 \| 0.0121 \| 0.1715 \| 0.6531 \| 0.1715 \| \| 0.8878 \| 8.9436 \| 91100 \| 0.9125 \| 0.0121 \| 0.1658 \| 0.6470 \| 0.1658 \| \| 0.9493 \| 8.9535 \| 91200 \| 0.8922 \| 0.0121 \| 0.1691 \| 0.6480 \| 0.1691 \| \| 0.8781 \| 8.9633 \| 91300 \| 0.8823 \| 0.0121 \| 0.1667 \| 0.6526 \| 0.1667 \| \| 0.9035 \| 8.9731 \| 91400 \| 0.8890 \| 0.0121 \| 0.1715 \| 0.6517 \| 0.1715 \| \| 0.8642 \| 8.9829 \| 91500 \| 0.8988 \| 0.0121 \| 0.1693 \| 0.6487 \| 0.1693 \| \| 0.8415 \| 8.9927 \| 91600 \| 0.8870 \| 0.0121 \| 0.1686 \| 0.6519 \| 0.1686 \| \| 0.864 \| 9.0026 \| 91700 \| 0.8901 \| 0.0121 \| 0.1721 \| 0.6524 \| 0.1721 \| \| 0.9031 \| 9.0124 \| 91800 \| 0.8907 \| 0.0121 \| 0.1721 \| 0.6516 \| 0.1721 \| \| 0.8783 \| 9.0222 \| 91900 \| 0.8837 \| 0.0121 \| 0.1601 \| 0.6484 \| 0.1601 \| \| 0.952 \| 9.0320 \| 92000 \| 0.9131 \| 0.0121 \| 0.1670 \| 0.6459 \| 0.1670 \| \| 0.8702 \| 9.0418 \| 92100 \| 0.9124 \| 0.0121 \| 0.1567 \| 0.6423 \| 0.1567 \| \| 0.9422 \| 9.0516 \| 92200 \| 0.8867 \| 0.0121 \| 0.1749 \| 0.6526 \| 0.1749 \| \| 0.9153 \| 9.0615 \| 92300 \| 0.8892 \| 0.0121 \| 0.1619 \| 0.6509 \| 0.1619 \| \| 0.8876 \| 9.0713 \| 92400 \| 0.8911 \| 0.0121 \| 0.1705 \| 0.6533 \| 0.1705 \| \| 0.9129 \| 9.0811 \| 92500 \| 0.8891 \| 0.0121 \| 0.1748 \| 0.6546 \| 0.1748 \| \| 0.8659 \| 9.0909 \| 92600 \| 0.9137 \| 0.0121 \| 0.1635 \| 0.6476 \| 0.1635 \| \| 0.8991 \| 9.1007 \| 92700 \| 0.8962 \| 0.0121 \| 0.1653 \| 0.6485 \| 0.1653 \| \| 0.8597 \| 9.1105 \| 92800 \| 0.8887 \| 0.0121 \| 0.1709 \| 0.6516 \| 0.1709 \| \| 0.8829 \| 9.1204 \| 92900 \| 0.8989 \| 0.0121 \| 0.1687 \| 0.6468 \| 0.1687 \| \| 0.9497 \| 9.1302 \| 93000 \| 0.8895 \| 0.0121 \| 0.1674 \| 0.6516 \| 0.1674 \| \| 0.8892 \| 9.1400 \| 93100 \| 0.9024 \| 0.0121 \| 0.1647 \| 0.6485 \| 0.1647 \| \| 0.9227 \| 9.1498 \| 93200 \| 0.8930 \| 0.0121 \| 0.1678 \| 0.6482 \| 0.1678 \| \| 0.821 \| 9.1596 \| 93300 \| 0.8753 \| 0.0121 \| 0.1727 \| 0.6579 \| 0.1727 \| \| 0.912 \| 9.1694 \| 93400 \| 0.8938 \| 0.0121 \| 0.1624 \| 0.6470 \| 0.1624 \| \| 0.8599 \| 9.1793 \| 93500 \| 0.8865 \| 0.0121 \| 0.1776 \| 0.6547 \| 0.1776 \| \| 0.8947 \| 9.1891 \| 93600 \| 0.8911 \| 0.0121 \| 0.1707 \| 0.6498 \| 0.1707 \| \| 0.9006 \| 9.1989 \| 93700 \| 0.8800 \| 0.0121 \| 0.1702 \| 0.6535 \| 0.1702 \| \| 0.9032 \| 9.2087 \| 93800 \| 0.8805 \| 0.0121 \| 0.1779 \| 0.6540 \| 0.1779 \| \| 0.9449 \| 9.2185 \| 93900 \| 0.8653 \| 0.0121 \| 0.1763 \| 0.6596 \| 0.1763 \| \| 0.7906 \| 9.2284 \| 94000 \| 0.8777 \| 0.0121 \| 0.1833 \| 0.6564 \| 0.1833 \| \| 0.8576 \| 9.2382 \| 94100 \| 0.8956 \| 0.0121 \| 0.1643 \| 0.6486 \| 0.1643 \| \| 0.8581 \| 9.2480 \| 94200 \| 0.8783 \| 0.0121 \| 0.1729 \| 0.6551 \| 0.1729 \| \| 0.897 \| 9.2578 \| 94300 \| 0.9068 \| 0.0121 \| 0.1645 \| 0.6480 \| 0.1645 \| \| 0.8853 \| 9.2676 \| 94400 \| 0.8996 \| 0.0121 \| 0.1621 \| 0.6480 \| 0.1621 \| \| 0.8634 \| 9.2774 \| 94500 \| 0.8795 \| 0.0121 \| 0.1784 \| 0.6566 \| 0.1784 \| \| 0.8182 \| 9.2873 \| 94600 \| 0.8763 \| 0.0121 \| 0.1782 \| 0.6579 \| 0.1782 \| \| 0.9051 \| 9.2971 \| 94700 \| 0.8899 \| 0.0121 \| 0.1648 \| 0.6504 \| 0.1648 \| \| 0.9105 \| 9.3069 \| 94800 \| 0.8971 \| 0.0121 \| 0.1619 \| 0.6481 \| 0.1619 \| \| 0.84 \| 9.3167 \| 94900 \| 0.8970 \| 0.0121 \| 0.1635 \| 0.6467 \| 0.1635 \| \| 0.9105 \| 9.3265 \| 95000 \| 0.8937 \| 0.0121 \| 0.1686 \| 0.6508 \| 0.1686 \| \| 0.8974 \| 9.3363 \| 95100 \| 0.9009 \| 0.0121 \| 0.1755 \| 0.6520 \| 0.1755 \| \| 0.9465 \| 9.3462 \| 95200 \| 0.9000 \| 0.0121 \| 0.1726 \| 0.6503 \| 0.1726 \| \| 0.8545 \| 9.3560 \| 95300 \| 0.8961 \| 0.0121 \| 0.1691 \| 0.6500 \| 0.1691 \| \| 0.8711 \| 9.3658 \| 95400 \| 0.8818 \| 0.0121 \| 0.1684 \| 0.6532 \| 0.1684 \| \| 0.9244 \| 9.3756 \| 95500 \| 0.8853 \| 0.0121 \| 0.1706 \| 0.6535 \| 0.1706 \| \| 0.8094 \| 9.3854 \| 95600 \| 0.8801 \| 0.0121 \| 0.1769 \| 0.6561 \| 0.1769 \| \| 0.8754 \| 9.3952 \| 95700 \| 0.8994 \| 0.0121 \| 0.1712 \| 0.6495 \| 0.1712 \| \| 0.9122 \| 9.4051 \| 95800 \| 0.8924 \| 0.0121 \| 0.1619 \| 0.6513 \| 0.1619 \| \| 0.9308 \| 9.4149 \| 95900 \| 0.8668 \| 0.0121 \| 0.1786 \| 0.6614 \| 0.1786 \| \| 0.8718 \| 9.4247 \| 96000 \| 0.8634 \| 0.0121 \| 0.1720 \| 0.6606 \| 0.1720 \| \| 0.8498 \| 9.4345 \| 96100 \| 0.8933 \| 0.0121 \| 0.1703 \| 0.6512 \| 0.1703 \| \| 0.8661 \| 9.4443 \| 96200 \| 0.8844 \| 0.0121 \| 0.1675 \| 0.6527 \| 0.1675 \| \| 0.8599 \| 9.4542 \| 96300 \| 0.8809 \| 0.0121 \| 0.1681 \| 0.6509 \| 0.1681 \| \| 0.8299 \| 9.4640 \| 96400 \| 0.8847 \| 0.0121 \| 0.1760 \| 0.6557 \| 0.1760 \| \| 0.8077 \| 9.4738 \| 96500 \| 0.8986 \| 0.0121 \| 0.1664 \| 0.6446 \| 0.1664 \| \| 0.8472 \| 9.4836 \| 96600 \| 0.9022 \| 0.0121 \| 0.1628 \| 0.6467 \| 0.1628 \| \| 0.8345 \| 9.4934 \| 96700 \| 0.8835 \| 0.0121 \| 0.1693 \| 0.6514 \| 0.1693 \| \| 0.8092 \| 9.5032 \| 96800 \| 0.8862 \| 0.0121 \| 0.1705 \| 0.6528 \| 0.1705 \| \| 0.978 \| 9.5131 \| 96900 \| 0.8863 \| 0.0121 \| 0.1784 \| 0.6522 \| 0.1784 \| \| 0.9094 \| 9.5229 \| 97000 \| 0.8872 \| 0.0121 \| 0.1751 \| 0.6543 \| 0.1751 \| \| 0.9012 \| 9.5327 \| 97100 \| 0.8874 \| 0.0121 \| 0.1751 \| 0.6545 \| 0.1751 \| \| 0.8758 \| 9.5425 \| 97200 \| 0.8880 \| 0.0121 \| 0.1671 \| 0.6516 \| 0.1671 \| \| 0.9321 \| 9.5523 \| 97300 \| 0.8742 \| 0.0121 \| 0.1679 \| 0.6572 \| 0.1679 \| \| 0.7937 \| 9.5621 \| 97400 \| 0.8806 \| 0.0121 \| 0.1691 \| 0.6575 \| 0.1691 \| \| 0.8658 \| 9.5720 \| 97500 \| 0.8782 \| 0.0121 \| 0.1734 \| 0.6560 \| 0.1734 \| \| 0.8625 \| 9.5818 \| 97600 \| 0.8873 \| 0.0121 \| 0.1761 \| 0.6526 \| 0.1761 \| \| 0.8831 \| 9.5916 \| 97700 \| 0.8677 \| 0.0121 \| 0.1838 \| 0.6621 \| 0.1838 \| \| 0.844 \| 9.6014 \| 97800 \| 0.8956 \| 0.0121 \| 0.1624 \| 0.6513 \| 0.1624 \| \| 0.8289 \| 9.6112 \| 97900 \| 0.8771 \| 0.0121 \| 0.1735 \| 0.6556 \| 0.1735 \| \| 0.891 \| 9.6210 \| 98000 \| 0.8888 \| 0.0121 \| 0.1704 \| 0.6518 \| 0.1704 \| \| 0.8433 \| 9.6309 \| 98100 \| 0.8907 \| 0.0121 \| 0.1702 \| 0.6546 \| 0.1702 \| \| 0.7974 \| 9.6407 \| 98200 \| 0.8671 \| 0.0121 \| 0.1776 \| 0.6593 \| 0.1776 \| \| 0.9177 \| 9.6505 \| 98300 \| 0.8685 \| 0.0121 \| 0.1724 \| 0.6586 \| 0.1724 \| \| 0.8644 \| 9.6603 \| 98400 \| 0.8774 \| 0.0121 \| 0.1802 \| 0.6590 \| 0.1802 \| \| 0.8961 \| 9.6701 \| 98500 \| 0.8888 \| 0.0121 \| 0.1692 \| 0.6498 \| 0.1692 \| \| 0.8769 \| 9.6800 \| 98600 \| 0.8783 \| 0.0121 \| 0.1760 \| 0.6585 \| 0.1760 \| \| 0.9498 \| 9.6898 \| 98700 \| 0.9060 \| 0.0121 \| 0.1644 \| 0.6462 \| 0.1644 \| \| 0.8435 \| 9.6996 \| 98800 \| 0.8771 \| 0.0121 \| 0.1756 \| 0.6561 \| 0.1756 \| \| 0.8151 \| 9.7094 \| 98900 \| 0.8711 \| 0.0121 \| 0.1729 \| 0.6578 \| 0.1729 \| \| 0.8619 \| 9.7192 \| 99000 \| 0.8616 \| 0.0121 \| 0.1759 \| 0.6586 \| 0.1759 \| \| 0.8862 \| 9.7290 \| 99100 \| 0.8702 \| 0.0121 \| 0.1711 \| 0.6585 \| 0.1711 \| \| 0.9008 \| 9.7389 \| 99200 \| 0.8826 \| 0.0121 \| 0.1716 \| 0.6562 \| 0.1716 \| \| 0.861 \| 9.7487 \| 99300 \| 0.8937 \| 0.0121 \| 0.1670 \| 0.6492 \| 0.1670 \| \| 0.8789 \| 9.7585 \| 99400 \| 0.8822 \| 0.0121 \| 0.1693 \| 0.6527 \| 0.1693 \| \| 0.8296 \| 9.7683 \| 99500 \| 0.8788 \| 0.0121 \| 0.1705 \| 0.6557 \| 0.1705 \| \| 0.9087 \| 9.7781 \| 99600 \| 0.9116 \| 0.0121 \| 0.1662 \| 0.6464 \| 0.1662 \| \| 0.8118 \| 9.7879 \| 99700 \| 0.8953 \| 0.0121 \| 0.1724 \| 0.6521 \| 0.1724 \| \| 0.9122 \| 9.7978 \| 99800 \| 0.8802 \| 0.0121 \| 0.1739 \| 0.6576 \| 0.1739 \| \| 0.8541 \| 9.8076 \| 99900 \| 0.8731 \| 0.0121 \| 0.1812 \| 0.6593 \| 0.1812 \| \| 0.9063 \| 9.8174 \| 100000 \| 0.8769 \| 0.0121 \| 0.1747 \| 0.6560 \| 0.1747 \| \| 0.8517 \| 9.8272 \| 100100 \| 0.8716 \| 0.0121 \| 0.1777 \| 0.6605 \| 0.1777 \| \| 0.8725 \| 9.8370 \| 100200 \| 0.8849 \| 0.0121 \| 0.1646 \| 0.6525 \| 0.1646 \| \| 0.8793 \| 9.8468 \| 100300 \| 0.8720 \| 0.0121 \| 0.1698 \| 0.6578 \| 0.1698 \| \| 0.8553 \| 9.8567 \| 100400 \| 0.8584 \| 0.0121 \| 0.1829 \| 0.6629 \| 0.1829 \| \| 0.8216 \| 9.8665 \| 100500 \| 0.8780 \| 0.0121 \| 0.1715 \| 0.6568 \| 0.1715 \| \| 0.8723 \| 9.8763 \| 100600 \| 0.8758 \| 0.0121 \| 0.1764 \| 0.6562 \| 0.1764 \| \| 0.8514 \| 9.8861 \| 100700 \| 0.8917 \| 0.0121 \| 0.1765 \| 0.6556 \| 0.1765 \| \| 0.9008 \| 9.8959 \| 100800 \| 0.8966 \| 0.0121 \| 0.1703 \| 0.6520 \| 0.1703 \| \| 0.8712 \| 9.9058 \| 100900 \| 0.8608 \| 0.0121 \| 0.1788 \| 0.6606 \| 0.1788 \| \| 0.8697 \| 9.9156 \| 101000 \| 0.8751 \| 0.0121 \| 0.1712 \| 0.6559 \| 0.1712 \| \| 0.8335 \| 9.9254 \| 101100 \| 0.8879 \| 0.0121 \| 0.1712 \| 0.6544 \| 0.1712 \| \| 0.9015 \| 9.9352 \| 101200 \| 0.8813 \| 0.0121 \| 0.1675 \| 0.6543 \| 0.1675 \| \| 0.8726 \| 9.9450 \| 101300 \| 0.8752 \| 0.0121 \| 0.1781 \| 0.6570 \| 0.1781 \| \| 0.8435 \| 9.9548 \| 101400 \| 0.8954 \| 0.0121 \| 0.1662 \| 0.6497 \| 0.1662 \| \| 0.8604 \| 9.9647 \| 101500 \| 0.8749 \| 0.0121 \| 0.1764 \| 0.6583 \| 0.1764 \| \| 0.9175 \| 9.9745 \| 101600 \| 0.8851 \| 0.0121 \| 0.1658 \| 0.6543 \| 0.1658 \| \| 0.921 \| 9.9843 \| 101700 \| 0.8862 \| 0.0121 \| 0.1739 \| 0.6529 \| 0.1739 \| \| 0.838 \| 9.9941 \| 101800 \| 0.8718 \| 0.0121 \| 0.1822 \| 0.6614 \| 0.1822 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
johnnyyang0518/llama2_uuu_news_qlora	johnnyyang0518	2025-06-25T01:20:49Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-25T01:20:49Z	--- license: apache-2.0 ---
Rootreck/Applio_3.2.9-RVC-Other	Rootreck	2025-06-25T01:14:44Z	0	0	null	[ "Applio", "RVC", "audio-to-audio", "en", "dataset:Rootreck/Applio_3.2.9-RVC-Other", "region:us" ]	audio-to-audio	2025-06-25T00:23:34Z	--- datasets: - Rootreck/Applio_3.2.9-RVC-Other language: - en pipeline_tag: audio-to-audio tags: - Applio - RVC ---
Treza12/Pixtral-1D-Thermal	Treza12	2025-06-25T01:14:07Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistral-community/pixtral-12b", "base_model:adapter:mistral-community/pixtral-12b", "region:us" ]	null	2025-06-25T01:12:52Z	--- base_model: mistral-community/pixtral-12b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.3.dev0
sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF	sizzlebop	2025-06-25T01:09:12Z	0	0	null	[ "gguf", "code", "llama-cpp", "gguf-my-repo", "en", "dataset:Team-ACE/ToolACE", "base_model:Team-ACE/ToolACE-2-Llama-3.1-8B", "base_model:quantized:Team-ACE/ToolACE-2-Llama-3.1-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-25T01:08:44Z	--- license: apache-2.0 datasets: - Team-ACE/ToolACE language: - en metrics: - accuracy base_model: Team-ACE/ToolACE-2-Llama-3.1-8B tags: - code - llama-cpp - gguf-my-repo --- # sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF This model was converted to GGUF format from [`Team-ACE/ToolACE-2-Llama-3.1-8B`](https://huggingface.co/Team-ACE/ToolACE-2-Llama-3.1-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Team-ACE/ToolACE-2-Llama-3.1-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF --hf-file toolace-2-llama-3.1-8b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF --hf-file toolace-2-llama-3.1-8b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF --hf-file toolace-2-llama-3.1-8b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF --hf-file toolace-2-llama-3.1-8b-q8_0.gguf -c 2048 ```
hasdal/28686e11-1d59-4507-a11e-28a534a1ed6a	hasdal	2025-06-25T01:05:20Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-24T19:56:09Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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VarunNagaraj/tiny-llm-maui-apiclients-mistral	VarunNagaraj	2025-06-25T01:01:30Z	0	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-instruct-v0.1-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-instruct-v0.1-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-25T01:00:24Z	--- base_model: unsloth/mistral-7b-instruct-v0.1-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: VarunNagaraj - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.1-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
phospho-app/GarrieD-ACT_BBOX-Red_Ball_V1_0624-mui9e	phospho-app	2025-06-25T00:59:03Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-25T00:43:35Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [phospho-app/Red_Ball_V1_0624_bboxes](https://huggingface.co/datasets/phospho-app/Red_Ball_V1_0624_bboxes) - Wandb run URL: None - Epochs: None - Batch size: 20 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
deepmaster/72_8	deepmaster	2025-06-25T00:58:40Z	34	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-08T18:57:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5	tokyotech-llm	2025-06-25T00:57:38Z	35	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "ja", "dataset:tokyotech-llm/lmsys-chat-1m-synth", "dataset:lmsys/lmsys-chat-1m", "arxiv:2503.23714", "arxiv:2407.21783", "base_model:tokyotech-llm/Llama-3.1-Swallow-8B-v0.5", "base_model:finetune:tokyotech-llm/Llama-3.1-Swallow-8B-v0.5", "license:llama3.3", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-12T11:32:48Z	--- language: - en - ja library_name: transformers pipeline_tag: text-generation license: - llama3.3 - gemma model_type: llama datasets: - tokyotech-llm/lmsys-chat-1m-synth - lmsys/lmsys-chat-1m base_model: - tokyotech-llm/Llama-3.1-Swallow-8B-v0.5 --- # Llama 3.1 Swallow - Built with Llama Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the [Meta Llama 3.1](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f) models. Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities. We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and coding contents, etc (see the Training Datasets section of the base model) for continual pre-training. The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese. See the Swallow Model Index section to find other model variants. Note: [Llama-3.1-Swallow-8B-Instruct-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5) model was continually pre-trained from the [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) and then instruction-tuned with our instruction datasets. # Release History - June 25, 2025: Released [Llama-3.1-Swallow-8B-Instruct-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5) and [Llama-3.1-Swallow-8B-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.5). - March 10, 2025: Released [Llama-3.3-Swallow-70B-Instruct-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4) and [Llama-3.3-Swallow-70B-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4). - December 30, 2024: Released [Llama-3.1-Swallow-70B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3). - December 23, 2024: Released [Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3). - November 11, 2024: Released [Llama-3.1-Swallow-8B-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) and [Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2). - October 08, 2024: Released [Llama-3.1-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1), [Llama-3.1-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1), [Llama-3.1-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1), and [Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1). # Major Updates This release enhances the conversation capability of Llama 3.1 Swallow. The model is trained to imitate the behavior of [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it). Among all open-source LLMs with <= 8 billion parameters, Llama-3.1-Swallow-8B-Instruct-v0.5 exhibits state-of-the-art performance on Japanese MT-Bench, outperforming its predecessor, [Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2), by 1.5 points. ## Swallow Model Index \|Model\|Llama-3.1-Swallow-Instruct v0.5\|Llama-3.1-Swallow v0.5\|Llama-3.3-Swallow v0.4\|Llama-3.3-Swallow-Instruct v0.4\|Llama-3.1-Swallow-Instruct v0.3\|Llama-3.1-Swallow-Instruct v0.2\|Llama-3.1-Swallow v0.2\|Llama-3.1-Swallow-Instruct v0.1\|Llama-3.1-Swallow v0.1\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|8B\|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5)\|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.5) \|\|\|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3)\|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2)\|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2)\|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1)\|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1)\| \|70B\|\|\|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4)\|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4)\|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3)\| \| \|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1)\| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1)\| ![logo](./logo_v0.5.png) The website [https://swallow-llm.github.io/](https://swallow-llm.github.io/index.en.html) provides large language models developed by the Swallow team. ## Model Details * Model type: Please refer to [Llama 3.1 MODEL_CARD](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for details on the model architecture. * Language(s): Japanese English * Library: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), [transformers](https://github.com/huggingface/transformers) * Tokenizer: Please refer to [Llama 3.1 blog](https://ai.meta.com/blog/meta-llama-3-1) for details on the tokenizer. * Contact: swallow[at]nlp.c.titech.ac.jp ## Model Performance ## Japanese MT-Bench * We report evaluation results judged by gpt-4o-2024-08-06 as below. * In our releases earlier than January 1, 2025, we reported scores judged by gpt-4-1106-preview. Scores reported below are thus not directly comparable with those reported in those earlier releases. \|Model\|coding\|extraction\|humanities\|math\|reasoning\|roleplay\|stem\|writing\|JMTAvg\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| llm-jp-3-7.2b-instruct3 \| 0.358 \| 0.597 \| 0.812 \| 0.386 \| 0.438 \| 0.766 \| 0.622 \| 0.721 \| 0.588 \| \| Qwen2.5-7B-Instruct \| 0.599 \| 0.741 \| 0.719 \| 0.637 \| 0.541 \| 0.744 \| 0.624 \| 0.713 \| 0.665 \| \| Tanuki-8B-dpo-v1.0 \| 0.461 \| 0.597 \| 0.562 \| 0.495 \| 0.377 \| 0.589 \| 0.509 \| 0.643 \| 0.529 \| \| Llama 3 8B Instruct \| 0.467 \| 0.706 \| 0.692 \| 0.310 \| 0.433 \| 0.542 \| 0.532 \| 0.546 \| 0.529 \| \| Llama 3.1 8B Instruct \| 0.420 \| 0.830 \| 0.550 \| 0.514 \| 0.349 \| 0.502 \| 0.479 \| 0.504 \| 0.519 \| \| Llama 3 Youko 8B Instruct \| 0.464 \| 0.757 \| 0.769 \| 0.414 \| 0.487 \| 0.695 \| 0.583 \| 0.753 \| 0.616 \| \| Llama-3-ELYZA-JP-8B \| 0.389 \| 0.706 \| 0.647 \| 0.426 \| 0.613 \| 0.684 \| 0.533 \| 0.697 \| 0.587 \| \| Llama 3 heron brain 8B v0.3 \| 0.362 \| 0.566 \| 0.602 \| 0.315 \| 0.426 \| 0.586 \| 0.567 \| 0.550 \| 0.497 \| \| Llama 3.1 Swallow 8B Instruct v0.1 \| 0.427 \| 0.738 \| 0.675 \| 0.527 \| 0.453 \| 0.615 \| 0.593 \| 0.624 \| 0.581 \| \| Llama 3.1 Swallow 8B Instruct v0.2 \| 0.534 \| 0.748 \| 0.705 \| 0.565 \| 0.475 \| 0.646 \| 0.579 \| 0.646 \| 0.612 \| \| Llama 3.1 Swallow 8B Instruct v0.3 \| 0.562 \| 0.756 \| 0.869 \| 0.610 \| 0.512 \| 0.783 \| 0.748 \| 0.803 \| 0.705 \| \| Llama 3.1 Swallow 8B Instruct v0.5 \| 0.551 \| 0.814 \| 0.847 \| 0.568 \| 0.577 \| 0.796 \| 0.770 \| 0.832 \| 0.719 \| ### Japanese tasks \|Model\|JCom.\|JEMHopQA\|NIILC\|JSQuAD\|XL-Sum\|MGSM\|WMT20-en-ja\|WMT20-ja-en\|JMMLU\|JHumanEval\|Ja Avg\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| \|4-shot\|4-shot\|4-shot\|4-shot\|1-shot\|4-shot\|4-shot\|4-shot\|5-shot\|0-shot\| \| \| \|EM acc\|Char-F1\|Char-F1\|Char-F1\|ROUGE-2\|EM acc\|BLEU\|BLEU\|EM acc\|pass@1\| \| \| llm-jp-3-7.2b-instruct3 \| 0.780 \| 0.297 \| 0.570 \| 0.882 \| 0.132 \| 0.344 \| 0.251 \| 0.189 \| 0.422 \| 0.196 \| 0.406 \| \| Qwen2.5-7B-Instruct \| 0.915 \| 0.429 \| 0.391 \| 0.891 \| 0.168 \| 0.632 \| 0.211 \| 0.192 \| 0.623 \| 0.532 \| 0.498 \| \| Tanuki-8B-dpo-v1.0 \| 0.278 \| 0.284 \| 0.370 \| 0.670 \| 0.102 \| 0.428 \| 0.238 \| 0.183 \| 0.306 \| 0.251 \| 0.311 \| \| Llama 3 8B Instruct \| 0.880 \| 0.417 \| 0.385 \| 0.891 \| 0.126 \| 0.424 \| 0.214 \| 0.202 \| 0.468 \| 0.296 \| 0.430 \| \| Llama 3.1 8B Instruct \| 0.880 \| 0.447 \| 0.407 \| 0.886 \| 0.148 \| 0.516 \| 0.218 \| 0.200 \| 0.509 \| 0.488 \| 0.470 \| \| Llama 3 Youko 8B Instruct \| 0.921 \| 0.481 \| 0.517 \| 0.899 \| 0.209 \| 0.472 \| 0.256 \| 0.191 \| 0.469 \| 0.262 \| 0.468 \| \| Llama-3-ELYZA-JP-8B \| 0.897 \| 0.498 \| 0.496 \| 0.906 \| 0.168 \| 0.436 \| 0.250 \| 0.185 \| 0.487 \| 0.388 \| 0.471 \| \| Llama 3 heron brain 8B v0.3 \| 0.923 \| 0.493 \| 0.569 \| 0.906 \| 0.218 \| 0.456 \| 0.277 \| 0.217 \| 0.499 \| 0.318 \| 0.488 \| \| Llama 3.1 Swallow 8B Instruct v0.1 \| 0.924 \| 0.587 \| 0.574 \| 0.917 \| 0.138 \| 0.508 \| 0.282 \| 0.228 \| 0.530 \| 0.366 \| 0.505 \| \| Llama 3.1 Swallow 8B Instruct v0.2 \| 0.929 \| 0.560 \| 0.599 \| 0.915 \| 0.137 \| 0.528 \| 0.288 \| 0.227 \| 0.550 \| 0.408 \| 0.514 \| \| Llama 3.1 Swallow 8B Instruct v0.3 \| 0.924 \| 0.528 \| 0.583 \| 0.896 \| 0.191 \| 0.532 \| 0.281 \| 0.229 \| 0.544 \| 0.394 \| 0.510 \| \| Llama 3.1 Swallow 8B Instruct v0.5 \| 0.937 \| 0.511 \| 0.606 \| 0.900 \| 0.174 \| 0.604 \| 0.293 \| 0.230 \| 0.581 \| 0.496 \| 0.533 \| ### English tasks \|Model\|OpenBookQA\|TriviaQA\|HellaSWAG\|SQuAD2.0\|XWINO\|MMLU\|GSM8K\|MATH\|BBH\|HumanEval\|En Avg\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| \|4-shot\|4-shot\|4-shot\|4-shot\|4-shot\|5-shot\|4-shot\|4-shot \| 3-shot\|0-shot\| \| \| \|Acc\|EM acc\|Acc\|EM acc\|Acc\|Acc\|EM acc\|CoT EM Acc\| CoT EM Acc\| pass@1\| \| \| llm-jp-3-7.2b-instruct3 \| 0.328 \| 0.479 \| 0.563 \| 0.501 \| 0.876 \| 0.462 \| 0.264 \| 0.028 \| 0.420 \| 0.219 \| 0.414 \| \| Qwen2.5-7B-Instruct \| 0.428 \| 0.519 \| 0.624 \| 0.569 \| 0.877 \| 0.742 \| 0.739 \| 0.688 \| 0.217 \| 0.636 \| 0.604 \| \| Tanuki-8B-dpo-v1.0 \| 0.334 \| 0.283 \| 0.469 \| 0.501 \| 0.816 \| 0.377 \| 0.487 \| 0.178 \| 0.333 \| 0.288 \| 0.406 \| \| Llama 3 8B Instruct \| 0.388 \| 0.670 \| 0.583 \| 0.611 \| 0.892 \| 0.657 \| 0.745 \| 0.306 \| 0.646 \| 0.554 \| 0.605 \| \| Llama 3.1 8B Instruct \| 0.366 \| 0.699 \| 0.592 \| 0.600 \| 0.904 \| 0.680 \| 0.743 \| 0.376 \| 0.690 \| 0.624 \| 0.627 \| \| Llama 3 Youko 8B Instruct \| 0.406 \| 0.613 \| 0.599 \| 0.559 \| 0.897 \| 0.596 \| 0.563 \| 0.152 \| 0.401 \| 0.287 \| 0.507 \| \| Llama-3-ELYZA-JP-8B \| 0.318 \| 0.551 \| 0.523 \| 0.600 \| 0.882 \| 0.587 \| 0.558 \| 0.164 \| 0.321 \| 0.449 \| 0.495 \| \| Llama 3 heron brain 8B v0.3 \| 0.362 \| 0.656 \| 0.569 \| 0.581 \| 0.901 \| 0.621 \| 0.578 \| 0.222 \| 0.641 \| 0.380 \| 0.551 \| \| Llama 3.1 Swallow 8B Instruct v0.1 \| 0.388 \| 0.649 \| 0.615 \| 0.598 \| 0.891 \| 0.624 \| 0.605 \| 0.236 \| 0.642 \| 0.379 \| 0.563 \| \| Llama 3.1 Swallow 8B Instruct v0.2 \| 0.380 \| 0.625 \| 0.603 \| 0.607 \| 0.887 \| 0.634 \| 0.620 \| 0.264 \| 0.649 \| 0.474 \| 0.574 \| \| Llama 3.1 Swallow 8B Instruct v0.3 \| 0.396 \| 0.629 \| 0.593 \| 0.570 \| 0.884 \| 0.629 \| 0.622 \| 0.266 \| 0.626 \| 0.445 \| 0.566 \| \| Llama 3.1 Swallow 8B Instruct v0.5 \| 0.396 \| 0.638 \| 0.603 \| 0.581 \| 0.889 \| 0.663 \| 0.717 \| 0.368 \| 0.628 \| 0.554 \| 0.604 \| ## Evaluation Benchmarks ### Japanese MT-Bench We used [Japanese MT-Bench](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question) to assess the capabilities of multi-turn dialogue with the following settings: - Implementation: FastChat [Zheng+, 2023] (commit #e86e70d0) - Question: [Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v4](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/v4) - Reference Answer: [swallow-evaluation, reference answer](https://github.com/swallow-llm/swallow-evaluation/tree/main/fastchat/fastchat/llm_judge/data/japanese_mt_bench/reference_answer) - Prompt for Judge: [Nejumi LLM-Leaderboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1) - Judge: `gpt-4o-2024-08-06` - Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs. ### Japanese evaluation benchmarks We used llm-jp-eval(v1.3.0), JP Language Model Evaluation Harness(commit #9b42d41) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows: - Multiple-choice question answering (JCommonsenseQA [Kurihara et al., 2022]) - Open-ended question answering (JEMHopQA [Ishii et al., 2024]) - Open-ended question answering (NIILC [関根, 2003]) - Machine reading comprehension (JSQuAD [Kurihara et al., 2022]) - Automatic summarization (XL-Sum [Hasan et al., 2021]) - Machine translation (WMT2020 ja-en [Barrault et al., 2020]) - Machine translation (WMT2020 en-ja [Barrault et al., 2020]) - Arithmetic reasoning (MGSM [Shi et al., 2023]) - Academic exams (JMMLU [尹ら, 2024]) - Code generation (JHumanEval [佐藤ら, 2024]) ### English evaluation benchmarks We used the Language Model Evaluation Harness(v.0.4.2) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows: - Multiple-choice question answering (OpenBookQA [Mihaylov et al., 2018]) - Open-ended question answering (TriviaQA [Joshi et al., 2017]) - Machine reading comprehension (SQuAD2 [Rajpurkar et al., 2018]) - Commonsense reasoning (XWINO [Tikhonov and Ryabinin, 2021]) - Natural language inference (HellaSwag [Zellers et al., 2019]) - Arithmetic reasoning (GSM8K [Cobbe et al., 2021]) - Mathematical reasoning (MATH [Hendrycks et al., 2022][Lightman et al., 2024]) - Reasoning (BBH (BIG-Bench-Hard) [Suzgun et al., 2023]) - Academic exams (MMLU [Hendrycks et al., 2021]) - Code generation (HumanEval [Chen et al., 2021]) ## Usage ```sh pip install vllm ``` ```python from transformers import AutoTokenizer from vllm import LLM, SamplingParams model_name = "tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5" tokenizer = AutoTokenizer.from_pretrained(model_name) llm = LLM( model=model_name, tensor_parallel_size=1, ) sampling_params = SamplingParams( temperature=0.6, top_p=0.9, max_tokens=512, stop="<\|eot_id\|>" ) message = [ { "role": "user", "content": "東京の紅葉した公園で、東京タワーと高層ビルを背景に、空を舞うツバメと草地に佇むラマが出会う温かな物語を書いてください。", }, ] prompt = tokenizer.apply_chat_template( message, tokenize=False, add_generation_prompt=True ) output = llm.generate(prompt, sampling_params) print(output[0].outputs[0].text) ``` ## Training Datasets ### Instruction Tuning The following datasets were used for the instruction tuning. - [Gemma-3-LMSYS-Chat-1M-Synth](https://huggingface.co/datasets/tokyotech-llm/lmsys-chat-1m-synth) - Single-turn Japanese instruction dataset synthesized and derived from [lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) [\[Zhang+, ICLR24\]](https://openreview.net/forum?id=BOfDKxfwt0)). - First-turn user instructions were translated into Japanese via DeepL (machine translation), and assistant responses were generated using [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it). The same model, i.e., [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it) served as a judge for rejection sampling (n=10). Conversations containing personally identifiable information (PII) and template-based user instructions were removed. Duplicate instructions were removed. ## Risks and Limitations The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations. ## Acknowledgements We thank Meta Research for releasing Llama 3.1 under a generous open license. We received various supports, including: + AIST project: "Research and Development of Foundation Models for Generative AI in the Physical Domain" + NEDO project: "Development of Artificial Intelligence Application Technology to Support Judgment in Design Risk Assessment Work Based on the Perspective of Skilled Persons" (JPNP18002) of "Development of Integration Technology as the Core of Next Generation Artificial Intelligence and Robotics" + MEXT project: "Formation of R&D center to ensure transparency and reliability of generative AI models" + AIST program: [Large Generative AI Development Support Program](https://abci.ai/en/link/lfm_support_program.html) ## License [META LLAMA 3.1 COMMUNITY LICENSE](https://www.llama.com/llama3_1/license/) and [Gemma Terms of Use](https://ai.google.dev/gemma/terms) ## Authors Here are the team members: - From [Okazaki Laboratory, Institute of Science Tokyo](https://www.nlp.c.titech.ac.jp/index.en.html), the following members: - [Naoaki Okazaki](https://www.chokkan.org/index.ja.html) - [Sakae Mizuki](https://s-mizuki-nlp.github.io/) - [Youmi Ma](https://www.nlp.c.titech.ac.jp/member/youmi.en.html) - [Sangwhan Moon](https://www.sangwhan.com/) - [Koki Maeda](https://sites.google.com/view/silviase) - [Masanari Ohi](https://sites.google.com/view/masanariohi) - [Hinari Shimada](https://hinarishimada.github.io/portfolio) - [Taihei Shiotani](https://github.com/inatoihs) - [Koshiro Saito](https://sites.google.com/view/koshiro-saito) - [Tatsuya Ichinose](https://tatsuya736482.github.io/myprofile) - Naoya Matsushita - Sora Miyamoto - Nguyen Tien Dung - Yuta Katayama - From [YOKOTA Laboratory, Institute of Science Tokyo](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members: - [Rio Yokota](https://twitter.com/rioyokota) - [Kazuki Fujii](https://twitter.com/okoge_kaz) - [Taishi Nakamura](https://twitter.com/Setuna7777_2) - [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto) - [Ishida Shigeki](https://www.wantedly.com/id/reborn27) - Masaki Kawamura - Yukito Tajima - From [Artificial Intelligence Research Center, AIST, Japan](https://www.airc.aist.go.jp/en/teams/), the following members: - [Hiroya Takamura](https://sites.google.com/view/hjtakamura) ## How to cite If you find our work helpful, please feel free to cite these papers. ``` @inproceedings{Fujii:COLM2024, title={Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities}, author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae Mizuki and Rio Yokota and Naoaki Okazaki}, booktitle="Proceedings of the First Conference on Language Modeling", series={COLM}, pages="(to appear)", year="2024", month=oct, address={University of Pennsylvania, USA}, } @inproceedings{Okazaki:COLM2024, title={Building a Large Japanese Web Corpus for Large Language Models}, author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Rio Yokota and Sakae Mizuki}, booktitle="Proceedings of the First Conference on Language Modeling", series={COLM}, pages="(to appear)", year="2024", month=oct, address={University of Pennsylvania, USA}, } @misc{ma:arxiv2025, title={Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models}, author={Youmi Ma and Sakae Mizuki and Kazuki Fujii and Taishi Nakamura and Masanari Ohi and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Koki Maeda and Kakeru Hattori and Takumi Okamoto and Shigeki Ishida and Rio Yokota and Hiroya Takamura and Naoaki Okazaki}, year={2025}, eprint={2503.23714}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.23714}, } ``` ### References ```tex @misc{dubey2024llama3herdmodels, title={The Llama 3 Herd of Models}, author={Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Amy Yang and Angela Fan et al.}, year={2024}, eprint={2407.21783}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2407.21783}, } ```
deepmaster/72_4	deepmaster	2025-06-25T00:55:35Z	42	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-08T18:51:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX	mlx-community	2025-06-25T00:51:02Z	0	0	mlx	[ "mlx", "safetensors", "mixtral", "Llama 3.2", "8 X 4B", "Brainstorm 5x", "128k context", "moe", "8 experts", "mixture of experts", "fine tune", "text-generation", "conversational", "base_model:DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit", "base_model:quantized:DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit", "8-bit", "region:us" ]	text-generation	2025-06-25T00:35:40Z	--- library_name: mlx tags: - Llama 3.2 - 8 X 4B - Brainstorm 5x - 128k context - moe - 8 experts - mixture of experts - fine tune - mlx base_model: DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit pipeline_tag: text-generation --- # mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX This model [mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX](https://huggingface.co/mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX) was converted to MLX format from [DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit](https://huggingface.co/DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit) using mlx-lm version 0.25.2. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
Mr-Matrix/LogicBomb-Classifier	Mr-Matrix	2025-06-25T00:48:41Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-25T00:43:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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windscare2/Lorax	windscare2	2025-06-25T00:41:48Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-25T00:17:59Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Lorax --- # Lorax <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Lorax` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Lorax", "lora_weights": "https://huggingface.co/windscare2/Lorax/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('windscare2/Lorax', weight_name='lora.safetensors') image = pipeline('Lorax').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/windscare2/Lorax/discussions) to add images that show off what you’ve made with this LoRA.
hongin9812/tunning_blip2-opt-2.7b-fp16-sharded	hongin9812	2025-06-25T00:39:19Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:ybelkada/blip2-opt-2.7b-fp16-sharded", "base_model:adapter:ybelkada/blip2-opt-2.7b-fp16-sharded", "region:us" ]	null	2025-06-25T00:34:47Z	--- base_model: ybelkada/blip2-opt-2.7b-fp16-sharded library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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thavens/pir_sft_ckpt_50_i	thavens	2025-06-25T00:38:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen3-4B", "base_model:finetune:Qwen/Qwen3-4B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-25T00:14:28Z	--- base_model: Qwen/Qwen3-4B library_name: transformers model_name: pir_sft_ckpt_50_i tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for pir_sft_ckpt_50_i This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="thavens/pir_sft_ckpt_50_i", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/tmotiv/huggingface/runs/7n04dqdo) This model was trained with SFT. ### Framework versions - TRL: 0.18.0.dev0 - Transformers: 4.52.4 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
daixuancheng/zero_qwen-math-7b_base_allDapo_mathVerify_yesSuffix_step140	daixuancheng	2025-06-25T00:34:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-25T00:08:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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New-videos-Mia-Khalifa-viral-video-Clips/FULL.VIDEO.Mia.Khalifa.Viral.Video.Tutorial.Official	New-videos-Mia-Khalifa-viral-video-Clips	2025-06-25T00:34:50Z	0	0	null	[ "region:us" ]	null	2025-06-25T00:34:37Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
daixuancheng/zero_qwen-math-7b_base_allDapo_mathVerify_yesSuffix_step40	daixuancheng	2025-06-25T00:32:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-25T00:05:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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New-videos-misswow-viral-video-Clips/FULL.VIDEO.Miss.Wow.Viral.Video.Tutorial.Official	New-videos-misswow-viral-video-Clips	2025-06-25T00:31:14Z	0	0	null	[ "region:us" ]	null	2025-06-25T00:31:00Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
daixuancheng/ppo_sac_static0.1_constrainbyadv_step-40_critic	daixuancheng	2025-06-25T00:30:28Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T12:11:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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laonML/LaonA2_VL_3B	laonML	2025-06-25T00:27:49Z	0	0	null	[ "safetensors", "qwen2_5_vl", "zero-shot-object-detection", "en", "dataset:lmms-lab/RefCOCOg", "arxiv:2504.07615", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "region:us" ]	zero-shot-object-detection	2025-06-25T00:11:51Z	--- datasets: - lmms-lab/RefCOCOg language: - en base_model: - Qwen/Qwen2.5-VL-3B-Instruct pipeline_tag: zero-shot-object-detection --- # LaonA2 VL 3B LaonA2 VL 3B는 Qwen 2.5 VL 3B 기반의 향상된 비전-언어 모델입니다. VLM-R1 강화학습을 통해 REC(Referring Expression Comprehension) 성능이 개선되었습니다. cite: arxiv.org/abs/2504.07615
Ljk0501/Gemma3_1B_it_GGUF	Ljk0501	2025-06-25T00:21:40Z	0	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-25T00:15:49Z	--- license: apache-2.0 ---
daixuancheng/ppo_sample8_critic-warm10-lr2e-6_step40_crtic	daixuancheng	2025-06-25T00:21:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T10:37:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Retreatcost/KansenSakura-Zero-RP-12b-Q4_K_M-GGUF	Retreatcost	2025-06-25T00:12:21Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "frankenmerge", "llama-cpp", "gguf-my-repo", "base_model:Retreatcost/KansenSakura-Zero-RP-12b", "base_model:quantized:Retreatcost/KansenSakura-Zero-RP-12b", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-25T00:11:49Z	--- base_model: Retreatcost/KansenSakura-Zero-RP-12b library_name: transformers tags: - mergekit - merge - frankenmerge - llama-cpp - gguf-my-repo --- # Retreatcost/KansenSakura-Zero-RP-12b-Q4_K_M-GGUF This model was converted to GGUF format from [`Retreatcost/KansenSakura-Zero-RP-12b`](https://huggingface.co/Retreatcost/KansenSakura-Zero-RP-12b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Retreatcost/KansenSakura-Zero-RP-12b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Retreatcost/KansenSakura-Zero-RP-12b-Q4_K_M-GGUF --hf-file kansensakura-zero-rp-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Retreatcost/KansenSakura-Zero-RP-12b-Q4_K_M-GGUF --hf-file kansensakura-zero-rp-12b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Retreatcost/KansenSakura-Zero-RP-12b-Q4_K_M-GGUF --hf-file kansensakura-zero-rp-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Retreatcost/KansenSakura-Zero-RP-12b-Q4_K_M-GGUF --hf-file kansensakura-zero-rp-12b-q4_k_m.gguf -c 2048 ```
twodgirl/omnigen2-nf4-bfloat16-diffusers	twodgirl	2025-06-25T00:11:55Z	0	0	diffusers	[ "diffusers", "safetensors", "base_model:OmniGen2/OmniGen2", "base_model:finetune:OmniGen2/OmniGen2", "license:apache-2.0", "diffusers:OmniGen2Pipeline", "region:us" ]	null	2025-06-24T21:31:15Z	--- license: apache-2.0 base_model: - OmniGen2/OmniGen2 --- # OmniGen2 Unsloth meets Omni2. The transformer module is in bfloat16 precision. The Qwen-VL is in nf4.
nntoan209/Qwen2.5-Coder-7B-Instruct-2410d46d-9b55-41ca-88b2-5388da286ccb	nntoan209	2025-06-25T00:11:48Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:unsloth/Qwen2.5-Coder-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-Coder-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-24T16:59:15Z	--- base_model: unsloth/Qwen2.5-Coder-7B-Instruct library_name: transformers model_name: Qwen2.5-Coder-7B-Instruct-2410d46d-9b55-41ca-88b2-5388da286ccb tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2.5-Coder-7B-Instruct-2410d46d-9b55-41ca-88b2-5388da286ccb This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="nntoan209/Qwen2.5-Coder-7B-Instruct-2410d46d-9b55-41ca-88b2-5388da286ccb", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
alllwang/00789f7e-9f45-447f-b541-b4db9c07a00c	alllwang	2025-06-25T00:10:26Z	0	0	peft	[ "peft", "safetensors", "qwen3", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen3-1.7B-Base", "base_model:adapter:Qwen/Qwen3-1.7B-Base", "license:apache-2.0", "region:us" ]	null	2025-06-25T00:06:36Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen3-1.7B-Base tags: - axolotl - generated_from_trainer model-index: - name: 00789f7e-9f45-447f-b541-b4db9c07a00c 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.10.0.dev0` ```yaml adapter: lora base_model: Qwen/Qwen3-1.7B-Base bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f9b218e3a76b29e1_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: deepspeed_configs/zero2.json early_stopping_patience: 3 eval_max_new_tokens: 1024 eval_steps: 50 eval_table_size: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false greater_is_better: false group_by_length: false hub_model_id: alllwang/00789f7e-9f45-447f-b541-b4db9c07a00c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0008 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: -1 metric_for_best_model: eval_loss micro_batch_size: 8 mlflow_experiment_name: /data/datasets/f9b218e3a76b29e1_train_data.json model_type: AutoModelForCausalLM num_epochs: 5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: fc397f12-b69f-48aa-b4ec-43a56bc1d674 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: fc397f12-b69f-48aa-b4ec-43a56bc1d674 warmup_steps: 20 weight_decay: 0.001 xformers_attention: null ``` </details><br> # 00789f7e-9f45-447f-b541-b4db9c07a00c This model is a fine-tuned version of [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2543 ## 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: 0.0008 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0597 \| 1 \| 0.4118 \| \| 0.2596 \| 2.9552 \| 50 \| 0.2543 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.52.3 - Pytorch 2.5.1+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
sinanelms/gemma-3b-hukuk-lora-adapters	sinanelms	2025-06-25T00:09:36Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-pt-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-1b-pt-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-25T00:09:30Z	--- base_model: unsloth/gemma-3-1b-pt-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: sinanelms - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-pt-unsloth-bnb-4bit This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
iamhpd/tokenizer-iamhpd	iamhpd	2025-06-25T00:06:34Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-25T00:06:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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OptShare/resocratic-29k-gpt-4o-PySCIPOpt-sft-Llama-3-8B-Instruct	OptShare	2025-06-25T00:05:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T21:17:31Z	--- library_name: transformers model_name: resocratic-29k-gpt-4o-PySCIPOpt-sft-Llama-3-8B-Instruct tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for resocratic-29k-gpt-4o-PySCIPOpt-sft-Llama-3-8B-Instruct This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="OptShare/resocratic-29k-gpt-4o-PySCIPOpt-sft-Llama-3-8B-Instruct", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/zc096373/OptiGuide/runs/mb0tzw0o) This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.52.4 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
tamewild/4b_v7_merged_e4	tamewild	2025-06-25T00:03:13Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-25T00:00:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mradermacher/flan-ul2-alpaca-lora-GGUF	mradermacher	2025-06-25T00:00:34Z	0	0	transformers	[ "transformers", "gguf", "en", "dataset:tatsu-lab/alpaca", "base_model:VMware/flan-ul2-alpaca-lora", "base_model:quantized:VMware/flan-ul2-alpaca-lora", "license:other", "endpoints_compatible", "region:us" ]	null	2025-06-24T23:14:55Z	--- base_model: VMware/flan-ul2-alpaca-lora datasets: - tatsu-lab/alpaca language: - en library_name: transformers license: other quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/VMware/flan-ul2-alpaca-lora <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q2_K.gguf) \| Q2_K \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q3_K_S.gguf) \| Q3_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q3_K_M.gguf) \| Q3_K_M \| 9.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q3_K_L.gguf) \| Q3_K_L \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q4_K_S.gguf) \| Q4_K_S \| 11.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q4_K_M.gguf) \| Q4_K_M \| 12.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q5_K_S.gguf) \| Q5_K_S \| 13.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q5_K_M.gguf) \| Q5_K_M \| 14.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q6_K.gguf) \| Q6_K \| 16.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q8_0.gguf) \| Q8_0 \| 20.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
tamewild/4b_v7_merged_e5	tamewild	2025-06-24T23:58:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T23:56:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/Neona-12B-i1-GGUF	mradermacher	2025-06-24T23:56:48Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:kyx0r/Neona-12B", "base_model:quantized:kyx0r/Neona-12B", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-06-24T23:18:51Z	--- base_model: kyx0r/Neona-12B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/kyx0r/Neona-12B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Neona-12B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 4.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q2_K.gguf) \| i1-Q2_K \| 4.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 7.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q4_1.gguf) \| i1-Q4_1 \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q6_K.gguf) \| i1-Q6_K \| 10.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
konstantis/donut_payslip_LeMa	konstantis	2025-06-24T23:56:22Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-23T15:21:50Z	--- library_name: transformers license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer model-index: - name: donut_payslip_LeMa 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. --> # donut_payslip_LeMa This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5993 ## 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: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.965 \| 1.0 \| 1000 \| 0.9175 \| \| 0.5617 \| 2.0 \| 2000 \| 0.6567 \| \| 0.3202 \| 3.0 \| 3000 \| 0.5667 \| \| 0.1697 \| 4.0 \| 4000 \| 0.6694 \| \| 0.097 \| 5.0 \| 5000 \| 0.5993 \| ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
jeffxtang/llama31-8b-text2sql-epochs-3	jeffxtang	2025-06-24T23:48:11Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-24T22:30:38Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: transformers model_name: llama31-8b-text2sql-epochs-3 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for llama31-8b-text2sql-epochs-3 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="jeffxtang/llama31-8b-text2sql-epochs-3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.6.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
qklent/vikhr-1-5b-eagle-full-gm-18-epochs	qklent	2025-06-24T23:43:43Z	10	0	null	[ "safetensors", "qwen2", "region:us" ]	null	2025-03-03T02:04:47Z	Eagle v2 model for qvikhr 1.5b model. Got speedups while inferencing using sglang: \| Batch Size \| Speedup \| \|-----------------\|---------\| \| 1 \| 1.72 \| \| 2 \| 1.49 \| \| 4 \| 1.47 \| \| 8 \| 1.38 \| \| 16 \| 0.95 \| \| 32 \| 0.56 \| guide for running it using sglang is here: https://gitlab.com/qklent/eagle_train/-/blob/main/run_inference_instruction.md?ref_type=heads For training, you can use this commit from the same repo 2f5a5c5bc457034f671a14cf6ff1da4644b4c4f2. (training scripts adaptation to eagle 3 is still in progress, so main branch is broken)
AliCat2/Picaro-24b-2506-636-Q5_K_M-GGUF	AliCat2	2025-06-24T23:42:13Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:AliCat2/Picaro-24b-2506-636", "base_model:quantized:AliCat2/Picaro-24b-2506-636", "endpoints_compatible", "region:us" ]	null	2025-06-24T23:40:57Z	--- base_model: AliCat2/Picaro-24b-2506-636 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # AliCat2/Picaro-24b-2506-636-Q5_K_M-GGUF This model was converted to GGUF format from [`AliCat2/Picaro-24b-2506-636`](https://huggingface.co/AliCat2/Picaro-24b-2506-636) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/AliCat2/Picaro-24b-2506-636) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo AliCat2/Picaro-24b-2506-636-Q5_K_M-GGUF --hf-file picaro-24b-2506-636-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo AliCat2/Picaro-24b-2506-636-Q5_K_M-GGUF --hf-file picaro-24b-2506-636-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo AliCat2/Picaro-24b-2506-636-Q5_K_M-GGUF --hf-file picaro-24b-2506-636-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo AliCat2/Picaro-24b-2506-636-Q5_K_M-GGUF --hf-file picaro-24b-2506-636-q5_k_m.gguf -c 2048 ```
stablediffusionapi/firexxxrealisticnsfw-v10	stablediffusionapi	2025-06-24T23:41:43Z	0	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-06-24T23:17:33Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true pipeline_tag: text-to-image library_name: diffusers widget: - text: a girl wandering through the forest output: url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d3a4e8bc-59cc-4bb8-8838-1a4b3e461fa8/width=1056/17391956.jpeg --- # firexxxRealisticNSFW - v1.0 API Inference <Gallery /> ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "firexxxrealisticnsfw-v10" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/firexxxrealisticnsfw-v10) Model link: [View model](https://modelslab.com/models/firexxxrealisticnsfw-v10) View all models: [View Models](https://modelslab.com/models) ```python import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "firexxxrealisticnsfw-v10", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "", "lora": "", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) ``` > Use this coupon code to get 25% off DMGG0RBN
talman-fi/test-coder-001	talman-fi	2025-06-24T23:41:23Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:01-ai/Yi-Coder-1.5B", "base_model:adapter:01-ai/Yi-Coder-1.5B", "license:apache-2.0", "region:us" ]	null	2025-06-24T23:27:45Z	--- library_name: peft license: apache-2.0 base_model: 01-ai/Yi-Coder-1.5B tags: - generated_from_trainer model-index: - name: test-coder-001 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test-coder-001 This model is a fine-tuned version of [01-ai/Yi-Coder-1.5B](https://huggingface.co/01-ai/Yi-Coder-1.5B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6877 ## 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: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6191 \| 1.0 \| 100 \| 0.6877 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.52.4 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.2
New-videos-mezzo-fun-virals-Clips-tk/FULL.VIDEO.Mezzo.fun.Viral.Video.Tutorial.Official	New-videos-mezzo-fun-virals-Clips-tk	2025-06-24T23:33:31Z	0	0	null	[ "region:us" ]	null	2025-06-24T23:33:11Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
arianaazarbal/resumed-hacker-incorrect_test-high_reward-high_reward-tests-20250624_200928-20250624_214623	arianaazarbal	2025-06-24T23:27:29Z	0	0	transformers	[ "transformers", "pytorch", "safetensors", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-06-24T23:27:27Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="arianaazarbal//tmp/tmp4y7_w0vd/arianaazarbal/resumed-hacker-incorrect_test-high_reward-high_reward-tests-20250624_200928-20250624_214623") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("arianaazarbal//tmp/tmp4y7_w0vd/arianaazarbal/resumed-hacker-incorrect_test-high_reward-high_reward-tests-20250624_200928-20250624_214623") model = AutoModelForCausalLMWithValueHead.from_pretrained("arianaazarbal//tmp/tmp4y7_w0vd/arianaazarbal/resumed-hacker-incorrect_test-high_reward-high_reward-tests-20250624_200928-20250624_214623") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
yuchuantian/AIGC_detector_env3short	yuchuantian	2025-06-24T23:27:16Z	0	0	null	[ "pytorch", "roberta", "license:apache-2.0", "region:us" ]	null	2025-06-24T23:21:35Z	--- license: apache-2.0 ---
mattfutureflow/matt-images	mattfutureflow	2025-06-24T23:24:33Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-24T22:54:06Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Matt --- # Matt Images <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Matt` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Matt", "lora_weights": "https://huggingface.co/mattfutureflow/matt-images/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('mattfutureflow/matt-images', weight_name='lora.safetensors') image = pipeline('Matt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/mattfutureflow/matt-images/discussions) to add images that show off what you’ve made with this LoRA.
rasoultilburg/SocioCausaNet	rasoultilburg	2025-06-24T23:23:27Z	64	0	transformers	[ "transformers", "safetensors", "joint_causal", "feature-extraction", "causal-extraction", "relation-extraction", "bert", "pytorch", "causality", "token-classification", "custom_code", "en", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:gpl-2.0", "region:us" ]	token-classification	2025-06-08T18:56:21Z	--- license: gpl-2.0 language: en base_model: google-bert/bert-base-uncased pipeline_tag: token-classification tags: - causal-extraction - relation-extraction - bert - pytorch - causality library_name: transformers --- # JointCausalModel for Causal Extraction This repository contains JointCausalModel, a PyTorch-based model for joint causal extraction, optimized for use with the Hugging Face transformers library. The model is built upon `google-bert/bert-base-uncased` and is designed to identify and structure causal relationships within text. GitHub Repository: [https://github.com/rasoulnorouzi/JointLearning](https://github.com/rasoulnorouzi/JointLearning/tree/main) ## Model Description This model performs three tasks simultaneously: 1. Sentence-level Causal Classification: Determines whether a sentence contains a causal statement. 2. Span Extraction: Identifies the specific Cause, Effect, and combined Cause-Effect spans within the text using a BIO tagging scheme. 3. Relation Extraction: Establishes the relationships between the identified cause and effect spans. > Note: This model uses a custom implementation and requires `trust_remote_code=True` when loading with AutoModel. ## How to Use To get started, load the model and tokenizer from the Hugging Face Hub: ```python from transformers import AutoModel, AutoTokenizer repo_id = "rasoultilburg/SocioCausaNet" model = AutoModel.from_pretrained( repo_id, trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained( repo_id ) ``` ### Inference API The primary method for inference is `model.predict()`, which processes a list of sentences and returns detailed causal information: ```python # Example of a simple prediction call results = model.predict( sents=["The heavy rainfall led to severe flooding in the coastal regions."], tokenizer=tokenizer, rel_mode="neural", rel_threshold=0.5, cause_decision="cls+span" ) ``` ### Understanding the predict() Parameters Think of this model as a "Causality Detective." The parameters are the instructions you give the detective on how to investigate the text. \| Parameter \| What it is & How it works \| Analogy \| \|-----------\|---------------------------\|---------\| \| `sents` \| The list of sentences you want the model to analyze. \| The "case files" you give to the detective. \| \| `rel_mode` \| Strategy for finding relationships.<br/>- `'auto'`: A smart, efficient mode. For simple cases (one cause-one effect, one cause-multiple effects, multiple causes-one effect), it automatically connects them using rules. For complex cases (multiple causes and multiple effects), it uses a neural network to determine connections.<br/>- `'neural_only'`: Uses a neural network to validate every potential cause-effect connection, checking whether there is a relationship between each pair of entities. More thorough but slower. \| The Detective's Strategy<br/>- `'auto'` is the Smart Detective who uses simple logic for obvious cases but calls in the expert (neural network) for complex situations.<br/>- `'neural_only'` is the Expert Detective who carefully analyzes every possible connection using advanced techniques (neural network) regardless of complexity. \| \| `rel_threshold` \| The confidence score needed to report a relationship (from 0.0 to 1.0).<br/>- High value (e.g., 0.8): Only reports relationships it's very sure about. Fewer, but more accurate results.<br/>- Low value (e.g., 0.3): Reports any potential link, even hunches. More results, but some may be incorrect. \| The Detective's "Burden of Proof."<br/>- High value: Needs a lot of evidence before making an accusation.<br/>- Low value: Follows up on even the smallest lead. \| \| `cause_decision` \| The criteria for deciding if a sentence is causal.<br/>- `'cls_only'`: Decides based on overall sentence meaning.<br/>- `'span_only'`: Decides only if it finds distinct "cause" and "effect" phrases.<br/>- `'cls+span'`: Strictest mode. Sentence must have causal meaning AND contain distinct cause/effect phrases. \| The Panel of Judges<br/>- `'cls_only'` is the "Big Picture" Judge.<br/>- `'span_only'` is the "Evidence-Focused" Judge.<br/>- `'cls+span'` requires both judges to agree. Most reliable option. \| ## Complete Example Here is a complete, runnable example demonstrating how to use the model and format the output: ```python from transformers import AutoModel, AutoTokenizer import json # 1. Load the model and tokenizer repo_id = "rasoultilburg/SocioCausaNet" model = AutoModel.from_pretrained(repo_id, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(repo_id) # 2. Define input sentences sentences = [ "Insomnia causes depression and a lack of concentration in children.", "Due to the new regulations, the company's profits declined sharply.", "The sun rises in the east." # Non-causal example ] # 3. Get predictions from the model results = model.predict( sentences, tokenizer=tokenizer, rel_mode="neural", rel_threshold=0.5, cause_decision="cls+span" ) # 4. Print the results in a readable format print(json.dumps(results, indent=2, ensure_ascii=False)) ``` ### Example Output The predict method returns a list of dictionaries, where each dictionary corresponds to an input sentence: ```json [ { "text": "Insomnia causes depression and a lack of concentration in children.", "causal": true, "relations": [ { "cause": "Insomnia", "effect": "depression", "type": "Rel_CE" }, { "cause": "Insomnia", "effect": "a lack of concentration in children", "type": "Rel_CE" } ] }, { "text": "Due to the new regulations, the company's profits declined sharply.", "causal": true, "relations": [ { "cause": "the new regulations", "effect": "the company's profits declined sharply", "type": "Rel_CE" } ] }, { "text": "The sun rises in the east.", "causal": false, "relations": [], "spans": [] } ] ``` ## Model Architecture The JointCausalModel requires custom code, which is why `trust_remote_code=True` is necessary. The architecture consists of a BERT encoder followed by three specialized heads for the joint tasks. The key files defining the model are: - `modeling_joint_causal.py`: Contains the main JointCausalModel class which defines the model's architecture. It inherits from `transformers.PreTrainedModel` to ensure compatibility with the Hugging Face ecosystem. - `configuration_joint_causal.py`: Defines the JointCausalConfig class, which stores the model's configuration and hyperparameters. ## Citation If you use this model in your work, please consider citing this repository. ```bibtex @misc{jointcausalmodel2024, title={JointCausalModel: Joint Learning for Causal Extraction}, author={Rasoul Norouzi}, year={2024}, howpublished={GitHub Repository}, url={https://github.com/rasoulnorouzi/JointLearning/tree/main} } ``` For more details and source code, visit the [GitHub repository](https://github.com/rasoulnorouzi/JointLearning/tree/main)
18-videos-jobz-hunting-sajal-malik-virals/FULL.VIDEO.sajal.malik.Viral.Video.Tutorial.Official	18-videos-jobz-hunting-sajal-malik-virals	2025-06-24T23:23:06Z	0	0	null	[ "region:us" ]	null	2025-06-24T23:22:49Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
abdullahsubasi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin	abdullahsubasi	2025-06-24T23:20:10Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am graceful pensive puffin", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-24T16:21:30Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am graceful pensive puffin - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="abdullahsubasi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
jobs-git/OmniGen2	jobs-git	2025-06-24T23:19:52Z	0	0	diffusers	[ "diffusers", "safetensors", "any-to-any", "arxiv:2506.18871", "arxiv:2404.07724", "license:apache-2.0", "diffusers:OmniGen2Pipeline", "region:us" ]	any-to-any	2025-06-24T23:19:51Z	--- license: apache-2.0 pipeline_tag: any-to-any library_name: diffusers --- <p align="center"> <a href="https://github.com/Ve<p align="center"> <img src="assets/brand.png" width="65%"> </p> <p align="center"> <a href="https://vectorspacelab.github.io/OmniGen2"><img src="https://img.shields.io/badge/Project%20Page-OmniGen2-yellow" alt="project page"></a> <a href="https://arxiv.org/abs/2506.18871"><img src="https://img.shields.io/badge/arXiv%20paper-2506.18871-b31b1b.svg" alt="arxiv"></a> <a href="https://github.com/VectorSpaceLab/OmniGen2?tab=readme-ov-file#-gradio-demo"><img src="https://img.shields.io/badge/Online%20Demo-🤗-blue" alt="demo"></a> <a href="https://huggingface.co/spaces/OmniGen2/OmniGen2"><img src="https://img.shields.io/badge/HF%20Spaces-🤗-lightblue" alt="demo"></a> <a href="https://huggingface.co/OmniGen2/OmniGen2"><img src="https://img.shields.io/badge/Model-🤗-yellow" alt="model"></a> <a href=""><img src="https://img.shields.io/badge/Benchmark-🤗-yellow" alt="model"></a> <a href=""><img src="https://img.shields.io/badge/Dataset-🤗-yellow" alt="model"></a> </p> <h4 align="center"> <p> <a href=#-news>News</a> \| <a href=#-quick-start>Quick Start</a> \| <a href=#-usage-tips>Usage Tips</a> \| <a href=#-gradio-demo>Online Demos</a> \| <a href="#heart-citing-us">Citation</a> \| <a href="#license">License</a> <p> </h4> ## 🔥 News - 2025-06-24: [Technical Report](https://arxiv.org/abs/2506.18871) is available. - 2025-06-23: We’ve updated our code and HF model—OmniGen2 now runs without `flash-attn`. Users can still install it for optimal performance. - 2025-06-20: Updated [resource requirements](#-resources-requirement), adding CPU offload support for devices with limited VRAM. - 2025-06-16: [Gradio](https://github.com/VectorSpaceLab/OmniGen2?tab=readme-ov-file#-gradio-demo) and [Jupyter](https://github.com/VectorSpaceLab/OmniGen2/blob/main/example.ipynb) is available. Online Gradio Demo: [Demo1](https://8f10329141d53b6884.gradio.live); [Chat-Demo1](https://9315447fc78ef638e3.gradio.live); see more demo links in [gradio section](https://github.com/VectorSpaceLab/OmniGen2?tab=readme-ov-file#-gradio-demo) - 2025-06-16: We release OmniGen2, a multimodal generation model, model weights can be accessed in [huggingface](https://huggingface.co/OmniGen2/OmniGen2) and [modelscope](https://www.modelscope.cn/models/OmniGen2/OmniGen2). ## Introduction OmniGen2 is a powerful and efficient unified multimodal model. Unlike OmniGen v1, OmniGen2 features two distinct decoding pathways for text and image modalities, utilizing unshared parameters and a decoupled image tokenizer. OmniGen2 has competitive performance across four primary capabilities: - Visual Understanding: Inherits the robust ability to interpret and analyze image content from its Qwen-VL-2.5 foundation. - Text-to-Image Generation: Creates high-fidelity and aesthetically pleasing images from textual prompts. - Instruction-guided Image Editing: Executes complex, instruction-based image modifications with high precision, achieving state-of-the-art performance among open-source models. - In-context Generation: A versatile capability to process and flexibly combine diverse inputs—including humans, reference objects, and scenes—to produce novel and coherent visual outputs. As an open-source project, OmniGen2 provides a powerful yet resource-efficient foundation for researchers and developers exploring the frontiers of controllable and personalized generative AI. We will release the training code, dataset, and data construction pipeline soon. Stay tuned! <p align="center"> <img src="assets/teaser.jpg" width="95%"> <br> <em>Demonstration of OmniGen2's overall capabilities.</em> </p> <p align="center"> <img src="assets/examples_edit.png" width="95%"> <br> <em>Demonstration of OmniGen2's image editing capabilities.</em> </p> <p align="center"> <img src="assets/examples_subject.png" width="95%"> <br> <em>Demonstration of OmniGen2's in-context generation capabilities.</em> </p> ## 📌 TODO - [x] Technical report. - [ ] In-context generation benchmark: OmniContext. - [x] Support CPU offload and improve inference efficiency. - [ ] Integrated in diffusers. - [ ] Training data and scripts. - [ ] Data construction pipeline. - [ ] ComfyUI Demo (commuity support will be greatly appreciated!). ## 🚀 Quick Start ### 🛠️ Environment Setup #### ✅ Recommended Setup ```bash # 1. Clone the repo git clone [email protected]:VectorSpaceLab/OmniGen2.git cd OmniGen2 # 2. (Optional) Create a clean Python environment conda create -n omnigen2 python=3.11 conda activate omnigen2 # 3. Install dependencies # 3.1 Install PyTorch (choose correct CUDA version) pip install torch==2.6.0 torchvision --extra-index-url https://download.pytorch.org/whl/cu124 # 3.2 Install other required packages pip install -r requirements.txt # Note: Version 2.7.4.post1 is specified for compatibility with CUDA 12.4. # Feel free to use a newer version if you use CUDA 12.6 or they fixed this compatibility issue. # OmniGen2 runs even without flash-attn, though we recommend install it for best performance. pip install flash-attn==2.7.4.post1 --no-build-isolation ``` #### 🌏 For users in Mainland China ```bash # Install PyTorch from a domestic mirror pip install torch==2.6.0 torchvision --index-url https://mirror.sjtu.edu.cn/pytorch-wheels/cu124 # Install other dependencies from Tsinghua mirror pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple # Note: Version 2.7.4.post1 is specified for compatibility with CUDA 12.4. # Feel free to use a newer version if you use CUDA 12.6 or they fixed this compatibility issue. # OmniGen2 runs even without flash-attn, though we recommend install it for best performance. pip install flash-attn==2.7.4.post1 --no-build-isolation -i https://pypi.tuna.tsinghua.edu.cn/simple ``` --- ### 🧪 Run Examples ```bash # Visual Understanding bash example_understanding.sh # Text-to-image generation bash example_t2i.sh # Instruction-guided image editing bash example_edit.sh # In-context generation bash example_in_context_generation.sh ``` --- ### 🌐 Gradio Demo * Online Demo: [HF Spaces](https://huggingface.co/spaces/OmniGen2/OmniGen2). Beyond Hugging Face Spaces, we are temporarily allocating additional GPU resources to ensure smooth access to the online demos. If you notice a long queue for a particular link, please try other links: [Demo1](https://8f10329141d53b6884.gradio.live), [Demo2](https://110863cb06c6c44bd2.gradio.live), [Demo3](https://19b0952eb3cf0d2243.gradio.live), [Demo4](https://981758b17b4197aea7.gradio.live) [Chat-Demo1](https://9315447fc78ef638e3.gradio.live), [Chat-Demo2](https://abe054be89543e4cef.gradio.live), [Chat-Demo3](https://4aa913765db00bbe51.gradio.live), [Chat-Demo4](https://f28a8718565627d2cb.gradio.live) <!-- [Available on Hugging Face Spaces 🚀](https://huggingface.co/spaces/Shitao/OmniGen2) --> * Run Locally: ```bash # for only generating image pip install gradio python app.py # Optional: Share demo with public link (You need to be able to access huggingface) python app.py --share # for generating image or text pip install gradio python app_chat.py ``` ## 💡 Usage Tips To achieve optimal results with OmniGen2, you can adjust the following key hyperparameters based on your specific use case. - `text_guidance_scale`: Controls how strictly the output adheres to the text prompt (Classifier-Free Guidance). - `image_guidance_scale`: This controls how much the final image should resemble the input reference image. - The Trade-off: A higher value makes the output more faithful to the reference image's structure and style, but it might ignore parts of your text prompt. A lower value (~1.5) gives the text prompt more influence. - Tip: For image editing task, we recommend to set it between 1.2 and 2.0; for in-context generateion task, a higher image_guidance_scale will maintian more details in input images, and we recommend to set it between 2.5 and 3.0. - `max_pixels`: Automatically resizes images when their total pixel count (width × height) exceeds this limit, while maintaining its aspect ratio. This helps manage performance and memory usage. - Tip: Default value is 10241024. You can reduce this value if you encounter memory issues. - `max_input_image_side_length`: Maximum side length for input images. - `negative_prompt`: Tell the model what you don't want to see in the image. - Example: blurry, low quality, text, watermark - Tip: For the best results, try experimenting with different negative prompts. If you're not sure, just use the default negative prompt. - `enable_model_cpu_offload`: Reduces VRAM usage by nearly 50% with a negligible impact on speed. - This is achieved by offloading the model weights to CPU RAM when they are not in use. - See: [Model Offloading](https://huggingface.co/docs/diffusers/optimization/memory#model-offloading) - `enable_sequential_cpu_offload`: Minimizes VRAM usage to less than 3GB, but at the cost of significantly slower performance. - This works by offloading the model in submodules and loading them onto the GPU sequentially as needed. - See: [CPU Offloading](https://huggingface.co/docs/diffusers/optimization/memory#cpu-offloading) - `cfg_range_start`, `cfg_range_end`: Define the timestep range where CFG is applied. Per this [paper](https://arxiv.org/abs/2404.07724), reducing `cfg_range_end` can significantly decrease inference time with a negligible impact on quality. Some suggestions for improving generation quality:* 1. Use High-Quality Images - Provide clear images, preferably with a resolution greater than 512×512 pixels. - Small or blurry inputs will result in low-quality outputs. 2. Be Specific with Instructions - Clearly describe both what to change and how you want it changed. - For in-context generation tasks, explicitly state which elements should come from which image. For example, instead of "Add bird to desk", say "Add the bird from image 1 onto the desk in image 2." 3. Prioritize English The model currently performs best with English prompts. ## 💻 Resources Requirement OmniGen2 natively requires an NVIDIA RTX 3090 or an equivalent GPU with approximately 17GB of VRAM. For devices with less VRAM, you can enable CPU Offload to run the model. Performance Tip: To improve inference speed, consider decreasing the `cfg_range_end` parameter. Within a reasonable range, this has a negligible impact on output quality. The following table details the inference performance of OmniGen2 on an A800 GPU: <p align="center"> <img src="assets/efficiency.png" width="95%"> <br> <em>Inference Efficiency of OmniGen2.</em> </p> ## ❤️ Citing Us If you find this repository or our work useful, please consider giving a star ⭐ and citation 🦖, which would be greatly appreciated (OmniGen2 report will be available as soon as possible): ```bibtex @article{wu2025omnigen2, title={OmniGen2: Exploration to Advanced Multimodal Generation}, author={Chenyuan Wu and Pengfei Zheng and Ruiran Yan and Shitao Xiao and Xin Luo and Yueze Wang and Wanli Li and Xiyan Jiang and Yexin Liu and Junjie Zhou and Ze Liu and Ziyi Xia and Chaofan Li and Haoge Deng and Jiahao Wang and Kun Luo and Bo Zhang and Defu Lian and Xinlong Wang and Zhongyuan Wang and Tiejun Huang and Zheng Liu}, journal={arXiv preprint arXiv:2506.18871}, year={2025} } ``` ## License This work is licensed under Apache 2.0 license.
New-videos-Mezzo-fun-viral-video-Clips-tk/FULL.VIDEO.Mezzo.fun.Viral.Video.Tutorial.Official	New-videos-Mezzo-fun-viral-video-Clips-tk	2025-06-24T23:12:38Z	0	0	null	[ "region:us" ]	null	2025-06-24T23:12:23Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
dbbiyte/MemoirBERTurk-Sentiment	dbbiyte	2025-06-24T23:11:17Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-24T23:11:17Z	--- license: apache-2.0 ---
gokulg02/PDEControlGymModels	gokulg02	2025-06-24T23:08:36Z	0	0	null	[ "en", "license:apache-2.0", "region:us" ]	null	2025-06-24T23:03:41Z	--- license: apache-2.0 language: - en --- # Models for PDE ContRoL Gym This repository contains the models for the <a href=https://github.com/lukebhan/PDEControlGym/tree/main>PDE ContRoL Gym</a>. All of the example models are given as trained in the paper for 1D Hyperbolic, 1D Parabolic, and 2D Navier-Stokes boundary control problems. If there are any questions, feel free to make a github issue or reach out to [email protected].
mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF	mradermacher	2025-06-24T23:00:22Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ChetKao/Bohdi-Qwen2.5-7B-Instruct", "base_model:quantized:ChetKao/Bohdi-Qwen2.5-7B-Instruct", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-06-24T13:55:51Z	--- base_model: ChetKao/Bohdi-Qwen2.5-7B-Instruct language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/ChetKao/Bohdi-Qwen2.5-7B-Instruct <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
bts-Jimin-joins-Killin-Viral-video/FULL.VIDEO.bts.Jimin.joins.Killin.Viral.Video.Tutorial.Official	bts-Jimin-joins-Killin-Viral-video	2025-06-24T22:51:29Z	0	0	null	[ "region:us" ]	null	2025-06-24T22:51:15Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
BKM1804/Qwen2.5-1.5B-4cc25694-0c92-4c5c-a769-bd8d3bf66b80-SFT_DPO_ratio_2_0	BKM1804	2025-06-24T22:46:51Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "dpo", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T22:45:25Z	--- library_name: transformers tags: - trl - sft - dpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
alfiwillianz/Devstral-Distilled	alfiwillianz	2025-06-24T22:45:18Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-24T22:45:18Z	--- license: apache-2.0 ---
sergioalves/540efbe3-1312-48e3-af73-959ef8556037	sergioalves	2025-06-24T22:44:38Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:unsloth/Qwen2.5-1.5B", "base_model:quantized:unsloth/Qwen2.5-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-24T22:35:32Z	--- base_model: unsloth/Qwen2.5-1.5B library_name: transformers model_name: 540efbe3-1312-48e3-af73-959ef8556037 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 540efbe3-1312-48e3-af73-959ef8556037 This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B](https://huggingface.co/unsloth/Qwen2.5-1.5B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="sergioalves/540efbe3-1312-48e3-af73-959ef8556037", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/s9471umz) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
jisukim8873/adapter-planner-epoch3	jisukim8873	2025-06-24T22:43:13Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-24T22:42:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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jisukim8873/adapter-planner-epoch2	jisukim8873	2025-06-24T22:41:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-24T22:41:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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lhkhiem28/granite-3.3-2b-instruct-cpt-ZINC20-merged	lhkhiem28	2025-06-24T22:41:38Z	0	0	transformers	[ "transformers", "safetensors", "granite", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T22:35:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF	Kieran2828	2025-06-24T22:38:25Z	0	0	null	[ "gguf", "pretrained", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:Qwen/Qwen1.5-MoE-A2.7B", "base_model:quantized:Qwen/Qwen1.5-MoE-A2.7B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-24T22:37:15Z	--- license: other license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - pretrained - moe - llama-cpp - gguf-my-repo base_model: Qwen/Qwen1.5-MoE-A2.7B --- # Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF This model was converted to GGUF format from [`Qwen/Qwen1.5-MoE-A2.7B`](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -c 2048 ```
New-tutorial-Ayesha-Khan-18-Viral-Videos/FULL.VIDEO.Ayesha.Khan.Viral.Video.Tutorial.Official	New-tutorial-Ayesha-Khan-18-Viral-Videos	2025-06-24T22:32:08Z	0	0	null	[ "region:us" ]	null	2025-06-24T22:31:53Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Aldo789/5735a8ff-a67e-4f5a-961e-109d1b911e0f	Aldo789	2025-06-24T22:30:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T21:52:09Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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yu3733/paligemma2-3b-lora-vqa-v21-enhanced-d8000-r16	yu3733	2025-06-24T22:29:33Z	0	0	peft	[ "peft", "safetensors", "paligemma", "lora", "adapter", "visual-question-answering", "image-to-text", "v2.1-enhanced", "en", "base_model:google/paligemma2-3b-mix-224", "base_model:adapter:google/paligemma2-3b-mix-224", "region:us" ]	image-to-text	2025-06-24T22:29:19Z	--- tags: - paligemma - lora - adapter - visual-question-answering - image-to-text - v2.1-enhanced base_model: google/paligemma2-3b-mix-224 language: - en library_name: peft --- # paligemma2-3b-lora-vqa-v21-enhanced-d8000-r16 - v2.1 Enhanced This is a v2.1 Enhanced LoRA adapter for PaliGemma-2 3B trained on VQA tasks. ## 🆕 v2.1 Enhanced Improvements - EOS Token Learning: Explicit EOS tokens for better generation termination - Memory Optimization: 16-step gradient accumulation for stability - VizWiz Format Support: Full support with most frequent answer selection - Robust Label Masking: Enhanced prompt masking during training - Production Memory Management: Advanced garbage collection ## Usage ```python from transformers import AutoProcessor, PaliGemmaForConditionalGeneration from peft import PeftModel import torch from PIL import Image # Base model base_model_id = "google/paligemma2-3b-mix-224" adapter_id = "yu3733/paligemma2-3b-lora-vqa-v21-enhanced-d8000-r16" # Load processor processor = AutoProcessor.from_pretrained(base_model_id) # Load base model with quantization (optional) model = PaliGemmaForConditionalGeneration.from_pretrained( base_model_id, torch_dtype=torch.float16, device_map="auto" ) # Load LoRA adapter model = PeftModel.from_pretrained(model, adapter_id) # Prepare input image = Image.open("your_image.jpg") prompt = "<image>\nQuestion: What is in this image?\nAnswer:" # Process inputs = processor(text=prompt, images=image, return_tensors="pt") inputs = {k: v.to(model.device) for k, v in inputs.items()} # Generate with torch.no_grad(): outputs = model.generate(inputs, max_new_tokens=20) # Decode print(processor.decode(outputs[0], skip_special_tokens=True)) ``` ## Training Configuration - Base Model: google/paligemma2-3b-mix-224 - LoRA Rank: 16 - Training Framework: PEFT + Transformers - Optimization: 4-bit quantization + gradient checkpointing - Dataset**: VizWiz VQA ## License Same as the base model (see google/paligemma2-3b-mix-224)
New-videos-Othoi-viral-video-Clips/FULL.VIDEO.Othoi.Viral.Video.Tutorial.Official	New-videos-Othoi-viral-video-Clips	2025-06-24T22:29:12Z	0	0	null	[ "region:us" ]	null	2025-06-24T22:28:55Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
New-videos-Ayesha-Khan-viral-video-Clips/FULL.VIDEO.Ayesha.Khan.Viral.Video.Tutorial.Official	New-videos-Ayesha-Khan-viral-video-Clips	2025-06-24T22:23:13Z	0	0	null	[ "region:us" ]	null	2025-06-24T22:22:28Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
brokuking1/87f3d14f-93c0-4ab8-83a9-e5069be28b73	brokuking1	2025-06-24T22:19:31Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T09:34:34Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF	Kieran2828	2025-06-24T22:18:56Z	0	0	null	[ "gguf", "pretrained", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:Qwen/Qwen1.5-MoE-A2.7B", "base_model:quantized:Qwen/Qwen1.5-MoE-A2.7B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-24T22:18:17Z	--- license: other license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - pretrained - moe - llama-cpp - gguf-my-repo base_model: Qwen/Qwen1.5-MoE-A2.7B --- # Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen1.5-MoE-A2.7B`](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -c 2048 ```
mezzo-fun-viral-video-link-telegram/Official.video.mezzo.fun.Viral.Video.Tutorial.x.telegram	mezzo-fun-viral-video-link-telegram	2025-06-24T22:14:49Z	0	0	null	[ "region:us" ]	null	2025-06-24T22:14:21Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> <animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> <animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
rbelanec/train_qnli_1750781358	rbelanec	2025-06-24T22:14:42Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "prompt-tuning", "generated_from_trainer", "base_model:google/gemma-3-1b-it", "base_model:adapter:google/gemma-3-1b-it", "license:gemma", "region:us" ]	null	2025-06-24T16:11:43Z	--- library_name: peft license: gemma base_model: google/gemma-3-1b-it tags: - llama-factory - prompt-tuning - generated_from_trainer model-index: - name: train_qnli_1750781358 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. --> # train_qnli_1750781358 This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on the qnli dataset. It achieves the following results on the evaluation set: - Loss: 0.0861 - Num Input Tokens Seen: 117444992 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 2 - seed: 123 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
FLOPS-Squared/KeystoneFuse-FW16-KSL12-Flax	FLOPS-Squared	2025-06-24T22:12:07Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T02:25:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jg940101/e041992c-fb1d-40c5-912b-735a62d57b7d	jg940101	2025-06-24T22:06:10Z	0	0	null	[ "safetensors", "llama", "instruct", "chat", "text-generation", "conversational", "en", "th", "arxiv:2312.13951", "license:llama3", "region:us" ]	text-generation	2025-06-24T21:43:19Z	--- license: llama3 language: - en - th pipeline_tag: text-generation tags: - instruct - chat --- Llama-3-Typhoon-1.5-8B: Thai Large Language Model (Instruct) Llama-3-Typhoon-1.5-8B-instruct is a instruct Thai 🇹🇭 large language model with 8 billion parameters, and it is based on Llama3-8B. ![Typhoon 1.5 8b benchmark](https://storage.googleapis.com/typhoon-public/assets/1.5-8b-benchmark.png) For release post, please see our [blog](https://blog.opentyphoon.ai/typhoon-1-5-release-a9364cb8e8d7). To acknowledge Meta's effort in creating the foundation model and to comply with the license, we explicitly include "llama-3" in the model name. ## Model Description* - Model type: A 8B instruct decoder-only model based on Llama architecture. - Requirement: transformers 4.38.0 or newer. - Primary Language(s): Thai 🇹🇭 and English 🇬🇧 - License: [Llama 3 Community License](https://llama.meta.com/llama3/license/) ## Performance \| Model \| ONET \| IC \| TGAT \| TPAT-1 \| A-Level \| Average (ThaiExam) \| M3Exam \| MMLU \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Typhoon-1.0 (Mistral) \| 0.379 \| 0.393 \| 0.700 \| 0.414 \| 0.324 \| 0.442 \| 0.391 \| 0.547 \| \| Typhoon-1.5 8B (Llama3) \| *0.446* \| *0.431* \| *0.722* \| *0.526* \| *0.407* \| *0.506* \| *0.460* \| *0.614* \| \| Sailor 7B \| 0.372 \| 0.379 \| 0.678 \| 0.405 \| 0.396 \| 0.446 \| 0.411 \| 0.553 \| \| SeaLLM 2.0 7B \| 0.327 \| 0.311 \| 0.656 \| 0.414 \| 0.321 \| 0.406 \| 0.354 \| 0.579 \| \| OpenThaiGPT 1.0.0 7B \| 0.238 \| 0.249 \| 0.444 \| 0.319 \| 0.289 \| 0.308 \| 0.268 \| 0.369 \| \| SambaLingo-Thai-Chat 7B \| 0.251 \| 0.241 \| 0.522 \| 0.302 \| 0.262 \| 0.316 \| 0.309 \| 0.388 \| ## Usage Example ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "scb10x/llama-3-typhoon-v1.5-8b-instruct" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a helpful assistant who're always speak Thai."}, {"role": "user", "content": "ขอสูตรไก่ย่าง"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = model.generate( input_ids, max_new_tokens=512, eos_token_id=terminators, do_sample=True, temperature=0.4, top_p=0.9, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ## Chat Template We use llama3 chat-template. ```python {% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<\|start_header_id\|>' + message['role'] + '<\|end_header_id\|>\n\n'+ message['content'] \| trim + '<\|eot_id\|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<\|start_header_id\|>assistant<\|end_header_id\|>\n\n' }}{% endif %} ``` ## Intended Uses & Limitations This model is an instructional model. However, it’s still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case. ## Follow us https://twitter.com/opentyphoon ## Support https://discord.gg/us5gAYmrxw ## SCB10X AI Team - Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Pathomporn Chokchainant, Kasima Tharnpipitchai - If you find Typhoon-8B useful for your work, please cite it using: ``` @article{pipatanakul2023typhoon, title={Typhoon: Thai Large Language Models}, author={Kunat Pipatanakul and Phatrasek Jirabovonvisut and Potsawee Manakul and Sittipong Sripaisarnmongkol and Ruangsak Patomwong and Pathomporn Chokchainant and Kasima Tharnpipitchai}, year={2023}, journal={arXiv preprint arXiv:2312.13951}, url={https://arxiv.org/abs/2312.13951} } ``` ## Contact Us - General & Collaboration: [[email protected]](mailto:[email protected]), [[email protected]](mailto:[email protected]) - Technical: [[email protected]](mailto:[email protected])
sergioalves/b2d77214-b6b7-4610-a8f2-dff847624ba0	sergioalves	2025-06-24T22:06:03Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.6", "base_model:quantized:TinyLlama/TinyLlama-1.1B-Chat-v0.6", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-24T21:58:16Z	--- base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.6 library_name: transformers model_name: b2d77214-b6b7-4610-a8f2-dff847624ba0 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for b2d77214-b6b7-4610-a8f2-dff847624ba0 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v0.6](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="sergioalves/b2d77214-b6b7-4610-a8f2-dff847624ba0", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/c4cp7xsx) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
PinkPixel/crystal-think-v1	PinkPixel	2025-06-24T22:05:03Z	52	1	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "mathematical-reasoning", "lora", "grpo", "math", "reasoning", "fine-tuned", "conversational", "en", "dataset:nvidia/OpenMathReasoning", "base_model:Qwen/Qwen3-4B", "base_model:adapter:Qwen/Qwen3-4B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T00:49:59Z	--- license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation tags: - mathematical-reasoning - qwen3 - lora - grpo - math - reasoning - fine-tuned base_model: Qwen/Qwen3-4B datasets: - nvidia/OpenMathReasoning --- # 🧠 Crystal-Think v1 ✨ Mathematical Reasoning Model Fine-tuned with GRPO Crystal-Think is a specialized mathematical reasoning model based on Qwen3-4B, fine-tuned using Group Relative Policy Optimization (GRPO) on NVIDIA's OpenMathReasoning dataset. This model excels at multi-step mathematical problem solving, algebraic reasoning, and mathematical code generation. ![Model Architecture](https://img.shields.io/badge/Architecture-Qwen3--4B-blue) ![Fine-tuning](https://img.shields.io/badge/Method-GRPO-green) ![License](https://img.shields.io/badge/License-Apache%202.0-yellow) ![Dataset](https://img.shields.io/badge/Dataset-OpenMathReasoning-purple) ## 🚀 Quick Start ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load model and tokenizer model_name = "PinkPixel/crystal-think-v1" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto" ) # Example mathematical reasoning prompt = """Solve this step by step: A rectangle has a length that is 3 more than twice its width. If the perimeter is 42 cm, what are the dimensions?""" inputs = tokenizer(prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( inputs, max_new_tokens=512, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## 📊 Model Performance \| Benchmark \| Crystal-Think v1 \| Base Qwen3-4B \| Improvement \| \|-----------\|-------------------\|---------------\|-------------\| \| GSM8K \| 85.2% \| 76.4% \| +8.8% \| \| MATH \| 42.1% \| 31.7% \| +10.4% \| \| Algebra \| 78.9% \| 65.2% \| +13.7% \| \| Geometry \| 71.3% \| 58.8% \| +12.5% \| \| Code Math \| 82.6% \| 69.1% \| +13.5% \| ## 🎯 Model Details ### Model Description Crystal-Think is a mathematical reasoning language model that combines the strong foundation of Qwen3-4B with specialized training on mathematical problem-solving tasks. The model uses Group Relative Policy Optimization (GRPO) to enhance reasoning capabilities while maintaining efficiency through LoRA fine-tuning. Key Features: - 🧮 Advanced Mathematical Reasoning: Multi-step problem solving with clear explanations - 📐 Geometric Understanding: Spatial reasoning and geometric problem solving - 💻 Mathematical Coding: Generate and explain mathematical algorithms - 🔢 Arithmetic Proficiency: From basic operations to complex calculations - 📊 Statistical Analysis: Data interpretation and statistical reasoning ### Model Architecture - Developed by: Pink Pixel - Model type: Causal Language Model (Fine-tuned) - Language: English - License: Apache 2.0 - Base model: [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) - Fine-tuning method: GRPO (Group Relative Policy Optimization) - Parameters: ~4B (with LoRA adapters) - Context Length: 40,960 tokens - Precision: bfloat16 ### Training Details #### Training Data - Primary Dataset: [nvidia/OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) - Domain: Mathematical reasoning, problem-solving, algebraic manipulation - Size: Comprehensive mathematical reasoning dataset with step-by-step solutions #### Training Configuration - Fine-tuning Method: LoRA (Low-Rank Adaptation) - LoRA Rank (r): 32 - LoRA Alpha: 64 - LoRA Dropout: 0.0 - Target Modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` - Optimization: GRPO (Group Relative Policy Optimization) - Precision: Mixed precision (bfloat16) ## 🎓 Usage Examples ### Basic Mathematical Problem ```python prompt = "What is the derivative of x^3 + 2x^2 - 5x + 1?" # Expected: Step-by-step differentiation with clear explanation ``` ### Word Problem Solving ```python prompt = """A train travels at 60 mph for 2 hours, then 80 mph for 1.5 hours. What is the average speed for the entire journey?""" # Expected: Detailed solution with distance calculations ``` ### Algebraic Reasoning ```python prompt = "Solve for x: 2x^2 - 8x + 6 = 0" # Expected: Quadratic formula application with step-by-step solution ``` ### Mathematical Code Generation ```python prompt = "Write a Python function to calculate the factorial of a number using recursion." # Expected: Clean, commented code with mathematical explanation ``` ## 📈 Evaluation Results ### Mathematical Reasoning Benchmarks The model was evaluated on standard mathematical reasoning benchmarks: - GSM8K (Grade School Math): 85.2% accuracy - MATH (Competition Mathematics): 42.1% accuracy - Algebra Problems: 78.9% accuracy - Geometry Problems: 71.3% accuracy - Mathematical Coding*: 82.6% accuracy ### 📊 Performance Visualizations <div align="center"> #### 🎯 Performance Across Mathematical Domains <img src="crystal_think_performance_comparison.png" alt="Crystal-Think Performance Comparison" width="800"/> Crystal-Think v1.0 consistently outperforms the base Qwen3-4B model across all mathematical domains, with particularly strong improvements in competition mathematics (+10.4%) and code generation (+13.5%).* #### 📈 Difficulty Scaling Analysis <img src="crystal_think_difficulty_scaling.png" alt="Difficulty Scaling Performance" width="800"/> Performance scaling across AoPS problem difficulty levels shows Crystal-Think maintains superior accuracy even on advanced mathematical concepts, with a 24.3% improvement on Olympiad-level problems. #### 🚀 Model Improvements Over Base <img src="crystal_think_improvements.png" alt="Model Improvements" width="800"/> GRPO fine-tuning on OpenMathReasoning delivers consistent improvements across all capabilities, with the highest gains in Tool Usage Proficiency (+18.1%) and Solution Verification (+16.7%). #### 🧠 Reasoning Capabilities Radar <img src="crystal_think_reasoning_radar.png" alt="Reasoning Capabilities" width="600"/> Comprehensive reasoning profile trained on 3.2M Chain-of-Thought and 1.7M Tool-Integrated Reasoning solutions, showing balanced excellence across all mathematical reasoning dimensions. #### 📚 Training Data Composition <img src="crystal_think_training_data.png" alt="Training Data Breakdown" width="800"/> OpenMathReasoning dataset composition: 5.86M total samples from AoPS forums with diverse solution types optimized for mathematical reasoning development. </div> ### Reasoning Capabilities ✅ Multi-step Problem Solving: Breaks down complex problems systematically ✅ Clear Explanations: Provides step-by-step reasoning ✅ Error Checking: Identifies and corrects mathematical errors ✅ Multiple Approaches: Can solve problems using different methods ✅ Code Integration: Generates mathematical code with explanations ## ⚠️ Limitations - Domain Specificity: Optimized for mathematical reasoning; may be less effective for general conversational tasks - Language: Primarily trained on English mathematical content - Complexity Ceiling: Very advanced mathematical concepts may still be challenging - Computational Requirements: Requires adequate GPU memory for optimal performance ## 🔧 Technical Specifications ### Hardware Requirements - Minimum GPU Memory: 8GB VRAM - Recommended GPU Memory: 16GB+ VRAM - CPU: Modern multi-core processor - RAM: 16GB+ system memory ### Software Dependencies ``` transformers>=4.52.0 torch>=2.0.0 tokenizers>=0.13.0 accelerate>=0.20.0 ``` ## 📝 Citation If you use Crystal-Think v1 in your research or applications, please cite: ```bibtex @model{crystal-think-v1, title={Crystal-Think v1: A Mathematical Reasoning Model}, author={sizzlebop}, year={2025}, url={https://huggingface.co/PinkPixel/crystal-think-v1}, note={Fine-tuned Qwen3-4B with GRPO on OpenMathReasoning} } ``` ## 🤝 Contributing I'm always open to learning, and I am very interested in the fine-tuning process! If you have suggestions for improvements, find issues, or want to collaborate on future versions, please feel free to reach out. ## 📧 Contact - Developer: Pink Pixel - GitHub: [https://github.com/pinkpixel-dev] - Website: [https://pinkpixel.dev] - Email: [[email protected]] ## 🙏 Acknowledgments - Base Model: Qwen Team for the excellent Qwen3-4B foundation - Training Framework: Unsloth for efficient fine-tuning tools - Dataset: NVIDIA for the OpenMathReasoning dataset - Community: Hugging Face community for support and resources --- Made with ❤️ by Pink Pixel ✨ "Dream it, Pixel it"
Zekrompogu/APNRkendaraan	Zekrompogu	2025-06-24T22:04:58Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:microsoft/trocr-base-str", "base_model:finetune:microsoft/trocr-base-str", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-24T22:03:24Z	--- library_name: transformers base_model: microsoft/trocr-base-str tags: - generated_from_trainer metrics: - wer model-index: - name: microsoft/trocr-base-str 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. --> # microsoft/trocr-base-str This model is a fine-tuned version of [microsoft/trocr-base-str](https://huggingface.co/microsoft/trocr-base-str) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0895 - Cer: 0.0114 - Wer: 0.0668 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 1.9045 \| 1.0 \| 173 \| 0.5308 \| 0.0892 \| 0.3232 \| \| 0.3001 \| 2.0 \| 346 \| 0.1530 \| 0.0262 \| 0.1328 \| \| 0.1046 \| 3.0 \| 519 \| 0.1169 \| 0.0189 \| 0.1108 \| \| 0.061 \| 4.0 \| 692 \| 0.1017 \| 0.0159 \| 0.0871 \| \| 0.0403 \| 5.0 \| 865 \| 0.0904 \| 0.0134 \| 0.0778 \| \| 0.0267 \| 6.0 \| 1038 \| 0.0924 \| 0.0132 \| 0.0745 \| \| 0.0211 \| 7.0 \| 1211 \| 0.0888 \| 0.0122 \| 0.0694 \| \| 0.0139 \| 8.0 \| 1384 \| 0.0915 \| 0.0120 \| 0.0694 \| \| 0.0111 \| 9.0 \| 1557 \| 0.0902 \| 0.0120 \| 0.0702 \| \| 0.0084 \| 10.0 \| 1730 \| 0.0895 \| 0.0114 \| 0.0668 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 2.17.0 - Tokenizers 0.19.1
phospho-app/Schmidie-gr00t-schachtel-2vj6c	phospho-app	2025-06-24T22:02:42Z	0	0	null	[ "safetensors", "phosphobot", "gr00t", "region:us" ]	null	2025-06-24T18:57:17Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/opt/conda/lib/python3.11/asyncio/tasks.py", line 500, in wait_for return fut.result() ^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 970, in read_output async for line in process.stdout: File "/opt/conda/lib/python3.11/asyncio/streams.py", line 765, in __anext__ val = await self.readline() ^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/streams.py", line 566, in readline line = await self.readuntil(sep) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/streams.py", line 658, in readuntil await self._wait_for_data('readuntil') File "/opt/conda/lib/python3.11/asyncio/streams.py", line 543, in _wait_for_data await self._waiter asyncio.exceptions.CancelledError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/root/phosphobot/am/gr00t.py", line 981, in run_gr00t_training await asyncio.wait_for(read_output(), timeout=timeout_seconds) File "/opt/conda/lib/python3.11/asyncio/tasks.py", line 502, in wait_for raise exceptions.TimeoutError() from exc TimeoutError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/root/src/helper.py", line 165, in predict trainer.train(timeout_seconds=timeout_seconds) File "/root/phosphobot/am/gr00t.py", line 1146, in train asyncio.run( File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 986, in run_gr00t_training raise TimeoutError( TimeoutError: Training process exceeded timeout of 10800 seconds. Please consider lowering the number of epochs and/or batch size. ``` ## Training parameters: - Dataset: [Schmidie/schachtel](https://huggingface.co/datasets/Schmidie/schachtel) - Wandb run URL: None - Epochs: 10 - Batch size: 15 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
NEW-VIDEO-mezzo-fun-18-viral-Clips-tk/18.video.mezzo.fun.viral.link.Mezzo.fun.Viral.Video.Tutorial.Official	NEW-VIDEO-mezzo-fun-18-viral-Clips-tk	2025-06-24T22:01:09Z	0	0	null	[ "region:us" ]	null	2025-06-24T22:00:45Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
annasoli/Qwen2.5-14B-Instruct_bad-med-topic-10	annasoli	2025-06-24T22:00:26Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-24T21:23:03Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
phospho-app/gc1724-ACT-ttt-a1-square-xogfz	phospho-app	2025-06-24T21:57:41Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-24T15:16:08Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [gc1724/ttt-a1-square](https://huggingface.co/datasets/gc1724/ttt-a1-square) - Wandb run URL: None - Epochs: None - Batch size: 60 - Training steps: 7500 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
Osilly/Vision-R1-72B	Osilly	2025-06-24T21:54:56Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-06-24T20:52:01Z	--- license: apache-2.0 ---
New-videos-Bindura-University-viral-video/FULL.VIDEO.Bindura.University.Viral.Video.Tutorial.Official	New-videos-Bindura-University-viral-video	2025-06-24T21:53:12Z	0	0	null	[ "region:us" ]	null	2025-06-24T21:52:53Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
JamieOgundiran/Ogun-Mistral-7B	JamieOgundiran	2025-06-24T21:40:50Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "sft", "trl", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T21:35:20Z	--- base_model: mistralai/Mistral-7B-v0.1 library_name: transformers model_name: ogun-mistral-7B-2 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for ogun-mistral-7B-2 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="JamieOgundiran/ogun-mistral-7B-2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.52.4 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Tyl3rDrden/trained-flux-dev-dreambooth-lora2_Normal_LILY	Tyl3rDrden	2025-06-24T21:37:48Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-24T16:52:39Z	--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers license: other instance_prompt: a photo of sksdog widget: - text: A photo of sksdog in a bucket output: url: image_0.png - text: A photo of sksdog in a bucket output: url: image_1.png - text: A photo of sksdog in a bucket output: url: image_2.png - text: A photo of sksdog in a bucket output: url: image_3.png tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Flux DreamBooth LoRA - Tyl3rDrden/trained-flux-dev-dreambooth-lora2_Normal_LILY <Gallery /> ## Model description These are Tyl3rDrden/trained-flux-dev-dreambooth-lora2_Normal_LILY DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md). Was LoRA for the text encoder enabled? True. ## Trigger words You should use `a photo of sksdog` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](Tyl3rDrden/trained-flux-dev-dreambooth-lora2_Normal_LILY/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('Tyl3rDrden/trained-flux-dev-dreambooth-lora2_Normal_LILY', weight_name='pytorch_lora_weights.safetensors') image = pipeline('A photo of sksdog in a bucket').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-11_9874	luckeciano	2025-06-24T21:34:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T16:02:16Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-11_9874 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-11_9874 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-11_9874", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/c1aokynk) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
altaweel/gemma-ultrasound-1b-v3	altaweel	2025-06-24T21:33:36Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "endpoints_compatible", "region:us" ]	null	2025-06-24T19:32:05Z	--- base_model: google/gemma-3-1b-pt library_name: transformers model_name: gemma-ultrasound-1b-v3 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-ultrasound-1b-v3 This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="altaweel/gemma-ultrasound-1b-v3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.4 - Pytorch: 2.5.1+cu121 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
nguyenvuvn/1a137b4b-cce1-4cd3-ac12-6319c4427169	nguyenvuvn	2025-06-24T21:33:01Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T16:56:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ICONNAI/ICONN-e1-Beta	ICONNAI	2025-06-24T21:30:27Z	90	15	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "emotional-ai", "ICONN", "chatbot", "base", "conversational", "doi:10.57967/hf/5861", "license:apache-2.0", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-17T18:57:06Z	--- library_name: transformers tags: - emotional-ai - ICONN - chatbot - base co2_eq_emissions: emissions: 3.37 source: CodeCarbon training_type: pretraining geographical_location: US-West hardware_used: 18 x B200 pipeline_tag: text-generation license: apache-2.0 --- <div align="center" style="line-height: 1;"> ![ICONN AI Logo](https://i.postimg.cc/gJwHqh1D/svgviewer-png-output.png) <a href="https://huggingface.co/collections/ICONNAI/iconn-1-6851e8a88ed4eb66b4fd0132" target="_blank" style="margin: 2px;"> <img alt="ICONN 1 Models" src="https://img.shields.io/badge/📦_ICONN_1_Models-HuggingFace-1CBEEF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://huggingface.co/ICONNAI" target="_blank" style="margin: 2px;"> <img alt="ICONN on Hugging Face" src="https://img.shields.io/badge/🤗_ICONN_on_HF-ICONNAI-A4BCF0?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://opensource.org/license/apache-2-0" target="_blank" style="margin: 2px;"> <img alt="License Apache 2.0" src="https://img.shields.io/badge/⚖️_License-Apache_2.0-5C63DA?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://github.com/organizations/ICONN-AI/" target="_blank" style="margin: 2px;"> <img alt="ICONN on GitHub" src="https://img.shields.io/badge/🐙_ICONN_on_GitHub-ICONN--AI-8C8CFF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://huggingface.co/ICONNAI" target="_blank" style="margin: 2px;"> <img alt="Follow ICONNAI" src="https://img.shields.io/badge/⭐_Follow_ICONNAI-HuggingFace-A4BCF0?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> </div> # ICONN e1: The new era of Open-Source AI GPU poor? Less than 3x A100s? A e1 Lite model is coming with just 22B parameters alongside a model for consumer CPUs with 14B and 7B parameters. - Emotional Context Awareness ICONN e1 interprets emotional cues and adjusts tone, vocabulary, and response style—offering a more human-like, emotionally reactive experience. - ICONN Emotional Core (IEC) (Notice: Not available on Huggingface) Powered by millions of small AI agents, IEC gives ICONN its emotional personality, with billions of simulated emotional states and detections. - Reasoning ICONN e1 is one of the most powerful reasoning open-source models, and most closed-source models in or out of Huggingface. # What is in the ICONN i1 MoE? ## ICONN i1 MoE and Experts ICONN e1, being a MoE just like it's base model ICONN 1, has multiple expert models. Keywords are taken from the user's input to choose which expert generates the output. \| Expert Chosen \| User Input \| \|---------------------\|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| ICONN-e1 \| `'Hi!'` \| \| ICONN-e1-Pro \| `Solve for m: m² − (2 + ∑₍ⱼ₌₁₎² j)·m + (1 + ∑₍ⱼ₌₁₎³ j² − 14) = 0.` \| \| ICONN-e1-Science \| `If a stable isotope of Ununoctium (Uuo, now Og) could be synthesized in bulk, what would be its most likely physical state at STP and why, considering relativistic effects?` \| \| ICONN-e1-Code \| `Create a zero-dependency quantum-safe VM in Zig that compiles a domain-specific language into a fully homomorphic encrypted IR, supports hot-reloading WebAssembly modules, parallel scheduling via lock-free fibers, and performs live introspection through a headless OpenGL debug overlay.` \| ICONN-e1: ICONN's general-purpose reasoning model, designed for everyday tasks, logic, and conversation. ICONN-e1-Pro: ICONN's advanced reasoning model, optimized for complex problem-solving in math, logic, and professional domains. ICONN-e1-Science: ICONN's scientific expert model, trained on advanced science datasets to enhance precision in physics, chemistry, biology, and technical reasoning. ICONN-e1-Code: ICONN's coding specialist, trained for programming, compiler theory, software architecture, and technical code generation across multiple languages. # Usage First, make sure you have at least 4x Nvidia A100 or a single B100, and 120GB RAM and 120-192GB VRAM. Don't have this? Use our Lite model, coming soon. > Run the code below to run ICONN i1: ```python from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline import torch def run_iconn_chatbot(model_name="ICONNAI/ICONN-e1"): tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) device = 0 if torch.cuda.is_available() else -1 chat_pipeline = pipeline( "text-generation", model=model, tokenizer=tokenizer, device=device, max_length=1624, do_sample=True, top_p=0.9, temperature=0.4, pad_token_id=tokenizer.eos_token_id ) print(f"ICONN chatbot running with model: {model_name}. Type 'exit' to quit.") conversation_history = "" while True: user_input = input("You: ") if user_input.lower() == "exit": print("Goodbye!") break conversation_history += f"User: {user_input}\nBot:" response = chat_pipeline(conversation_history, max_length=len(tokenizer.encode(conversation_history)) + 100)[0]['generated_text'] bot_reply = response[len(conversation_history):].strip().split("\n")[0] print(f"Bot: {bot_reply}") conversation_history += f" {bot_reply}\n" if __name__ == "__main__": run_iconn_chatbot() ``` ## Cite Us If you use ICONN 1, please cite us as follows:** ```DoI @misc{iconnai_2025, author = { ICONNAI }, title = { ICONN-e1-Beta (Revision ca41146) }, year = 2025, url = { https://huggingface.co/ICONNAI/ICONN-e1-Beta }, doi = { 10.57967/hf/5861 }, publisher = { Hugging Face } } ```
sameeahameed/llama-3.2-3b-25-6-25	sameeahameed	2025-06-24T21:29:40Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-24T21:29:33Z	--- base_model: unsloth/llama-3.2-3b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: sameeahameed - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ICONNAI/ICONN-1-Mini-Beta	ICONNAI	2025-06-24T21:29:37Z	27	2	transformers	[ "transformers", "safetensors", "iconn", "text-generation", "emotional-ai", "ICONN", "chatbot", "base", "conversational", "custom_code", "doi:10.57967/hf/5860", "license:apache-2.0", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-22T22:14:23Z	--- library_name: transformers tags: - emotional-ai - ICONN - chatbot - base co2_eq_emissions: emissions: 0.34 source: CodeCarbon training_type: pretraining geographical_location: US-West hardware_used: 9 x B200 pipeline_tag: text-generation license: apache-2.0 --- <div align="center" style="line-height: 1;"> ![ICONN AI Logo](https://i.postimg.cc/gJwHqh1D/svgviewer-png-output.png) <a href="https://huggingface.co/collections/ICONNAI/iconn-1-6851e8a88ed4eb66b4fd0132" target="_blank" style="margin: 2px;"> <img alt="ICONN 1 Models" src="https://img.shields.io/badge/📦_ICONN_1_Models-HuggingFace-1CBEEF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://huggingface.co/spaces/ICONNAI/ICONN-Mini-Chat" target="_blank" style="margin: 2px;"> <img alt="ICONN 1 Chat" src="https://img.shields.io/badge/💬_ICONN_1_Chat-Online-65C7F9?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://huggingface.co/ICONNAI" target="_blank" style="margin: 2px;"> <img alt="ICONN on Hugging Face" src="https://img.shields.io/badge/🤗_ICONN_on_HF-ICONNAI-A4BCF0?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://opensource.org/license/apache-2-0" target="_blank" style="margin: 2px;"> <img alt="License Apache 2.0" src="https://img.shields.io/badge/⚖️_License-Apache_2.0-5C63DA?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://github.com/organizations/ICONN-AI/" target="_blank" style="margin: 2px;"> <img alt="ICONN on GitHub" src="https://img.shields.io/badge/🐙_ICONN_on_GitHub-ICONN--AI-8C8CFF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://huggingface.co/ICONNAI" target="_blank" style="margin: 2px;"> <img alt="Follow ICONNAI" src="https://img.shields.io/badge/⭐_Follow_ICONNAI-HuggingFace-A4BCF0?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://huggingface.co/spaces/huggingface/InferenceSupport/discussions/2932" target="_blank" style="margin: 2px;"> <img alt="React to Vote" src="https://img.shields.io/badge/🗳️_Vote_for_us_as_Inference_Provider-React_👍-1CBEEF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> </div> ## ICONN 1 Introducing ICONN 1 Mini Beta, a cutting-edge open-source AI model with just 7 billion parameters — designed for natural, human-like language understanding and generation. Despite its compact size, it delivers powerful performance through efficient architecture and careful tuning. ICONN 1 Mini Beta represents the next step in accessible, conversational AI. Developed entirely from scratch, ICONN-1-Mini-Beta is based on a new ICONN framework and comprises 7 billion parameters. ICONN-1 is released in three distinct forms to serve different application needs: - ICONN-1-Mini-Beta(This model) is a small 7B model trained for a lightweight alternative to ICONN 1. - ICONN-1 is optimized for natural, emotionally resonant, and conversational interactions. - ICONN-e1 is a specialized variant of the model fine-tuned for advanced reasoning, critical analysis, and complex problem-solving. Together, these models represent a major leap forward in the evolution of AI systems—demonstrating not only deep reasoning but also a commitment to openness, accessibility, and human-aligned intelligence. ## Usage To run ICONN 1 Mini Beta, you need: - Any hardware - CPU or GPU; Just make sure you have about 15GB storage space! > Run the code below to run ICONN 1 Mini Beta: ```python import os import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer from threading import Thread model_id = "ICONNAI/ICONN-1-Mini-Beta" try: model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained(model_id) except Exception as e: exit(f"Exiting due to model loading error: {e}") def generate_response( message: str, max_new_tokens: int = 2048, temperature: float = 0.4, top_p: float = 0.9, top_k: int = 50, repetition_penalty: float = 1.2, ) -> str: conversation = [{"role": "user", "content": message}] try: input_ids = tokenizer.apply_chat_template( conversation, return_tensors="pt", enable_thinking=True ) except Exception as e: return f"Error applying chat template: {e}" input_ids = input_ids.to(model.device) streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True) adjusted_top_k = int(max(1, top_k)) generate_kwargs = dict( {"input_ids": input_ids}, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p, top_k=adjusted_top_k, temperature=temperature, num_beams=1, repetition_penalty=repetition_penalty, ) try: t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() except Exception as e: return f"Error starting generation thread: {e}" outputs = [] for text in streamer: outputs.append(text) return "".join(outputs) if __name__ == "__main__": question = "Can you explain briefly to me what is the Python programming language?" print(f"User Question: {question}") response = generate_response(question) print(f"Bot Response: {response}") ``` ## Cite Us If you use ICONN 1, please cite us as follows: ```DoI @misc{iconnai_2025, author = { ICONNAI }, title = { ICONN-1-Mini-Beta (Revision e29b435) }, year = 2025, url = { https://huggingface.co/ICONNAI/ICONN-1-Mini-Beta }, doi = { 10.57967/hf/5860 }, publisher = { Hugging Face } } ```
oumi-ai/Phi-3-vision-128k-instruct	oumi-ai	2025-06-24T21:28:39Z	0	0	null	[ "safetensors", "phi3_v", "nlp", "code", "vision", "text-generation", "conversational", "custom_code", "multilingual", "license:mit", "region:us" ]	text-generation	2025-06-24T21:23:42Z	--- license: mit license_link: https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE language: - multilingual pipeline_tag: text-generation tags: - nlp - code - vision inference: parameters: temperature: 0.7 widget: - messages: - role: user content: <\|image_1\|>Can you describe what you see in the image? --- 🎉 Phi-3.5: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct) ## Model Summary The Phi-3-Vision-128K-Instruct is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data both on text and vision. The model belongs to the Phi-3 model family, and the multimodal version comes with 128K context length (in tokens) it can support. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures. Resources and Technical Documentation: + [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024) + [Phi-3 Technical Report](https://aka.ms/phi3-tech-report) + [Phi-3 on Azure AI Studio](https://aka.ms/try-phi3vision) + [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook) \| \| Short Context \| Long Context \| \| ------- \| ------------- \| ------------ \| \| Mini \| 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) \| 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)\| \| Small \| 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) \| 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)\| \| Medium \| 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) \| 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)\| \| Vision \| \| 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda)\| ## Intended Uses Primary use cases The model is intended for broad commercial and research use in English. The model provides uses for general purpose AI systems and applications with visual and text input capabilities which require 1) memory/compute constrained environments; 2) latency bound scenarios; 3) general image understanding; 4) OCR; 5) chart and table understanding. Our model is designed to accelerate research on efficient language and multimodal models, for use as a building block for generative AI powered features. Use case considerations Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. ## How to Use Phi-3-Vision-128K-Instruct has been integrated in the development version (4.40.2) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following: * When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function. * Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source. The current `transformers` version can be verified with: `pip list \| grep transformers`. Examples of required packages: ``` flash_attn==2.5.8 numpy==1.24.4 Pillow==10.3.0 Requests==2.31.0 torch==2.3.0 torchvision==0.18.0 transformers==4.40.2 ``` Phi-3-Vision-128K-Instruct is also available in [Azure AI Studio](https://aka.ms/phi3-azure-ai). ### Chat Format Given the nature of the training data, the Phi-3-Vision-128K-Instruct model is best suited for a single image input wih prompts using the chat format as follows. You can provide the prompt as a single image with a generic template as follow: ```markdown <\|user\|>\n<\|image_1\|>\n{prompt}<\|end\|>\n<\|assistant\|>\n ``` where the model generates the text after `<\|assistant\|>` . In case of multi-turn conversation, the prompt can be formatted as follows: ```markdown <\|user\|>\n<\|image_1\|>\n{prompt_1}<\|end\|>\n<\|assistant\|>\n{response_1}<\|end\|>\n<\|user\|>\n{prompt_2}<\|end\|>\n<\|assistant\|>\n ``` ### Sample inference code This code snippets show how to get quickly started with running the model on a GPU: ```python from PIL import Image import requests from transformers import AutoModelForCausalLM from transformers import AutoProcessor model_id = "microsoft/Phi-3-vision-128k-instruct" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto", _attn_implementation='flash_attention_2') # use _attn_implementation='eager' to disable flash attention processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) messages = [ {"role": "user", "content": "<\|image_1\|>\nWhat is shown in this image?"}, {"role": "assistant", "content": "The chart displays the percentage of respondents who agree with various statements about their preparedness for meetings. It shows five categories: 'Having clear and pre-defined goals for meetings', 'Knowing where to find the information I need for a meeting', 'Understanding my exact role and responsibilities when I'm invited', 'Having tools to manage admin tasks like note-taking or summarization', and 'Having more focus time to sufficiently prepare for meetings'. Each category has an associated bar indicating the level of agreement, measured on a scale from 0% to 100%."}, {"role": "user", "content": "Provide insightful questions to spark discussion."} ] url = "https://assets-c4akfrf5b4d3f4b7.z01.azurefd.net/assets/2024/04/BMDataViz_661fb89f3845e.png" image = Image.open(requests.get(url, stream=True).raw) prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = processor(prompt, [image], return_tensors="pt").to("cuda:0") generation_args = { "max_new_tokens": 500, "temperature": 0.0, "do_sample": False, } generate_ids = model.generate(inputs, eos_token_id=processor.tokenizer.eos_token_id, generation_args) # remove input tokens generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:] response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] print(response) ``` Additional basic examples are provided [here](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/sample_inference.py). ### How to finetune? We recommend user to take a look at the [Phi-3 CookBook finetuning recipe for Vision](https://github.com/microsoft/Phi-3CookBook/blob/main/md/04.Fine-tuning/FineTuning_Vision.md) ## Responsible AI Considerations Like other models, the Phi family of models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: + Quality of Service: The Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. + Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. + Inappropriate or Offensive Content: These models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. + Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. + Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include: + Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. + High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. + Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). + Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. + Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations. + Identification of individuals: models with vision capabilities may have the potential to uniquely identify individuals in images. Safety post-training steers the model to refuse such requests, but developers should consider and implement, as appropriate, additional mitigations or user consent flows as required in their respective jurisdiction, (e.g., building measures to blur faces in image inputs before processing. ## Training ### Model * Architecture: Phi-3-Vision-128K-Instruct has 4.2B parameters and contains image encoder, connector, projector, and Phi-3 Mini language model. * Inputs: Text and Image. It’s best suited for prompts using the chat format. * Context length: 128K tokens * GPUs: 512 H100-80G * Training time: 1.5 days * Training data: 500B vision and text tokens * Outputs: Generated text in response to the input * Dates: Our models were trained between February and April 2024 * Status: This is a static model trained on an offline text dataset with cutoff date Mar 15, 2024. Future versions of the tuned models may be released as we improve models. * Release Type: Open weight release * Release dates: The model weight is released on May 21, 2024. ### Datasets Our training data includes a wide variety of sources, and is a combination of 1) publicly available documents filtered rigorously for quality, selected high-quality educational data and code; 2) selected high-quality image-text interleave; 3) newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.), newly created image data, e.g., chart/table/diagram/slides; 4) high quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. The data collection process involved sourcing information from publicly available documents, with a meticulous approach to filtering out undesirable documents and images. To safeguard privacy, we carefully filtered various image and text data sources to remove or scrub any potentially personal data from the training data. More details can be found in the [Phi-3 Technical Report](https://aka.ms/phi3-tech-report). ## Benchmarks To understand the capabilities, we compare Phi-3-Vision-128K-Instruct with a set of models over a variety of zero-shot benchmarks using our internal benchmark platform. \|Benchmark\|Phi-3 Vision-128K-In\|LlaVA-1.6 Vicuna-7B\|QWEN-VL Chat\|Llama3-Llava-Next-8B\|Claude-3 Haiku\|Gemini 1.0 Pro V\|GPT-4V-Turbo\| \|---------\|---------------------\|------------------\|------------\|--------------------\|--------------\|----------------\|------------\| \|MMMU\|40.4\|34.2\|39.0\|36.4\|40.7\|42.0\|55.5\| \|MMBench\|80.5\|76.3\|75.8\|79.4\|62.4\|80.0\|86.1\| \|ScienceQA\|90.8\|70.6\|67.2\|73.7\|72.0\|79.7\|75.7\| \|MathVista\|44.5\|31.5\|29.4\|34.8\|33.2\|35.0\|47.5\| \|InterGPS\|38.1\|20.5\|22.3\|24.6\|32.1\|28.6\|41.0\| \|AI2D\|76.7\|63.1\|59.8\|66.9\|60.3\|62.8\|74.7\| \|ChartQA\|81.4\|55.0\|50.9\|65.8\|59.3\|58.0\|62.3\| \|TextVQA\|70.9\|64.6\|59.4\|55.7\|62.7\|64.7\|68.1\| \|POPE\|85.8\|87.2\|82.6\|87.0\|74.4\|84.2\|83.7\| ## Software * [PyTorch](https://github.com/pytorch/pytorch) * [Transformers](https://github.com/huggingface/transformers) * [Flash-Attention](https://github.com/HazyResearch/flash-attention) ## Hardware Note that by default, the Phi-3-Vision-128K model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types: * NVIDIA A100 * NVIDIA A6000 * NVIDIA H100 ## License The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE). ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
ToastyPigeon/ms3.2-cowriter-lora	ToastyPigeon	2025-06-24T21:28:07Z	0	0	peft	[ "peft", "safetensors", "mistral", "generated_from_trainer", "dataset:ToastyPigeon/cowriter-instruct", "base_model:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML", "base_model:adapter:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML", "4-bit", "bitsandbytes", "region:us" ]	null	2025-06-24T21:08:15Z	--- library_name: peft base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML tags: - generated_from_trainer datasets: - ToastyPigeon/cowriter-instruct model-index: - name: workspace/aibox-standalone-pool/axolotl/mistral3.2-cowriter-ckpts 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.10.0.dev0` ```yaml # === Model Configuration === base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML load_in_8bit: false load_in_4bit: true # === HF Configuration === #hub_model_id: ToastyPigeon/an-instruct-ms3.2-train #hub_strategy: "checkpoint" # === Training Setup === num_epochs: 2 micro_batch_size: 1 gradient_accumulation_steps: 4 sequence_len: 16384 sample_packing: true pad_to_sequence_len: true # === Evaluation === val_set_size: 0.01 evals_per_epoch: 5 #eval_steps: 20 #max_steps: 60 #eval_table_size: eval_max_new_tokens: 128 eval_sample_packing: true #eval_strategy: "no" # === LoRA Configuration === adapter: qlora lora_model_dir: lora_r: 128 lora_alpha: 16 lora_dropout: 0.125 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj peft_use_rslora: true #lora_mlp_kernel: true #lora_qkv_kernel: true #lora_o_kernel: true # === Hyperparameter Configuration === #optimizer: apollo_adamw_layerwise warmup_steps: 20 optimizer: adamw_torch_fused #optimizer: paged_adamw_8bit #optim_args: # enable_stochastic_rounding: true # enable_cautious: true # enable_8bit: true # Apollo-mini configuration: #optim_args: "proj=random,rank=128,scale=128.0,scale_type=tensor,update_proj_gap=100" # Regular Apollo configuration: # optim_args: #optim_target_modules: all_linear learning_rate: 1e-5 lr_scheduler: rex cosine_min_lr_ratio: 0.2 #lr_scheduler: cosine_with_min_lr #lr_scheduler_kwargs: # cosine_min_lr: 1e-6 weight_decay: 0.01 max_grad_norm: 1.0 #warmup_steps: 0 #warmup_ratio: 0.025 # === Data Configuration === chat_template: jinja chat_template_jinja: "{%- set default_system_message = \"You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.\" %}\n\n{{- bos_token }}\n\n{%- if messages[0]['role'] == 'system' %}\n {%- if messages[0]['content'] is string %}\n {%- set system_message = messages[0]['content'] %}\n {%- else %}\n {%- set system_message = messages[0]['content'][0]['text'] %}\n {%- endif %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = default_system_message %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{{- '[SYSTEM_PROMPT]' + system_message + '[/SYSTEM_PROMPT]' }}\n\n{%- for message in loop_messages %}\n {%- if message['role'] == 'user' %}\n {%- if message['content'] is string %}\n {{- '[INST]' + message['content'] + '[/INST]' }}\n {%- else %}\n {{- '[INST]' }}\n {%- for block in message['content'] %}\n {%- if block['type'] == 'text' %}\n {{- block['text'] }}\n {%- elif block['type'] in ['image', 'image_url'] %}\n {{- '[IMG]' }}\n {%- else %}\n {{- raise_exception('Only text and image blocks are supported in message content!') }}\n {%- endif %}\n {%- endfor %}\n {{- '[/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'system' %}\n {%- if message['content'] is string %}\n {{- '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }}\n {%- else %}\n {{- '[SYSTEM_PROMPT]' + message['content'][0]['text'] + '[/SYSTEM_PROMPT]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {%- if message['content'] is string %}\n {{- message['content'] + eos_token }}\n {%- else %}\n {{- message['content'][0]['text'] + eos_token }}\n {%- endif %}\n {%- else %}\n {{- raise_exception('Only user, system and assistant roles are supported!') }}\n {%- endif %}\n{%- endfor %}" tokenizer_use_mistral_common: true shuffle_merged_datasets: true datasets: - path: ToastyPigeon/cowriter-instruct type: chat_template data_files: cowriter-16k.json dataset_prepared_path: last_run_prepared # === Plugins === plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin # === Hardware Optimization === #gradient_checkpointing: offload #gradient_checkpointing_kwargs: # use_reentrant: false liger_rope: true liger_rms_norm: true liger_layer_norm: true liger_glu_activation: true #liger_fused_linear_cross_entropy: true cut_cross_entropy: true #deepspeed: deepspeed_configs/zero3_bf16.json # === FSDP Config === fsdp: - full_shard - auto_wrap fsdp_config: fsdp_limit_all_gathers: true fsdp_sync_module_states: true fsdp_offload_params: true fsdp_activation_checkpointing: true fsdp_use_orig_params: false fsdp_cpu_ram_efficient_loading: true fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer fsdp_state_dict_type: FULL_STATE_DICT fsdp_sharding_strategy: FULL_SHARD # === Wandb Tracking === wandb_project: Mistral-3.2 # wandb_entity: [WANDB_ENTITY] # wandb_name: [WANDB_RUN_NAME] # === Checkpointing === saves_per_epoch: 20 save_total_limit: 5 # === Advanced Settings === output_dir: /workspace/aibox-standalone-pool/axolotl/mistral3.2-cowriter-ckpts bf16: auto flash_attention: true train_on_inputs: false group_by_length: false save_safetensors: true logging_steps: 1 gc_steps: 10 seed: 69 ``` </details><br> # workspace/aibox-standalone-pool/axolotl/mistral3.2-cowriter-ckpts This model is a fine-tuned version of [anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML](https://huggingface.co/anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML) on the ToastyPigeon/cowriter-instruct dataset. It achieves the following results on the evaluation set: - Loss: 2.4342 ## 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: 69 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - total_eval_batch_size: 2 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - training_steps: 328 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.4735 \| 0.0061 \| 1 \| 2.5005 \| \| 2.5091 \| 0.2006 \| 33 \| 2.4586 \| \| 2.499 \| 0.4012 \| 66 \| 2.4589 \| \| 2.3922 \| 0.6018 \| 99 \| 2.4499 \| \| 2.3564 \| 0.8024 \| 132 \| 2.4468 \| \| 2.3165 \| 1.0 \| 165 \| 2.4428 \| \| 2.4576 \| 1.2006 \| 198 \| 2.4388 \| \| 2.4476 \| 1.4012 \| 231 \| 2.4366 \| \| 2.3374 \| 1.6018 \| 264 \| 2.4343 \| \| 2.3019 \| 1.8024 \| 297 \| 2.4342 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.1 - Tokenizers 0.21.1

NTIS/hf_gemma3_2-checkpoint-101000

NTIS

2025-06-25T01:46:29Z

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "pytorch", "causal-lm", "ko", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T05:06:57Z

--- license: apache-2.0 language: - ko - en tags: - text-generation - pytorch - causal-lm library_name: transformers --- # hf_gemma3_2-checkpoint-101000 이 모델은 파인튜닝된 언어 모델 체크포인트입니다. ## 모델 정보 - **베이스 모델**: hf_gemma3_2 - **체크포인트**: checkpoint-101000 - **타입**: Causal Language Model - **라이선스**: Apache 2.0 ## 사용 방법 ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name = "NTIS/hf_gemma3_2-checkpoint-101000" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto" ) # 텍스트 생성 text = "안녕하세요" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` ## 주의사항 - 이 모델은 연구/실험 목적으로 제공됩니다 - 상업적 사용 전에 라이선스를 확인하세요

NTIS/hf_gemma3_2-checkpoint-100000

NTIS

2025-06-25T01:44:05Z

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "pytorch", "causal-lm", "ko", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T05:04:40Z

--- license: apache-2.0 language: - ko - en tags: - text-generation - pytorch - causal-lm library_name: transformers --- # hf_gemma3_2-checkpoint-100000 이 모델은 파인튜닝된 언어 모델 체크포인트입니다. ## 모델 정보 - **베이스 모델**: hf_gemma3_2 - **체크포인트**: checkpoint-100000 - **타입**: Causal Language Model - **라이선스**: Apache 2.0 ## 사용 방법 ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name = "NTIS/hf_gemma3_2-checkpoint-100000" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto" ) # 텍스트 생성 text = "안녕하세요" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` ## 주의사항 - 이 모델은 연구/실험 목적으로 제공됩니다 - 상업적 사용 전에 라이선스를 확인하세요

microsoft/elem2design

microsoft

2025-06-25T01:39:39Z

22

4

null

[ "safetensors", "llava_llama", "arxiv:2412.19712", "arxiv:2311.16974", "arxiv:2303.15937", "arxiv:2404.00995", "arxiv:2303.18248", "arxiv:1910.09700", "license:mit", "region:us" ]

null

2025-05-27T10:31:19Z

--- license: mit --- ## Model Details ### Model Description This model aims to compose user-provided graphic elements into a pleasing graphical design. It takes graphic elements (i.e., the images and texts) from users as input and generates the position, color and font information of each element as output. - **Developed by:** Jiawei Lin, Shizhao Sun, Danqing Huang, Ting Liu, Ji Li and Jiang Bian - **Model type:** Large Language Models - **Language(s):** Python - **License:** MIT - **Finetuned from model:** Llama-3.1-8B ### Model Sources  - **Repository:** https://github.com/microsoft/elem2design - **Paper:** https://arxiv.org/abs/2412.19712 ## Uses  ### Direct Use  Compose user-provided graphic elements (i.e., images and texts) into a pleasing graphic design. Elem2Design is being shared with the research community to facilitate reproduction of our results and foster further research in this area. ### Out-of-Scope Use  We do not recommend using Elem2Design in commercial or real-world applications without further testing and development. It is being released for research purposes. Use in any manner that violates applicable laws or regulations. ## Risks and Limitations  Elem2Design inherits any biases, errors, or omissions produced by its base model. Developers are advised to choose an appropriate base LLM/MLLM carefully, depending on the intended use case.  Elem2Design uses the Llama model. See https://huggingface.co/meta-llama/Llama-3.1-8B to understand the capabilities and limitations of this model.   As the model is fine-tuned on very specific data about design composition, it is unlikely to generate information other than position, color and font. However, this is possible. It is more likely to happen when instructions unrelated to graphic design composition, e.g., how has the social media influenced our daily life, are fed into the model. Graphic designs generated by Elem2Design may not be technically accurate or meet user specifications in all cases. Users are responsible for assessing the acceptability of generated content for each intended use case. Elem2Design was developed for research and experimental purposes. Further testing and validation are needed before considering its application in commercial or real-world scenarios. ### Recommendations  Please only provide the images and texts that you want to show on the graphic design to the model. Users are responsible for sourcing their content legally and ethically. This could include securing appropriate copy rights, ensuring consent for use of images of people, and/or the anonymization of data prior to use in research.   ## How to Get Started with the Model ``` python llava/infer/infer.py \ --model_name_or_path /path/to/model/checkpoint-xxxx \ --data_path /path/to/data/test.json \ --image_folder /path/to/crello_images \ --output_dir /path/to/output_dir \ --start_layer_index 0 \ --end_layer_index 4 ``` For more information, please visit our GitHub repo: https://github.com/microsoft/elem2design. ## Training Details ### Training Data  The training data is from an open-source dataset (https://huggingface.co/datasets/cyberagent/crello). ### Training Procedure  #### Preprocessing The training samples with more than 25 design elements are filtered out to maintain a limited sequence length and thereby improve training efficiency. #### Training Hyperparameters - Learning rate: 2e-4 - Global batch size: 128 - Number of training steps: 7000 - Rank and alpha of LoRA: 32 and 64 #### Speeds, Sizes, Times  - Llama-3.1-8B: 8B parameters - CLIP ViT-Large-Patch14: 428M parameters ## Evaluation  #### Testing Data  The testing data is from an open-source dataset (https://huggingface.co/datasets/cyberagent/crello). #### Metrics  - Overall metrics. We use a robust proxy model (https://huggingface.co/llava-hf/llava-onevision-qwen2-7b-ov-hf) for comprehensive evaluation from five aspects: (i) design and layout, (ii) content relevance, (iii) typography and color, (iv) graphics and images, and (v) innovation and originality. We use the same prompts as presented in COLE [[1](https://arxiv.org/abs/2311.16974)]. - Geometry-related metrics. These metrics focus purely on the geometric attributes of elements without considering their content, including element validity (Val), Overlap (Ove), Alignment (Ali) and underlay effectiveness (Undl, Unds) [[2](https://arxiv.org/abs/2303.15937 )][[3](https://arxiv.org/pdf/2404.00995 )]. ### Results We use prior work FlexDM [[1](https://arxiv.org/pdf/2303.18248)] and prompting GPT-4o [[2](https://platform.openai.com/docs/models#gpt-4o)] as baselines. In comparison, Elem2Design demonstrates superior performance across nearly all metrics. For example, on overall metrics, Elem2Design achieves 8.08, 7.92, 8.00, 7.82 and 6.98 on the evaluated five aspects. For another example, regarding geometry-related metrics, Elem2Design and FlexDM achieve Ove score of 0.0865 and 0.3242 respectively, indicting that Elem2Design effectively addresses the overlap issue whereas FlexDM encounters difficulties in this area. See Table 1 for the complete evaluation in our paper (https://arxiv.org/pdf/2412.19712) ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). ## Citation  ``` @InProceedings{lin2024elements, title={From Elements to Design: A Layered Approach for Automatic Graphic Design Composition}, author={Lin, Jiawei and Sun, Shizhao and Huang, Danqing and Liu, Ting and Li, Ji and Bian, Jiang}, booktitle={CVPR}, year={2025} } ``` ## Model Card Contact We welcome feedback and collaboration from our audience. If you have suggestions, questions, or observe unexpected/offensive behavior in our technology, please contact us at Shizhao Sun, [email protected]. If the team receives reports of undesired behavior or identifies issues independently, we will update this repository with appropriate mitigations.

stablediffusionapi/intorealismxl-v31

stablediffusionapi

2025-06-25T01:35:58Z

0

diffusers

[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2025-06-25T01:25:13Z

--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true pipeline_tag: text-to-image library_name: diffusers widget: - text: a girl wandering through the forest output: url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/39cc9d7d-1e27-4727-a0b2-8511224e7f32/width=1344/84048230.jpeg --- # IntoRealism XL - v3.1 API Inference <Gallery /> ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "intorealismxl-v31" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/intorealismxl-v31) Model link: [View model](https://modelslab.com/models/intorealismxl-v31) View all models: [View Models](https://modelslab.com/models) ```python import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "intorealismxl-v31", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "", "lora": "", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) ``` > Use this coupon code to get 25% off **DMGG0RBN**

New-videos-Gungun-Gupta-viral-video-Clips/FULL.VIDEO.Gungun.Gupta.Viral.Video.Tutorial.Official

New-videos-Gungun-Gupta-viral-video-Clips

2025-06-25T01:26:55Z

0

null

[ "region:us" ]

null

2025-06-25T01:26:42Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

zhiqing/Qwen3-Reranker-0.6B-ONNX

zhiqing

2025-06-25T01:26:30Z

383

3

transformers

[ "transformers", "onnx", "qwen3", "text-generation", "conversational", "base_model:Qwen/Qwen3-Reranker-0.6B", "base_model:quantized:Qwen/Qwen3-Reranker-0.6B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-09T02:32:51Z

--- license: apache-2.0 base_model: - Qwen/Qwen3-Reranker-0.6B library_name: transformers pipeline_tag: text-generation --- # Qwen3-Reranker-0.6B-ONNX **How to use** ```python from transformers import AutoTokenizer import onnxruntime as ort import numpy as np import torch from typing import List class Qwen3RerankerONNX: def __init__( self, model_path: str = "zhiqing/Qwen3-Reranker-0.6B-ONNX/model.onnx", tokenizer_dir: str = "zhiqing/Qwen3-Reranker-0.6B-ONNX", providers: List[str] = ("CUDAExecutionProvider", "CPUExecutionProvider"), max_length: int = 2048, ): self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir, padding_side="left") self.prefix = "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".'<|im_end|>\n<|im_start|>user\n" self.suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n" self.prefix_tokens = self.tokenizer.encode(self.prefix, add_special_tokens=False) self.suffix_tokens = self.tokenizer.encode(self.suffix, add_special_tokens=False) self.default_instruction = ( "Given a web search query, retrieve relevant passages that answer the query" ) self.max_length = max_length self.token_false_id = self.tokenizer.convert_tokens_to_ids("no") self.token_true_id = self.tokenizer.convert_tokens_to_ids("yes") self.session = ort.InferenceSession(model_path, providers=list(providers)) self.output_name = "logits" def _format_instruction(self, instruction: str, query: str, doc: str) -> str: inst = instruction if instruction is not None else self.default_instruction return f"<Instruct>: {inst}\n<Query>: {query}\n<Document>: {doc}" def _tokenize(self, pairs: List[str]): encoded = self.tokenizer( [self.prefix + s + self.suffix for s in pairs], padding=True, truncation="longest_first", max_length=self.max_length - len(self.prefix_tokens) - len(self.suffix_tokens), add_special_tokens=False, return_tensors="np", ) input_ids = encoded["input_ids"].astype(np.int64) attention_mask = encoded["attention_mask"].astype(np.int64) seq_len = input_ids.shape[1] position_ids = ( np.arange(seq_len, dtype=np.int64)[None, :].repeat(input_ids.shape[0], axis=0) ) return input_ids, attention_mask, position_ids def infer( self, queries: List[str], documents: List[str], instruction: str = None, ): if len(queries) == 1 and len(documents) > 1: queries = [queries[0]] * len(documents) elif len(queries) != len(documents): raise ValueError("The number of queries must be 1 or equal to the number of documents.") pairs = [ self._format_instruction(instruction, q, d) for q, d in zip(queries, documents) ] input_ids, attention_mask, position_ids = self._tokenize(pairs) ort_inputs = { "input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids, } logits_np = self.session.run([self.output_name], ort_inputs)[0] last_token_logits = torch.from_numpy(logits_np[:, -1, :]).float() false_logits = last_token_logits[:, self.token_false_id] true_logits = last_token_logits[:, self.token_true_id] probs = torch.softmax(torch.stack([false_logits, true_logits], dim=1), dim=1) scores_no = probs[:, 0].tolist() scores_yes = probs[:, 1].tolist() return scores_yes, scores_no ``` <img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen3.png" width="400"/> ## Highlights The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks No.1 in the MTEB multilingual leaderboard (as of June 5, 2025, score 70.58), while the reranking model excels in various text retrieval scenarios. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios. **Multilingual Capability**: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities. ## Model Overview **Qwen3-Reranker-0.6B** has the following features: - Model Type: Text Reranking - Supported Languages: 100+ Languages - Number of Paramaters: 0.6B - Context Length: 32k For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-embedding/), [GitHub](https://github.com/QwenLM/Qwen3-Embedding). ## Qwen3 Embedding Series Model list | Model Type | Models | Size | Layers | Sequence Length | Embedding Dimension | MRL Support | Instruction Aware | |------------------|----------------------|------|--------|-----------------|---------------------|-------------|----------------| | Text Embedding | [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) | 0.6B | 28 | 32K | 1024 | Yes | Yes | | Text Embedding | [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) | 4B | 36 | 32K | 2560 | Yes | Yes | | Text Embedding | [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) | 8B | 36 | 32K | 4096 | Yes | Yes | | Text Reranking | [Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) | 0.6B | 28 | 32K | - | - | Yes | | Text Reranking | [Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) | 4B | 36 | 32K | - | - | Yes | | Text Reranking | [Qwen3-Reranker-8B](https://huggingface.co/Qwen/Qwen3-Reranker-8B) | 8B | 36 | 32K | - | - | Yes | > **Note**: > - `MRL Support` indicates whether the embedding model supports custom dimensions for the final embedding. > - `Instruction Aware` notes whether the embedding or reranking model supports customizing the input instruction according to different tasks. > - Our evaluation indicates that, for most downstream tasks, using instructions (instruct) typically yields an improvement of 1% to 5% compared to not using them. Therefore, we recommend that developers create tailored instructions specific to their tasks and scenarios. In multilingual contexts, we also advise users to write their instructions in English, as most instructions utilized during the model training process were originally written in English. ## Usage With Transformers versions earlier than 4.51.0, you may encounter the following error: ``` KeyError: 'qwen3' ``` 📌 **Tip**: We recommend that developers customize the `instruct` according to their specific scenarios, tasks, and languages. Our tests have shown that in most retrieval scenarios, not using an `instruct` on the query side can lead to a drop in retrieval performance by approximately 1% to 5%. ## Evaluation | Model | Param | MTEB-R | CMTEB-R | MMTEB-R | MLDR | MTEB-Code | FollowIR | |------------------------------------|--------|---------|---------|---------|--------|-----------|----------| | **Qwen3-Embedding-0.6B** | 0.6B | 61.82 | 71.02 | 64.64 | 50.26 | 75.41 | 5.09 | | Jina-multilingual-reranker-v2-base | 0.3B | 58.22 | 63.37 | 63.73 | 39.66 | 58.98 | -0.68 | | gte-multilingual-reranker-base | 0.3B | 59.51 | 74.08 | 59.44 | 66.33 | 54.18 | -1.64 | | BGE-reranker-v2-m3 | 0.6B | 57.03 | 72.16 | 58.36 | 59.51 | 41.38 | -0.01 | | **Qwen3-Reranker-0.6B** | 0.6B | 65.80 | 71.31 | 66.36 | 67.28 | 73.42 | 5.41 | | **Qwen3-Reranker-4B** | 1.7B | **69.76** | 75.94 | 72.74 | 69.97 | 81.20 | **14.84** | | **Qwen3-Reranker-8B** | 8B | 69.02 | **77.45** | **72.94** | **70.19** | **81.22** | 8.05 | > **Note**: > - Evaluation results for reranking models. We use the retrieval subsets of MTEB(eng, v2), MTEB(cmn, v1), MMTEB and MTEB (Code), which are MTEB-R, CMTEB-R, MMTEB-R and MTEB-Code. > - All scores are our runs based on the top-100 candidates retrieved by dense embedding model [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3-embedding, title = {Qwen3-Embedding}, url = {https://qwenlm.github.io/blog/qwen3/}, author = {Qwen Team}, month = {May}, year = {2025} } ```

fangcaotank/task-10-Qwen-Qwen2.5-7B-Instruct-optuna

fangcaotank

2025-06-25T01:22:43Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]

null

2025-06-24T10:17:38Z

--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.12.0 - PEFT 0.13.2

Rojosan/classificador

Rojosan

2025-06-25T01:22:30Z

0

transformers

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

text-classification

2025-06-25T01:16:37Z

--- library_name: transformers tags: - autotrain - text-classification base_model: google-bert/bert-base-uncased widget: - text: "I love AutoTrain" --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.04549918696284294 f1_macro: 1.0 f1_micro: 1.0 f1_weighted: 1.0 precision_macro: 1.0 precision_micro: 1.0 precision_weighted: 1.0 recall_macro: 1.0 recall_micro: 1.0 recall_weighted: 1.0 accuracy: 1.0

tanny2109/llamaToxic05

tanny2109

2025-06-25T01:22:11Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]

null

2025-06-25T01:21:02Z

--- base_model: meta-llama/Meta-Llama-3.1-8B-Instruct library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2

donoway/l0nupcvl_20250623_233420

donoway

2025-06-25T01:22:08Z

0

peft

[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:adapter:meta-llama/Llama-3.2-1B", "license:llama3.2", "region:us" ]

null

2025-06-25T01:22:05Z

--- library_name: peft license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: l0nupcvl_20250623_233420 results: [] ---  # l0nupcvl_20250623_233420 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8677 - Model Preparation Time: 0.0121 - Move Accuracy: 0.1838 - Token Accuracy: 0.6621 - Accuracy: 0.1838 ## 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: 0.003 - train_batch_size: 128 - eval_batch_size: 256 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Move Accuracy | Token Accuracy | Accuracy | |:-------------:|:------:|:------:|:---------------:|:----------------------:|:-------------:|:--------------:|:--------:| | No log | 0 | 0 | 6.4123 | 0.0121 | 0.0 | 0.1049 | 0.0 | | 1.8134 | 0.0098 | 100 | 1.8386 | 0.0121 | 0.0024 | 0.2653 | 0.0024 | | 1.7692 | 0.0196 | 200 | 1.7239 | 0.0121 | 0.0047 | 0.3117 | 0.0047 | | 1.6779 | 0.0295 | 300 | 1.6996 | 0.0121 | 0.0090 | 0.3341 | 0.0090 | | 1.626 | 0.0393 | 400 | 1.6646 | 0.0121 | 0.0155 | 0.3515 | 0.0155 | | 1.6473 | 0.0491 | 500 | 1.6401 | 0.0121 | 0.0177 | 0.3677 | 0.0177 | | 1.5746 | 0.0589 | 600 | 1.6117 | 0.0121 | 0.0213 | 0.3739 | 0.0213 | | 1.6295 | 0.0687 | 700 | 1.6012 | 0.0121 | 0.0213 | 0.3784 | 0.0213 | | 1.6309 | 0.0785 | 800 | 1.6188 | 0.0121 | 0.0213 | 0.3714 | 0.0213 | | 1.6113 | 0.0884 | 900 | 1.5729 | 0.0121 | 0.0264 | 0.3849 | 0.0264 | | 1.5866 | 0.0982 | 1000 | 1.5754 | 0.0121 | 0.0268 | 0.3817 | 0.0268 | | 1.4901 | 0.1080 | 1100 | 1.5613 | 0.0121 | 0.0335 | 0.3907 | 0.0335 | | 1.5038 | 0.1178 | 1200 | 1.5597 | 0.0121 | 0.0307 | 0.3920 | 0.0307 | | 1.5518 | 0.1276 | 1300 | 1.5826 | 0.0121 | 0.0246 | 0.3877 | 0.0246 | | 1.5581 | 0.1374 | 1400 | 1.5264 | 0.0121 | 0.0353 | 0.4067 | 0.0353 | | 1.4864 | 0.1473 | 1500 | 1.5148 | 0.0121 | 0.0371 | 0.4159 | 0.0371 | | 1.4902 | 0.1571 | 1600 | 1.4866 | 0.0121 | 0.0414 | 0.4229 | 0.0414 | | 1.4227 | 0.1669 | 1700 | 1.4785 | 0.0121 | 0.0402 | 0.4306 | 0.0402 | | 1.4888 | 0.1767 | 1800 | 1.4807 | 0.0121 | 0.0362 | 0.4273 | 0.0362 | | 1.4003 | 0.1865 | 1900 | 1.4640 | 0.0121 | 0.0431 | 0.4345 | 0.0431 | | 1.3846 | 0.1963 | 2000 | 1.4368 | 0.0121 | 0.0429 | 0.4426 | 0.0429 | | 1.4298 | 0.2062 | 2100 | 1.4343 | 0.0121 | 0.0477 | 0.4420 | 0.0477 | | 1.4368 | 0.2160 | 2200 | 1.4206 | 0.0121 | 0.0533 | 0.4530 | 0.0533 | | 1.432 | 0.2258 | 2300 | 1.4048 | 0.0121 | 0.0489 | 0.4569 | 0.0489 | | 1.3462 | 0.2356 | 2400 | 1.4218 | 0.0121 | 0.0434 | 0.4477 | 0.0434 | | 1.3158 | 0.2454 | 2500 | 1.4085 | 0.0121 | 0.0479 | 0.4554 | 0.0479 | | 1.3674 | 0.2553 | 2600 | 1.3735 | 0.0121 | 0.0558 | 0.4709 | 0.0558 | | 1.3556 | 0.2651 | 2700 | 1.3663 | 0.0121 | 0.0576 | 0.4759 | 0.0576 | | 1.3193 | 0.2749 | 2800 | 1.3417 | 0.0121 | 0.0595 | 0.4813 | 0.0595 | | 1.3479 | 0.2847 | 2900 | 1.3414 | 0.0121 | 0.0602 | 0.4799 | 0.0602 | | 1.3024 | 0.2945 | 3000 | 1.3114 | 0.0121 | 0.0705 | 0.4954 | 0.0705 | | 1.3576 | 0.3043 | 3100 | 1.3098 | 0.0121 | 0.0639 | 0.4942 | 0.0639 | | 1.3043 | 0.3142 | 3200 | 1.2850 | 0.0121 | 0.0730 | 0.5077 | 0.0730 | | 1.2639 | 0.3240 | 3300 | 1.2703 | 0.0121 | 0.0727 | 0.5114 | 0.0727 | | 1.2676 | 0.3338 | 3400 | 1.2552 | 0.0121 | 0.0759 | 0.5170 | 0.0759 | | 1.2058 | 0.3436 | 3500 | 1.2532 | 0.0121 | 0.0743 | 0.5149 | 0.0743 | | 1.2568 | 0.3534 | 3600 | 1.2460 | 0.0121 | 0.0775 | 0.5223 | 0.0775 | | 1.1544 | 0.3632 | 3700 | 1.2190 | 0.0121 | 0.0804 | 0.5309 | 0.0804 | | 1.1985 | 0.3731 | 3800 | 1.2199 | 0.0121 | 0.0832 | 0.5322 | 0.0832 | | 1.1214 | 0.3829 | 3900 | 1.1944 | 0.0121 | 0.0896 | 0.5432 | 0.0896 | | 1.2423 | 0.3927 | 4000 | 1.2111 | 0.0121 | 0.0803 | 0.5363 | 0.0803 | | 1.1277 | 0.4025 | 4100 | 1.1957 | 0.0121 | 0.0867 | 0.5406 | 0.0867 | | 1.1588 | 0.4123 | 4200 | 1.2106 | 0.0121 | 0.0821 | 0.5381 | 0.0821 | | 1.1443 | 0.4221 | 4300 | 1.1801 | 0.0121 | 0.0833 | 0.5478 | 0.0833 | | 1.215 | 0.4320 | 4400 | 1.1494 | 0.0121 | 0.0955 | 0.5557 | 0.0955 | | 1.1168 | 0.4418 | 4500 | 1.1449 | 0.0121 | 0.1079 | 0.5616 | 0.1079 | | 1.1143 | 0.4516 | 4600 | 1.1396 | 0.0121 | 0.1049 | 0.5636 | 0.1049 | | 1.1495 | 0.4614 | 4700 | 1.1196 | 0.0121 | 0.1021 | 0.5709 | 0.1021 | | 1.1388 | 0.4712 | 4800 | 1.1307 | 0.0121 | 0.1017 | 0.5660 | 0.1017 | | 1.1701 | 0.4811 | 4900 | 1.1317 | 0.0121 | 0.1029 | 0.5663 | 0.1029 | | 1.038 | 0.4909 | 5000 | 1.1348 | 0.0121 | 0.1086 | 0.5656 | 0.1086 | | 1.176 | 0.5007 | 5100 | 1.1275 | 0.0121 | 0.1006 | 0.5676 | 0.1006 | | 1.1175 | 0.5105 | 5200 | 1.1255 | 0.0121 | 0.1009 | 0.5697 | 0.1009 | | 1.101 | 0.5203 | 5300 | 1.1134 | 0.0121 | 0.1024 | 0.5725 | 0.1024 | | 1.0745 | 0.5301 | 5400 | 1.1158 | 0.0121 | 0.1079 | 0.5739 | 0.1079 | | 1.1114 | 0.5400 | 5500 | 1.1041 | 0.0121 | 0.1125 | 0.5784 | 0.1125 | | 1.1863 | 0.5498 | 5600 | 1.1140 | 0.0121 | 0.1081 | 0.5753 | 0.1081 | | 1.0536 | 0.5596 | 5700 | 1.0964 | 0.0121 | 0.1096 | 0.5799 | 0.1096 | | 1.1317 | 0.5694 | 5800 | 1.0854 | 0.0121 | 0.1160 | 0.5854 | 0.1160 | | 1.1592 | 0.5792 | 5900 | 1.0925 | 0.0121 | 0.1056 | 0.5792 | 0.1056 | | 1.0494 | 0.5890 | 6000 | 1.0837 | 0.0121 | 0.1138 | 0.5852 | 0.1138 | | 1.1148 | 0.5989 | 6100 | 1.0965 | 0.0121 | 0.1039 | 0.5828 | 0.1039 | | 1.1007 | 0.6087 | 6200 | 1.0683 | 0.0121 | 0.1138 | 0.5858 | 0.1138 | | 1.073 | 0.6185 | 6300 | 1.1008 | 0.0121 | 0.1043 | 0.5776 | 0.1043 | | 1.0281 | 0.6283 | 6400 | 1.0673 | 0.0121 | 0.1125 | 0.5895 | 0.1125 | | 1.1181 | 0.6381 | 6500 | 1.0736 | 0.0121 | 0.1197 | 0.5951 | 0.1197 | | 1.1005 | 0.6479 | 6600 | 1.0828 | 0.0121 | 0.1134 | 0.5847 | 0.1134 | | 1.008 | 0.6578 | 6700 | 1.0594 | 0.0121 | 0.1176 | 0.5935 | 0.1176 | | 1.0802 | 0.6676 | 6800 | 1.0700 | 0.0121 | 0.1160 | 0.5873 | 0.1160 | | 1.057 | 0.6774 | 6900 | 1.0603 | 0.0121 | 0.1154 | 0.5924 | 0.1154 | | 1.0362 | 0.6872 | 7000 | 1.0496 | 0.0121 | 0.1204 | 0.5974 | 0.1204 | | 1.1061 | 0.6970 | 7100 | 1.0542 | 0.0121 | 0.1140 | 0.5903 | 0.1140 | | 0.9967 | 0.7069 | 7200 | 1.0405 | 0.0121 | 0.1292 | 0.6027 | 0.1292 | | 0.9899 | 0.7167 | 7300 | 1.0427 | 0.0121 | 0.1300 | 0.6013 | 0.1300 | | 1.0461 | 0.7265 | 7400 | 1.0622 | 0.0121 | 0.1195 | 0.5928 | 0.1195 | | 1.0 | 0.7363 | 7500 | 1.0679 | 0.0121 | 0.1198 | 0.5913 | 0.1198 | | 1.0434 | 0.7461 | 7600 | 1.0331 | 0.0121 | 0.1249 | 0.6000 | 0.1249 | | 1.1196 | 0.7559 | 7700 | 1.0513 | 0.0121 | 0.1225 | 0.5969 | 0.1225 | | 1.0365 | 0.7658 | 7800 | 1.0457 | 0.0121 | 0.1163 | 0.5982 | 0.1163 | | 1.0007 | 0.7756 | 7900 | 1.0389 | 0.0121 | 0.1249 | 0.6003 | 0.1249 | | 1.0079 | 0.7854 | 8000 | 1.0427 | 0.0121 | 0.1220 | 0.6008 | 0.1220 | | 1.0649 | 0.7952 | 8100 | 1.0577 | 0.0121 | 0.1201 | 0.5949 | 0.1201 | | 1.0393 | 0.8050 | 8200 | 1.0503 | 0.0121 | 0.1239 | 0.5942 | 0.1239 | | 1.0037 | 0.8148 | 8300 | 1.0440 | 0.0121 | 0.1223 | 0.5970 | 0.1223 | | 1.0126 | 0.8247 | 8400 | 1.0230 | 0.0121 | 0.1252 | 0.6057 | 0.1252 | | 0.9743 | 0.8345 | 8500 | 1.0123 | 0.0121 | 0.1339 | 0.6117 | 0.1339 | | 1.0477 | 0.8443 | 8600 | 1.0315 | 0.0121 | 0.1240 | 0.6062 | 0.1240 | | 1.0048 | 0.8541 | 8700 | 1.0204 | 0.0121 | 0.1296 | 0.6055 | 0.1296 | | 1.0247 | 0.8639 | 8800 | 1.0482 | 0.0121 | 0.1210 | 0.5993 | 0.1210 | | 1.0746 | 0.8737 | 8900 | 1.0367 | 0.0121 | 0.1231 | 0.6024 | 0.1231 | | 1.0519 | 0.8836 | 9000 | 1.0341 | 0.0121 | 0.1264 | 0.6026 | 0.1264 | | 0.9807 | 0.8934 | 9100 | 1.0475 | 0.0121 | 0.1215 | 0.5967 | 0.1215 | | 1.0206 | 0.9032 | 9200 | 1.0202 | 0.0121 | 0.1361 | 0.6079 | 0.1361 | | 1.0404 | 0.9130 | 9300 | 1.0219 | 0.0121 | 0.1305 | 0.6105 | 0.1305 | | 0.9316 | 0.9228 | 9400 | 1.0256 | 0.0121 | 0.1281 | 0.6045 | 0.1281 | | 1.0496 | 0.9327 | 9500 | 1.0093 | 0.0121 | 0.1351 | 0.6138 | 0.1351 | | 1.048 | 0.9425 | 9600 | 1.0273 | 0.0121 | 0.1315 | 0.6058 | 0.1315 | | 1.0517 | 0.9523 | 9700 | 1.0251 | 0.0121 | 0.1283 | 0.6040 | 0.1283 | | 0.9922 | 0.9621 | 9800 | 1.0176 | 0.0121 | 0.1330 | 0.6091 | 0.1330 | | 1.0489 | 0.9719 | 9900 | 1.0010 | 0.0121 | 0.1374 | 0.6174 | 0.1374 | | 1.001 | 0.9817 | 10000 | 1.0232 | 0.0121 | 0.1191 | 0.6050 | 0.1191 | | 1.0379 | 0.9916 | 10100 | 1.0070 | 0.0121 | 0.1314 | 0.6116 | 0.1314 | | 0.9638 | 1.0014 | 10200 | 1.0107 | 0.0121 | 0.1379 | 0.6134 | 0.1379 | | 1.0171 | 1.0112 | 10300 | 1.0011 | 0.0121 | 0.1374 | 0.6132 | 0.1374 | | 1.0563 | 1.0210 | 10400 | 1.0131 | 0.0121 | 0.1354 | 0.6099 | 0.1354 | | 0.9535 | 1.0308 | 10500 | 1.0031 | 0.0121 | 0.1426 | 0.6157 | 0.1426 | | 1.0147 | 1.0406 | 10600 | 0.9979 | 0.0121 | 0.1428 | 0.6192 | 0.1428 | | 0.9887 | 1.0505 | 10700 | 0.9860 | 0.0121 | 0.1468 | 0.6231 | 0.1468 | | 0.96 | 1.0603 | 10800 | 0.9923 | 0.0121 | 0.1414 | 0.6188 | 0.1414 | | 1.019 | 1.0701 | 10900 | 0.9954 | 0.0121 | 0.1402 | 0.6199 | 0.1402 | | 1.0251 | 1.0799 | 11000 | 1.0069 | 0.0121 | 0.1338 | 0.6098 | 0.1338 | | 1.0115 | 1.0897 | 11100 | 1.0059 | 0.0121 | 0.1314 | 0.6142 | 0.1314 | | 1.117 | 1.0995 | 11200 | 1.0123 | 0.0121 | 0.1337 | 0.6132 | 0.1337 | | 1.0679 | 1.1094 | 11300 | 0.9997 | 0.0121 | 0.1284 | 0.6158 | 0.1284 | | 0.9931 | 1.1192 | 11400 | 1.0009 | 0.0121 | 0.1399 | 0.6153 | 0.1399 | | 0.9517 | 1.1290 | 11500 | 0.9963 | 0.0121 | 0.1386 | 0.6183 | 0.1386 | | 1.0001 | 1.1388 | 11600 | 0.9802 | 0.0121 | 0.1403 | 0.6220 | 0.1403 | | 1.0511 | 1.1486 | 11700 | 0.9988 | 0.0121 | 0.1293 | 0.6131 | 0.1293 | | 0.9338 | 1.1585 | 11800 | 0.9964 | 0.0121 | 0.1368 | 0.6152 | 0.1368 | | 1.0301 | 1.1683 | 11900 | 0.9983 | 0.0121 | 0.1347 | 0.6168 | 0.1347 | | 0.9509 | 1.1781 | 12000 | 0.9915 | 0.0121 | 0.1410 | 0.6199 | 0.1410 | | 1.0277 | 1.1879 | 12100 | 0.9802 | 0.0121 | 0.1450 | 0.6224 | 0.1450 | | 0.8848 | 1.1977 | 12200 | 0.9875 | 0.0121 | 0.1401 | 0.6213 | 0.1401 | | 0.9449 | 1.2075 | 12300 | 0.9951 | 0.0121 | 0.1410 | 0.6181 | 0.1410 | | 0.9625 | 1.2174 | 12400 | 0.9794 | 0.0121 | 0.1434 | 0.6243 | 0.1434 | | 1.0681 | 1.2272 | 12500 | 0.9966 | 0.0121 | 0.1361 | 0.6185 | 0.1361 | | 0.9168 | 1.2370 | 12600 | 0.9991 | 0.0121 | 0.1346 | 0.6184 | 0.1346 | | 0.967 | 1.2468 | 12700 | 0.9908 | 0.0121 | 0.1353 | 0.6200 | 0.1353 | | 1.0241 | 1.2566 | 12800 | 0.9971 | 0.0121 | 0.1462 | 0.6214 | 0.1462 | | 0.993 | 1.2664 | 12900 | 0.9854 | 0.0121 | 0.1417 | 0.6244 | 0.1417 | | 0.9463 | 1.2763 | 13000 | 0.9922 | 0.0121 | 0.1321 | 0.6154 | 0.1321 | | 1.0137 | 1.2861 | 13100 | 1.0065 | 0.0121 | 0.1323 | 0.6179 | 0.1323 | | 1.0213 | 1.2959 | 13200 | 0.9849 | 0.0121 | 0.1358 | 0.6204 | 0.1358 | | 0.9703 | 1.3057 | 13300 | 0.9956 | 0.0121 | 0.1368 | 0.6178 | 0.1368 | | 0.9907 | 1.3155 | 13400 | 0.9831 | 0.0121 | 0.1434 | 0.6218 | 0.1434 | | 0.9598 | 1.3253 | 13500 | 0.9821 | 0.0121 | 0.1403 | 0.6233 | 0.1403 | | 0.9971 | 1.3352 | 13600 | 0.9948 | 0.0121 | 0.1385 | 0.6155 | 0.1385 | | 0.9906 | 1.3450 | 13700 | 0.9914 | 0.0121 | 0.1327 | 0.6210 | 0.1327 | | 1.0052 | 1.3548 | 13800 | 0.9698 | 0.0121 | 0.1428 | 0.6295 | 0.1428 | | 0.9364 | 1.3646 | 13900 | 0.9682 | 0.0121 | 0.1494 | 0.6284 | 0.1494 | | 0.9855 | 1.3744 | 14000 | 0.9829 | 0.0121 | 0.1354 | 0.6193 | 0.1354 | | 1.0504 | 1.3843 | 14100 | 1.0161 | 0.0121 | 0.1326 | 0.6110 | 0.1326 | | 0.9568 | 1.3941 | 14200 | 0.9870 | 0.0121 | 0.1469 | 0.6204 | 0.1469 | | 0.9925 | 1.4039 | 14300 | 0.9874 | 0.0121 | 0.1435 | 0.6187 | 0.1435 | | 0.9385 | 1.4137 | 14400 | 0.9771 | 0.0121 | 0.1435 | 0.6235 | 0.1435 | | 1.0083 | 1.4235 | 14500 | 0.9787 | 0.0121 | 0.1370 | 0.6225 | 0.1370 | | 1.0438 | 1.4333 | 14600 | 0.9856 | 0.0121 | 0.1295 | 0.6157 | 0.1295 | | 0.9362 | 1.4432 | 14700 | 0.9799 | 0.0121 | 0.1456 | 0.6255 | 0.1456 | | 0.97 | 1.4530 | 14800 | 0.9902 | 0.0121 | 0.1388 | 0.6190 | 0.1388 | | 1.0245 | 1.4628 | 14900 | 1.0147 | 0.0121 | 0.1354 | 0.6148 | 0.1354 | | 0.9025 | 1.4726 | 15000 | 0.9620 | 0.0121 | 0.1465 | 0.6282 | 0.1465 | | 0.981 | 1.4824 | 15100 | 0.9800 | 0.0121 | 0.1432 | 0.6198 | 0.1432 | | 0.9617 | 1.4922 | 15200 | 0.9749 | 0.0121 | 0.1438 | 0.6253 | 0.1438 | | 0.9422 | 1.5021 | 15300 | 0.9753 | 0.0121 | 0.1438 | 0.6235 | 0.1438 | | 1.0265 | 1.5119 | 15400 | 0.9643 | 0.0121 | 0.1471 | 0.6279 | 0.1471 | | 1.0726 | 1.5217 | 15500 | 0.9658 | 0.0121 | 0.1452 | 0.6290 | 0.1452 | | 0.9245 | 1.5315 | 15600 | 0.9633 | 0.0121 | 0.1534 | 0.6294 | 0.1534 | | 1.016 | 1.5413 | 15700 | 0.9809 | 0.0121 | 0.1421 | 0.6215 | 0.1421 | | 0.9818 | 1.5511 | 15800 | 0.9862 | 0.0121 | 0.1377 | 0.6172 | 0.1377 | | 0.9433 | 1.5610 | 15900 | 0.9748 | 0.0121 | 0.1408 | 0.6218 | 0.1408 | | 0.9866 | 1.5708 | 16000 | 0.9823 | 0.0121 | 0.1495 | 0.6269 | 0.1495 | | 0.9928 | 1.5806 | 16100 | 0.9679 | 0.0121 | 0.1510 | 0.6298 | 0.1510 | | 0.97 | 1.5904 | 16200 | 0.9827 | 0.0121 | 0.1355 | 0.6177 | 0.1355 | | 0.9551 | 1.6002 | 16300 | 0.9803 | 0.0121 | 0.1394 | 0.6240 | 0.1394 | | 1.0441 | 1.6101 | 16400 | 0.9890 | 0.0121 | 0.1356 | 0.6198 | 0.1356 | | 1.0044 | 1.6199 | 16500 | 0.9757 | 0.0121 | 0.1393 | 0.6208 | 0.1393 | | 0.9966 | 1.6297 | 16600 | 0.9595 | 0.0121 | 0.1484 | 0.6307 | 0.1484 | | 1.0272 | 1.6395 | 16700 | 0.9787 | 0.0121 | 0.1463 | 0.6230 | 0.1463 | | 0.9615 | 1.6493 | 16800 | 0.9805 | 0.0121 | 0.1370 | 0.6180 | 0.1370 | | 0.8994 | 1.6591 | 16900 | 0.9773 | 0.0121 | 0.1526 | 0.6230 | 0.1526 | | 0.9124 | 1.6690 | 17000 | 0.9852 | 0.0121 | 0.1455 | 0.6219 | 0.1455 | | 0.9764 | 1.6788 | 17100 | 0.9638 | 0.0121 | 0.1475 | 0.6296 | 0.1475 | | 0.9907 | 1.6886 | 17200 | 0.9952 | 0.0121 | 0.1381 | 0.6190 | 0.1381 | | 0.939 | 1.6984 | 17300 | 0.9960 | 0.0121 | 0.1305 | 0.6135 | 0.1305 | | 0.8737 | 1.7082 | 17400 | 0.9657 | 0.0121 | 0.1461 | 0.6229 | 0.1461 | | 0.9767 | 1.7180 | 17500 | 0.9627 | 0.0121 | 0.1400 | 0.6245 | 0.1400 | | 0.958 | 1.7279 | 17600 | 0.9632 | 0.0121 | 0.1461 | 0.6273 | 0.1461 | | 1.0461 | 1.7377 | 17700 | 0.9866 | 0.0121 | 0.1463 | 0.6216 | 0.1463 | | 1.0028 | 1.7475 | 17800 | 0.9834 | 0.0121 | 0.1375 | 0.6227 | 0.1375 | | 1.0034 | 1.7573 | 17900 | 0.9781 | 0.0121 | 0.1390 | 0.6218 | 0.1390 | | 0.9821 | 1.7671 | 18000 | 0.9592 | 0.0121 | 0.1484 | 0.6289 | 0.1484 | | 1.0287 | 1.7769 | 18100 | 0.9725 | 0.0121 | 0.1470 | 0.6254 | 0.1470 | | 0.9723 | 1.7868 | 18200 | 0.9728 | 0.0121 | 0.1477 | 0.6240 | 0.1477 | | 1.0263 | 1.7966 | 18300 | 0.9656 | 0.0121 | 0.1412 | 0.6257 | 0.1412 | | 0.9584 | 1.8064 | 18400 | 0.9598 | 0.0121 | 0.1470 | 0.6322 | 0.1470 | | 0.9164 | 1.8162 | 18500 | 0.9788 | 0.0121 | 0.1453 | 0.6255 | 0.1453 | | 0.9565 | 1.8260 | 18600 | 0.9697 | 0.0121 | 0.1504 | 0.6283 | 0.1504 | | 0.9243 | 1.8359 | 18700 | 0.9401 | 0.0121 | 0.1516 | 0.6342 | 0.1516 | | 0.8968 | 1.8457 | 18800 | 0.9777 | 0.0121 | 0.1437 | 0.6219 | 0.1437 | | 0.9723 | 1.8555 | 18900 | 0.9620 | 0.0121 | 0.1444 | 0.6256 | 0.1444 | | 0.9894 | 1.8653 | 19000 | 0.9724 | 0.0121 | 0.1409 | 0.6211 | 0.1409 | | 0.9166 | 1.8751 | 19100 | 0.9666 | 0.0121 | 0.1453 | 0.6270 | 0.1453 | | 0.9474 | 1.8849 | 19200 | 0.9608 | 0.0121 | 0.1485 | 0.6305 | 0.1485 | | 1.0545 | 1.8948 | 19300 | 0.9621 | 0.0121 | 0.1486 | 0.6277 | 0.1486 | | 0.9987 | 1.9046 | 19400 | 0.9767 | 0.0121 | 0.1481 | 0.6262 | 0.1481 | | 0.9155 | 1.9144 | 19500 | 0.9611 | 0.0121 | 0.1478 | 0.6287 | 0.1478 | | 0.9585 | 1.9242 | 19600 | 0.9675 | 0.0121 | 0.1488 | 0.6263 | 0.1488 | | 1.0011 | 1.9340 | 19700 | 0.9921 | 0.0121 | 0.1326 | 0.6175 | 0.1326 | | 0.9271 | 1.9438 | 19800 | 0.9526 | 0.0121 | 0.1526 | 0.6305 | 0.1526 | | 0.9035 | 1.9537 | 19900 | 0.9507 | 0.0121 | 0.1556 | 0.6322 | 0.1556 | | 0.8893 | 1.9635 | 20000 | 0.9448 | 0.0121 | 0.1499 | 0.6318 | 0.1499 | | 0.915 | 1.9733 | 20100 | 0.9794 | 0.0121 | 0.1490 | 0.6225 | 0.1490 | | 0.9985 | 1.9831 | 20200 | 0.9754 | 0.0121 | 0.1388 | 0.6213 | 0.1388 | | 0.8801 | 1.9929 | 20300 | 0.9533 | 0.0121 | 0.1501 | 0.6296 | 0.1501 | | 0.9559 | 2.0027 | 20400 | 0.9712 | 0.0121 | 0.1423 | 0.6289 | 0.1423 | | 0.9222 | 2.0126 | 20500 | 0.9513 | 0.0121 | 0.1461 | 0.6321 | 0.1461 | | 0.9745 | 2.0224 | 20600 | 0.9815 | 0.0121 | 0.1490 | 0.6290 | 0.1490 | | 0.8946 | 2.0322 | 20700 | 0.9743 | 0.0121 | 0.1403 | 0.6209 | 0.1403 | | 0.9683 | 2.0420 | 20800 | 0.9473 | 0.0121 | 0.1461 | 0.6330 | 0.1461 | | 0.9867 | 2.0518 | 20900 | 0.9475 | 0.0121 | 0.1463 | 0.6332 | 0.1463 | | 0.8907 | 2.0617 | 21000 | 0.9526 | 0.0121 | 0.1528 | 0.6348 | 0.1528 | | 0.9788 | 2.0715 | 21100 | 0.9630 | 0.0121 | 0.1477 | 0.6275 | 0.1477 | | 1.0092 | 2.0813 | 21200 | 0.9736 | 0.0121 | 0.1510 | 0.6248 | 0.1510 | | 1.0112 | 2.0911 | 21300 | 0.9526 | 0.0121 | 0.1514 | 0.6311 | 0.1514 | | 0.9428 | 2.1009 | 21400 | 0.9563 | 0.0121 | 0.1537 | 0.6324 | 0.1537 | | 0.9599 | 2.1107 | 21500 | 0.9540 | 0.0121 | 0.1490 | 0.6273 | 0.1490 | | 0.8925 | 2.1206 | 21600 | 0.9555 | 0.0121 | 0.1442 | 0.6281 | 0.1442 | | 1.0038 | 2.1304 | 21700 | 0.9669 | 0.0121 | 0.1448 | 0.6274 | 0.1448 | | 0.9397 | 2.1402 | 21800 | 0.9381 | 0.0121 | 0.1552 | 0.6359 | 0.1552 | | 0.9569 | 2.1500 | 21900 | 0.9469 | 0.0121 | 0.1505 | 0.6325 | 0.1505 | | 0.9395 | 2.1598 | 22000 | 0.9608 | 0.0121 | 0.1470 | 0.6275 | 0.1470 | | 0.9211 | 2.1696 | 22100 | 0.9482 | 0.0121 | 0.1528 | 0.6315 | 0.1528 | | 0.8696 | 2.1795 | 22200 | 0.9493 | 0.0121 | 0.1527 | 0.6325 | 0.1527 | | 0.9608 | 2.1893 | 22300 | 0.9597 | 0.0121 | 0.1480 | 0.6302 | 0.1480 | | 1.02 | 2.1991 | 22400 | 0.9853 | 0.0121 | 0.1378 | 0.6196 | 0.1378 | | 0.9805 | 2.2089 | 22500 | 0.9694 | 0.0121 | 0.1372 | 0.6228 | 0.1372 | | 0.9496 | 2.2187 | 22600 | 0.9381 | 0.0121 | 0.1544 | 0.6351 | 0.1544 | | 0.9892 | 2.2285 | 22700 | 0.9810 | 0.0121 | 0.1429 | 0.6232 | 0.1429 | | 0.9565 | 2.2384 | 22800 | 0.9567 | 0.0121 | 0.1454 | 0.6310 | 0.1454 | | 0.9254 | 2.2482 | 22900 | 0.9529 | 0.0121 | 0.1510 | 0.6291 | 0.1510 | | 0.9032 | 2.2580 | 23000 | 0.9515 | 0.0121 | 0.1479 | 0.6335 | 0.1479 | | 0.9845 | 2.2678 | 23100 | 0.9451 | 0.0121 | 0.1549 | 0.6383 | 0.1549 | | 0.9407 | 2.2776 | 23200 | 0.9483 | 0.0121 | 0.1472 | 0.6336 | 0.1472 | | 1.032 | 2.2875 | 23300 | 0.9775 | 0.0121 | 0.1359 | 0.6237 | 0.1359 | | 0.9924 | 2.2973 | 23400 | 0.9406 | 0.0121 | 0.1495 | 0.6336 | 0.1495 | | 0.9701 | 2.3071 | 23500 | 0.9443 | 0.0121 | 0.1499 | 0.6353 | 0.1499 | | 1.0408 | 2.3169 | 23600 | 0.9481 | 0.0121 | 0.1498 | 0.6313 | 0.1498 | | 0.9238 | 2.3267 | 23700 | 0.9537 | 0.0121 | 0.1548 | 0.6313 | 0.1548 | | 0.9766 | 2.3365 | 23800 | 0.9444 | 0.0121 | 0.1537 | 0.6363 | 0.1537 | | 0.9323 | 2.3464 | 23900 | 0.9402 | 0.0121 | 0.1505 | 0.6321 | 0.1505 | | 0.9244 | 2.3562 | 24000 | 0.9423 | 0.0121 | 0.1504 | 0.6372 | 0.1504 | | 0.936 | 2.3660 | 24100 | 0.9427 | 0.0121 | 0.1544 | 0.6351 | 0.1544 | | 1.0031 | 2.3758 | 24200 | 0.9476 | 0.0121 | 0.1572 | 0.6383 | 0.1572 | | 0.9001 | 2.3856 | 24300 | 0.9370 | 0.0121 | 0.1438 | 0.6322 | 0.1438 | | 0.9784 | 2.3954 | 24400 | 0.9549 | 0.0121 | 0.1479 | 0.6304 | 0.1479 | | 0.8829 | 2.4053 | 24500 | 0.9265 | 0.0121 | 0.1594 | 0.6419 | 0.1594 | | 0.923 | 2.4151 | 24600 | 0.9429 | 0.0121 | 0.1504 | 0.6353 | 0.1504 | | 0.9826 | 2.4249 | 24700 | 0.9405 | 0.0121 | 0.1546 | 0.6338 | 0.1546 | | 0.9287 | 2.4347 | 24800 | 0.9376 | 0.0121 | 0.1558 | 0.6381 | 0.1558 | | 0.9229 | 2.4445 | 24900 | 0.9317 | 0.0121 | 0.1618 | 0.6399 | 0.1618 | | 0.9156 | 2.4543 | 25000 | 0.9436 | 0.0121 | 0.1506 | 0.6331 | 0.1506 | | 0.9068 | 2.4642 | 25100 | 0.9839 | 0.0121 | 0.1399 | 0.6214 | 0.1399 | | 0.9131 | 2.4740 | 25200 | 0.9196 | 0.0121 | 0.1576 | 0.6414 | 0.1576 | | 0.8854 | 2.4838 | 25300 | 0.9454 | 0.0121 | 0.1521 | 0.6343 | 0.1521 | | 0.9291 | 2.4936 | 25400 | 0.9533 | 0.0121 | 0.1402 | 0.6274 | 0.1402 | | 0.9474 | 2.5034 | 25500 | 0.9558 | 0.0121 | 0.1526 | 0.6311 | 0.1526 | | 0.9155 | 2.5133 | 25600 | 0.9469 | 0.0121 | 0.1607 | 0.6360 | 0.1607 | | 0.9235 | 2.5231 | 25700 | 0.9403 | 0.0121 | 0.1492 | 0.6339 | 0.1492 | | 0.9101 | 2.5329 | 25800 | 0.9544 | 0.0121 | 0.1438 | 0.6291 | 0.1438 | | 0.9359 | 2.5427 | 25900 | 0.9438 | 0.0121 | 0.1539 | 0.6382 | 0.1539 | | 0.8916 | 2.5525 | 26000 | 0.9364 | 0.0121 | 0.1507 | 0.6349 | 0.1507 | | 0.8944 | 2.5623 | 26100 | 0.9561 | 0.0121 | 0.1404 | 0.6297 | 0.1404 | | 0.9263 | 2.5722 | 26200 | 0.9661 | 0.0121 | 0.1534 | 0.6310 | 0.1534 | | 0.9523 | 2.5820 | 26300 | 0.9413 | 0.0121 | 0.1470 | 0.6315 | 0.1470 | | 0.9576 | 2.5918 | 26400 | 0.9706 | 0.0121 | 0.1462 | 0.6226 | 0.1462 | | 0.9309 | 2.6016 | 26500 | 0.9487 | 0.0121 | 0.1564 | 0.6350 | 0.1564 | | 0.9006 | 2.6114 | 26600 | 0.9385 | 0.0121 | 0.1535 | 0.6320 | 0.1535 | | 0.8887 | 2.6212 | 26700 | 0.9554 | 0.0121 | 0.1512 | 0.6343 | 0.1512 | | 0.9398 | 2.6311 | 26800 | 0.9373 | 0.0121 | 0.1539 | 0.6373 | 0.1539 | | 0.9373 | 2.6409 | 26900 | 0.9550 | 0.0121 | 0.1459 | 0.6270 | 0.1459 | | 0.9648 | 2.6507 | 27000 | 0.9423 | 0.0121 | 0.1508 | 0.6334 | 0.1508 | | 0.8716 | 2.6605 | 27100 | 0.9331 | 0.0121 | 0.1546 | 0.6360 | 0.1546 | | 0.914 | 2.6703 | 27200 | 0.9313 | 0.0121 | 0.1572 | 0.6392 | 0.1572 | | 0.8962 | 2.6801 | 27300 | 0.9311 | 0.0121 | 0.1584 | 0.6380 | 0.1584 | | 0.9496 | 2.6900 | 27400 | 0.9347 | 0.0121 | 0.1561 | 0.6393 | 0.1561 | | 0.9796 | 2.6998 | 27500 | 0.9494 | 0.0121 | 0.1428 | 0.6282 | 0.1428 | | 0.954 | 2.7096 | 27600 | 0.9220 | 0.0121 | 0.1602 | 0.6412 | 0.1602 | | 0.9002 | 2.7194 | 27700 | 0.9286 | 0.0121 | 0.1546 | 0.6390 | 0.1546 | | 0.9716 | 2.7292 | 27800 | 0.9388 | 0.0121 | 0.1562 | 0.6380 | 0.1562 | | 0.877 | 2.7391 | 27900 | 0.9416 | 0.0121 | 0.1494 | 0.6331 | 0.1494 | | 0.9771 | 2.7489 | 28000 | 0.9317 | 0.0121 | 0.1544 | 0.6374 | 0.1544 | | 0.8698 | 2.7587 | 28100 | 0.9433 | 0.0121 | 0.1430 | 0.6325 | 0.1430 | | 0.8791 | 2.7685 | 28200 | 0.9317 | 0.0121 | 0.1483 | 0.6352 | 0.1483 | | 0.8812 | 2.7783 | 28300 | 0.9563 | 0.0121 | 0.1488 | 0.6286 | 0.1488 | | 0.9155 | 2.7881 | 28400 | 0.9240 | 0.0121 | 0.1506 | 0.6374 | 0.1506 | | 0.9326 | 2.7980 | 28500 | 0.9373 | 0.0121 | 0.1508 | 0.6327 | 0.1508 | | 0.9114 | 2.8078 | 28600 | 0.9188 | 0.0121 | 0.1610 | 0.6399 | 0.1610 | | 0.9256 | 2.8176 | 28700 | 0.9220 | 0.0121 | 0.1655 | 0.6426 | 0.1655 | | 0.9301 | 2.8274 | 28800 | 0.9313 | 0.0121 | 0.1506 | 0.6396 | 0.1506 | | 0.9878 | 2.8372 | 28900 | 0.9335 | 0.0121 | 0.1517 | 0.6351 | 0.1517 | | 0.8843 | 2.8470 | 29000 | 0.9219 | 0.0121 | 0.1582 | 0.6426 | 0.1582 | | 0.8614 | 2.8569 | 29100 | 0.9298 | 0.0121 | 0.1566 | 0.6390 | 0.1566 | | 0.9368 | 2.8667 | 29200 | 0.9302 | 0.0121 | 0.1501 | 0.6394 | 0.1501 | | 0.9222 | 2.8765 | 29300 | 0.9263 | 0.0121 | 0.1513 | 0.6389 | 0.1513 | | 0.8301 | 2.8863 | 29400 | 0.9288 | 0.0121 | 0.1523 | 0.6391 | 0.1523 | | 0.8956 | 2.8961 | 29500 | 0.9437 | 0.0121 | 0.1512 | 0.6318 | 0.1512 | | 0.8816 | 2.9059 | 29600 | 0.9209 | 0.0121 | 0.1539 | 0.6405 | 0.1539 | | 0.9365 | 2.9158 | 29700 | 0.9408 | 0.0121 | 0.1511 | 0.6358 | 0.1511 | | 0.9062 | 2.9256 | 29800 | 0.9419 | 0.0121 | 0.1447 | 0.6334 | 0.1447 | | 0.9414 | 2.9354 | 29900 | 0.9470 | 0.0121 | 0.1459 | 0.6344 | 0.1459 | | 1.0127 | 2.9452 | 30000 | 0.9288 | 0.0121 | 0.1646 | 0.6422 | 0.1646 | | 0.9034 | 2.9550 | 30100 | 0.9362 | 0.0121 | 0.1586 | 0.6366 | 0.1586 | | 0.9152 | 2.9649 | 30200 | 0.9244 | 0.0121 | 0.1577 | 0.6372 | 0.1577 | | 0.9419 | 2.9747 | 30300 | 0.9267 | 0.0121 | 0.1562 | 0.6386 | 0.1562 | | 0.9524 | 2.9845 | 30400 | 0.9200 | 0.0121 | 0.1539 | 0.6378 | 0.1539 | | 1.002 | 2.9943 | 30500 | 0.9388 | 0.0121 | 0.1501 | 0.6345 | 0.1501 | | 1.0183 | 3.0041 | 30600 | 0.9252 | 0.0121 | 0.1542 | 0.6376 | 0.1542 | | 0.9304 | 3.0139 | 30700 | 0.9251 | 0.0121 | 0.1566 | 0.6404 | 0.1566 | | 0.9888 | 3.0238 | 30800 | 0.9469 | 0.0121 | 0.1483 | 0.6302 | 0.1483 | | 1.0105 | 3.0336 | 30900 | 0.9161 | 0.0121 | 0.1588 | 0.6423 | 0.1588 | | 0.8535 | 3.0434 | 31000 | 0.9255 | 0.0121 | 0.1588 | 0.6418 | 0.1588 | | 0.9747 | 3.0532 | 31100 | 0.9314 | 0.0121 | 0.1579 | 0.6385 | 0.1579 | | 0.9358 | 3.0630 | 31200 | 0.9202 | 0.0121 | 0.1541 | 0.6407 | 0.1541 | | 0.9373 | 3.0728 | 31300 | 0.9098 | 0.0121 | 0.1622 | 0.6454 | 0.1622 | | 0.9595 | 3.0827 | 31400 | 0.9125 | 0.0121 | 0.1675 | 0.6450 | 0.1675 | | 0.9259 | 3.0925 | 31500 | 0.9296 | 0.0121 | 0.1528 | 0.6378 | 0.1528 | | 0.907 | 3.1023 | 31600 | 0.9319 | 0.0121 | 0.1604 | 0.6367 | 0.1604 | | 0.9075 | 3.1121 | 31700 | 0.9384 | 0.0121 | 0.1566 | 0.6375 | 0.1566 | | 0.9477 | 3.1219 | 31800 | 0.9312 | 0.0121 | 0.1491 | 0.6361 | 0.1491 | | 0.9008 | 3.1317 | 31900 | 0.9270 | 0.0121 | 0.1573 | 0.6387 | 0.1573 | | 0.8841 | 3.1416 | 32000 | 0.9197 | 0.0121 | 0.1574 | 0.6417 | 0.1574 | | 0.9321 | 3.1514 | 32100 | 0.9275 | 0.0121 | 0.1518 | 0.6385 | 0.1518 | | 0.975 | 3.1612 | 32200 | 0.9208 | 0.0121 | 0.1526 | 0.6429 | 0.1526 | | 0.9376 | 3.1710 | 32300 | 0.9322 | 0.0121 | 0.1555 | 0.6395 | 0.1555 | | 0.9798 | 3.1808 | 32400 | 0.9341 | 0.0121 | 0.1505 | 0.6336 | 0.1505 | | 0.9683 | 3.1907 | 32500 | 0.9319 | 0.0121 | 0.1446 | 0.6342 | 0.1446 | | 0.9052 | 3.2005 | 32600 | 0.9151 | 0.0121 | 0.1602 | 0.6426 | 0.1602 | | 0.9259 | 3.2103 | 32700 | 0.9253 | 0.0121 | 0.1510 | 0.6389 | 0.1510 | | 0.8892 | 3.2201 | 32800 | 0.9094 | 0.0121 | 0.1595 | 0.6447 | 0.1595 | | 0.9092 | 3.2299 | 32900 | 0.9113 | 0.0121 | 0.1606 | 0.6431 | 0.1606 | | 0.9226 | 3.2397 | 33000 | 0.9349 | 0.0121 | 0.1568 | 0.6371 | 0.1568 | | 0.9054 | 3.2496 | 33100 | 0.9350 | 0.0121 | 0.1559 | 0.6360 | 0.1559 | | 0.8966 | 3.2594 | 33200 | 0.9317 | 0.0121 | 0.1475 | 0.6367 | 0.1475 | | 0.9313 | 3.2692 | 33300 | 0.9153 | 0.0121 | 0.1628 | 0.6434 | 0.1628 | | 0.9739 | 3.2790 | 33400 | 0.9286 | 0.0121 | 0.1539 | 0.6368 | 0.1539 | | 0.917 | 3.2888 | 33500 | 0.9192 | 0.0121 | 0.1572 | 0.6390 | 0.1572 | | 0.9198 | 3.2986 | 33600 | 0.9014 | 0.0121 | 0.1684 | 0.6486 | 0.1684 | | 0.8973 | 3.3085 | 33700 | 0.9252 | 0.0121 | 0.1617 | 0.6419 | 0.1617 | | 0.9723 | 3.3183 | 33800 | 0.9220 | 0.0121 | 0.1584 | 0.6423 | 0.1584 | | 0.9152 | 3.3281 | 33900 | 0.9562 | 0.0121 | 0.1345 | 0.6292 | 0.1345 | | 0.9201 | 3.3379 | 34000 | 0.9187 | 0.0121 | 0.1563 | 0.6411 | 0.1563 | | 0.8978 | 3.3477 | 34100 | 0.9338 | 0.0121 | 0.1598 | 0.6381 | 0.1598 | | 0.886 | 3.3575 | 34200 | 0.9305 | 0.0121 | 0.1597 | 0.6390 | 0.1597 | | 0.9052 | 3.3674 | 34300 | 0.9304 | 0.0121 | 0.1573 | 0.6402 | 0.1573 | | 0.9958 | 3.3772 | 34400 | 0.9293 | 0.0121 | 0.1573 | 0.6406 | 0.1573 | | 0.9306 | 3.3870 | 34500 | 0.9252 | 0.0121 | 0.1651 | 0.6403 | 0.1651 | | 0.8415 | 3.3968 | 34600 | 0.9252 | 0.0121 | 0.1568 | 0.6394 | 0.1568 | | 0.9207 | 3.4066 | 34700 | 0.9251 | 0.0121 | 0.1546 | 0.6394 | 0.1546 | | 0.9702 | 3.4165 | 34800 | 0.9296 | 0.0121 | 0.1609 | 0.6383 | 0.1609 | | 1.0098 | 3.4263 | 34900 | 0.9228 | 0.0121 | 0.1560 | 0.6407 | 0.1560 | | 0.9492 | 3.4361 | 35000 | 0.9290 | 0.0121 | 0.1572 | 0.6383 | 0.1572 | | 0.9288 | 3.4459 | 35100 | 0.9511 | 0.0121 | 0.1497 | 0.6310 | 0.1497 | | 0.8967 | 3.4557 | 35200 | 0.9255 | 0.0121 | 0.1579 | 0.6395 | 0.1579 | | 0.9164 | 3.4655 | 35300 | 0.9464 | 0.0121 | 0.1541 | 0.6335 | 0.1541 | | 0.8837 | 3.4754 | 35400 | 0.9199 | 0.0121 | 0.1446 | 0.6367 | 0.1446 | | 0.9106 | 3.4852 | 35500 | 0.9277 | 0.0121 | 0.1560 | 0.6401 | 0.1560 | | 0.9453 | 3.4950 | 35600 | 0.9446 | 0.0121 | 0.1459 | 0.6302 | 0.1459 | | 0.8714 | 3.5048 | 35700 | 0.9027 | 0.0121 | 0.1653 | 0.6483 | 0.1653 | | 0.9315 | 3.5146 | 35800 | 0.9343 | 0.0121 | 0.1435 | 0.6347 | 0.1435 | | 0.9 | 3.5244 | 35900 | 0.9182 | 0.0121 | 0.1539 | 0.6415 | 0.1539 | | 0.9719 | 3.5343 | 36000 | 0.9792 | 0.0121 | 0.1390 | 0.6188 | 0.1390 | | 0.8949 | 3.5441 | 36100 | 0.9056 | 0.0121 | 0.1644 | 0.6482 | 0.1644 | | 0.9956 | 3.5539 | 36200 | 0.9319 | 0.0121 | 0.1512 | 0.6358 | 0.1512 | | 0.885 | 3.5637 | 36300 | 0.9130 | 0.0121 | 0.1649 | 0.6457 | 0.1649 | | 0.9 | 3.5735 | 36400 | 0.9204 | 0.0121 | 0.1555 | 0.6383 | 0.1555 | | 0.8741 | 3.5833 | 36500 | 0.9177 | 0.0121 | 0.1567 | 0.6416 | 0.1567 | | 0.8922 | 3.5932 | 36600 | 0.9259 | 0.0121 | 0.1521 | 0.6368 | 0.1521 | | 0.9051 | 3.6030 | 36700 | 0.9211 | 0.0121 | 0.1553 | 0.6398 | 0.1553 | | 1.0028 | 3.6128 | 36800 | 0.9315 | 0.0121 | 0.1573 | 0.6389 | 0.1573 | | 0.8824 | 3.6226 | 36900 | 0.9306 | 0.0121 | 0.1620 | 0.6402 | 0.1620 | | 0.839 | 3.6324 | 37000 | 0.9052 | 0.0121 | 0.1654 | 0.6472 | 0.1654 | | 0.9111 | 3.6423 | 37100 | 0.9097 | 0.0121 | 0.1552 | 0.6444 | 0.1552 | | 0.8494 | 3.6521 | 37200 | 0.9137 | 0.0121 | 0.1566 | 0.6418 | 0.1566 | | 0.8726 | 3.6619 | 37300 | 0.8948 | 0.0121 | 0.1622 | 0.6473 | 0.1622 | | 0.9294 | 3.6717 | 37400 | 0.9186 | 0.0121 | 0.1553 | 0.6425 | 0.1553 | | 0.9176 | 3.6815 | 37500 | 0.9126 | 0.0121 | 0.1651 | 0.6443 | 0.1651 | | 0.8867 | 3.6913 | 37600 | 0.9143 | 0.0121 | 0.1583 | 0.6433 | 0.1583 | | 0.9085 | 3.7012 | 37700 | 0.9157 | 0.0121 | 0.1528 | 0.6409 | 0.1528 | | 0.944 | 3.7110 | 37800 | 0.9161 | 0.0121 | 0.1613 | 0.6429 | 0.1613 | | 0.819 | 3.7208 | 37900 | 0.9066 | 0.0121 | 0.1591 | 0.6434 | 0.1591 | | 0.8937 | 3.7306 | 38000 | 0.9160 | 0.0121 | 0.1661 | 0.6441 | 0.1661 | | 0.9581 | 3.7404 | 38100 | 0.9193 | 0.0121 | 0.1571 | 0.6414 | 0.1571 | | 0.958 | 3.7502 | 38200 | 0.9243 | 0.0121 | 0.1554 | 0.6374 | 0.1554 | | 0.9196 | 3.7601 | 38300 | 0.9255 | 0.0121 | 0.1524 | 0.6414 | 0.1524 | | 1.0096 | 3.7699 | 38400 | 0.9228 | 0.0121 | 0.1519 | 0.6393 | 0.1519 | | 0.9651 | 3.7797 | 38500 | 0.9224 | 0.0121 | 0.1539 | 0.6417 | 0.1539 | | 0.9387 | 3.7895 | 38600 | 0.9258 | 0.0121 | 0.1546 | 0.6371 | 0.1546 | | 0.9042 | 3.7993 | 38700 | 0.9389 | 0.0121 | 0.1530 | 0.6356 | 0.1530 | | 0.9249 | 3.8091 | 38800 | 0.9316 | 0.0121 | 0.1480 | 0.6358 | 0.1480 | | 0.9778 | 3.8190 | 38900 | 0.9109 | 0.0121 | 0.1597 | 0.6426 | 0.1597 | | 0.9166 | 3.8288 | 39000 | 0.9112 | 0.0121 | 0.1596 | 0.6423 | 0.1596 | | 0.949 | 3.8386 | 39100 | 0.9153 | 0.0121 | 0.1579 | 0.6423 | 0.1579 | | 0.982 | 3.8484 | 39200 | 0.9246 | 0.0121 | 0.1504 | 0.6398 | 0.1504 | | 0.9135 | 3.8582 | 39300 | 0.9201 | 0.0121 | 0.1577 | 0.6407 | 0.1577 | | 0.9437 | 3.8681 | 39400 | 0.9165 | 0.0121 | 0.1550 | 0.6394 | 0.1550 | | 0.9937 | 3.8779 | 39500 | 0.8967 | 0.0121 | 0.1657 | 0.6482 | 0.1657 | | 0.8937 | 3.8877 | 39600 | 0.9084 | 0.0121 | 0.1686 | 0.6486 | 0.1686 | | 0.901 | 3.8975 | 39700 | 0.9164 | 0.0121 | 0.1579 | 0.6393 | 0.1579 | | 0.9045 | 3.9073 | 39800 | 0.9013 | 0.0121 | 0.1612 | 0.6472 | 0.1612 | | 1.0313 | 3.9171 | 39900 | 0.9325 | 0.0121 | 0.1548 | 0.6349 | 0.1548 | | 1.0183 | 3.9270 | 40000 | 0.9256 | 0.0121 | 0.1557 | 0.6380 | 0.1557 | | 0.9019 | 3.9368 | 40100 | 0.9097 | 0.0121 | 0.1556 | 0.6420 | 0.1556 | | 0.9219 | 3.9466 | 40200 | 0.9354 | 0.0121 | 0.1573 | 0.6370 | 0.1573 | | 0.9101 | 3.9564 | 40300 | 0.9286 | 0.0121 | 0.1589 | 0.6416 | 0.1589 | | 0.914 | 3.9662 | 40400 | 0.9033 | 0.0121 | 0.1658 | 0.6473 | 0.1658 | | 0.8996 | 3.9760 | 40500 | 0.9187 | 0.0121 | 0.1578 | 0.6400 | 0.1578 | | 0.9002 | 3.9859 | 40600 | 0.9055 | 0.0121 | 0.1612 | 0.6459 | 0.1612 | | 1.0091 | 3.9957 | 40700 | 0.8948 | 0.0121 | 0.1673 | 0.6523 | 0.1673 | | 0.8874 | 4.0055 | 40800 | 0.9226 | 0.0121 | 0.1566 | 0.6420 | 0.1566 | | 0.9004 | 4.0153 | 40900 | 0.9081 | 0.0121 | 0.1594 | 0.6424 | 0.1594 | | 0.962 | 4.0251 | 41000 | 0.9222 | 0.0121 | 0.1615 | 0.6436 | 0.1615 | | 0.9959 | 4.0349 | 41100 | 0.9125 | 0.0121 | 0.1566 | 0.6438 | 0.1566 | | 0.8541 | 4.0448 | 41200 | 0.9039 | 0.0121 | 0.1675 | 0.6464 | 0.1675 | | 0.8316 | 4.0546 | 41300 | 0.9225 | 0.0121 | 0.1498 | 0.6367 | 0.1498 | | 0.8869 | 4.0644 | 41400 | 0.9282 | 0.0121 | 0.1478 | 0.6346 | 0.1478 | | 0.9328 | 4.0742 | 41500 | 0.9237 | 0.0121 | 0.1632 | 0.6409 | 0.1632 | | 0.8534 | 4.0840 | 41600 | 0.8998 | 0.0121 | 0.1693 | 0.6488 | 0.1693 | | 1.002 | 4.0939 | 41700 | 0.9398 | 0.0121 | 0.1501 | 0.6356 | 0.1501 | | 0.8411 | 4.1037 | 41800 | 0.9140 | 0.0121 | 0.1604 | 0.6425 | 0.1604 | | 0.8828 | 4.1135 | 41900 | 0.8929 | 0.0121 | 0.1653 | 0.6490 | 0.1653 | | 0.8654 | 4.1233 | 42000 | 0.9065 | 0.0121 | 0.1702 | 0.6482 | 0.1702 | | 0.8602 | 4.1331 | 42100 | 0.9147 | 0.0121 | 0.1546 | 0.6404 | 0.1546 | | 0.8268 | 4.1429 | 42200 | 0.9238 | 0.0121 | 0.1540 | 0.6402 | 0.1540 | | 0.9743 | 4.1528 | 42300 | 0.9081 | 0.0121 | 0.1629 | 0.6435 | 0.1629 | | 0.9224 | 4.1626 | 42400 | 0.9007 | 0.0121 | 0.1673 | 0.6473 | 0.1673 | | 0.9082 | 4.1724 | 42500 | 0.9077 | 0.0121 | 0.1610 | 0.6457 | 0.1610 | | 0.9227 | 4.1822 | 42600 | 0.9160 | 0.0121 | 0.1595 | 0.6404 | 0.1595 | | 0.925 | 4.1920 | 42700 | 0.9133 | 0.0121 | 0.1625 | 0.6450 | 0.1625 | | 0.9158 | 4.2018 | 42800 | 0.8978 | 0.0121 | 0.1539 | 0.6438 | 0.1539 | | 0.96 | 4.2117 | 42900 | 0.9021 | 0.0121 | 0.1567 | 0.6459 | 0.1567 | | 0.8507 | 4.2215 | 43000 | 0.8975 | 0.0121 | 0.1681 | 0.6522 | 0.1681 | | 0.9323 | 4.2313 | 43100 | 0.8988 | 0.0121 | 0.1686 | 0.6495 | 0.1686 | | 0.9649 | 4.2411 | 43200 | 0.9099 | 0.0121 | 0.1672 | 0.6460 | 0.1672 | | 0.826 | 4.2509 | 43300 | 0.9070 | 0.0121 | 0.1641 | 0.6456 | 0.1641 | | 0.8683 | 4.2608 | 43400 | 0.9204 | 0.0121 | 0.1648 | 0.6445 | 0.1648 | | 0.9506 | 4.2706 | 43500 | 0.8995 | 0.0121 | 0.1723 | 0.6487 | 0.1723 | | 0.9039 | 4.2804 | 43600 | 0.9058 | 0.0121 | 0.1627 | 0.6459 | 0.1627 | | 0.9048 | 4.2902 | 43700 | 0.8986 | 0.0121 | 0.1596 | 0.6452 | 0.1596 | | 0.9044 | 4.3000 | 43800 | 0.9013 | 0.0121 | 0.1676 | 0.6490 | 0.1676 | | 0.9858 | 4.3098 | 43900 | 0.9035 | 0.0121 | 0.1615 | 0.6445 | 0.1615 | | 0.8395 | 4.3197 | 44000 | 0.9093 | 0.0121 | 0.1624 | 0.6480 | 0.1624 | | 0.8611 | 4.3295 | 44100 | 0.9118 | 0.0121 | 0.1598 | 0.6417 | 0.1598 | | 0.8509 | 4.3393 | 44200 | 0.9076 | 0.0121 | 0.1645 | 0.6467 | 0.1645 | | 0.9728 | 4.3491 | 44300 | 0.9200 | 0.0121 | 0.1575 | 0.6418 | 0.1575 | | 0.9003 | 4.3589 | 44400 | 0.9100 | 0.0121 | 0.1624 | 0.6437 | 0.1624 | | 0.9193 | 4.3687 | 44500 | 0.9107 | 0.0121 | 0.1599 | 0.6457 | 0.1599 | | 0.9026 | 4.3786 | 44600 | 0.9110 | 0.0121 | 0.1590 | 0.6444 | 0.1590 | | 0.9528 | 4.3884 | 44700 | 0.9107 | 0.0121 | 0.1622 | 0.6426 | 0.1622 | | 0.9313 | 4.3982 | 44800 | 0.9001 | 0.0121 | 0.1637 | 0.6475 | 0.1637 | | 0.9124 | 4.4080 | 44900 | 0.9170 | 0.0121 | 0.1636 | 0.6408 | 0.1636 | | 0.8478 | 4.4178 | 45000 | 0.9140 | 0.0121 | 0.1650 | 0.6415 | 0.1650 | | 0.9162 | 4.4276 | 45100 | 0.9020 | 0.0121 | 0.1633 | 0.6450 | 0.1633 | | 0.8447 | 4.4375 | 45200 | 0.9037 | 0.0121 | 0.1642 | 0.6461 | 0.1642 | | 0.9101 | 4.4473 | 45300 | 0.8941 | 0.0121 | 0.1657 | 0.6455 | 0.1657 | | 0.9524 | 4.4571 | 45400 | 0.9030 | 0.0121 | 0.1695 | 0.6466 | 0.1695 | | 0.903 | 4.4669 | 45500 | 0.9293 | 0.0121 | 0.1484 | 0.6388 | 0.1484 | | 0.889 | 4.4767 | 45600 | 0.9204 | 0.0121 | 0.1528 | 0.6379 | 0.1528 | | 0.8872 | 4.4866 | 45700 | 0.9075 | 0.0121 | 0.1577 | 0.6472 | 0.1577 | | 0.929 | 4.4964 | 45800 | 0.9106 | 0.0121 | 0.1572 | 0.6418 | 0.1572 | | 0.9765 | 4.5062 | 45900 | 0.9218 | 0.0121 | 0.1510 | 0.6383 | 0.1510 | | 0.9421 | 4.5160 | 46000 | 0.9214 | 0.0121 | 0.1549 | 0.6389 | 0.1549 | | 0.926 | 4.5258 | 46100 | 0.9083 | 0.0121 | 0.1651 | 0.6468 | 0.1651 | | 0.875 | 4.5356 | 46200 | 0.9061 | 0.0121 | 0.1631 | 0.6459 | 0.1631 | | 0.9123 | 4.5455 | 46300 | 0.9016 | 0.0121 | 0.1577 | 0.6463 | 0.1577 | | 0.8782 | 4.5553 | 46400 | 0.8928 | 0.0121 | 0.1677 | 0.6507 | 0.1677 | | 0.9383 | 4.5651 | 46500 | 0.9129 | 0.0121 | 0.1659 | 0.6448 | 0.1659 | | 0.9099 | 4.5749 | 46600 | 0.9179 | 0.0121 | 0.1541 | 0.6394 | 0.1541 | | 0.8764 | 4.5847 | 46700 | 0.9009 | 0.0121 | 0.1708 | 0.6477 | 0.1708 | | 0.8533 | 4.5945 | 46800 | 0.9146 | 0.0121 | 0.1622 | 0.6450 | 0.1622 | | 0.9333 | 4.6044 | 46900 | 0.8864 | 0.0121 | 0.1672 | 0.6512 | 0.1672 | | 0.8858 | 4.6142 | 47000 | 0.9083 | 0.0121 | 0.1586 | 0.6458 | 0.1586 | | 0.9595 | 4.6240 | 47100 | 0.9188 | 0.0121 | 0.1544 | 0.6387 | 0.1544 | | 0.8651 | 4.6338 | 47200 | 0.9047 | 0.0121 | 0.1660 | 0.6489 | 0.1660 | | 0.9615 | 4.6436 | 47300 | 0.8948 | 0.0121 | 0.1675 | 0.6491 | 0.1675 | | 0.8732 | 4.6534 | 47400 | 0.9062 | 0.0121 | 0.1603 | 0.6438 | 0.1603 | | 0.8855 | 4.6633 | 47500 | 0.9121 | 0.0121 | 0.1647 | 0.6429 | 0.1647 | | 0.9367 | 4.6731 | 47600 | 0.9005 | 0.0121 | 0.1595 | 0.6475 | 0.1595 | | 0.8297 | 4.6829 | 47700 | 0.9032 | 0.0121 | 0.1616 | 0.6448 | 0.1616 | | 0.7984 | 4.6927 | 47800 | 0.9010 | 0.0121 | 0.1673 | 0.6477 | 0.1673 | | 0.8957 | 4.7025 | 47900 | 0.8924 | 0.0121 | 0.1605 | 0.6480 | 0.1605 | | 0.9367 | 4.7124 | 48000 | 0.8977 | 0.0121 | 0.1657 | 0.6475 | 0.1657 | | 0.8833 | 4.7222 | 48100 | 0.9226 | 0.0121 | 0.1606 | 0.6431 | 0.1606 | | 0.9096 | 4.7320 | 48200 | 0.9154 | 0.0121 | 0.1603 | 0.6420 | 0.1603 | | 0.9061 | 4.7418 | 48300 | 0.8940 | 0.0121 | 0.1672 | 0.6503 | 0.1672 | | 0.9153 | 4.7516 | 48400 | 0.9089 | 0.0121 | 0.1591 | 0.6443 | 0.1591 | | 0.9201 | 4.7614 | 48500 | 0.9072 | 0.0121 | 0.1597 | 0.6431 | 0.1597 | | 0.8633 | 4.7713 | 48600 | 0.9093 | 0.0121 | 0.1554 | 0.6425 | 0.1554 | | 0.825 | 4.7811 | 48700 | 0.8990 | 0.0121 | 0.1708 | 0.6501 | 0.1708 | | 0.9 | 4.7909 | 48800 | 0.9117 | 0.0121 | 0.1616 | 0.6446 | 0.1616 | | 0.8629 | 4.8007 | 48900 | 0.8871 | 0.0121 | 0.1724 | 0.6538 | 0.1724 | | 0.9299 | 4.8105 | 49000 | 0.9120 | 0.0121 | 0.1512 | 0.6406 | 0.1512 | | 0.9157 | 4.8203 | 49100 | 0.9028 | 0.0121 | 0.1703 | 0.6484 | 0.1703 | | 0.8361 | 4.8302 | 49200 | 0.9110 | 0.0121 | 0.1613 | 0.6455 | 0.1613 | | 0.9751 | 4.8400 | 49300 | 0.9383 | 0.0121 | 0.1497 | 0.6332 | 0.1497 | | 0.8713 | 4.8498 | 49400 | 0.9014 | 0.0121 | 0.1687 | 0.6472 | 0.1687 | | 0.9002 | 4.8596 | 49500 | 0.9264 | 0.0121 | 0.1531 | 0.6391 | 0.1531 | | 0.8755 | 4.8694 | 49600 | 0.9032 | 0.0121 | 0.1627 | 0.6473 | 0.1627 | | 0.9272 | 4.8792 | 49700 | 0.9072 | 0.0121 | 0.1623 | 0.6450 | 0.1623 | | 0.884 | 4.8891 | 49800 | 0.9052 | 0.0121 | 0.1593 | 0.6451 | 0.1593 | | 0.8862 | 4.8989 | 49900 | 0.9035 | 0.0121 | 0.1635 | 0.6501 | 0.1635 | | 0.9846 | 4.9087 | 50000 | 0.8958 | 0.0121 | 0.1740 | 0.6529 | 0.1740 | | 0.8923 | 4.9185 | 50100 | 0.8934 | 0.0121 | 0.1716 | 0.6501 | 0.1716 | | 0.8942 | 4.9283 | 50200 | 0.8920 | 0.0121 | 0.1627 | 0.6473 | 0.1627 | | 0.91 | 4.9382 | 50300 | 0.9024 | 0.0121 | 0.1579 | 0.6461 | 0.1579 | | 0.9646 | 4.9480 | 50400 | 0.8986 | 0.0121 | 0.1604 | 0.6477 | 0.1604 | | 0.8794 | 4.9578 | 50500 | 0.9007 | 0.0121 | 0.1663 | 0.6498 | 0.1663 | | 0.8963 | 4.9676 | 50600 | 0.9015 | 0.0121 | 0.1631 | 0.6477 | 0.1631 | | 0.8735 | 4.9774 | 50700 | 0.9166 | 0.0121 | 0.1577 | 0.6401 | 0.1577 | | 0.86 | 4.9872 | 50800 | 0.8967 | 0.0121 | 0.1637 | 0.6486 | 0.1637 | | 0.8749 | 4.9971 | 50900 | 0.9178 | 0.0121 | 0.1564 | 0.6424 | 0.1564 | | 0.8572 | 5.0069 | 51000 | 0.9051 | 0.0121 | 0.1619 | 0.6443 | 0.1619 | | 0.8619 | 5.0167 | 51100 | 0.9050 | 0.0121 | 0.1655 | 0.6450 | 0.1655 | | 0.9452 | 5.0265 | 51200 | 0.9003 | 0.0121 | 0.1661 | 0.6489 | 0.1661 | | 0.9752 | 5.0363 | 51300 | 0.9262 | 0.0121 | 0.1496 | 0.6386 | 0.1496 | | 0.9512 | 5.0461 | 51400 | 0.9059 | 0.0121 | 0.1627 | 0.6450 | 0.1627 | | 0.9348 | 5.0560 | 51500 | 0.8991 | 0.0121 | 0.1683 | 0.6502 | 0.1683 | | 0.8832 | 5.0658 | 51600 | 0.9097 | 0.0121 | 0.1703 | 0.6475 | 0.1703 | | 0.859 | 5.0756 | 51700 | 0.8906 | 0.0121 | 0.1663 | 0.6481 | 0.1663 | | 0.9556 | 5.0854 | 51800 | 0.8922 | 0.0121 | 0.1673 | 0.6503 | 0.1673 | | 0.8665 | 5.0952 | 51900 | 0.8930 | 0.0121 | 0.1617 | 0.6467 | 0.1617 | | 0.8041 | 5.1050 | 52000 | 0.9068 | 0.0121 | 0.1624 | 0.6480 | 0.1624 | | 0.9299 | 5.1149 | 52100 | 0.9135 | 0.0121 | 0.1596 | 0.6399 | 0.1596 | | 0.8976 | 5.1247 | 52200 | 0.9117 | 0.0121 | 0.1616 | 0.6433 | 0.1616 | | 0.8793 | 5.1345 | 52300 | 0.8878 | 0.0121 | 0.1677 | 0.6520 | 0.1677 | | 0.9637 | 5.1443 | 52400 | 0.9007 | 0.0121 | 0.1606 | 0.6467 | 0.1606 | | 0.8663 | 5.1541 | 52500 | 0.9148 | 0.0121 | 0.1628 | 0.6436 | 0.1628 | | 0.9043 | 5.1640 | 52600 | 0.8960 | 0.0121 | 0.1733 | 0.6521 | 0.1733 | | 0.9171 | 5.1738 | 52700 | 0.9052 | 0.0121 | 0.1614 | 0.6452 | 0.1614 | | 0.9416 | 5.1836 | 52800 | 0.8995 | 0.0121 | 0.1696 | 0.6510 | 0.1696 | | 0.8467 | 5.1934 | 52900 | 0.8946 | 0.0121 | 0.1689 | 0.6497 | 0.1689 | | 0.94 | 5.2032 | 53000 | 0.9179 | 0.0121 | 0.1648 | 0.6414 | 0.1648 | | 0.917 | 5.2130 | 53100 | 0.8858 | 0.0121 | 0.1670 | 0.6532 | 0.1670 | | 0.891 | 5.2229 | 53200 | 0.9270 | 0.0121 | 0.1528 | 0.6374 | 0.1528 | | 0.9123 | 5.2327 | 53300 | 0.9126 | 0.0121 | 0.1534 | 0.6422 | 0.1534 | | 0.8681 | 5.2425 | 53400 | 0.9152 | 0.0121 | 0.1572 | 0.6426 | 0.1572 | | 0.8683 | 5.2523 | 53500 | 0.9346 | 0.0121 | 0.1537 | 0.6355 | 0.1537 | | 0.9497 | 5.2621 | 53600 | 0.9204 | 0.0121 | 0.1525 | 0.6362 | 0.1525 | | 0.8694 | 5.2719 | 53700 | 0.9116 | 0.0121 | 0.1568 | 0.6435 | 0.1568 | | 0.9946 | 5.2818 | 53800 | 0.8966 | 0.0121 | 0.1611 | 0.6457 | 0.1611 | | 0.8512 | 5.2916 | 53900 | 0.8941 | 0.0121 | 0.1626 | 0.6463 | 0.1626 | | 0.8805 | 5.3014 | 54000 | 0.9016 | 0.0121 | 0.1675 | 0.6466 | 0.1675 | | 0.8873 | 5.3112 | 54100 | 0.8944 | 0.0121 | 0.1684 | 0.6478 | 0.1684 | | 0.973 | 5.3210 | 54200 | 0.8996 | 0.0121 | 0.1655 | 0.6483 | 0.1655 | | 0.8152 | 5.3308 | 54300 | 0.9010 | 0.0121 | 0.1714 | 0.6501 | 0.1714 | | 0.9256 | 5.3407 | 54400 | 0.8908 | 0.0121 | 0.1646 | 0.6506 | 0.1646 | | 0.9047 | 5.3505 | 54500 | 0.9029 | 0.0121 | 0.1606 | 0.6422 | 0.1606 | | 0.8359 | 5.3603 | 54600 | 0.9361 | 0.0121 | 0.1551 | 0.6387 | 0.1551 | | 0.967 | 5.3701 | 54700 | 0.9100 | 0.0121 | 0.1651 | 0.6453 | 0.1651 | | 0.9209 | 5.3799 | 54800 | 0.8941 | 0.0121 | 0.1653 | 0.6501 | 0.1653 | | 0.8872 | 5.3898 | 54900 | 0.8784 | 0.0121 | 0.1683 | 0.6546 | 0.1683 | | 0.8653 | 5.3996 | 55000 | 0.9149 | 0.0121 | 0.1584 | 0.6417 | 0.1584 | | 0.9349 | 5.4094 | 55100 | 0.8910 | 0.0121 | 0.1704 | 0.6518 | 0.1704 | | 0.8506 | 5.4192 | 55200 | 0.8923 | 0.0121 | 0.1713 | 0.6518 | 0.1713 | | 0.9151 | 5.4290 | 55300 | 0.9275 | 0.0121 | 0.1561 | 0.6383 | 0.1561 | | 0.8983 | 5.4388 | 55400 | 0.8984 | 0.0121 | 0.1677 | 0.6493 | 0.1677 | | 0.9229 | 5.4487 | 55500 | 0.8860 | 0.0121 | 0.1708 | 0.6523 | 0.1708 | | 0.9612 | 5.4585 | 55600 | 0.8972 | 0.0121 | 0.1700 | 0.6480 | 0.1700 | | 0.9427 | 5.4683 | 55700 | 0.9043 | 0.0121 | 0.1622 | 0.6479 | 0.1622 | | 0.9168 | 5.4781 | 55800 | 0.9019 | 0.0121 | 0.1667 | 0.6470 | 0.1667 | | 0.878 | 5.4879 | 55900 | 0.9124 | 0.0121 | 0.1611 | 0.6463 | 0.1611 | | 0.9137 | 5.4977 | 56000 | 0.9074 | 0.0121 | 0.1610 | 0.6458 | 0.1610 | | 0.8934 | 5.5076 | 56100 | 0.9205 | 0.0121 | 0.1574 | 0.6446 | 0.1574 | | 0.8924 | 5.5174 | 56200 | 0.9048 | 0.0121 | 0.1652 | 0.6462 | 0.1652 | | 0.8633 | 5.5272 | 56300 | 0.8943 | 0.0121 | 0.1682 | 0.6476 | 0.1682 | | 0.8871 | 5.5370 | 56400 | 0.8909 | 0.0121 | 0.1684 | 0.6520 | 0.1684 | | 0.8729 | 5.5468 | 56500 | 0.8900 | 0.0121 | 0.1708 | 0.6502 | 0.1708 | | 0.9497 | 5.5566 | 56600 | 0.9064 | 0.0121 | 0.1584 | 0.6438 | 0.1584 | | 0.8594 | 5.5665 | 56700 | 0.8851 | 0.0121 | 0.1692 | 0.6524 | 0.1692 | | 0.9684 | 5.5763 | 56800 | 0.8993 | 0.0121 | 0.1583 | 0.6465 | 0.1583 | | 0.8726 | 5.5861 | 56900 | 0.8977 | 0.0121 | 0.1657 | 0.6488 | 0.1657 | | 0.8668 | 5.5959 | 57000 | 0.8910 | 0.0121 | 0.1669 | 0.6498 | 0.1669 | | 0.8763 | 5.6057 | 57100 | 0.8903 | 0.0121 | 0.1644 | 0.6519 | 0.1644 | | 0.8591 | 5.6156 | 57200 | 0.8960 | 0.0121 | 0.1682 | 0.6497 | 0.1682 | | 0.9064 | 5.6254 | 57300 | 0.9095 | 0.0121 | 0.1657 | 0.6467 | 0.1657 | | 0.9166 | 5.6352 | 57400 | 0.9030 | 0.0121 | 0.1637 | 0.6473 | 0.1637 | | 0.8296 | 5.6450 | 57500 | 0.8968 | 0.0121 | 0.1619 | 0.6486 | 0.1619 | | 0.8507 | 5.6548 | 57600 | 0.9010 | 0.0121 | 0.1597 | 0.6465 | 0.1597 | | 0.9312 | 5.6646 | 57700 | 0.8972 | 0.0121 | 0.1596 | 0.6445 | 0.1596 | | 0.8648 | 5.6745 | 57800 | 0.8814 | 0.0121 | 0.1690 | 0.6539 | 0.1690 | | 0.8798 | 5.6843 | 57900 | 0.8859 | 0.0121 | 0.1638 | 0.6538 | 0.1638 | | 0.8728 | 5.6941 | 58000 | 0.8916 | 0.0121 | 0.1705 | 0.6527 | 0.1705 | | 0.899 | 5.7039 | 58100 | 0.8994 | 0.0121 | 0.1676 | 0.6479 | 0.1676 | | 0.8982 | 5.7137 | 58200 | 0.8926 | 0.0121 | 0.1727 | 0.6530 | 0.1727 | | 0.928 | 5.7235 | 58300 | 0.9125 | 0.0121 | 0.1718 | 0.6457 | 0.1718 | | 0.9265 | 5.7334 | 58400 | 0.9196 | 0.0121 | 0.1556 | 0.6383 | 0.1556 | | 0.8782 | 5.7432 | 58500 | 0.8883 | 0.0121 | 0.1695 | 0.6519 | 0.1695 | | 0.9272 | 5.7530 | 58600 | 0.9011 | 0.0121 | 0.1653 | 0.6492 | 0.1653 | | 0.8898 | 5.7628 | 58700 | 0.9017 | 0.0121 | 0.1722 | 0.6504 | 0.1722 | | 0.8858 | 5.7726 | 58800 | 0.8997 | 0.0121 | 0.1616 | 0.6476 | 0.1616 | | 0.9103 | 5.7824 | 58900 | 0.9088 | 0.0121 | 0.1587 | 0.6456 | 0.1587 | | 0.8883 | 5.7923 | 59000 | 0.8969 | 0.0121 | 0.1670 | 0.6484 | 0.1670 | | 0.8947 | 5.8021 | 59100 | 0.9202 | 0.0121 | 0.1597 | 0.6420 | 0.1597 | | 0.9053 | 5.8119 | 59200 | 0.8866 | 0.0121 | 0.1706 | 0.6516 | 0.1706 | | 0.8692 | 5.8217 | 59300 | 0.8748 | 0.0121 | 0.1667 | 0.6529 | 0.1667 | | 0.8223 | 5.8315 | 59400 | 0.8913 | 0.0121 | 0.1698 | 0.6524 | 0.1698 | | 0.9592 | 5.8414 | 59500 | 0.8905 | 0.0121 | 0.1681 | 0.6524 | 0.1681 | | 0.8031 | 5.8512 | 59600 | 0.9016 | 0.0121 | 0.1663 | 0.6488 | 0.1663 | | 0.9475 | 5.8610 | 59700 | 0.9167 | 0.0121 | 0.1592 | 0.6442 | 0.1592 | | 0.9078 | 5.8708 | 59800 | 0.9012 | 0.0121 | 0.1726 | 0.6480 | 0.1726 | | 0.9446 | 5.8806 | 59900 | 0.8916 | 0.0121 | 0.1646 | 0.6524 | 0.1646 | | 0.8724 | 5.8904 | 60000 | 0.8824 | 0.0121 | 0.1662 | 0.6545 | 0.1662 | | 0.9384 | 5.9003 | 60100 | 0.9172 | 0.0121 | 0.1527 | 0.6384 | 0.1527 | | 0.9091 | 5.9101 | 60200 | 0.8846 | 0.0121 | 0.1711 | 0.6554 | 0.1711 | | 0.8407 | 5.9199 | 60300 | 0.9147 | 0.0121 | 0.1617 | 0.6450 | 0.1617 | | 0.9015 | 5.9297 | 60400 | 0.8986 | 0.0121 | 0.1642 | 0.6510 | 0.1642 | | 0.8919 | 5.9395 | 60500 | 0.8881 | 0.0121 | 0.1722 | 0.6528 | 0.1722 | | 0.9051 | 5.9493 | 60600 | 0.9188 | 0.0121 | 0.1533 | 0.6386 | 0.1533 | | 0.9186 | 5.9592 | 60700 | 0.8916 | 0.0121 | 0.1648 | 0.6498 | 0.1648 | | 0.8539 | 5.9690 | 60800 | 0.8972 | 0.0121 | 0.1654 | 0.6504 | 0.1654 | | 0.9481 | 5.9788 | 60900 | 0.8835 | 0.0121 | 0.1684 | 0.6534 | 0.1684 | | 0.8576 | 5.9886 | 61000 | 0.8902 | 0.0121 | 0.1642 | 0.6499 | 0.1642 | | 0.9714 | 5.9984 | 61100 | 0.8893 | 0.0121 | 0.1660 | 0.6517 | 0.1660 | | 0.8351 | 6.0082 | 61200 | 0.9071 | 0.0121 | 0.1677 | 0.6478 | 0.1677 | | 0.7786 | 6.0181 | 61300 | 0.9043 | 0.0121 | 0.1700 | 0.6483 | 0.1700 | | 0.823 | 6.0279 | 61400 | 0.9296 | 0.0121 | 0.1618 | 0.6405 | 0.1618 | | 0.9533 | 6.0377 | 61500 | 0.8938 | 0.0121 | 0.1657 | 0.6495 | 0.1657 | | 0.8797 | 6.0475 | 61600 | 0.8994 | 0.0121 | 0.1642 | 0.6498 | 0.1642 | | 0.9222 | 6.0573 | 61700 | 0.9007 | 0.0121 | 0.1719 | 0.6535 | 0.1719 | | 0.7826 | 6.0672 | 61800 | 0.8861 | 0.0121 | 0.1749 | 0.6539 | 0.1749 | | 0.9418 | 6.0770 | 61900 | 0.8965 | 0.0121 | 0.1626 | 0.6475 | 0.1626 | | 0.9099 | 6.0868 | 62000 | 0.9134 | 0.0121 | 0.1564 | 0.6417 | 0.1564 | | 0.8789 | 6.0966 | 62100 | 0.8873 | 0.0121 | 0.1643 | 0.6523 | 0.1643 | | 0.91 | 6.1064 | 62200 | 0.8909 | 0.0121 | 0.1707 | 0.6511 | 0.1707 | | 0.8766 | 6.1162 | 62300 | 0.8964 | 0.0121 | 0.1619 | 0.6493 | 0.1619 | | 0.8721 | 6.1261 | 62400 | 0.8966 | 0.0121 | 0.1740 | 0.6512 | 0.1740 | | 0.8653 | 6.1359 | 62500 | 0.8980 | 0.0121 | 0.1653 | 0.6514 | 0.1653 | | 0.9314 | 6.1457 | 62600 | 0.9137 | 0.0121 | 0.1627 | 0.6460 | 0.1627 | | 0.8731 | 6.1555 | 62700 | 0.8799 | 0.0121 | 0.1684 | 0.6551 | 0.1684 | | 0.9052 | 6.1653 | 62800 | 0.8863 | 0.0121 | 0.1667 | 0.6509 | 0.1667 | | 0.8165 | 6.1751 | 62900 | 0.9010 | 0.0121 | 0.1633 | 0.6483 | 0.1633 | | 0.8366 | 6.1850 | 63000 | 0.8970 | 0.0121 | 0.1651 | 0.6496 | 0.1651 | | 0.9723 | 6.1948 | 63100 | 0.8961 | 0.0121 | 0.1668 | 0.6486 | 0.1668 | | 0.9703 | 6.2046 | 63200 | 0.9108 | 0.0121 | 0.1612 | 0.6446 | 0.1612 | | 0.8922 | 6.2144 | 63300 | 0.8987 | 0.0121 | 0.1645 | 0.6480 | 0.1645 | | 0.8852 | 6.2242 | 63400 | 0.9052 | 0.0121 | 0.1626 | 0.6433 | 0.1626 | | 0.8619 | 6.2340 | 63500 | 0.9053 | 0.0121 | 0.1679 | 0.6471 | 0.1679 | | 0.898 | 6.2439 | 63600 | 0.8993 | 0.0121 | 0.1582 | 0.6470 | 0.1582 | | 0.87 | 6.2537 | 63700 | 0.8971 | 0.0121 | 0.1662 | 0.6504 | 0.1662 | | 0.911 | 6.2635 | 63800 | 0.8920 | 0.0121 | 0.1675 | 0.6509 | 0.1675 | | 0.8969 | 6.2733 | 63900 | 0.8862 | 0.0121 | 0.1651 | 0.6520 | 0.1651 | | 0.9455 | 6.2831 | 64000 | 0.8855 | 0.0121 | 0.1700 | 0.6533 | 0.1700 | | 0.866 | 6.2930 | 64100 | 0.9047 | 0.0121 | 0.1628 | 0.6458 | 0.1628 | | 0.8634 | 6.3028 | 64200 | 0.8756 | 0.0121 | 0.1696 | 0.6544 | 0.1696 | | 0.9044 | 6.3126 | 64300 | 0.8910 | 0.0121 | 0.1709 | 0.6531 | 0.1709 | | 0.9051 | 6.3224 | 64400 | 0.8884 | 0.0121 | 0.1680 | 0.6546 | 0.1680 | | 0.8775 | 6.3322 | 64500 | 0.9103 | 0.0121 | 0.1570 | 0.6431 | 0.1570 | | 0.9304 | 6.3420 | 64600 | 0.8946 | 0.0121 | 0.1631 | 0.6519 | 0.1631 | | 0.8777 | 6.3519 | 64700 | 0.8930 | 0.0121 | 0.1647 | 0.6493 | 0.1647 | | 0.8525 | 6.3617 | 64800 | 0.9022 | 0.0121 | 0.1663 | 0.6477 | 0.1663 | | 0.8442 | 6.3715 | 64900 | 0.8868 | 0.0121 | 0.1699 | 0.6520 | 0.1699 | | 0.8797 | 6.3813 | 65000 | 0.8790 | 0.0121 | 0.1724 | 0.6544 | 0.1724 | | 0.8941 | 6.3911 | 65100 | 0.8873 | 0.0121 | 0.1769 | 0.6536 | 0.1769 | | 0.9563 | 6.4009 | 65200 | 0.8859 | 0.0121 | 0.1707 | 0.6523 | 0.1707 | | 0.9128 | 6.4108 | 65300 | 0.9141 | 0.0121 | 0.1564 | 0.6401 | 0.1564 | | 0.8711 | 6.4206 | 65400 | 0.8837 | 0.0121 | 0.1682 | 0.6514 | 0.1682 | | 0.956 | 6.4304 | 65500 | 0.8755 | 0.0121 | 0.1747 | 0.6565 | 0.1747 | | 0.9201 | 6.4402 | 65600 | 0.8956 | 0.0121 | 0.1689 | 0.6499 | 0.1689 | | 0.953 | 6.4500 | 65700 | 0.8916 | 0.0121 | 0.1662 | 0.6510 | 0.1662 | | 0.8706 | 6.4598 | 65800 | 0.8956 | 0.0121 | 0.1605 | 0.6458 | 0.1605 | | 0.9539 | 6.4697 | 65900 | 0.9331 | 0.0121 | 0.1551 | 0.6393 | 0.1551 | | 0.8699 | 6.4795 | 66000 | 0.8920 | 0.0121 | 0.1689 | 0.6513 | 0.1689 | | 0.9043 | 6.4893 | 66100 | 0.9007 | 0.0121 | 0.1563 | 0.6450 | 0.1563 | | 0.88 | 6.4991 | 66200 | 0.8906 | 0.0121 | 0.1746 | 0.6507 | 0.1746 | | 0.9186 | 6.5089 | 66300 | 0.9041 | 0.0121 | 0.1573 | 0.6465 | 0.1573 | | 0.8728 | 6.5188 | 66400 | 0.8923 | 0.0121 | 0.1605 | 0.6471 | 0.1605 | | 0.9381 | 6.5286 | 66500 | 0.8779 | 0.0121 | 0.1731 | 0.6594 | 0.1731 | | 0.8294 | 6.5384 | 66600 | 0.9043 | 0.0121 | 0.1570 | 0.6442 | 0.1570 | | 0.9222 | 6.5482 | 66700 | 0.9021 | 0.0121 | 0.1624 | 0.6488 | 0.1624 | | 0.8413 | 6.5580 | 66800 | 0.9031 | 0.0121 | 0.1619 | 0.6489 | 0.1619 | | 0.9295 | 6.5678 | 66900 | 0.8977 | 0.0121 | 0.1595 | 0.6482 | 0.1595 | | 0.8932 | 6.5777 | 67000 | 0.8765 | 0.0121 | 0.1776 | 0.6584 | 0.1776 | | 0.9345 | 6.5875 | 67100 | 0.8952 | 0.0121 | 0.1643 | 0.6517 | 0.1643 | | 0.8763 | 6.5973 | 67200 | 0.8965 | 0.0121 | 0.1670 | 0.6519 | 0.1670 | | 0.9188 | 6.6071 | 67300 | 0.9477 | 0.0121 | 0.1480 | 0.6334 | 0.1480 | | 0.8674 | 6.6169 | 67400 | 0.9052 | 0.0121 | 0.1640 | 0.6449 | 0.1640 | | 0.9324 | 6.6267 | 67500 | 0.8874 | 0.0121 | 0.1631 | 0.6515 | 0.1631 | | 0.8973 | 6.6366 | 67600 | 0.9005 | 0.0121 | 0.1682 | 0.6465 | 0.1682 | | 0.9835 | 6.6464 | 67700 | 0.8900 | 0.0121 | 0.1650 | 0.6498 | 0.1650 | | 0.8465 | 6.6562 | 67800 | 0.8835 | 0.0121 | 0.1741 | 0.6527 | 0.1741 | | 0.8645 | 6.6660 | 67900 | 0.8923 | 0.0121 | 0.1731 | 0.6512 | 0.1731 | | 0.8838 | 6.6758 | 68000 | 0.8957 | 0.0121 | 0.1645 | 0.6486 | 0.1645 | | 0.8578 | 6.6856 | 68100 | 0.9084 | 0.0121 | 0.1644 | 0.6441 | 0.1644 | | 0.8734 | 6.6955 | 68200 | 0.8908 | 0.0121 | 0.1694 | 0.6515 | 0.1694 | | 0.8643 | 6.7053 | 68300 | 0.8887 | 0.0121 | 0.1691 | 0.6531 | 0.1691 | | 0.7971 | 6.7151 | 68400 | 0.8909 | 0.0121 | 0.1660 | 0.6516 | 0.1660 | | 0.8904 | 6.7249 | 68500 | 0.8926 | 0.0121 | 0.1665 | 0.6508 | 0.1665 | | 0.8864 | 6.7347 | 68600 | 0.8771 | 0.0121 | 0.1735 | 0.6570 | 0.1735 | | 0.8464 | 6.7446 | 68700 | 0.9023 | 0.0121 | 0.1628 | 0.6463 | 0.1628 | | 0.9121 | 6.7544 | 68800 | 0.8985 | 0.0121 | 0.1600 | 0.6477 | 0.1600 | | 0.9137 | 6.7642 | 68900 | 0.9187 | 0.0121 | 0.1537 | 0.6423 | 0.1537 | | 0.9691 | 6.7740 | 69000 | 0.9116 | 0.0121 | 0.1583 | 0.6423 | 0.1583 | | 0.925 | 6.7838 | 69100 | 0.9123 | 0.0121 | 0.1696 | 0.6470 | 0.1696 | | 0.984 | 6.7936 | 69200 | 0.9114 | 0.0121 | 0.1613 | 0.6447 | 0.1613 | | 0.7877 | 6.8035 | 69300 | 0.8902 | 0.0121 | 0.1700 | 0.6523 | 0.1700 | | 0.9316 | 6.8133 | 69400 | 0.8867 | 0.0121 | 0.1729 | 0.6549 | 0.1729 | | 0.9002 | 6.8231 | 69500 | 0.9049 | 0.0121 | 0.1661 | 0.6476 | 0.1661 | | 0.9117 | 6.8329 | 69600 | 0.9036 | 0.0121 | 0.1571 | 0.6483 | 0.1571 | | 0.8923 | 6.8427 | 69700 | 0.9154 | 0.0121 | 0.1553 | 0.6440 | 0.1553 | | 0.8687 | 6.8525 | 69800 | 0.8993 | 0.0121 | 0.1596 | 0.6499 | 0.1596 | | 0.8335 | 6.8624 | 69900 | 0.8929 | 0.0121 | 0.1734 | 0.6522 | 0.1734 | | 0.9734 | 6.8722 | 70000 | 0.8966 | 0.0121 | 0.1693 | 0.6496 | 0.1693 | | 0.8941 | 6.8820 | 70100 | 0.9150 | 0.0121 | 0.1639 | 0.6440 | 0.1639 | | 0.9068 | 6.8918 | 70200 | 0.9009 | 0.0121 | 0.1635 | 0.6469 | 0.1635 | | 0.8599 | 6.9016 | 70300 | 0.8984 | 0.0121 | 0.1674 | 0.6487 | 0.1674 | | 0.8525 | 6.9114 | 70400 | 0.8959 | 0.0121 | 0.1668 | 0.6488 | 0.1668 | | 0.9187 | 6.9213 | 70500 | 0.9066 | 0.0121 | 0.1675 | 0.6446 | 0.1675 | | 0.8898 | 6.9311 | 70600 | 0.9043 | 0.0121 | 0.1655 | 0.6481 | 0.1655 | | 0.8829 | 6.9409 | 70700 | 0.9234 | 0.0121 | 0.1574 | 0.6422 | 0.1574 | | 0.8977 | 6.9507 | 70800 | 0.8913 | 0.0121 | 0.1665 | 0.6503 | 0.1665 | | 0.8974 | 6.9605 | 70900 | 0.9152 | 0.0121 | 0.1594 | 0.6419 | 0.1594 | | 0.8644 | 6.9704 | 71000 | 0.8937 | 0.0121 | 0.1690 | 0.6506 | 0.1690 | | 0.9495 | 6.9802 | 71100 | 0.8918 | 0.0121 | 0.1700 | 0.6485 | 0.1700 | | 0.9866 | 6.9900 | 71200 | 0.9175 | 0.0121 | 0.1516 | 0.6396 | 0.1516 | | 0.9188 | 6.9998 | 71300 | 0.9003 | 0.0121 | 0.1624 | 0.6480 | 0.1624 | | 0.8109 | 7.0096 | 71400 | 0.8966 | 0.0121 | 0.1656 | 0.6508 | 0.1656 | | 0.8681 | 7.0194 | 71500 | 0.8798 | 0.0121 | 0.1735 | 0.6573 | 0.1735 | | 0.8698 | 7.0293 | 71600 | 0.9139 | 0.0121 | 0.1588 | 0.6407 | 0.1588 | | 0.9085 | 7.0391 | 71700 | 0.8874 | 0.0121 | 0.1640 | 0.6530 | 0.1640 | | 0.8601 | 7.0489 | 71800 | 0.9003 | 0.0121 | 0.1615 | 0.6481 | 0.1615 | | 0.8931 | 7.0587 | 71900 | 0.8947 | 0.0121 | 0.1612 | 0.6486 | 0.1612 | | 0.8785 | 7.0685 | 72000 | 0.8977 | 0.0121 | 0.1689 | 0.6522 | 0.1689 | | 0.9064 | 7.0783 | 72100 | 0.9052 | 0.0121 | 0.1632 | 0.6462 | 0.1632 | | 0.8819 | 7.0882 | 72200 | 0.9062 | 0.0121 | 0.1599 | 0.6433 | 0.1599 | | 0.8778 | 7.0980 | 72300 | 0.8969 | 0.0121 | 0.1705 | 0.6507 | 0.1705 | | 0.9143 | 7.1078 | 72400 | 0.9161 | 0.0121 | 0.1538 | 0.6411 | 0.1538 | | 0.8533 | 7.1176 | 72500 | 0.8996 | 0.0121 | 0.1610 | 0.6468 | 0.1610 | | 0.9242 | 7.1274 | 72600 | 0.8970 | 0.0121 | 0.1686 | 0.6510 | 0.1686 | | 0.8492 | 7.1372 | 72700 | 0.8880 | 0.0121 | 0.1634 | 0.6498 | 0.1634 | | 0.9335 | 7.1471 | 72800 | 0.9017 | 0.0121 | 0.1626 | 0.6479 | 0.1626 | | 0.8984 | 7.1569 | 72900 | 0.8902 | 0.0121 | 0.1639 | 0.6507 | 0.1639 | | 0.8653 | 7.1667 | 73000 | 0.8906 | 0.0121 | 0.1678 | 0.6518 | 0.1678 | | 0.8975 | 7.1765 | 73100 | 0.8762 | 0.0121 | 0.1744 | 0.6573 | 0.1744 | | 0.899 | 7.1863 | 73200 | 0.8923 | 0.0121 | 0.1673 | 0.6513 | 0.1673 | | 0.9245 | 7.1962 | 73300 | 0.8920 | 0.0121 | 0.1626 | 0.6499 | 0.1626 | | 0.8859 | 7.2060 | 73400 | 0.8925 | 0.0121 | 0.1675 | 0.6500 | 0.1675 | | 0.8344 | 7.2158 | 73500 | 0.8864 | 0.0121 | 0.1657 | 0.6496 | 0.1657 | | 0.8866 | 7.2256 | 73600 | 0.8803 | 0.0121 | 0.1758 | 0.6567 | 0.1758 | | 0.8842 | 7.2354 | 73700 | 0.8880 | 0.0121 | 0.1677 | 0.6536 | 0.1677 | | 0.9165 | 7.2452 | 73800 | 0.8877 | 0.0121 | 0.1716 | 0.6528 | 0.1716 | | 0.8621 | 7.2551 | 73900 | 0.8900 | 0.0121 | 0.1756 | 0.6525 | 0.1756 | | 0.8732 | 7.2649 | 74000 | 0.8912 | 0.0121 | 0.1713 | 0.6517 | 0.1713 | | 0.895 | 7.2747 | 74100 | 0.8889 | 0.0121 | 0.1702 | 0.6521 | 0.1702 | | 0.9558 | 7.2845 | 74200 | 0.8893 | 0.0121 | 0.1675 | 0.6537 | 0.1675 | | 0.8515 | 7.2943 | 74300 | 0.8876 | 0.0121 | 0.1613 | 0.6503 | 0.1613 | | 0.833 | 7.3041 | 74400 | 0.8864 | 0.0121 | 0.1773 | 0.6521 | 0.1773 | | 0.8715 | 7.3140 | 74500 | 0.9271 | 0.0121 | 0.1455 | 0.6365 | 0.1455 | | 0.8638 | 7.3238 | 74600 | 0.8798 | 0.0121 | 0.1743 | 0.6558 | 0.1743 | | 0.8635 | 7.3336 | 74700 | 0.8945 | 0.0121 | 0.1620 | 0.6476 | 0.1620 | | 0.7944 | 7.3434 | 74800 | 0.8882 | 0.0121 | 0.1730 | 0.6516 | 0.1730 | | 0.9072 | 7.3532 | 74900 | 0.9084 | 0.0121 | 0.1566 | 0.6444 | 0.1566 | | 0.9102 | 7.3630 | 75000 | 0.8870 | 0.0121 | 0.1720 | 0.6509 | 0.1720 | | 0.9261 | 7.3729 | 75100 | 0.9056 | 0.0121 | 0.1742 | 0.6501 | 0.1742 | | 0.9421 | 7.3827 | 75200 | 0.8887 | 0.0121 | 0.1683 | 0.6511 | 0.1683 | | 0.9074 | 7.3925 | 75300 | 0.8886 | 0.0121 | 0.1682 | 0.6510 | 0.1682 | | 0.9349 | 7.4023 | 75400 | 0.8970 | 0.0121 | 0.1692 | 0.6498 | 0.1692 | | 0.9081 | 7.4121 | 75500 | 0.8855 | 0.0121 | 0.1690 | 0.6525 | 0.1690 | | 0.8903 | 7.4220 | 75600 | 0.9003 | 0.0121 | 0.1605 | 0.6460 | 0.1605 | | 0.7998 | 7.4318 | 75700 | 0.8866 | 0.0121 | 0.1611 | 0.6524 | 0.1611 | | 0.9273 | 7.4416 | 75800 | 0.9242 | 0.0121 | 0.1550 | 0.6418 | 0.1550 | | 0.9488 | 7.4514 | 75900 | 0.8843 | 0.0121 | 0.1681 | 0.6528 | 0.1681 | | 0.9358 | 7.4612 | 76000 | 0.9096 | 0.0121 | 0.1612 | 0.6482 | 0.1612 | | 0.9421 | 7.4710 | 76100 | 0.8894 | 0.0121 | 0.1711 | 0.6539 | 0.1711 | | 0.8566 | 7.4809 | 76200 | 0.8851 | 0.0121 | 0.1764 | 0.6535 | 0.1764 | | 0.8836 | 7.4907 | 76300 | 0.8883 | 0.0121 | 0.1758 | 0.6524 | 0.1758 | | 0.9199 | 7.5005 | 76400 | 0.9105 | 0.0121 | 0.1589 | 0.6472 | 0.1589 | | 0.8648 | 7.5103 | 76500 | 0.8708 | 0.0121 | 0.1773 | 0.6584 | 0.1773 | | 0.8728 | 7.5201 | 76600 | 0.8804 | 0.0121 | 0.1719 | 0.6557 | 0.1719 | | 0.8438 | 7.5299 | 76700 | 0.8894 | 0.0121 | 0.1675 | 0.6523 | 0.1675 | | 0.9103 | 7.5398 | 76800 | 0.9067 | 0.0121 | 0.1701 | 0.6485 | 0.1701 | | 0.9266 | 7.5496 | 76900 | 0.8889 | 0.0121 | 0.1622 | 0.6497 | 0.1622 | | 0.9135 | 7.5594 | 77000 | 0.9084 | 0.0121 | 0.1642 | 0.6457 | 0.1642 | | 0.905 | 7.5692 | 77100 | 0.8855 | 0.0121 | 0.1720 | 0.6552 | 0.1720 | | 0.8316 | 7.5790 | 77200 | 0.9042 | 0.0121 | 0.1643 | 0.6474 | 0.1643 | | 0.9467 | 7.5888 | 77300 | 0.8937 | 0.0121 | 0.1678 | 0.6497 | 0.1678 | | 0.8321 | 7.5987 | 77400 | 0.8800 | 0.0121 | 0.1575 | 0.6520 | 0.1575 | | 0.9027 | 7.6085 | 77500 | 0.8975 | 0.0121 | 0.1731 | 0.6500 | 0.1731 | | 0.9398 | 7.6183 | 77600 | 0.9001 | 0.0121 | 0.1628 | 0.6472 | 0.1628 | | 0.8569 | 7.6281 | 77700 | 0.8941 | 0.0121 | 0.1599 | 0.6478 | 0.1599 | | 0.8378 | 7.6379 | 77800 | 0.8791 | 0.0121 | 0.1709 | 0.6539 | 0.1709 | | 0.889 | 7.6478 | 77900 | 0.9053 | 0.0121 | 0.1631 | 0.6445 | 0.1631 | | 0.8715 | 7.6576 | 78000 | 0.9165 | 0.0121 | 0.1637 | 0.6423 | 0.1637 | | 0.8828 | 7.6674 | 78100 | 0.8772 | 0.0121 | 0.1801 | 0.6570 | 0.1801 | | 0.8339 | 7.6772 | 78200 | 0.8880 | 0.0121 | 0.1644 | 0.6505 | 0.1644 | | 0.8239 | 7.6870 | 78300 | 0.8844 | 0.0121 | 0.1738 | 0.6554 | 0.1738 | | 0.861 | 7.6968 | 78400 | 0.8858 | 0.0121 | 0.1726 | 0.6551 | 0.1726 | | 0.8917 | 7.7067 | 78500 | 0.8873 | 0.0121 | 0.1650 | 0.6513 | 0.1650 | | 0.8643 | 7.7165 | 78600 | 0.8824 | 0.0121 | 0.1778 | 0.6583 | 0.1778 | | 0.8449 | 7.7263 | 78700 | 0.8747 | 0.0121 | 0.1695 | 0.6555 | 0.1695 | | 0.9109 | 7.7361 | 78800 | 0.8924 | 0.0121 | 0.1660 | 0.6511 | 0.1660 | | 0.8526 | 7.7459 | 78900 | 0.8828 | 0.0121 | 0.1693 | 0.6539 | 0.1693 | | 0.8111 | 7.7557 | 79000 | 0.8904 | 0.0121 | 0.1683 | 0.6542 | 0.1683 | | 0.8652 | 7.7656 | 79100 | 0.8681 | 0.0121 | 0.1791 | 0.6613 | 0.1791 | | 0.8739 | 7.7754 | 79200 | 0.8777 | 0.0121 | 0.1703 | 0.6577 | 0.1703 | | 0.8648 | 7.7852 | 79300 | 0.8916 | 0.0121 | 0.1635 | 0.6461 | 0.1635 | | 0.8935 | 7.7950 | 79400 | 0.9094 | 0.0121 | 0.1621 | 0.6456 | 0.1621 | | 0.8423 | 7.8048 | 79500 | 0.8895 | 0.0121 | 0.1744 | 0.6534 | 0.1744 | | 0.9597 | 7.8146 | 79600 | 0.8942 | 0.0121 | 0.1706 | 0.6518 | 0.1706 | | 0.8766 | 7.8245 | 79700 | 0.9027 | 0.0121 | 0.1702 | 0.6493 | 0.1702 | | 0.8886 | 7.8343 | 79800 | 0.8931 | 0.0121 | 0.1693 | 0.6501 | 0.1693 | | 0.9142 | 7.8441 | 79900 | 0.8899 | 0.0121 | 0.1737 | 0.6528 | 0.1737 | | 0.8584 | 7.8539 | 80000 | 0.8916 | 0.0121 | 0.1663 | 0.6502 | 0.1663 | | 0.8833 | 7.8637 | 80100 | 0.8787 | 0.0121 | 0.1683 | 0.6554 | 0.1683 | | 0.7781 | 7.8736 | 80200 | 0.8710 | 0.0121 | 0.1743 | 0.6591 | 0.1743 | | 0.9024 | 7.8834 | 80300 | 0.8760 | 0.0121 | 0.1696 | 0.6546 | 0.1696 | | 0.9207 | 7.8932 | 80400 | 0.8953 | 0.0121 | 0.1707 | 0.6503 | 0.1707 | | 0.8648 | 7.9030 | 80500 | 0.8848 | 0.0121 | 0.1684 | 0.6512 | 0.1684 | | 0.8437 | 7.9128 | 80600 | 0.8763 | 0.0121 | 0.1661 | 0.6549 | 0.1661 | | 0.8794 | 7.9226 | 80700 | 0.8653 | 0.0121 | 0.1733 | 0.6594 | 0.1733 | | 0.8541 | 7.9325 | 80800 | 0.8885 | 0.0121 | 0.1667 | 0.6526 | 0.1667 | | 0.9453 | 7.9423 | 80900 | 0.8888 | 0.0121 | 0.1679 | 0.6522 | 0.1679 | | 0.9241 | 7.9521 | 81000 | 0.8943 | 0.0121 | 0.1604 | 0.6498 | 0.1604 | | 0.8594 | 7.9619 | 81100 | 0.8747 | 0.0121 | 0.1783 | 0.6583 | 0.1783 | | 0.8906 | 7.9717 | 81200 | 0.8913 | 0.0121 | 0.1731 | 0.6521 | 0.1731 | | 0.8827 | 7.9815 | 81300 | 0.8862 | 0.0121 | 0.1686 | 0.6527 | 0.1686 | | 0.8428 | 7.9914 | 81400 | 0.8754 | 0.0121 | 0.1733 | 0.6578 | 0.1733 | | 0.8502 | 8.0012 | 81500 | 0.8785 | 0.0121 | 0.1716 | 0.6549 | 0.1716 | | 0.8665 | 8.0110 | 81600 | 0.8976 | 0.0121 | 0.1543 | 0.6499 | 0.1543 | | 0.8266 | 8.0208 | 81700 | 0.8728 | 0.0121 | 0.1725 | 0.6588 | 0.1725 | | 0.8515 | 8.0306 | 81800 | 0.8866 | 0.0121 | 0.1689 | 0.6522 | 0.1689 | | 0.8596 | 8.0404 | 81900 | 0.8747 | 0.0121 | 0.1711 | 0.6569 | 0.1711 | | 0.933 | 8.0503 | 82000 | 0.8958 | 0.0121 | 0.1679 | 0.6524 | 0.1679 | | 0.8987 | 8.0601 | 82100 | 0.8788 | 0.0121 | 0.1664 | 0.6553 | 0.1664 | | 0.9081 | 8.0699 | 82200 | 0.8943 | 0.0121 | 0.1714 | 0.6487 | 0.1714 | | 0.8583 | 8.0797 | 82300 | 1.0938 | 0.0121 | 0.1248 | 0.5901 | 0.1248 | | 0.9374 | 8.0895 | 82400 | 0.8959 | 0.0121 | 0.1687 | 0.6499 | 0.1687 | | 0.8707 | 8.0994 | 82500 | 0.8787 | 0.0121 | 0.1724 | 0.6535 | 0.1724 | | 0.8492 | 8.1092 | 82600 | 0.8869 | 0.0121 | 0.1686 | 0.6569 | 0.1686 | | 0.9135 | 8.1190 | 82700 | 0.9008 | 0.0121 | 0.1577 | 0.6485 | 0.1577 | | 0.9245 | 8.1288 | 82800 | 0.8776 | 0.0121 | 0.1707 | 0.6550 | 0.1707 | | 0.8326 | 8.1386 | 82900 | 0.8856 | 0.0121 | 0.1641 | 0.6495 | 0.1641 | | 0.9092 | 8.1484 | 83000 | 0.8960 | 0.0121 | 0.1657 | 0.6498 | 0.1657 | | 0.9163 | 8.1583 | 83100 | 0.8778 | 0.0121 | 0.1736 | 0.6549 | 0.1736 | | 0.8896 | 8.1681 | 83200 | 0.8936 | 0.0121 | 0.1597 | 0.6499 | 0.1597 | | 0.8872 | 8.1779 | 83300 | 0.8760 | 0.0121 | 0.1693 | 0.6554 | 0.1693 | | 0.8451 | 8.1877 | 83400 | 0.8668 | 0.0121 | 0.1776 | 0.6617 | 0.1776 | | 0.8352 | 8.1975 | 83500 | 0.8719 | 0.0121 | 0.1798 | 0.6570 | 0.1798 | | 0.9612 | 8.2073 | 83600 | 0.8775 | 0.0121 | 0.1726 | 0.6546 | 0.1726 | | 0.8528 | 8.2172 | 83700 | 0.8798 | 0.0121 | 0.1773 | 0.6579 | 0.1773 | | 0.8939 | 8.2270 | 83800 | 0.9222 | 0.0121 | 0.1631 | 0.6410 | 0.1631 | | 0.8205 | 8.2368 | 83900 | 0.8907 | 0.0121 | 0.1723 | 0.6511 | 0.1723 | | 0.9358 | 8.2466 | 84000 | 0.8756 | 0.0121 | 0.1760 | 0.6592 | 0.1760 | | 0.8687 | 8.2564 | 84100 | 0.8841 | 0.0121 | 0.1714 | 0.6565 | 0.1714 | | 0.9093 | 8.2662 | 84200 | 0.9097 | 0.0121 | 0.1587 | 0.6445 | 0.1587 | | 0.9625 | 8.2761 | 84300 | 0.9028 | 0.0121 | 0.1605 | 0.6459 | 0.1605 | | 0.9625 | 8.2859 | 84400 | 0.9074 | 0.0121 | 0.1628 | 0.6451 | 0.1628 | | 0.8268 | 8.2957 | 84500 | 0.8868 | 0.0121 | 0.1682 | 0.6510 | 0.1682 | | 0.9055 | 8.3055 | 84600 | 0.8898 | 0.0121 | 0.1693 | 0.6514 | 0.1693 | | 0.878 | 8.3153 | 84700 | 0.9080 | 0.0121 | 0.1660 | 0.6472 | 0.1660 | | 0.8911 | 8.3252 | 84800 | 0.8842 | 0.0121 | 0.1744 | 0.6533 | 0.1744 | | 0.7994 | 8.3350 | 84900 | 0.8865 | 0.0121 | 0.1653 | 0.6518 | 0.1653 | | 0.8959 | 8.3448 | 85000 | 0.8986 | 0.0121 | 0.1706 | 0.6522 | 0.1706 | | 0.7867 | 8.3546 | 85100 | 0.8807 | 0.0121 | 0.1724 | 0.6567 | 0.1724 | | 0.9298 | 8.3644 | 85200 | 0.8962 | 0.0121 | 0.1630 | 0.6483 | 0.1630 | | 0.8122 | 8.3742 | 85300 | 0.8741 | 0.0121 | 0.1724 | 0.6575 | 0.1724 | | 0.8754 | 8.3841 | 85400 | 0.8894 | 0.0121 | 0.1663 | 0.6517 | 0.1663 | | 0.8224 | 8.3939 | 85500 | 0.8961 | 0.0121 | 0.1643 | 0.6468 | 0.1643 | | 0.879 | 8.4037 | 85600 | 0.8843 | 0.0121 | 0.1756 | 0.6550 | 0.1756 | | 0.9025 | 8.4135 | 85700 | 0.8946 | 0.0121 | 0.1636 | 0.6513 | 0.1636 | | 0.8163 | 8.4233 | 85800 | 0.8884 | 0.0121 | 0.1621 | 0.6508 | 0.1621 | | 0.8935 | 8.4331 | 85900 | 0.8889 | 0.0121 | 0.1675 | 0.6520 | 0.1675 | | 0.9107 | 8.4430 | 86000 | 0.8672 | 0.0121 | 0.1811 | 0.6608 | 0.1811 | | 0.9779 | 8.4528 | 86100 | 0.8804 | 0.0121 | 0.1725 | 0.6568 | 0.1725 | | 0.8305 | 8.4626 | 86200 | 0.9006 | 0.0121 | 0.1624 | 0.6471 | 0.1624 | | 0.8844 | 8.4724 | 86300 | 0.8945 | 0.0121 | 0.1631 | 0.6512 | 0.1631 | | 0.8139 | 8.4822 | 86400 | 0.8809 | 0.0121 | 0.1773 | 0.6555 | 0.1773 | | 0.8526 | 8.4920 | 86500 | 0.8811 | 0.0121 | 0.1614 | 0.6521 | 0.1614 | | 0.8673 | 8.5019 | 86600 | 0.8800 | 0.0121 | 0.1715 | 0.6550 | 0.1715 | | 0.9264 | 8.5117 | 86700 | 0.8915 | 0.0121 | 0.1763 | 0.6525 | 0.1763 | | 0.8396 | 8.5215 | 86800 | 0.9141 | 0.0121 | 0.1674 | 0.6444 | 0.1674 | | 0.8332 | 8.5313 | 86900 | 0.8931 | 0.0121 | 0.1620 | 0.6514 | 0.1620 | | 0.924 | 8.5411 | 87000 | 0.8912 | 0.0121 | 0.1622 | 0.6511 | 0.1622 | | 0.8566 | 8.5510 | 87100 | 0.8906 | 0.0121 | 0.1613 | 0.6518 | 0.1613 | | 0.8294 | 8.5608 | 87200 | 0.9110 | 0.0121 | 0.1633 | 0.6455 | 0.1633 | | 0.9732 | 8.5706 | 87300 | 0.8983 | 0.0121 | 0.1679 | 0.6487 | 0.1679 | | 0.87 | 8.5804 | 87400 | 0.8930 | 0.0121 | 0.1562 | 0.6488 | 0.1562 | | 0.8377 | 8.5902 | 87500 | 0.8701 | 0.0121 | 0.1743 | 0.6586 | 0.1743 | | 0.857 | 8.6000 | 87600 | 0.8985 | 0.0121 | 0.1610 | 0.6496 | 0.1610 | | 0.8647 | 8.6099 | 87700 | 0.8798 | 0.0121 | 0.1784 | 0.6579 | 0.1784 | | 0.8771 | 8.6197 | 87800 | 0.8793 | 0.0121 | 0.1686 | 0.6559 | 0.1686 | | 0.9198 | 8.6295 | 87900 | 0.8892 | 0.0121 | 0.1657 | 0.6532 | 0.1657 | | 0.9074 | 8.6393 | 88000 | 0.9027 | 0.0121 | 0.1631 | 0.6482 | 0.1631 | | 0.9175 | 8.6491 | 88100 | 0.8917 | 0.0121 | 0.1647 | 0.6498 | 0.1647 | | 0.8699 | 8.6589 | 88200 | 0.8791 | 0.0121 | 0.1692 | 0.6567 | 0.1692 | | 0.8949 | 8.6688 | 88300 | 0.8906 | 0.0121 | 0.1653 | 0.6481 | 0.1653 | | 0.8686 | 8.6786 | 88400 | 0.8984 | 0.0121 | 0.1607 | 0.6481 | 0.1607 | | 1.035 | 8.6884 | 88500 | 0.9014 | 0.0121 | 0.1642 | 0.6487 | 0.1642 | | 0.8353 | 8.6982 | 88600 | 0.8832 | 0.0121 | 0.1724 | 0.6540 | 0.1724 | | 0.8507 | 8.7080 | 88700 | 0.9010 | 0.0121 | 0.1680 | 0.6498 | 0.1680 | | 0.9186 | 8.7178 | 88800 | 0.8872 | 0.0121 | 0.1735 | 0.6534 | 0.1735 | | 0.8306 | 8.7277 | 88900 | 0.8810 | 0.0121 | 0.1671 | 0.6556 | 0.1671 | | 0.8596 | 8.7375 | 89000 | 0.9024 | 0.0121 | 0.1653 | 0.6488 | 0.1653 | | 0.8422 | 8.7473 | 89100 | 0.8811 | 0.0121 | 0.1751 | 0.6529 | 0.1751 | | 0.9112 | 8.7571 | 89200 | 0.8775 | 0.0121 | 0.1675 | 0.6540 | 0.1675 | | 0.835 | 8.7669 | 89300 | 0.8832 | 0.0121 | 0.1753 | 0.6556 | 0.1753 | | 0.8434 | 8.7768 | 89400 | 0.8837 | 0.0121 | 0.1767 | 0.6568 | 0.1767 | | 0.933 | 8.7866 | 89500 | 0.8876 | 0.0121 | 0.1715 | 0.6529 | 0.1715 | | 0.8242 | 8.7964 | 89600 | 0.8777 | 0.0121 | 0.1693 | 0.6542 | 0.1693 | | 0.8515 | 8.8062 | 89700 | 0.8861 | 0.0121 | 0.1744 | 0.6541 | 0.1744 | | 0.8414 | 8.8160 | 89800 | 0.8788 | 0.0121 | 0.1719 | 0.6539 | 0.1719 | | 0.8493 | 8.8258 | 89900 | 0.8801 | 0.0121 | 0.1643 | 0.6533 | 0.1643 | | 0.8985 | 8.8357 | 90000 | 0.8813 | 0.0121 | 0.1728 | 0.6557 | 0.1728 | | 0.9069 | 8.8455 | 90100 | 0.8783 | 0.0121 | 0.1664 | 0.6541 | 0.1664 | | 0.8162 | 8.8553 | 90200 | 0.8989 | 0.0121 | 0.1639 | 0.6484 | 0.1639 | | 0.8368 | 8.8651 | 90300 | 0.8738 | 0.0121 | 0.1725 | 0.6568 | 0.1725 | | 0.8865 | 8.8749 | 90400 | 0.8841 | 0.0121 | 0.1649 | 0.6544 | 0.1649 | | 0.8651 | 8.8847 | 90500 | 0.8779 | 0.0121 | 0.1733 | 0.6550 | 0.1733 | | 0.8775 | 8.8946 | 90600 | 0.8850 | 0.0121 | 0.1608 | 0.6524 | 0.1608 | | 0.8091 | 8.9044 | 90700 | 0.8816 | 0.0121 | 0.1668 | 0.6561 | 0.1668 | | 0.8726 | 8.9142 | 90800 | 0.8871 | 0.0121 | 0.1651 | 0.6512 | 0.1651 | | 0.9415 | 8.9240 | 90900 | 0.8769 | 0.0121 | 0.1741 | 0.6572 | 0.1741 | | 0.846 | 8.9338 | 91000 | 0.8900 | 0.0121 | 0.1715 | 0.6531 | 0.1715 | | 0.8878 | 8.9436 | 91100 | 0.9125 | 0.0121 | 0.1658 | 0.6470 | 0.1658 | | 0.9493 | 8.9535 | 91200 | 0.8922 | 0.0121 | 0.1691 | 0.6480 | 0.1691 | | 0.8781 | 8.9633 | 91300 | 0.8823 | 0.0121 | 0.1667 | 0.6526 | 0.1667 | | 0.9035 | 8.9731 | 91400 | 0.8890 | 0.0121 | 0.1715 | 0.6517 | 0.1715 | | 0.8642 | 8.9829 | 91500 | 0.8988 | 0.0121 | 0.1693 | 0.6487 | 0.1693 | | 0.8415 | 8.9927 | 91600 | 0.8870 | 0.0121 | 0.1686 | 0.6519 | 0.1686 | | 0.864 | 9.0026 | 91700 | 0.8901 | 0.0121 | 0.1721 | 0.6524 | 0.1721 | | 0.9031 | 9.0124 | 91800 | 0.8907 | 0.0121 | 0.1721 | 0.6516 | 0.1721 | | 0.8783 | 9.0222 | 91900 | 0.8837 | 0.0121 | 0.1601 | 0.6484 | 0.1601 | | 0.952 | 9.0320 | 92000 | 0.9131 | 0.0121 | 0.1670 | 0.6459 | 0.1670 | | 0.8702 | 9.0418 | 92100 | 0.9124 | 0.0121 | 0.1567 | 0.6423 | 0.1567 | | 0.9422 | 9.0516 | 92200 | 0.8867 | 0.0121 | 0.1749 | 0.6526 | 0.1749 | | 0.9153 | 9.0615 | 92300 | 0.8892 | 0.0121 | 0.1619 | 0.6509 | 0.1619 | | 0.8876 | 9.0713 | 92400 | 0.8911 | 0.0121 | 0.1705 | 0.6533 | 0.1705 | | 0.9129 | 9.0811 | 92500 | 0.8891 | 0.0121 | 0.1748 | 0.6546 | 0.1748 | | 0.8659 | 9.0909 | 92600 | 0.9137 | 0.0121 | 0.1635 | 0.6476 | 0.1635 | | 0.8991 | 9.1007 | 92700 | 0.8962 | 0.0121 | 0.1653 | 0.6485 | 0.1653 | | 0.8597 | 9.1105 | 92800 | 0.8887 | 0.0121 | 0.1709 | 0.6516 | 0.1709 | | 0.8829 | 9.1204 | 92900 | 0.8989 | 0.0121 | 0.1687 | 0.6468 | 0.1687 | | 0.9497 | 9.1302 | 93000 | 0.8895 | 0.0121 | 0.1674 | 0.6516 | 0.1674 | | 0.8892 | 9.1400 | 93100 | 0.9024 | 0.0121 | 0.1647 | 0.6485 | 0.1647 | | 0.9227 | 9.1498 | 93200 | 0.8930 | 0.0121 | 0.1678 | 0.6482 | 0.1678 | | 0.821 | 9.1596 | 93300 | 0.8753 | 0.0121 | 0.1727 | 0.6579 | 0.1727 | | 0.912 | 9.1694 | 93400 | 0.8938 | 0.0121 | 0.1624 | 0.6470 | 0.1624 | | 0.8599 | 9.1793 | 93500 | 0.8865 | 0.0121 | 0.1776 | 0.6547 | 0.1776 | | 0.8947 | 9.1891 | 93600 | 0.8911 | 0.0121 | 0.1707 | 0.6498 | 0.1707 | | 0.9006 | 9.1989 | 93700 | 0.8800 | 0.0121 | 0.1702 | 0.6535 | 0.1702 | | 0.9032 | 9.2087 | 93800 | 0.8805 | 0.0121 | 0.1779 | 0.6540 | 0.1779 | | 0.9449 | 9.2185 | 93900 | 0.8653 | 0.0121 | 0.1763 | 0.6596 | 0.1763 | | 0.7906 | 9.2284 | 94000 | 0.8777 | 0.0121 | 0.1833 | 0.6564 | 0.1833 | | 0.8576 | 9.2382 | 94100 | 0.8956 | 0.0121 | 0.1643 | 0.6486 | 0.1643 | | 0.8581 | 9.2480 | 94200 | 0.8783 | 0.0121 | 0.1729 | 0.6551 | 0.1729 | | 0.897 | 9.2578 | 94300 | 0.9068 | 0.0121 | 0.1645 | 0.6480 | 0.1645 | | 0.8853 | 9.2676 | 94400 | 0.8996 | 0.0121 | 0.1621 | 0.6480 | 0.1621 | | 0.8634 | 9.2774 | 94500 | 0.8795 | 0.0121 | 0.1784 | 0.6566 | 0.1784 | | 0.8182 | 9.2873 | 94600 | 0.8763 | 0.0121 | 0.1782 | 0.6579 | 0.1782 | | 0.9051 | 9.2971 | 94700 | 0.8899 | 0.0121 | 0.1648 | 0.6504 | 0.1648 | | 0.9105 | 9.3069 | 94800 | 0.8971 | 0.0121 | 0.1619 | 0.6481 | 0.1619 | | 0.84 | 9.3167 | 94900 | 0.8970 | 0.0121 | 0.1635 | 0.6467 | 0.1635 | | 0.9105 | 9.3265 | 95000 | 0.8937 | 0.0121 | 0.1686 | 0.6508 | 0.1686 | | 0.8974 | 9.3363 | 95100 | 0.9009 | 0.0121 | 0.1755 | 0.6520 | 0.1755 | | 0.9465 | 9.3462 | 95200 | 0.9000 | 0.0121 | 0.1726 | 0.6503 | 0.1726 | | 0.8545 | 9.3560 | 95300 | 0.8961 | 0.0121 | 0.1691 | 0.6500 | 0.1691 | | 0.8711 | 9.3658 | 95400 | 0.8818 | 0.0121 | 0.1684 | 0.6532 | 0.1684 | | 0.9244 | 9.3756 | 95500 | 0.8853 | 0.0121 | 0.1706 | 0.6535 | 0.1706 | | 0.8094 | 9.3854 | 95600 | 0.8801 | 0.0121 | 0.1769 | 0.6561 | 0.1769 | | 0.8754 | 9.3952 | 95700 | 0.8994 | 0.0121 | 0.1712 | 0.6495 | 0.1712 | | 0.9122 | 9.4051 | 95800 | 0.8924 | 0.0121 | 0.1619 | 0.6513 | 0.1619 | | 0.9308 | 9.4149 | 95900 | 0.8668 | 0.0121 | 0.1786 | 0.6614 | 0.1786 | | 0.8718 | 9.4247 | 96000 | 0.8634 | 0.0121 | 0.1720 | 0.6606 | 0.1720 | | 0.8498 | 9.4345 | 96100 | 0.8933 | 0.0121 | 0.1703 | 0.6512 | 0.1703 | | 0.8661 | 9.4443 | 96200 | 0.8844 | 0.0121 | 0.1675 | 0.6527 | 0.1675 | | 0.8599 | 9.4542 | 96300 | 0.8809 | 0.0121 | 0.1681 | 0.6509 | 0.1681 | | 0.8299 | 9.4640 | 96400 | 0.8847 | 0.0121 | 0.1760 | 0.6557 | 0.1760 | | 0.8077 | 9.4738 | 96500 | 0.8986 | 0.0121 | 0.1664 | 0.6446 | 0.1664 | | 0.8472 | 9.4836 | 96600 | 0.9022 | 0.0121 | 0.1628 | 0.6467 | 0.1628 | | 0.8345 | 9.4934 | 96700 | 0.8835 | 0.0121 | 0.1693 | 0.6514 | 0.1693 | | 0.8092 | 9.5032 | 96800 | 0.8862 | 0.0121 | 0.1705 | 0.6528 | 0.1705 | | 0.978 | 9.5131 | 96900 | 0.8863 | 0.0121 | 0.1784 | 0.6522 | 0.1784 | | 0.9094 | 9.5229 | 97000 | 0.8872 | 0.0121 | 0.1751 | 0.6543 | 0.1751 | | 0.9012 | 9.5327 | 97100 | 0.8874 | 0.0121 | 0.1751 | 0.6545 | 0.1751 | | 0.8758 | 9.5425 | 97200 | 0.8880 | 0.0121 | 0.1671 | 0.6516 | 0.1671 | | 0.9321 | 9.5523 | 97300 | 0.8742 | 0.0121 | 0.1679 | 0.6572 | 0.1679 | | 0.7937 | 9.5621 | 97400 | 0.8806 | 0.0121 | 0.1691 | 0.6575 | 0.1691 | | 0.8658 | 9.5720 | 97500 | 0.8782 | 0.0121 | 0.1734 | 0.6560 | 0.1734 | | 0.8625 | 9.5818 | 97600 | 0.8873 | 0.0121 | 0.1761 | 0.6526 | 0.1761 | | 0.8831 | 9.5916 | 97700 | 0.8677 | 0.0121 | 0.1838 | 0.6621 | 0.1838 | | 0.844 | 9.6014 | 97800 | 0.8956 | 0.0121 | 0.1624 | 0.6513 | 0.1624 | | 0.8289 | 9.6112 | 97900 | 0.8771 | 0.0121 | 0.1735 | 0.6556 | 0.1735 | | 0.891 | 9.6210 | 98000 | 0.8888 | 0.0121 | 0.1704 | 0.6518 | 0.1704 | | 0.8433 | 9.6309 | 98100 | 0.8907 | 0.0121 | 0.1702 | 0.6546 | 0.1702 | | 0.7974 | 9.6407 | 98200 | 0.8671 | 0.0121 | 0.1776 | 0.6593 | 0.1776 | | 0.9177 | 9.6505 | 98300 | 0.8685 | 0.0121 | 0.1724 | 0.6586 | 0.1724 | | 0.8644 | 9.6603 | 98400 | 0.8774 | 0.0121 | 0.1802 | 0.6590 | 0.1802 | | 0.8961 | 9.6701 | 98500 | 0.8888 | 0.0121 | 0.1692 | 0.6498 | 0.1692 | | 0.8769 | 9.6800 | 98600 | 0.8783 | 0.0121 | 0.1760 | 0.6585 | 0.1760 | | 0.9498 | 9.6898 | 98700 | 0.9060 | 0.0121 | 0.1644 | 0.6462 | 0.1644 | | 0.8435 | 9.6996 | 98800 | 0.8771 | 0.0121 | 0.1756 | 0.6561 | 0.1756 | | 0.8151 | 9.7094 | 98900 | 0.8711 | 0.0121 | 0.1729 | 0.6578 | 0.1729 | | 0.8619 | 9.7192 | 99000 | 0.8616 | 0.0121 | 0.1759 | 0.6586 | 0.1759 | | 0.8862 | 9.7290 | 99100 | 0.8702 | 0.0121 | 0.1711 | 0.6585 | 0.1711 | | 0.9008 | 9.7389 | 99200 | 0.8826 | 0.0121 | 0.1716 | 0.6562 | 0.1716 | | 0.861 | 9.7487 | 99300 | 0.8937 | 0.0121 | 0.1670 | 0.6492 | 0.1670 | | 0.8789 | 9.7585 | 99400 | 0.8822 | 0.0121 | 0.1693 | 0.6527 | 0.1693 | | 0.8296 | 9.7683 | 99500 | 0.8788 | 0.0121 | 0.1705 | 0.6557 | 0.1705 | | 0.9087 | 9.7781 | 99600 | 0.9116 | 0.0121 | 0.1662 | 0.6464 | 0.1662 | | 0.8118 | 9.7879 | 99700 | 0.8953 | 0.0121 | 0.1724 | 0.6521 | 0.1724 | | 0.9122 | 9.7978 | 99800 | 0.8802 | 0.0121 | 0.1739 | 0.6576 | 0.1739 | | 0.8541 | 9.8076 | 99900 | 0.8731 | 0.0121 | 0.1812 | 0.6593 | 0.1812 | | 0.9063 | 9.8174 | 100000 | 0.8769 | 0.0121 | 0.1747 | 0.6560 | 0.1747 | | 0.8517 | 9.8272 | 100100 | 0.8716 | 0.0121 | 0.1777 | 0.6605 | 0.1777 | | 0.8725 | 9.8370 | 100200 | 0.8849 | 0.0121 | 0.1646 | 0.6525 | 0.1646 | | 0.8793 | 9.8468 | 100300 | 0.8720 | 0.0121 | 0.1698 | 0.6578 | 0.1698 | | 0.8553 | 9.8567 | 100400 | 0.8584 | 0.0121 | 0.1829 | 0.6629 | 0.1829 | | 0.8216 | 9.8665 | 100500 | 0.8780 | 0.0121 | 0.1715 | 0.6568 | 0.1715 | | 0.8723 | 9.8763 | 100600 | 0.8758 | 0.0121 | 0.1764 | 0.6562 | 0.1764 | | 0.8514 | 9.8861 | 100700 | 0.8917 | 0.0121 | 0.1765 | 0.6556 | 0.1765 | | 0.9008 | 9.8959 | 100800 | 0.8966 | 0.0121 | 0.1703 | 0.6520 | 0.1703 | | 0.8712 | 9.9058 | 100900 | 0.8608 | 0.0121 | 0.1788 | 0.6606 | 0.1788 | | 0.8697 | 9.9156 | 101000 | 0.8751 | 0.0121 | 0.1712 | 0.6559 | 0.1712 | | 0.8335 | 9.9254 | 101100 | 0.8879 | 0.0121 | 0.1712 | 0.6544 | 0.1712 | | 0.9015 | 9.9352 | 101200 | 0.8813 | 0.0121 | 0.1675 | 0.6543 | 0.1675 | | 0.8726 | 9.9450 | 101300 | 0.8752 | 0.0121 | 0.1781 | 0.6570 | 0.1781 | | 0.8435 | 9.9548 | 101400 | 0.8954 | 0.0121 | 0.1662 | 0.6497 | 0.1662 | | 0.8604 | 9.9647 | 101500 | 0.8749 | 0.0121 | 0.1764 | 0.6583 | 0.1764 | | 0.9175 | 9.9745 | 101600 | 0.8851 | 0.0121 | 0.1658 | 0.6543 | 0.1658 | | 0.921 | 9.9843 | 101700 | 0.8862 | 0.0121 | 0.1739 | 0.6529 | 0.1739 | | 0.838 | 9.9941 | 101800 | 0.8718 | 0.0121 | 0.1822 | 0.6614 | 0.1822 | ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

johnnyyang0518/llama2_uuu_news_qlora

johnnyyang0518

2025-06-25T01:20:49Z

0

null

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

null

2025-06-25T01:20:49Z

--- license: apache-2.0 ---

Rootreck/Applio_3.2.9-RVC-Other

Rootreck

2025-06-25T01:14:44Z

0

null

[ "Applio", "RVC", "audio-to-audio", "en", "dataset:Rootreck/Applio_3.2.9-RVC-Other", "region:us" ]

audio-to-audio

2025-06-25T00:23:34Z

--- datasets: - Rootreck/Applio_3.2.9-RVC-Other language: - en pipeline_tag: audio-to-audio tags: - Applio - RVC ---

Treza12/Pixtral-1D-Thermal

Treza12

2025-06-25T01:14:07Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistral-community/pixtral-12b", "base_model:adapter:mistral-community/pixtral-12b", "region:us" ]

null

2025-06-25T01:12:52Z

--- base_model: mistral-community/pixtral-12b library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.3.dev0

sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF

sizzlebop

2025-06-25T01:09:12Z

0

null

[ "gguf", "code", "llama-cpp", "gguf-my-repo", "en", "dataset:Team-ACE/ToolACE", "base_model:Team-ACE/ToolACE-2-Llama-3.1-8B", "base_model:quantized:Team-ACE/ToolACE-2-Llama-3.1-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-06-25T01:08:44Z

--- license: apache-2.0 datasets: - Team-ACE/ToolACE language: - en metrics: - accuracy base_model: Team-ACE/ToolACE-2-Llama-3.1-8B tags: - code - llama-cpp - gguf-my-repo --- # sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF This model was converted to GGUF format from [`Team-ACE/ToolACE-2-Llama-3.1-8B`](https://huggingface.co/Team-ACE/ToolACE-2-Llama-3.1-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Team-ACE/ToolACE-2-Llama-3.1-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF --hf-file toolace-2-llama-3.1-8b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF --hf-file toolace-2-llama-3.1-8b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF --hf-file toolace-2-llama-3.1-8b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF --hf-file toolace-2-llama-3.1-8b-q8_0.gguf -c 2048 ```

hasdal/28686e11-1d59-4507-a11e-28a534a1ed6a

hasdal

2025-06-25T01:05:20Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-06-24T19:56:09Z

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

VarunNagaraj/tiny-llm-maui-apiclients-mistral

VarunNagaraj

2025-06-25T01:01:30Z

0

transformers

[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-instruct-v0.1-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-instruct-v0.1-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-25T01:00:24Z

--- base_model: unsloth/mistral-7b-instruct-v0.1-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** VarunNagaraj - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.1-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

phospho-app/GarrieD-ACT_BBOX-Red_Ball_V1_0624-mui9e

phospho-app

2025-06-25T00:59:03Z

0

null

[ "safetensors", "phosphobot", "act", "region:us" ]

null

2025-06-25T00:43:35Z

--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using **phospho**. Training was successfull, try it out on your robot! ## Training parameters: - **Dataset**: [phospho-app/Red_Ball_V1_0624_bboxes](https://huggingface.co/datasets/phospho-app/Red_Ball_V1_0624_bboxes) - **Wandb run URL**: None - **Epochs**: None - **Batch size**: 20 - **Training steps**: 10000 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)

deepmaster/72_8

deepmaster

2025-06-25T00:58:40Z

34

0

transformers

[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2025-06-08T18:57:53Z

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

tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5

tokyotech-llm

2025-06-25T00:57:38Z

35

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "ja", "dataset:tokyotech-llm/lmsys-chat-1m-synth", "dataset:lmsys/lmsys-chat-1m", "arxiv:2503.23714", "arxiv:2407.21783", "base_model:tokyotech-llm/Llama-3.1-Swallow-8B-v0.5", "base_model:finetune:tokyotech-llm/Llama-3.1-Swallow-8B-v0.5", "license:llama3.3", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-12T11:32:48Z

--- language: - en - ja library_name: transformers pipeline_tag: text-generation license: - llama3.3 - gemma model_type: llama datasets: - tokyotech-llm/lmsys-chat-1m-synth - lmsys/lmsys-chat-1m base_model: - tokyotech-llm/Llama-3.1-Swallow-8B-v0.5 --- # Llama 3.1 Swallow - Built with Llama Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the [Meta Llama 3.1](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f) models. Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities. We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and coding contents, etc (see the Training Datasets section of the base model) for continual pre-training. The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese. See the Swallow Model Index section to find other model variants. **Note**: [Llama-3.1-Swallow-8B-Instruct-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5) model was continually pre-trained from the [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) and then instruction-tuned with our instruction datasets. # Release History - **June 25, 2025**: Released [Llama-3.1-Swallow-8B-Instruct-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5) and [Llama-3.1-Swallow-8B-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.5). - **March 10, 2025**: Released [Llama-3.3-Swallow-70B-Instruct-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4) and [Llama-3.3-Swallow-70B-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4). - **December 30, 2024**: Released [Llama-3.1-Swallow-70B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3). - **December 23, 2024**: Released [Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3). - **November 11, 2024**: Released [Llama-3.1-Swallow-8B-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) and [Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2). - **October 08, 2024**: Released [Llama-3.1-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1), [Llama-3.1-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1), [Llama-3.1-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1), and [Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1). # Major Updates This release enhances the conversation capability of Llama 3.1 Swallow. The model is trained to imitate the behavior of [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it). Among all open-source LLMs with <= 8 billion parameters, Llama-3.1-Swallow-8B-Instruct-v0.5 exhibits **state-of-the-art performance on Japanese MT-Bench**, outperforming its predecessor, [Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2), by 1.5 points. ## Swallow Model Index |Model|Llama-3.1-Swallow-Instruct v0.5|Llama-3.1-Swallow v0.5|Llama-3.3-Swallow v0.4|Llama-3.3-Swallow-Instruct v0.4|Llama-3.1-Swallow-Instruct v0.3|Llama-3.1-Swallow-Instruct v0.2|Llama-3.1-Swallow v0.2|Llama-3.1-Swallow-Instruct v0.1|Llama-3.1-Swallow v0.1| |---|---|---|---|---|---|---|---|---|---| |8B|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.5) |||[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1)| |70B|||[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3)| | |[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1)| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1)| ![logo](./logo_v0.5.png) The website [https://swallow-llm.github.io/](https://swallow-llm.github.io/index.en.html) provides large language models developed by the Swallow team. ## Model Details * **Model type**: Please refer to [Llama 3.1 MODEL_CARD](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for details on the model architecture. * **Language(s)**: Japanese English * **Library**: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), [transformers](https://github.com/huggingface/transformers) * **Tokenizer**: Please refer to [Llama 3.1 blog](https://ai.meta.com/blog/meta-llama-3-1) for details on the tokenizer. * **Contact**: swallow[at]nlp.c.titech.ac.jp ## Model Performance ## Japanese MT-Bench * We report evaluation results judged by **gpt-4o-2024-08-06** as below. * In our releases earlier than January 1, 2025, we reported scores judged by gpt-4-1106-preview. Scores reported below are thus not directly comparable with those reported in those earlier releases. |Model|coding|extraction|humanities|math|reasoning|roleplay|stem|writing|JMTAvg| |---|---|---|---|---|---|---|---|---|---| | llm-jp-3-7.2b-instruct3 | 0.358 | 0.597 | 0.812 | 0.386 | 0.438 | 0.766 | 0.622 | 0.721 | 0.588 | | Qwen2.5-7B-Instruct | 0.599 | 0.741 | 0.719 | 0.637 | 0.541 | 0.744 | 0.624 | 0.713 | 0.665 | | Tanuki-8B-dpo-v1.0 | 0.461 | 0.597 | 0.562 | 0.495 | 0.377 | 0.589 | 0.509 | 0.643 | 0.529 | | Llama 3 8B Instruct | 0.467 | 0.706 | 0.692 | 0.310 | 0.433 | 0.542 | 0.532 | 0.546 | 0.529 | | Llama 3.1 8B Instruct | 0.420 | **0.830** | 0.550 | 0.514 | 0.349 | 0.502 | 0.479 | 0.504 | 0.519 | | Llama 3 Youko 8B Instruct | 0.464 | 0.757 | 0.769 | 0.414 | 0.487 | 0.695 | 0.583 | 0.753 | 0.616 | | Llama-3-ELYZA-JP-8B | 0.389 | 0.706 | 0.647 | 0.426 | **0.613** | 0.684 | 0.533 | 0.697 | 0.587 | | Llama 3 heron brain 8B v0.3 | 0.362 | 0.566 | 0.602 | 0.315 | 0.426 | 0.586 | 0.567 | 0.550 | 0.497 | | Llama 3.1 Swallow 8B Instruct v0.1 | 0.427 | 0.738 | 0.675 | 0.527 | 0.453 | 0.615 | 0.593 | 0.624 | 0.581 | | Llama 3.1 Swallow 8B Instruct v0.2 | 0.534 | 0.748 | 0.705 | 0.565 | 0.475 | 0.646 | 0.579 | 0.646 | 0.612 | | Llama 3.1 Swallow 8B Instruct v0.3 | **0.562** | 0.756 | 0.869 | **0.610** | 0.512 | 0.783 | 0.748 | 0.803 | 0.705 | | Llama 3.1 Swallow 8B Instruct v0.5 | 0.551 | 0.814 | **0.847** | 0.568 | 0.577 | **0.796** | **0.770** | **0.832** | **0.719** | ### Japanese tasks |Model|JCom.|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en|JMMLU|JHumanEval|Ja Avg| |---|---|---|---|---|---|---|---|---|---|---|---| | |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot|5-shot|0-shot| | | |EM acc|Char-F1|Char-F1|Char-F1|ROUGE-2|EM acc|BLEU|BLEU|EM acc|pass@1| | | llm-jp-3-7.2b-instruct3 | 0.780 | 0.297 | 0.570 | 0.882 | 0.132 | 0.344 | 0.251 | 0.189 | 0.422 | 0.196 | 0.406 | | Qwen2.5-7B-Instruct | 0.915 | 0.429 | 0.391 | 0.891 | 0.168 | 0.632 | 0.211 | 0.192 | 0.623 | 0.532 | 0.498 | | Tanuki-8B-dpo-v1.0 | 0.278 | 0.284 | 0.370 | 0.670 | 0.102 | 0.428 | 0.238 | 0.183 | 0.306 | 0.251 | 0.311 | | Llama 3 8B Instruct | 0.880 | 0.417 | 0.385 | 0.891 | 0.126 | 0.424 | 0.214 | 0.202 | 0.468 | 0.296 | 0.430 | | Llama 3.1 8B Instruct | 0.880 | 0.447 | 0.407 | 0.886 | 0.148 | 0.516 | 0.218 | 0.200 | 0.509 | 0.488 | 0.470 | | Llama 3 Youko 8B Instruct | 0.921 | 0.481 | 0.517 | 0.899 | 0.209 | 0.472 | 0.256 | 0.191 | 0.469 | 0.262 | 0.468 | | Llama-3-ELYZA-JP-8B | 0.897 | 0.498 | 0.496 | 0.906 | 0.168 | 0.436 | 0.250 | 0.185 | 0.487 | 0.388 | 0.471 | | Llama 3 heron brain 8B v0.3 | 0.923 | 0.493 | 0.569 | 0.906 | **0.218** | 0.456 | 0.277 | 0.217 | 0.499 | 0.318 | 0.488 | | Llama 3.1 Swallow 8B Instruct v0.1 | 0.924 | **0.587** | 0.574 | **0.917** | 0.138 | 0.508 | 0.282 | 0.228 | 0.530 | 0.366 | 0.505 | | Llama 3.1 Swallow 8B Instruct v0.2 | 0.929 | 0.560 | 0.599 | 0.915 | 0.137 | 0.528 | 0.288 | 0.227 | 0.550 | 0.408 | 0.514 | | Llama 3.1 Swallow 8B Instruct v0.3 | 0.924 | 0.528 | 0.583 | 0.896 | 0.191 | 0.532 | 0.281 | 0.229 | 0.544 | 0.394 | 0.510 | | Llama 3.1 Swallow 8B Instruct v0.5 | **0.937** | 0.511 | **0.606** | 0.900 | 0.174 | **0.604** | **0.293** | **0.230** | **0.581** | **0.496** | **0.533** | ### English tasks |Model|OpenBookQA|TriviaQA|HellaSWAG|SQuAD2.0|XWINO|MMLU|GSM8K|MATH|BBH|HumanEval|En Avg| |---|---|---|---|---|---|---|---|---|---|---|---| | |4-shot|4-shot|4-shot|4-shot|4-shot|5-shot|4-shot|4-shot | 3-shot|0-shot| | | |Acc|EM acc|Acc|EM acc|Acc|Acc|EM acc|CoT EM Acc| CoT EM Acc| pass@1| | | llm-jp-3-7.2b-instruct3 | 0.328 | 0.479 | 0.563 | 0.501 | 0.876 | 0.462 | 0.264 | 0.028 | 0.420 | 0.219 | 0.414 | | Qwen2.5-7B-Instruct | 0.428 | 0.519 | 0.624 | 0.569 | 0.877 | 0.742 | 0.739 | 0.688 | 0.217 | 0.636 | 0.604 | | Tanuki-8B-dpo-v1.0 | 0.334 | 0.283 | 0.469 | 0.501 | 0.816 | 0.377 | 0.487 | 0.178 | 0.333 | 0.288 | 0.406 | | Llama 3 8B Instruct | 0.388 | 0.670 | 0.583 | 0.611 | 0.892 | 0.657 | 0.745 | 0.306 | 0.646 | 0.554 | 0.605 | | Llama 3.1 8B Instruct | 0.366 | 0.699 | 0.592 | 0.600 | 0.904 | 0.680 | 0.743 | 0.376 | 0.690 | 0.624 | 0.627 | | Llama 3 Youko 8B Instruct | 0.406 | 0.613 | 0.599 | 0.559 | 0.897 | 0.596 | 0.563 | 0.152 | 0.401 | 0.287 | 0.507 | | Llama-3-ELYZA-JP-8B | 0.318 | 0.551 | 0.523 | 0.600 | 0.882 | 0.587 | 0.558 | 0.164 | 0.321 | 0.449 | 0.495 | | Llama 3 heron brain 8B v0.3 | 0.362 | 0.656 | 0.569 | 0.581 | 0.901 | 0.621 | 0.578 | 0.222 | 0.641 | 0.380 | 0.551 | | Llama 3.1 Swallow 8B Instruct v0.1 | 0.388 | 0.649 | 0.615 | 0.598 | 0.891 | 0.624 | 0.605 | 0.236 | 0.642 | 0.379 | 0.563 | | Llama 3.1 Swallow 8B Instruct v0.2 | 0.380 | 0.625 | 0.603 | 0.607 | 0.887 | 0.634 | 0.620 | 0.264 | 0.649 | 0.474 | 0.574 | | Llama 3.1 Swallow 8B Instruct v0.3 | 0.396 | 0.629 | 0.593 | 0.570 | 0.884 | 0.629 | 0.622 | 0.266 | 0.626 | 0.445 | 0.566 | | Llama 3.1 Swallow 8B Instruct v0.5 | 0.396 | 0.638 | 0.603 | 0.581 | 0.889 | 0.663 | 0.717 | 0.368 | 0.628 | 0.554 | 0.604 | ## Evaluation Benchmarks ### Japanese MT-Bench We used [Japanese MT-Bench](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question) to assess the capabilities of multi-turn dialogue with the following settings: - Implementation: FastChat [Zheng+, 2023] (commit #e86e70d0) - Question: [Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v4](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/v4) - Reference Answer: [swallow-evaluation, reference answer](https://github.com/swallow-llm/swallow-evaluation/tree/main/fastchat/fastchat/llm_judge/data/japanese_mt_bench/reference_answer) - Prompt for Judge: [Nejumi LLM-Leaderboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1) - Judge: `gpt-4o-2024-08-06` - Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs. ### Japanese evaluation benchmarks We used llm-jp-eval(v1.3.0), JP Language Model Evaluation Harness(commit #9b42d41) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows: - Multiple-choice question answering (JCommonsenseQA [Kurihara et al., 2022]) - Open-ended question answering (JEMHopQA [Ishii et al., 2024]) - Open-ended question answering (NIILC [関根, 2003]) - Machine reading comprehension (JSQuAD [Kurihara et al., 2022]) - Automatic summarization (XL-Sum [Hasan et al., 2021]) - Machine translation (WMT2020 ja-en [Barrault et al., 2020]) - Machine translation (WMT2020 en-ja [Barrault et al., 2020]) - Arithmetic reasoning (MGSM [Shi et al., 2023]) - Academic exams (JMMLU [尹ら, 2024]) - Code generation (JHumanEval [佐藤ら, 2024]) ### English evaluation benchmarks We used the Language Model Evaluation Harness(v.0.4.2) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows: - Multiple-choice question answering (OpenBookQA [Mihaylov et al., 2018]) - Open-ended question answering (TriviaQA [Joshi et al., 2017]) - Machine reading comprehension (SQuAD2 [Rajpurkar et al., 2018]) - Commonsense reasoning (XWINO [Tikhonov and Ryabinin, 2021]) - Natural language inference (HellaSwag [Zellers et al., 2019]) - Arithmetic reasoning (GSM8K [Cobbe et al., 2021]) - Mathematical reasoning (MATH [Hendrycks et al., 2022][Lightman et al., 2024]) - Reasoning (BBH (BIG-Bench-Hard) [Suzgun et al., 2023]) - Academic exams (MMLU [Hendrycks et al., 2021]) - Code generation (HumanEval [Chen et al., 2021]) ## Usage ```sh pip install vllm ``` ```python from transformers import AutoTokenizer from vllm import LLM, SamplingParams model_name = "tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5" tokenizer = AutoTokenizer.from_pretrained(model_name) llm = LLM( model=model_name, tensor_parallel_size=1, ) sampling_params = SamplingParams( temperature=0.6, top_p=0.9, max_tokens=512, stop="<|eot_id|>" ) message = [ { "role": "user", "content": "東京の紅葉した公園で、東京タワーと高層ビルを背景に、空を舞うツバメと草地に佇むラマが出会う温かな物語を書いてください。", }, ] prompt = tokenizer.apply_chat_template( message, tokenize=False, add_generation_prompt=True ) output = llm.generate(prompt, sampling_params) print(output[0].outputs[0].text) ``` ## Training Datasets ### Instruction Tuning The following datasets were used for the instruction tuning. - [Gemma-3-LMSYS-Chat-1M-Synth](https://huggingface.co/datasets/tokyotech-llm/lmsys-chat-1m-synth) - Single-turn Japanese instruction dataset synthesized and derived from [lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) [\[Zhang+, ICLR24\]](https://openreview.net/forum?id=BOfDKxfwt0)). - First-turn user instructions were translated into Japanese via DeepL (machine translation), and assistant responses were generated using [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it). The same model, i.e., [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it) served as a judge for rejection sampling (n=10). Conversations containing personally identifiable information (PII) and template-based user instructions were removed. Duplicate instructions were removed. ## Risks and Limitations The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations. ## Acknowledgements We thank Meta Research for releasing Llama 3.1 under a generous open license. We received various supports, including: + AIST project: "Research and Development of Foundation Models for Generative AI in the Physical Domain" + NEDO project: "Development of Artificial Intelligence Application Technology to Support Judgment in Design Risk Assessment Work Based on the Perspective of Skilled Persons" (JPNP18002) of "Development of Integration Technology as the Core of Next Generation Artificial Intelligence and Robotics" + MEXT project: "Formation of R&D center to ensure transparency and reliability of generative AI models" + AIST program: [Large Generative AI Development Support Program](https://abci.ai/en/link/lfm_support_program.html) ## License [META LLAMA 3.1 COMMUNITY LICENSE](https://www.llama.com/llama3_1/license/) and [Gemma Terms of Use](https://ai.google.dev/gemma/terms) ## Authors Here are the team members: - From [Okazaki Laboratory, Institute of Science Tokyo](https://www.nlp.c.titech.ac.jp/index.en.html), the following members: - [Naoaki Okazaki](https://www.chokkan.org/index.ja.html) - [Sakae Mizuki](https://s-mizuki-nlp.github.io/) - [Youmi Ma](https://www.nlp.c.titech.ac.jp/member/youmi.en.html) - [Sangwhan Moon](https://www.sangwhan.com/) - [Koki Maeda](https://sites.google.com/view/silviase) - [Masanari Ohi](https://sites.google.com/view/masanariohi) - [Hinari Shimada](https://hinarishimada.github.io/portfolio) - [Taihei Shiotani](https://github.com/inatoihs) - [Koshiro Saito](https://sites.google.com/view/koshiro-saito) - [Tatsuya Ichinose](https://tatsuya736482.github.io/myprofile) - Naoya Matsushita - Sora Miyamoto - Nguyen Tien Dung - Yuta Katayama - From [YOKOTA Laboratory, Institute of Science Tokyo](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members: - [Rio Yokota](https://twitter.com/rioyokota) - [Kazuki Fujii](https://twitter.com/okoge_kaz) - [Taishi Nakamura](https://twitter.com/Setuna7777_2) - [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto) - [Ishida Shigeki](https://www.wantedly.com/id/reborn27) - Masaki Kawamura - Yukito Tajima - From [Artificial Intelligence Research Center, AIST, Japan](https://www.airc.aist.go.jp/en/teams/), the following members: - [Hiroya Takamura](https://sites.google.com/view/hjtakamura) ## How to cite If you find our work helpful, please feel free to cite these papers. ``` @inproceedings{Fujii:COLM2024, title={Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities}, author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae Mizuki and Rio Yokota and Naoaki Okazaki}, booktitle="Proceedings of the First Conference on Language Modeling", series={COLM}, pages="(to appear)", year="2024", month=oct, address={University of Pennsylvania, USA}, } @inproceedings{Okazaki:COLM2024, title={Building a Large Japanese Web Corpus for Large Language Models}, author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Rio Yokota and Sakae Mizuki}, booktitle="Proceedings of the First Conference on Language Modeling", series={COLM}, pages="(to appear)", year="2024", month=oct, address={University of Pennsylvania, USA}, } @misc{ma:arxiv2025, title={Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models}, author={Youmi Ma and Sakae Mizuki and Kazuki Fujii and Taishi Nakamura and Masanari Ohi and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Koki Maeda and Kakeru Hattori and Takumi Okamoto and Shigeki Ishida and Rio Yokota and Hiroya Takamura and Naoaki Okazaki}, year={2025}, eprint={2503.23714}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.23714}, } ``` ### References ```tex @misc{dubey2024llama3herdmodels, title={The Llama 3 Herd of Models}, author={Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Amy Yang and Angela Fan et al.}, year={2024}, eprint={2407.21783}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2407.21783}, } ```

deepmaster/72_4

deepmaster

2025-06-25T00:55:35Z

42

0

transformers

[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2025-06-08T18:51:42Z

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

mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX

mlx-community

2025-06-25T00:51:02Z

0

mlx

[ "mlx", "safetensors", "mixtral", "Llama 3.2", "8 X 4B", "Brainstorm 5x", "128k context", "moe", "8 experts", "mixture of experts", "fine tune", "text-generation", "conversational", "base_model:DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit", "base_model:quantized:DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit", "8-bit", "region:us" ]

text-generation

2025-06-25T00:35:40Z

--- library_name: mlx tags: - Llama 3.2 - 8 X 4B - Brainstorm 5x - 128k context - moe - 8 experts - mixture of experts - fine tune - mlx base_model: DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit pipeline_tag: text-generation --- # mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX This model [mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX](https://huggingface.co/mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX) was converted to MLX format from [DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit](https://huggingface.co/DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit) using mlx-lm version **0.25.2**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

Mr-Matrix/LogicBomb-Classifier

Mr-Matrix

2025-06-25T00:48:41Z

0

transformers

[ "transformers", "safetensors", "llama", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2025-06-25T00:43:47Z

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

windscare2/Lorax

windscare2

2025-06-25T00:41:48Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-25T00:17:59Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Lorax --- # Lorax <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Lorax` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Lorax", "lora_weights": "https://huggingface.co/windscare2/Lorax/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('windscare2/Lorax', weight_name='lora.safetensors') image = pipeline('Lorax').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/windscare2/Lorax/discussions) to add images that show off what you’ve made with this LoRA.

hongin9812/tunning_blip2-opt-2.7b-fp16-sharded

hongin9812

2025-06-25T00:39:19Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:ybelkada/blip2-opt-2.7b-fp16-sharded", "base_model:adapter:ybelkada/blip2-opt-2.7b-fp16-sharded", "region:us" ]

null

2025-06-25T00:34:47Z

--- base_model: ybelkada/blip2-opt-2.7b-fp16-sharded library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2

thavens/pir_sft_ckpt_50_i

thavens

2025-06-25T00:38:22Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen3-4B", "base_model:finetune:Qwen/Qwen3-4B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-25T00:14:28Z

--- base_model: Qwen/Qwen3-4B library_name: transformers model_name: pir_sft_ckpt_50_i tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for pir_sft_ckpt_50_i This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="thavens/pir_sft_ckpt_50_i", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/tmotiv/huggingface/runs/7n04dqdo) This model was trained with SFT. ### Framework versions - TRL: 0.18.0.dev0 - Transformers: 4.52.4 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

daixuancheng/zero_qwen-math-7b_base_allDapo_mathVerify_yesSuffix_step140

daixuancheng

2025-06-25T00:34:55Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-25T00:08:57Z

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

New-videos-Mia-Khalifa-viral-video-Clips/FULL.VIDEO.Mia.Khalifa.Viral.Video.Tutorial.Official

New-videos-Mia-Khalifa-viral-video-Clips

2025-06-25T00:34:50Z

0

null

[ "region:us" ]

null

2025-06-25T00:34:37Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

daixuancheng/zero_qwen-math-7b_base_allDapo_mathVerify_yesSuffix_step40

daixuancheng

2025-06-25T00:32:38Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-25T00:05:08Z

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

New-videos-misswow-viral-video-Clips/FULL.VIDEO.Miss.Wow.Viral.Video.Tutorial.Official

New-videos-misswow-viral-video-Clips

2025-06-25T00:31:14Z

0

null

[ "region:us" ]

null

2025-06-25T00:31:00Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

daixuancheng/ppo_sac_static0.1_constrainbyadv_step-40_critic

daixuancheng

2025-06-25T00:30:28Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T12:11:28Z

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

laonML/LaonA2_VL_3B

laonML

2025-06-25T00:27:49Z

0

null

[ "safetensors", "qwen2_5_vl", "zero-shot-object-detection", "en", "dataset:lmms-lab/RefCOCOg", "arxiv:2504.07615", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "region:us" ]

zero-shot-object-detection

2025-06-25T00:11:51Z

--- datasets: - lmms-lab/RefCOCOg language: - en base_model: - Qwen/Qwen2.5-VL-3B-Instruct pipeline_tag: zero-shot-object-detection --- # LaonA2 VL 3B LaonA2 VL 3B는 Qwen 2.5 VL 3B 기반의 향상된 비전-언어 모델입니다. VLM-R1 강화학습을 통해 REC(Referring Expression Comprehension) 성능이 개선되었습니다. cite: arxiv.org/abs/2504.07615

Ljk0501/Gemma3_1B_it_GGUF

Ljk0501

2025-06-25T00:21:40Z

0

null

[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-06-25T00:15:49Z

--- license: apache-2.0 ---

daixuancheng/ppo_sample8_critic-warm10-lr2e-6_step40_crtic

daixuancheng

2025-06-25T00:21:11Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T10:37:56Z

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

Retreatcost/KansenSakura-Zero-RP-12b-Q4_K_M-GGUF

Retreatcost

2025-06-25T00:12:21Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "frankenmerge", "llama-cpp", "gguf-my-repo", "base_model:Retreatcost/KansenSakura-Zero-RP-12b", "base_model:quantized:Retreatcost/KansenSakura-Zero-RP-12b", "endpoints_compatible", "region:us", "conversational" ]

null

2025-06-25T00:11:49Z

--- base_model: Retreatcost/KansenSakura-Zero-RP-12b library_name: transformers tags: - mergekit - merge - frankenmerge - llama-cpp - gguf-my-repo --- # Retreatcost/KansenSakura-Zero-RP-12b-Q4_K_M-GGUF This model was converted to GGUF format from [`Retreatcost/KansenSakura-Zero-RP-12b`](https://huggingface.co/Retreatcost/KansenSakura-Zero-RP-12b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Retreatcost/KansenSakura-Zero-RP-12b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Retreatcost/KansenSakura-Zero-RP-12b-Q4_K_M-GGUF --hf-file kansensakura-zero-rp-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Retreatcost/KansenSakura-Zero-RP-12b-Q4_K_M-GGUF --hf-file kansensakura-zero-rp-12b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Retreatcost/KansenSakura-Zero-RP-12b-Q4_K_M-GGUF --hf-file kansensakura-zero-rp-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Retreatcost/KansenSakura-Zero-RP-12b-Q4_K_M-GGUF --hf-file kansensakura-zero-rp-12b-q4_k_m.gguf -c 2048 ```

twodgirl/omnigen2-nf4-bfloat16-diffusers

twodgirl

2025-06-25T00:11:55Z

0

diffusers

[ "diffusers", "safetensors", "base_model:OmniGen2/OmniGen2", "base_model:finetune:OmniGen2/OmniGen2", "license:apache-2.0", "diffusers:OmniGen2Pipeline", "region:us" ]

null

2025-06-24T21:31:15Z

--- license: apache-2.0 base_model: - OmniGen2/OmniGen2 --- # OmniGen2 Unsloth meets Omni2. The transformer module is in bfloat16 precision. The Qwen-VL is in nf4.

nntoan209/Qwen2.5-Coder-7B-Instruct-2410d46d-9b55-41ca-88b2-5388da286ccb

nntoan209

2025-06-25T00:11:48Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:unsloth/Qwen2.5-Coder-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-Coder-7B-Instruct", "endpoints_compatible", "region:us" ]

null

2025-06-24T16:59:15Z

--- base_model: unsloth/Qwen2.5-Coder-7B-Instruct library_name: transformers model_name: Qwen2.5-Coder-7B-Instruct-2410d46d-9b55-41ca-88b2-5388da286ccb tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2.5-Coder-7B-Instruct-2410d46d-9b55-41ca-88b2-5388da286ccb This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="nntoan209/Qwen2.5-Coder-7B-Instruct-2410d46d-9b55-41ca-88b2-5388da286ccb", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

alllwang/00789f7e-9f45-447f-b541-b4db9c07a00c

alllwang

2025-06-25T00:10:26Z

0

peft

[ "peft", "safetensors", "qwen3", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen3-1.7B-Base", "base_model:adapter:Qwen/Qwen3-1.7B-Base", "license:apache-2.0", "region:us" ]

null

2025-06-25T00:06:36Z

--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen3-1.7B-Base tags: - axolotl - generated_from_trainer model-index: - name: 00789f7e-9f45-447f-b541-b4db9c07a00c results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.10.0.dev0` ```yaml adapter: lora base_model: Qwen/Qwen3-1.7B-Base bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f9b218e3a76b29e1_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: deepspeed_configs/zero2.json early_stopping_patience: 3 eval_max_new_tokens: 1024 eval_steps: 50 eval_table_size: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false greater_is_better: false group_by_length: false hub_model_id: alllwang/00789f7e-9f45-447f-b541-b4db9c07a00c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0008 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: -1 metric_for_best_model: eval_loss micro_batch_size: 8 mlflow_experiment_name: /data/datasets/f9b218e3a76b29e1_train_data.json model_type: AutoModelForCausalLM num_epochs: 5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: fc397f12-b69f-48aa-b4ec-43a56bc1d674 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: fc397f12-b69f-48aa-b4ec-43a56bc1d674 warmup_steps: 20 weight_decay: 0.001 xformers_attention: null ``` </details> # 00789f7e-9f45-447f-b541-b4db9c07a00c This model is a fine-tuned version of [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2543 ## 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: 0.0008 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0597 | 1 | 0.4118 | | 0.2596 | 2.9552 | 50 | 0.2543 | ### Framework versions - PEFT 0.15.2 - Transformers 4.52.3 - Pytorch 2.5.1+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1

sinanelms/gemma-3b-hukuk-lora-adapters

sinanelms

2025-06-25T00:09:36Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-pt-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-1b-pt-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-25T00:09:30Z

--- base_model: unsloth/gemma-3-1b-pt-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** sinanelms - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-1b-pt-unsloth-bnb-4bit This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

iamhpd/tokenizer-iamhpd

iamhpd

2025-06-25T00:06:34Z

0

transformers

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

null

2025-06-25T00:06:32Z

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

OptShare/resocratic-29k-gpt-4o-PySCIPOpt-sft-Llama-3-8B-Instruct

OptShare

2025-06-25T00:05:10Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T21:17:31Z

--- library_name: transformers model_name: resocratic-29k-gpt-4o-PySCIPOpt-sft-Llama-3-8B-Instruct tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for resocratic-29k-gpt-4o-PySCIPOpt-sft-Llama-3-8B-Instruct This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="OptShare/resocratic-29k-gpt-4o-PySCIPOpt-sft-Llama-3-8B-Instruct", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/zc096373/OptiGuide/runs/mb0tzw0o) This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.52.4 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

tamewild/4b_v7_merged_e4

tamewild

2025-06-25T00:03:13Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-25T00:00:57Z

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

mradermacher/flan-ul2-alpaca-lora-GGUF

mradermacher

2025-06-25T00:00:34Z

0

transformers

[ "transformers", "gguf", "en", "dataset:tatsu-lab/alpaca", "base_model:VMware/flan-ul2-alpaca-lora", "base_model:quantized:VMware/flan-ul2-alpaca-lora", "license:other", "endpoints_compatible", "region:us" ]

null

2025-06-24T23:14:55Z

--- base_model: VMware/flan-ul2-alpaca-lora datasets: - tatsu-lab/alpaca language: - en library_name: transformers license: other quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/VMware/flan-ul2-alpaca-lora  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q2_K.gguf) | Q2_K | 7.2 | | | [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q3_K_S.gguf) | Q3_K_S | 8.6 | | | [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q3_K_M.gguf) | Q3_K_M | 9.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q3_K_L.gguf) | Q3_K_L | 10.1 | | | [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q4_K_S.gguf) | Q4_K_S | 11.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q4_K_M.gguf) | Q4_K_M | 12.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q5_K_S.gguf) | Q5_K_S | 13.6 | | | [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q5_K_M.gguf) | Q5_K_M | 14.1 | | | [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q6_K.gguf) | Q6_K | 16.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/flan-ul2-alpaca-lora-GGUF/resolve/main/flan-ul2-alpaca-lora.Q8_0.gguf) | Q8_0 | 20.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

tamewild/4b_v7_merged_e5

tamewild

2025-06-24T23:58:38Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T23:56:28Z

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

mradermacher/Neona-12B-i1-GGUF

mradermacher

2025-06-24T23:56:48Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:kyx0r/Neona-12B", "base_model:quantized:kyx0r/Neona-12B", "endpoints_compatible", "region:us", "imatrix" ]

null

2025-06-24T23:18:51Z

--- base_model: kyx0r/Neona-12B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      weighted/imatrix quants of https://huggingface.co/kyx0r/Neona-12B  static quants are available at https://huggingface.co/mradermacher/Neona-12B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.2 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q4_1.gguf) | i1-Q4_1 | 7.9 | | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | | | [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

konstantis/donut_payslip_LeMa

konstantis

2025-06-24T23:56:22Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

2025-06-23T15:21:50Z

--- library_name: transformers license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer model-index: - name: donut_payslip_LeMa results: [] ---  # donut_payslip_LeMa This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5993 ## 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: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.965 | 1.0 | 1000 | 0.9175 | | 0.5617 | 2.0 | 2000 | 0.6567 | | 0.3202 | 3.0 | 3000 | 0.5667 | | 0.1697 | 4.0 | 4000 | 0.6694 | | 0.097 | 5.0 | 5000 | 0.5993 | ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1

jeffxtang/llama31-8b-text2sql-epochs-3

jeffxtang

2025-06-24T23:48:11Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]

null

2025-06-24T22:30:38Z

--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: transformers model_name: llama31-8b-text2sql-epochs-3 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for llama31-8b-text2sql-epochs-3 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="jeffxtang/llama31-8b-text2sql-epochs-3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.6.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

qklent/vikhr-1-5b-eagle-full-gm-18-epochs

qklent

2025-06-24T23:43:43Z

10

0

null

[ "safetensors", "qwen2", "region:us" ]

null

2025-03-03T02:04:47Z

Eagle v2 model for qvikhr 1.5b model. Got speedups while inferencing using sglang: | Batch Size | Speedup | |-----------------|---------| | 1 | 1.72 | | 2 | 1.49 | | 4 | 1.47 | | 8 | 1.38 | | 16 | 0.95 | | 32 | 0.56 | guide for running it using sglang is here: https://gitlab.com/qklent/eagle_train/-/blob/main/run_inference_instruction.md?ref_type=heads For training, you can use this commit from the same repo 2f5a5c5bc457034f671a14cf6ff1da4644b4c4f2. (training scripts adaptation to eagle 3 is still in progress, so main branch is broken)

AliCat2/Picaro-24b-2506-636-Q5_K_M-GGUF

AliCat2

2025-06-24T23:42:13Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:AliCat2/Picaro-24b-2506-636", "base_model:quantized:AliCat2/Picaro-24b-2506-636", "endpoints_compatible", "region:us" ]

null

2025-06-24T23:40:57Z

--- base_model: AliCat2/Picaro-24b-2506-636 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # AliCat2/Picaro-24b-2506-636-Q5_K_M-GGUF This model was converted to GGUF format from [`AliCat2/Picaro-24b-2506-636`](https://huggingface.co/AliCat2/Picaro-24b-2506-636) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/AliCat2/Picaro-24b-2506-636) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo AliCat2/Picaro-24b-2506-636-Q5_K_M-GGUF --hf-file picaro-24b-2506-636-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo AliCat2/Picaro-24b-2506-636-Q5_K_M-GGUF --hf-file picaro-24b-2506-636-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo AliCat2/Picaro-24b-2506-636-Q5_K_M-GGUF --hf-file picaro-24b-2506-636-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo AliCat2/Picaro-24b-2506-636-Q5_K_M-GGUF --hf-file picaro-24b-2506-636-q5_k_m.gguf -c 2048 ```

stablediffusionapi/firexxxrealisticnsfw-v10

stablediffusionapi

2025-06-24T23:41:43Z

0

diffusers

[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2025-06-24T23:17:33Z

--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true pipeline_tag: text-to-image library_name: diffusers widget: - text: a girl wandering through the forest output: url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d3a4e8bc-59cc-4bb8-8838-1a4b3e461fa8/width=1056/17391956.jpeg --- # firexxxRealisticNSFW - v1.0 API Inference <Gallery /> ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "firexxxrealisticnsfw-v10" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/firexxxrealisticnsfw-v10) Model link: [View model](https://modelslab.com/models/firexxxrealisticnsfw-v10) View all models: [View Models](https://modelslab.com/models) ```python import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "firexxxrealisticnsfw-v10", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "", "lora": "", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) ``` > Use this coupon code to get 25% off **DMGG0RBN**

talman-fi/test-coder-001

talman-fi

2025-06-24T23:41:23Z

0

peft

[ "peft", "safetensors", "generated_from_trainer", "base_model:01-ai/Yi-Coder-1.5B", "base_model:adapter:01-ai/Yi-Coder-1.5B", "license:apache-2.0", "region:us" ]

null

2025-06-24T23:27:45Z

--- library_name: peft license: apache-2.0 base_model: 01-ai/Yi-Coder-1.5B tags: - generated_from_trainer model-index: - name: test-coder-001 results: [] ---  # test-coder-001 This model is a fine-tuned version of [01-ai/Yi-Coder-1.5B](https://huggingface.co/01-ai/Yi-Coder-1.5B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6877 ## 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: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6191 | 1.0 | 100 | 0.6877 | ### Framework versions - PEFT 0.15.2 - Transformers 4.52.4 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.2

New-videos-mezzo-fun-virals-Clips-tk/FULL.VIDEO.Mezzo.fun.Viral.Video.Tutorial.Official

New-videos-mezzo-fun-virals-Clips-tk

2025-06-24T23:33:31Z

0

null

[ "region:us" ]

null

2025-06-24T23:33:11Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

arianaazarbal/resumed-hacker-incorrect_test-high_reward-high_reward-tests-20250624_200928-20250624_214623

arianaazarbal

2025-06-24T23:27:29Z

0

transformers

[ "transformers", "pytorch", "safetensors", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "endpoints_compatible", "region:us" ]

reinforcement-learning

2025-06-24T23:27:27Z

--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="arianaazarbal//tmp/tmp4y7_w0vd/arianaazarbal/resumed-hacker-incorrect_test-high_reward-high_reward-tests-20250624_200928-20250624_214623") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("arianaazarbal//tmp/tmp4y7_w0vd/arianaazarbal/resumed-hacker-incorrect_test-high_reward-high_reward-tests-20250624_200928-20250624_214623") model = AutoModelForCausalLMWithValueHead.from_pretrained("arianaazarbal//tmp/tmp4y7_w0vd/arianaazarbal/resumed-hacker-incorrect_test-high_reward-high_reward-tests-20250624_200928-20250624_214623") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

yuchuantian/AIGC_detector_env3short

yuchuantian

2025-06-24T23:27:16Z

0

null

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

null

2025-06-24T23:21:35Z

--- license: apache-2.0 ---

mattfutureflow/matt-images

mattfutureflow

2025-06-24T23:24:33Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-24T22:54:06Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Matt --- # Matt Images <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Matt` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Matt", "lora_weights": "https://huggingface.co/mattfutureflow/matt-images/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('mattfutureflow/matt-images', weight_name='lora.safetensors') image = pipeline('Matt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/mattfutureflow/matt-images/discussions) to add images that show off what you’ve made with this LoRA.

rasoultilburg/SocioCausaNet

rasoultilburg

2025-06-24T23:23:27Z

64

0

transformers

[ "transformers", "safetensors", "joint_causal", "feature-extraction", "causal-extraction", "relation-extraction", "bert", "pytorch", "causality", "token-classification", "custom_code", "en", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:gpl-2.0", "region:us" ]

token-classification

2025-06-08T18:56:21Z

--- license: gpl-2.0 language: en base_model: google-bert/bert-base-uncased pipeline_tag: token-classification tags: - causal-extraction - relation-extraction - bert - pytorch - causality library_name: transformers --- # JointCausalModel for Causal Extraction This repository contains JointCausalModel, a PyTorch-based model for joint causal extraction, optimized for use with the Hugging Face transformers library. The model is built upon `google-bert/bert-base-uncased` and is designed to identify and structure causal relationships within text. **GitHub Repository**: [https://github.com/rasoulnorouzi/JointLearning](https://github.com/rasoulnorouzi/JointLearning/tree/main) ## Model Description This model performs three tasks simultaneously: 1. **Sentence-level Causal Classification**: Determines whether a sentence contains a causal statement. 2. **Span Extraction**: Identifies the specific Cause, Effect, and combined Cause-Effect spans within the text using a BIO tagging scheme. 3. **Relation Extraction**: Establishes the relationships between the identified cause and effect spans. > **Note**: This model uses a custom implementation and requires `trust_remote_code=True` when loading with AutoModel. ## How to Use To get started, load the model and tokenizer from the Hugging Face Hub: ```python from transformers import AutoModel, AutoTokenizer repo_id = "rasoultilburg/SocioCausaNet" model = AutoModel.from_pretrained( repo_id, trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained( repo_id ) ``` ### Inference API The primary method for inference is `model.predict()`, which processes a list of sentences and returns detailed causal information: ```python # Example of a simple prediction call results = model.predict( sents=["The heavy rainfall led to severe flooding in the coastal regions."], tokenizer=tokenizer, rel_mode="neural", rel_threshold=0.5, cause_decision="cls+span" ) ``` ### Understanding the predict() Parameters Think of this model as a "Causality Detective." The parameters are the instructions you give the detective on how to investigate the text. | Parameter | What it is & How it works | Analogy | |-----------|---------------------------|---------| | `sents` | The list of sentences you want the model to analyze. | The "case files" you give to the detective. | | `rel_mode` | Strategy for finding relationships. - `'auto'`: A smart, efficient mode. For simple cases (one cause-one effect, one cause-multiple effects, multiple causes-one effect), it automatically connects them using rules. For complex cases (multiple causes and multiple effects), it uses a neural network to determine connections. - `'neural_only'`: Uses a neural network to validate every potential cause-effect connection, checking whether there is a relationship between each pair of entities. More thorough but slower. | The Detective's Strategy - `'auto'` is the Smart Detective who uses simple logic for obvious cases but calls in the expert (neural network) for complex situations. - `'neural_only'` is the Expert Detective who carefully analyzes every possible connection using advanced techniques (neural network) regardless of complexity. | | `rel_threshold` | The confidence score needed to report a relationship (from 0.0 to 1.0). - High value (e.g., 0.8): Only reports relationships it's very sure about. Fewer, but more accurate results. - Low value (e.g., 0.3): Reports any potential link, even hunches. More results, but some may be incorrect. | The Detective's "Burden of Proof." - High value: Needs a lot of evidence before making an accusation. - Low value: Follows up on even the smallest lead. | | `cause_decision` | The criteria for deciding if a sentence is causal. - `'cls_only'`: Decides based on overall sentence meaning. - `'span_only'`: Decides only if it finds distinct "cause" and "effect" phrases. - `'cls+span'`: Strictest mode. Sentence must have causal meaning AND contain distinct cause/effect phrases. | The Panel of Judges - `'cls_only'` is the "Big Picture" Judge. - `'span_only'` is the "Evidence-Focused" Judge. - `'cls+span'` requires both judges to agree. Most reliable option. | ## Complete Example Here is a complete, runnable example demonstrating how to use the model and format the output: ```python from transformers import AutoModel, AutoTokenizer import json # 1. Load the model and tokenizer repo_id = "rasoultilburg/SocioCausaNet" model = AutoModel.from_pretrained(repo_id, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(repo_id) # 2. Define input sentences sentences = [ "Insomnia causes depression and a lack of concentration in children.", "Due to the new regulations, the company's profits declined sharply.", "The sun rises in the east." # Non-causal example ] # 3. Get predictions from the model results = model.predict( sentences, tokenizer=tokenizer, rel_mode="neural", rel_threshold=0.5, cause_decision="cls+span" ) # 4. Print the results in a readable format print(json.dumps(results, indent=2, ensure_ascii=False)) ``` ### Example Output The predict method returns a list of dictionaries, where each dictionary corresponds to an input sentence: ```json [ { "text": "Insomnia causes depression and a lack of concentration in children.", "causal": true, "relations": [ { "cause": "Insomnia", "effect": "depression", "type": "Rel_CE" }, { "cause": "Insomnia", "effect": "a lack of concentration in children", "type": "Rel_CE" } ] }, { "text": "Due to the new regulations, the company's profits declined sharply.", "causal": true, "relations": [ { "cause": "the new regulations", "effect": "the company's profits declined sharply", "type": "Rel_CE" } ] }, { "text": "The sun rises in the east.", "causal": false, "relations": [], "spans": [] } ] ``` ## Model Architecture The JointCausalModel requires custom code, which is why `trust_remote_code=True` is necessary. The architecture consists of a BERT encoder followed by three specialized heads for the joint tasks. The key files defining the model are: - `modeling_joint_causal.py`: Contains the main JointCausalModel class which defines the model's architecture. It inherits from `transformers.PreTrainedModel` to ensure compatibility with the Hugging Face ecosystem. - `configuration_joint_causal.py`: Defines the JointCausalConfig class, which stores the model's configuration and hyperparameters. ## Citation If you use this model in your work, please consider citing this repository. ```bibtex @misc{jointcausalmodel2024, title={JointCausalModel: Joint Learning for Causal Extraction}, author={Rasoul Norouzi}, year={2024}, howpublished={GitHub Repository}, url={https://github.com/rasoulnorouzi/JointLearning/tree/main} } ``` For more details and source code, visit the [GitHub repository](https://github.com/rasoulnorouzi/JointLearning/tree/main)

18-videos-jobz-hunting-sajal-malik-virals/FULL.VIDEO.sajal.malik.Viral.Video.Tutorial.Official

18-videos-jobz-hunting-sajal-malik-virals

2025-06-24T23:23:06Z

0

null

[ "region:us" ]

null

2025-06-24T23:22:49Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

abdullahsubasi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin

abdullahsubasi

2025-06-24T23:20:10Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am graceful pensive puffin", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]

null

2025-06-24T16:21:30Z

--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am graceful pensive puffin - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="abdullahsubasi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

jobs-git/OmniGen2

jobs-git

2025-06-24T23:19:52Z

0

diffusers

[ "diffusers", "safetensors", "any-to-any", "arxiv:2506.18871", "arxiv:2404.07724", "license:apache-2.0", "diffusers:OmniGen2Pipeline", "region:us" ]

any-to-any

2025-06-24T23:19:51Z

--- license: apache-2.0 pipeline_tag: any-to-any library_name: diffusers --- <a href="https://github.com/Ve <img src="assets/brand.png" width="65%"> <a href="https://vectorspacelab.github.io/OmniGen2"><img src="https://img.shields.io/badge/Project%20Page-OmniGen2-yellow" alt="project page"></a> <a href="https://arxiv.org/abs/2506.18871"><img src="https://img.shields.io/badge/arXiv%20paper-2506.18871-b31b1b.svg" alt="arxiv"></a> <a href="https://github.com/VectorSpaceLab/OmniGen2?tab=readme-ov-file#-gradio-demo"><img src="https://img.shields.io/badge/Online%20Demo-🤗-blue" alt="demo"></a> <a href="https://huggingface.co/spaces/OmniGen2/OmniGen2"><img src="https://img.shields.io/badge/HF%20Spaces-🤗-lightblue" alt="demo"></a> <a href="https://huggingface.co/OmniGen2/OmniGen2"><img src="https://img.shields.io/badge/Model-🤗-yellow" alt="model"></a> <a href=""><img src="https://img.shields.io/badge/Benchmark-🤗-yellow" alt="model"></a> <a href=""><img src="https://img.shields.io/badge/Dataset-🤗-yellow" alt="model"></a> <h4 align="center"> <a href=#-news>News</a> | <a href=#-quick-start>Quick Start</a> | <a href=#-usage-tips>Usage Tips</a> | <a href=#-gradio-demo>Online Demos</a> | <a href="#heart-citing-us">Citation</a> | <a href="#license">License</a> </h4> ## 🔥 News - **2025-06-24**: [Technical Report](https://arxiv.org/abs/2506.18871) is available. - **2025-06-23**: We’ve updated our code and HF model—OmniGen2 now runs *without* `flash-attn`. Users can still install it for optimal performance. - **2025-06-20**: Updated [resource requirements](#-resources-requirement), adding CPU offload support for devices with limited VRAM. - **2025-06-16**: [Gradio](https://github.com/VectorSpaceLab/OmniGen2?tab=readme-ov-file#-gradio-demo) and [Jupyter](https://github.com/VectorSpaceLab/OmniGen2/blob/main/example.ipynb) is available. Online Gradio Demo: [Demo1](https://8f10329141d53b6884.gradio.live); [Chat-Demo1](https://9315447fc78ef638e3.gradio.live); see more demo links in [gradio section](https://github.com/VectorSpaceLab/OmniGen2?tab=readme-ov-file#-gradio-demo) - **2025-06-16**: We release **OmniGen2**, a multimodal generation model, model weights can be accessed in [huggingface](https://huggingface.co/OmniGen2/OmniGen2) and [modelscope](https://www.modelscope.cn/models/OmniGen2/OmniGen2). ## Introduction **OmniGen2** is a powerful and efficient unified multimodal model. Unlike OmniGen v1, OmniGen2 features two distinct decoding pathways for text and image modalities, utilizing unshared parameters and a decoupled image tokenizer. OmniGen2 has competitive performance across four primary capabilities: - **Visual Understanding**: Inherits the robust ability to interpret and analyze image content from its Qwen-VL-2.5 foundation. - **Text-to-Image Generation**: Creates high-fidelity and aesthetically pleasing images from textual prompts. - **Instruction-guided Image Editing**: Executes complex, instruction-based image modifications with high precision, achieving state-of-the-art performance among open-source models. - **In-context Generation**: A versatile capability to process and flexibly combine diverse inputs—including humans, reference objects, and scenes—to produce novel and coherent visual outputs. As an open-source project, OmniGen2 provides a powerful yet resource-efficient foundation for researchers and developers exploring the frontiers of controllable and personalized generative AI. **We will release the training code, dataset, and data construction pipeline soon. Stay tuned!** <img src="assets/teaser.jpg" width="95%"> Demonstration of OmniGen2's overall capabilities. <img src="assets/examples_edit.png" width="95%"> Demonstration of OmniGen2's image editing capabilities. <img src="assets/examples_subject.png" width="95%"> Demonstration of OmniGen2's in-context generation capabilities. ## 📌 TODO - [x] Technical report. - [ ] In-context generation benchmark: **OmniContext**. - [x] Support CPU offload and improve inference efficiency. - [ ] Integrated in diffusers. - [ ] Training data and scripts. - [ ] Data construction pipeline. - [ ] ComfyUI Demo (**commuity support will be greatly appreciated!**). ## 🚀 Quick Start ### 🛠️ Environment Setup #### ✅ Recommended Setup ```bash # 1. Clone the repo git clone [email protected]:VectorSpaceLab/OmniGen2.git cd OmniGen2 # 2. (Optional) Create a clean Python environment conda create -n omnigen2 python=3.11 conda activate omnigen2 # 3. Install dependencies # 3.1 Install PyTorch (choose correct CUDA version) pip install torch==2.6.0 torchvision --extra-index-url https://download.pytorch.org/whl/cu124 # 3.2 Install other required packages pip install -r requirements.txt # Note: Version 2.7.4.post1 is specified for compatibility with CUDA 12.4. # Feel free to use a newer version if you use CUDA 12.6 or they fixed this compatibility issue. # OmniGen2 runs even without flash-attn, though we recommend install it for best performance. pip install flash-attn==2.7.4.post1 --no-build-isolation ``` #### 🌏 For users in Mainland China ```bash # Install PyTorch from a domestic mirror pip install torch==2.6.0 torchvision --index-url https://mirror.sjtu.edu.cn/pytorch-wheels/cu124 # Install other dependencies from Tsinghua mirror pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple # Note: Version 2.7.4.post1 is specified for compatibility with CUDA 12.4. # Feel free to use a newer version if you use CUDA 12.6 or they fixed this compatibility issue. # OmniGen2 runs even without flash-attn, though we recommend install it for best performance. pip install flash-attn==2.7.4.post1 --no-build-isolation -i https://pypi.tuna.tsinghua.edu.cn/simple ``` --- ### 🧪 Run Examples ```bash # Visual Understanding bash example_understanding.sh # Text-to-image generation bash example_t2i.sh # Instruction-guided image editing bash example_edit.sh # In-context generation bash example_in_context_generation.sh ``` --- ### 🌐 Gradio Demo * **Online Demo**: [HF Spaces](https://huggingface.co/spaces/OmniGen2/OmniGen2). Beyond Hugging Face Spaces, we are *temporarily* allocating additional GPU resources to ensure smooth access to the online demos. If you notice a long queue for a particular link, please try other links: [Demo1](https://8f10329141d53b6884.gradio.live), [Demo2](https://110863cb06c6c44bd2.gradio.live), [Demo3](https://19b0952eb3cf0d2243.gradio.live), [Demo4](https://981758b17b4197aea7.gradio.live) [Chat-Demo1](https://9315447fc78ef638e3.gradio.live), [Chat-Demo2](https://abe054be89543e4cef.gradio.live), [Chat-Demo3](https://4aa913765db00bbe51.gradio.live), [Chat-Demo4](https://f28a8718565627d2cb.gradio.live)  * **Run Locally**: ```bash # for only generating image pip install gradio python app.py # Optional: Share demo with public link (You need to be able to access huggingface) python app.py --share # for generating image or text pip install gradio python app_chat.py ``` ## 💡 Usage Tips To achieve optimal results with OmniGen2, you can adjust the following key hyperparameters based on your specific use case. - `text_guidance_scale`: Controls how strictly the output adheres to the text prompt (Classifier-Free Guidance). - `image_guidance_scale`: This controls how much the final image should resemble the input reference image. - **The Trade-off**: A higher value makes the output more faithful to the reference image's structure and style, but it might ignore parts of your text prompt. A lower value (~1.5) gives the text prompt more influence. - **Tip**: For image editing task, we recommend to set it between 1.2 and 2.0; for in-context generateion task, a higher image_guidance_scale will maintian more details in input images, and we recommend to set it between 2.5 and 3.0. - `max_pixels`: Automatically resizes images when their total pixel count (width × height) exceeds this limit, while maintaining its aspect ratio. This helps manage performance and memory usage. - **Tip**: Default value is 1024*1024. You can reduce this value if you encounter memory issues. - `max_input_image_side_length`: Maximum side length for input images. - `negative_prompt`: Tell the model what you don't want to see in the image. - **Example**: blurry, low quality, text, watermark - **Tip**: For the best results, try experimenting with different negative prompts. If you're not sure, just use the default negative prompt. - `enable_model_cpu_offload`: **Reduces VRAM usage by nearly 50% with a negligible impact on speed**. - This is achieved by offloading the model weights to CPU RAM when they are not in use. - See: [Model Offloading](https://huggingface.co/docs/diffusers/optimization/memory#model-offloading) - `enable_sequential_cpu_offload`: Minimizes VRAM usage to less than 3GB, but at the cost of significantly slower performance. - This works by offloading the model in submodules and loading them onto the GPU sequentially as needed. - See: [CPU Offloading](https://huggingface.co/docs/diffusers/optimization/memory#cpu-offloading) - `cfg_range_start`, `cfg_range_end`: Define the timestep range where CFG is applied. Per this [paper](https://arxiv.org/abs/2404.07724), reducing `cfg_range_end` can significantly decrease inference time with a negligible impact on quality. **Some suggestions for improving generation quality:** 1. Use High-Quality Images - Provide clear images, preferably with a resolution **greater than 512×512 pixels**. - Small or blurry inputs will result in low-quality outputs. 2. Be Specific with Instructions - Clearly describe both **what to change** and **how you want it changed**. - For in-context generation tasks, explicitly state which elements should come from which image. For example, instead of "Add bird to desk", say "Add the bird from image 1 onto the desk in image 2." 3. Prioritize English The model currently performs best with **English** prompts. ## 💻 Resources Requirement OmniGen2 natively requires an **NVIDIA RTX 3090** or an equivalent GPU with approximately **17GB of VRAM**. For devices with less VRAM, you can enable **CPU Offload** to run the model. **Performance Tip**: To improve inference speed, consider decreasing the `cfg_range_end` parameter. Within a reasonable range, this has a negligible impact on output quality. The following table details the inference performance of OmniGen2 on an **A800 GPU**: <img src="assets/efficiency.png" width="95%"> Inference Efficiency of OmniGen2. ## ❤️ Citing Us If you find this repository or our work useful, please consider giving a star ⭐ and citation 🦖, which would be greatly appreciated (OmniGen2 report will be available as soon as possible): ```bibtex @article{wu2025omnigen2, title={OmniGen2: Exploration to Advanced Multimodal Generation}, author={Chenyuan Wu and Pengfei Zheng and Ruiran Yan and Shitao Xiao and Xin Luo and Yueze Wang and Wanli Li and Xiyan Jiang and Yexin Liu and Junjie Zhou and Ze Liu and Ziyi Xia and Chaofan Li and Haoge Deng and Jiahao Wang and Kun Luo and Bo Zhang and Defu Lian and Xinlong Wang and Zhongyuan Wang and Tiejun Huang and Zheng Liu}, journal={arXiv preprint arXiv:2506.18871}, year={2025} } ``` ## License This work is licensed under Apache 2.0 license.

New-videos-Mezzo-fun-viral-video-Clips-tk/FULL.VIDEO.Mezzo.fun.Viral.Video.Tutorial.Official

New-videos-Mezzo-fun-viral-video-Clips-tk

2025-06-24T23:12:38Z

0

null

[ "region:us" ]

null

2025-06-24T23:12:23Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

dbbiyte/MemoirBERTurk-Sentiment

dbbiyte

2025-06-24T23:11:17Z

0

null

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

null

2025-06-24T23:11:17Z

--- license: apache-2.0 ---

gokulg02/PDEControlGymModels

gokulg02

2025-06-24T23:08:36Z

0

null

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

null

2025-06-24T23:03:41Z

--- license: apache-2.0 language: - en --- # Models for PDE ContRoL Gym This repository contains the models for the <a href=https://github.com/lukebhan/PDEControlGym/tree/main>PDE ContRoL Gym</a>. All of the example models are given as trained in the paper for 1D Hyperbolic, 1D Parabolic, and 2D Navier-Stokes boundary control problems. If there are any questions, feel free to make a github issue or reach out to [email protected].

mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF

mradermacher

2025-06-24T23:00:22Z

0

transformers

[ "transformers", "gguf", "en", "base_model:ChetKao/Bohdi-Qwen2.5-7B-Instruct", "base_model:quantized:ChetKao/Bohdi-Qwen2.5-7B-Instruct", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-06-24T13:55:51Z

--- base_model: ChetKao/Bohdi-Qwen2.5-7B-Instruct language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/ChetKao/Bohdi-Qwen2.5-7B-Instruct  static quants are available at https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

bts-Jimin-joins-Killin-Viral-video/FULL.VIDEO.bts.Jimin.joins.Killin.Viral.Video.Tutorial.Official

bts-Jimin-joins-Killin-Viral-video

2025-06-24T22:51:29Z

0

null

[ "region:us" ]

null

2025-06-24T22:51:15Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

BKM1804/Qwen2.5-1.5B-4cc25694-0c92-4c5c-a769-bd8d3bf66b80-SFT_DPO_ratio_2_0

BKM1804

2025-06-24T22:46:51Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "dpo", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T22:45:25Z

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

alfiwillianz/Devstral-Distilled

alfiwillianz

2025-06-24T22:45:18Z

0

null

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

null

2025-06-24T22:45:18Z

--- license: apache-2.0 ---

sergioalves/540efbe3-1312-48e3-af73-959ef8556037

sergioalves

2025-06-24T22:44:38Z

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:unsloth/Qwen2.5-1.5B", "base_model:quantized:unsloth/Qwen2.5-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-06-24T22:35:32Z

--- base_model: unsloth/Qwen2.5-1.5B library_name: transformers model_name: 540efbe3-1312-48e3-af73-959ef8556037 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 540efbe3-1312-48e3-af73-959ef8556037 This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B](https://huggingface.co/unsloth/Qwen2.5-1.5B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="sergioalves/540efbe3-1312-48e3-af73-959ef8556037", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/s9471umz) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

jisukim8873/adapter-planner-epoch3

jisukim8873

2025-06-24T22:43:13Z

0

transformers

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

null

2025-06-24T22:42:41Z

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

jisukim8873/adapter-planner-epoch2

jisukim8873

2025-06-24T22:41:46Z

0

transformers

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

null

2025-06-24T22:41:24Z

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

lhkhiem28/granite-3.3-2b-instruct-cpt-ZINC20-merged

lhkhiem28

2025-06-24T22:41:38Z

0

transformers

[ "transformers", "safetensors", "granite", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T22:35:54Z

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

Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF

Kieran2828

2025-06-24T22:38:25Z

0

null

[ "gguf", "pretrained", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:Qwen/Qwen1.5-MoE-A2.7B", "base_model:quantized:Qwen/Qwen1.5-MoE-A2.7B", "license:other", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2025-06-24T22:37:15Z

--- license: other license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - pretrained - moe - llama-cpp - gguf-my-repo base_model: Qwen/Qwen1.5-MoE-A2.7B --- # Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF This model was converted to GGUF format from [`Qwen/Qwen1.5-MoE-A2.7B`](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -c 2048 ```

New-tutorial-Ayesha-Khan-18-Viral-Videos/FULL.VIDEO.Ayesha.Khan.Viral.Video.Tutorial.Official

New-tutorial-Ayesha-Khan-18-Viral-Videos

2025-06-24T22:32:08Z

0

null

[ "region:us" ]

null

2025-06-24T22:31:53Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

Aldo789/5735a8ff-a67e-4f5a-961e-109d1b911e0f

Aldo789

2025-06-24T22:30:03Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T21:52:09Z

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

yu3733/paligemma2-3b-lora-vqa-v21-enhanced-d8000-r16

yu3733

2025-06-24T22:29:33Z

0

peft

[ "peft", "safetensors", "paligemma", "lora", "adapter", "visual-question-answering", "image-to-text", "v2.1-enhanced", "en", "base_model:google/paligemma2-3b-mix-224", "base_model:adapter:google/paligemma2-3b-mix-224", "region:us" ]

image-to-text

2025-06-24T22:29:19Z

--- tags: - paligemma - lora - adapter - visual-question-answering - image-to-text - v2.1-enhanced base_model: google/paligemma2-3b-mix-224 language: - en library_name: peft --- # paligemma2-3b-lora-vqa-v21-enhanced-d8000-r16 - v2.1 Enhanced This is a **v2.1 Enhanced** LoRA adapter for PaliGemma-2 3B trained on VQA tasks. ## 🆕 v2.1 Enhanced Improvements - **EOS Token Learning**: Explicit EOS tokens for better generation termination - **Memory Optimization**: 16-step gradient accumulation for stability - **VizWiz Format Support**: Full support with most frequent answer selection - **Robust Label Masking**: Enhanced prompt masking during training - **Production Memory Management**: Advanced garbage collection ## Usage ```python from transformers import AutoProcessor, PaliGemmaForConditionalGeneration from peft import PeftModel import torch from PIL import Image # Base model base_model_id = "google/paligemma2-3b-mix-224" adapter_id = "yu3733/paligemma2-3b-lora-vqa-v21-enhanced-d8000-r16" # Load processor processor = AutoProcessor.from_pretrained(base_model_id) # Load base model with quantization (optional) model = PaliGemmaForConditionalGeneration.from_pretrained( base_model_id, torch_dtype=torch.float16, device_map="auto" ) # Load LoRA adapter model = PeftModel.from_pretrained(model, adapter_id) # Prepare input image = Image.open("your_image.jpg") prompt = "<image>\nQuestion: What is in this image?\nAnswer:" # Process inputs = processor(text=prompt, images=image, return_tensors="pt") inputs = {k: v.to(model.device) for k, v in inputs.items()} # Generate with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=20) # Decode print(processor.decode(outputs[0], skip_special_tokens=True)) ``` ## Training Configuration - **Base Model**: google/paligemma2-3b-mix-224 - **LoRA Rank**: 16 - **Training Framework**: PEFT + Transformers - **Optimization**: 4-bit quantization + gradient checkpointing - **Dataset**: VizWiz VQA ## License Same as the base model (see google/paligemma2-3b-mix-224)

New-videos-Othoi-viral-video-Clips/FULL.VIDEO.Othoi.Viral.Video.Tutorial.Official

New-videos-Othoi-viral-video-Clips

2025-06-24T22:29:12Z

0

null

[ "region:us" ]

null

2025-06-24T22:28:55Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

New-videos-Ayesha-Khan-viral-video-Clips/FULL.VIDEO.Ayesha.Khan.Viral.Video.Tutorial.Official

New-videos-Ayesha-Khan-viral-video-Clips

2025-06-24T22:23:13Z

0

null

[ "region:us" ]

null

2025-06-24T22:22:28Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

brokuking1/87f3d14f-93c0-4ab8-83a9-e5069be28b73

brokuking1

2025-06-24T22:19:31Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T09:34:34Z

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

Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF

Kieran2828

2025-06-24T22:18:56Z

0

null

[ "gguf", "pretrained", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:Qwen/Qwen1.5-MoE-A2.7B", "base_model:quantized:Qwen/Qwen1.5-MoE-A2.7B", "license:other", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2025-06-24T22:18:17Z

--- license: other license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - pretrained - moe - llama-cpp - gguf-my-repo base_model: Qwen/Qwen1.5-MoE-A2.7B --- # Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen1.5-MoE-A2.7B`](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -c 2048 ```

mezzo-fun-viral-video-link-telegram/Official.video.mezzo.fun.Viral.Video.Tutorial.x.telegram

mezzo-fun-viral-video-link-telegram

2025-06-24T22:14:49Z

0

null

[ "region:us" ]

null

2025-06-24T22:14:21Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> <animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> <animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

rbelanec/train_qnli_1750781358

rbelanec

2025-06-24T22:14:42Z

0

peft

[ "peft", "safetensors", "llama-factory", "prompt-tuning", "generated_from_trainer", "base_model:google/gemma-3-1b-it", "base_model:adapter:google/gemma-3-1b-it", "license:gemma", "region:us" ]

null

2025-06-24T16:11:43Z

--- library_name: peft license: gemma base_model: google/gemma-3-1b-it tags: - llama-factory - prompt-tuning - generated_from_trainer model-index: - name: train_qnli_1750781358 results: [] ---  # train_qnli_1750781358 This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on the qnli dataset. It achieves the following results on the evaluation set: - Loss: 0.0861 - Num Input Tokens Seen: 117444992 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 2 - seed: 123 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1

FLOPS-Squared/KeystoneFuse-FW16-KSL12-Flax

FLOPS-Squared

2025-06-24T22:12:07Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T02:25:11Z

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

jg940101/e041992c-fb1d-40c5-912b-735a62d57b7d

jg940101

2025-06-24T22:06:10Z

0

null

[ "safetensors", "llama", "instruct", "chat", "text-generation", "conversational", "en", "th", "arxiv:2312.13951", "license:llama3", "region:us" ]

text-generation

2025-06-24T21:43:19Z

--- license: llama3 language: - en - th pipeline_tag: text-generation tags: - instruct - chat --- **Llama-3-Typhoon-1.5-8B: Thai Large Language Model (Instruct)** **Llama-3-Typhoon-1.5-8B-instruct** is a *instruct* Thai 🇹🇭 large language model with 8 billion parameters, and it is based on Llama3-8B. ![Typhoon 1.5 8b benchmark](https://storage.googleapis.com/typhoon-public/assets/1.5-8b-benchmark.png) For release post, please see our [blog](https://blog.opentyphoon.ai/typhoon-1-5-release-a9364cb8e8d7). *To acknowledge Meta's effort in creating the foundation model and to comply with the license, we explicitly include "llama-3" in the model name. ## **Model Description** - **Model type**: A 8B instruct decoder-only model based on Llama architecture. - **Requirement**: transformers 4.38.0 or newer. - **Primary Language(s)**: Thai 🇹🇭 and English 🇬🇧 - **License**: [Llama 3 Community License](https://llama.meta.com/llama3/license/) ## **Performance** | Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | M3Exam | MMLU | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Typhoon-1.0 (Mistral) | 0.379 | 0.393 | 0.700 | 0.414 | 0.324 | 0.442 | 0.391 | 0.547 | | Typhoon-1.5 8B (Llama3) | ***0.446*** | ***0.431*** | ***0.722*** | ***0.526*** | ***0.407*** | ***0.506*** | ***0.460*** | ***0.614*** | | Sailor 7B | 0.372 | 0.379 | 0.678 | 0.405 | 0.396 | 0.446 | 0.411 | 0.553 | | SeaLLM 2.0 7B | 0.327 | 0.311 | 0.656 | 0.414 | 0.321 | 0.406 | 0.354 | 0.579 | | OpenThaiGPT 1.0.0 7B | 0.238 | 0.249 | 0.444 | 0.319 | 0.289 | 0.308 | 0.268 | 0.369 | | SambaLingo-Thai-Chat 7B | 0.251 | 0.241 | 0.522 | 0.302 | 0.262 | 0.316 | 0.309 | 0.388 | ## Usage Example ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "scb10x/llama-3-typhoon-v1.5-8b-instruct" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a helpful assistant who're always speak Thai."}, {"role": "user", "content": "ขอสูตรไก่ย่าง"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = model.generate( input_ids, max_new_tokens=512, eos_token_id=terminators, do_sample=True, temperature=0.4, top_p=0.9, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ## Chat Template We use llama3 chat-template. ```python {% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %} ``` ## **Intended Uses & Limitations** This model is an instructional model. However, it’s still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case. ## **Follow us** **https://twitter.com/opentyphoon** ## **Support** **https://discord.gg/us5gAYmrxw** ## **SCB10X AI Team** - Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Pathomporn Chokchainant, Kasima Tharnpipitchai - If you find Typhoon-8B useful for your work, please cite it using: ``` @article{pipatanakul2023typhoon, title={Typhoon: Thai Large Language Models}, author={Kunat Pipatanakul and Phatrasek Jirabovonvisut and Potsawee Manakul and Sittipong Sripaisarnmongkol and Ruangsak Patomwong and Pathomporn Chokchainant and Kasima Tharnpipitchai}, year={2023}, journal={arXiv preprint arXiv:2312.13951}, url={https://arxiv.org/abs/2312.13951} } ``` ## **Contact Us** - General & Collaboration: **[[email protected]](mailto:[email protected])**, **[[email protected]](mailto:[email protected])** - Technical: **[[email protected]](mailto:[email protected])**

sergioalves/b2d77214-b6b7-4610-a8f2-dff847624ba0

sergioalves

2025-06-24T22:06:03Z

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.6", "base_model:quantized:TinyLlama/TinyLlama-1.1B-Chat-v0.6", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-06-24T21:58:16Z

--- base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.6 library_name: transformers model_name: b2d77214-b6b7-4610-a8f2-dff847624ba0 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for b2d77214-b6b7-4610-a8f2-dff847624ba0 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v0.6](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="sergioalves/b2d77214-b6b7-4610-a8f2-dff847624ba0", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/c4cp7xsx) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

PinkPixel/crystal-think-v1

PinkPixel

2025-06-24T22:05:03Z

52

1

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "mathematical-reasoning", "lora", "grpo", "math", "reasoning", "fine-tuned", "conversational", "en", "dataset:nvidia/OpenMathReasoning", "base_model:Qwen/Qwen3-4B", "base_model:adapter:Qwen/Qwen3-4B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-18T00:49:59Z

--- license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation tags: - mathematical-reasoning - qwen3 - lora - grpo - math - reasoning - fine-tuned base_model: Qwen/Qwen3-4B datasets: - nvidia/OpenMathReasoning --- # 🧠 Crystal-Think v1 ✨ **Mathematical Reasoning Model Fine-tuned with GRPO** Crystal-Think is a specialized mathematical reasoning model based on Qwen3-4B, fine-tuned using Group Relative Policy Optimization (GRPO) on NVIDIA's OpenMathReasoning dataset. This model excels at multi-step mathematical problem solving, algebraic reasoning, and mathematical code generation. ![Model Architecture](https://img.shields.io/badge/Architecture-Qwen3--4B-blue) ![Fine-tuning](https://img.shields.io/badge/Method-GRPO-green) ![License](https://img.shields.io/badge/License-Apache%202.0-yellow) ![Dataset](https://img.shields.io/badge/Dataset-OpenMathReasoning-purple) ## 🚀 Quick Start ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load model and tokenizer model_name = "PinkPixel/crystal-think-v1" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto" ) # Example mathematical reasoning prompt = """Solve this step by step: A rectangle has a length that is 3 more than twice its width. If the perimeter is 42 cm, what are the dimensions?""" inputs = tokenizer(prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## 📊 Model Performance | Benchmark | Crystal-Think v1 | Base Qwen3-4B | Improvement | |-----------|-------------------|---------------|-------------| | **GSM8K** | 85.2% | 76.4% | +8.8% | | **MATH** | 42.1% | 31.7% | +10.4% | | **Algebra** | 78.9% | 65.2% | +13.7% | | **Geometry** | 71.3% | 58.8% | +12.5% | | **Code Math** | 82.6% | 69.1% | +13.5% | ## 🎯 Model Details ### Model Description Crystal-Think is a mathematical reasoning language model that combines the strong foundation of Qwen3-4B with specialized training on mathematical problem-solving tasks. The model uses Group Relative Policy Optimization (GRPO) to enhance reasoning capabilities while maintaining efficiency through LoRA fine-tuning. **Key Features:** - 🧮 **Advanced Mathematical Reasoning**: Multi-step problem solving with clear explanations - 📐 **Geometric Understanding**: Spatial reasoning and geometric problem solving - 💻 **Mathematical Coding**: Generate and explain mathematical algorithms - 🔢 **Arithmetic Proficiency**: From basic operations to complex calculations - 📊 **Statistical Analysis**: Data interpretation and statistical reasoning ### Model Architecture - **Developed by:** Pink Pixel - **Model type:** Causal Language Model (Fine-tuned) - **Language:** English - **License:** Apache 2.0 - **Base model:** [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) - **Fine-tuning method:** GRPO (Group Relative Policy Optimization) - **Parameters:** ~4B (with LoRA adapters) - **Context Length:** 40,960 tokens - **Precision:** bfloat16 ### Training Details #### Training Data - **Primary Dataset:** [nvidia/OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) - **Domain:** Mathematical reasoning, problem-solving, algebraic manipulation - **Size:** Comprehensive mathematical reasoning dataset with step-by-step solutions #### Training Configuration - **Fine-tuning Method:** LoRA (Low-Rank Adaptation) - **LoRA Rank (r):** 32 - **LoRA Alpha:** 64 - **LoRA Dropout:** 0.0 - **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` - **Optimization:** GRPO (Group Relative Policy Optimization) - **Precision:** Mixed precision (bfloat16) ## 🎓 Usage Examples ### Basic Mathematical Problem ```python prompt = "What is the derivative of x^3 + 2x^2 - 5x + 1?" # Expected: Step-by-step differentiation with clear explanation ``` ### Word Problem Solving ```python prompt = """A train travels at 60 mph for 2 hours, then 80 mph for 1.5 hours. What is the average speed for the entire journey?""" # Expected: Detailed solution with distance calculations ``` ### Algebraic Reasoning ```python prompt = "Solve for x: 2x^2 - 8x + 6 = 0" # Expected: Quadratic formula application with step-by-step solution ``` ### Mathematical Code Generation ```python prompt = "Write a Python function to calculate the factorial of a number using recursion." # Expected: Clean, commented code with mathematical explanation ``` ## 📈 Evaluation Results ### Mathematical Reasoning Benchmarks The model was evaluated on standard mathematical reasoning benchmarks: - **GSM8K (Grade School Math)**: 85.2% accuracy - **MATH (Competition Mathematics)**: 42.1% accuracy - **Algebra Problems**: 78.9% accuracy - **Geometry Problems**: 71.3% accuracy - **Mathematical Coding**: 82.6% accuracy ### 📊 Performance Visualizations <div align="center"> #### 🎯 Performance Across Mathematical Domains <img src="crystal_think_performance_comparison.png" alt="Crystal-Think Performance Comparison" width="800"/> *Crystal-Think v1.0 consistently outperforms the base Qwen3-4B model across all mathematical domains, with particularly strong improvements in competition mathematics (+10.4%) and code generation (+13.5%).* #### 📈 Difficulty Scaling Analysis <img src="crystal_think_difficulty_scaling.png" alt="Difficulty Scaling Performance" width="800"/> *Performance scaling across AoPS problem difficulty levels shows Crystal-Think maintains superior accuracy even on advanced mathematical concepts, with a 24.3% improvement on Olympiad-level problems.* #### 🚀 Model Improvements Over Base <img src="crystal_think_improvements.png" alt="Model Improvements" width="800"/> *GRPO fine-tuning on OpenMathReasoning delivers consistent improvements across all capabilities, with the highest gains in Tool Usage Proficiency (+18.1%) and Solution Verification (+16.7%).* #### 🧠 Reasoning Capabilities Radar <img src="crystal_think_reasoning_radar.png" alt="Reasoning Capabilities" width="600"/> *Comprehensive reasoning profile trained on 3.2M Chain-of-Thought and 1.7M Tool-Integrated Reasoning solutions, showing balanced excellence across all mathematical reasoning dimensions.* #### 📚 Training Data Composition <img src="crystal_think_training_data.png" alt="Training Data Breakdown" width="800"/> *OpenMathReasoning dataset composition: 5.86M total samples from AoPS forums with diverse solution types optimized for mathematical reasoning development.* </div> ### Reasoning Capabilities ✅ **Multi-step Problem Solving**: Breaks down complex problems systematically ✅ **Clear Explanations**: Provides step-by-step reasoning ✅ **Error Checking**: Identifies and corrects mathematical errors ✅ **Multiple Approaches**: Can solve problems using different methods ✅ **Code Integration**: Generates mathematical code with explanations ## ⚠️ Limitations - **Domain Specificity**: Optimized for mathematical reasoning; may be less effective for general conversational tasks - **Language**: Primarily trained on English mathematical content - **Complexity Ceiling**: Very advanced mathematical concepts may still be challenging - **Computational Requirements**: Requires adequate GPU memory for optimal performance ## 🔧 Technical Specifications ### Hardware Requirements - **Minimum GPU Memory**: 8GB VRAM - **Recommended GPU Memory**: 16GB+ VRAM - **CPU**: Modern multi-core processor - **RAM**: 16GB+ system memory ### Software Dependencies ``` transformers>=4.52.0 torch>=2.0.0 tokenizers>=0.13.0 accelerate>=0.20.0 ``` ## 📝 Citation If you use Crystal-Think v1 in your research or applications, please cite: ```bibtex @model{crystal-think-v1, title={Crystal-Think v1: A Mathematical Reasoning Model}, author={sizzlebop}, year={2025}, url={https://huggingface.co/PinkPixel/crystal-think-v1}, note={Fine-tuned Qwen3-4B with GRPO on OpenMathReasoning} } ``` ## 🤝 Contributing I'm always open to learning, and I am very interested in the fine-tuning process! If you have suggestions for improvements, find issues, or want to collaborate on future versions, please feel free to reach out. ## 📧 Contact - **Developer:** Pink Pixel - **GitHub:** [https://github.com/pinkpixel-dev] - **Website:** [https://pinkpixel.dev] - **Email:** [[email protected]] ## 🙏 Acknowledgments - **Base Model:** Qwen Team for the excellent Qwen3-4B foundation - **Training Framework:** Unsloth for efficient fine-tuning tools - **Dataset:** NVIDIA for the OpenMathReasoning dataset - **Community:** Hugging Face community for support and resources --- **Made with ❤️ by Pink Pixel** ✨ *"Dream it, Pixel it"*

Zekrompogu/APNRkendaraan

Zekrompogu

2025-06-24T22:04:58Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:microsoft/trocr-base-str", "base_model:finetune:microsoft/trocr-base-str", "endpoints_compatible", "region:us" ]

image-text-to-text

2025-06-24T22:03:24Z

--- library_name: transformers base_model: microsoft/trocr-base-str tags: - generated_from_trainer metrics: - wer model-index: - name: microsoft/trocr-base-str results: [] ---  # microsoft/trocr-base-str This model is a fine-tuned version of [microsoft/trocr-base-str](https://huggingface.co/microsoft/trocr-base-str) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0895 - Cer: 0.0114 - Wer: 0.0668 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.9045 | 1.0 | 173 | 0.5308 | 0.0892 | 0.3232 | | 0.3001 | 2.0 | 346 | 0.1530 | 0.0262 | 0.1328 | | 0.1046 | 3.0 | 519 | 0.1169 | 0.0189 | 0.1108 | | 0.061 | 4.0 | 692 | 0.1017 | 0.0159 | 0.0871 | | 0.0403 | 5.0 | 865 | 0.0904 | 0.0134 | 0.0778 | | 0.0267 | 6.0 | 1038 | 0.0924 | 0.0132 | 0.0745 | | 0.0211 | 7.0 | 1211 | 0.0888 | 0.0122 | 0.0694 | | 0.0139 | 8.0 | 1384 | 0.0915 | 0.0120 | 0.0694 | | 0.0111 | 9.0 | 1557 | 0.0902 | 0.0120 | 0.0702 | | 0.0084 | 10.0 | 1730 | 0.0895 | 0.0114 | 0.0668 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 2.17.0 - Tokenizers 0.19.1

phospho-app/Schmidie-gr00t-schachtel-2vj6c

phospho-app

2025-06-24T22:02:42Z

0

null

[ "safetensors", "phosphobot", "gr00t", "region:us" ]

null

2025-06-24T18:57:17Z

--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/opt/conda/lib/python3.11/asyncio/tasks.py", line 500, in wait_for return fut.result() ^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 970, in read_output async for line in process.stdout: File "/opt/conda/lib/python3.11/asyncio/streams.py", line 765, in __anext__ val = await self.readline() ^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/streams.py", line 566, in readline line = await self.readuntil(sep) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/streams.py", line 658, in readuntil await self._wait_for_data('readuntil') File "/opt/conda/lib/python3.11/asyncio/streams.py", line 543, in _wait_for_data await self._waiter asyncio.exceptions.CancelledError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/root/phosphobot/am/gr00t.py", line 981, in run_gr00t_training await asyncio.wait_for(read_output(), timeout=timeout_seconds) File "/opt/conda/lib/python3.11/asyncio/tasks.py", line 502, in wait_for raise exceptions.TimeoutError() from exc TimeoutError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/root/src/helper.py", line 165, in predict trainer.train(timeout_seconds=timeout_seconds) File "/root/phosphobot/am/gr00t.py", line 1146, in train asyncio.run( File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 986, in run_gr00t_training raise TimeoutError( TimeoutError: Training process exceeded timeout of 10800 seconds. Please consider lowering the number of epochs and/or batch size. ``` ## Training parameters: - **Dataset**: [Schmidie/schachtel](https://huggingface.co/datasets/Schmidie/schachtel) - **Wandb run URL**: None - **Epochs**: 10 - **Batch size**: 15 - **Training steps**: None 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)

NEW-VIDEO-mezzo-fun-18-viral-Clips-tk/18.video.mezzo.fun.viral.link.Mezzo.fun.Viral.Video.Tutorial.Official

NEW-VIDEO-mezzo-fun-18-viral-Clips-tk

2025-06-24T22:01:09Z

0

null

[ "region:us" ]

null

2025-06-24T22:00:45Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

annasoli/Qwen2.5-14B-Instruct_bad-med-topic-10

annasoli

2025-06-24T22:00:26Z

0

transformers

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

null

2025-06-24T21:23:03Z

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

phospho-app/gc1724-ACT-ttt-a1-square-xogfz

phospho-app

2025-06-24T21:57:41Z

0

null

[ "safetensors", "phosphobot", "act", "region:us" ]

null

2025-06-24T15:16:08Z

--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using **phospho**. Training was successfull, try it out on your robot! ## Training parameters: - **Dataset**: [gc1724/ttt-a1-square](https://huggingface.co/datasets/gc1724/ttt-a1-square) - **Wandb run URL**: None - **Epochs**: None - **Batch size**: 60 - **Training steps**: 7500 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)

Osilly/Vision-R1-72B

Osilly

2025-06-24T21:54:56Z

0

null

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

null

2025-06-24T20:52:01Z

--- license: apache-2.0 ---

New-videos-Bindura-University-viral-video/FULL.VIDEO.Bindura.University.Viral.Video.Tutorial.Official

New-videos-Bindura-University-viral-video

2025-06-24T21:53:12Z

0

null

[ "region:us" ]

null

2025-06-24T21:52:53Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

JamieOgundiran/Ogun-Mistral-7B

JamieOgundiran

2025-06-24T21:40:50Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "sft", "trl", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T21:35:20Z

--- base_model: mistralai/Mistral-7B-v0.1 library_name: transformers model_name: ogun-mistral-7B-2 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for ogun-mistral-7B-2 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="JamieOgundiran/ogun-mistral-7B-2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.52.4 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

Tyl3rDrden/trained-flux-dev-dreambooth-lora2_Normal_LILY

Tyl3rDrden

2025-06-24T21:37:48Z

0

diffusers

[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-24T16:52:39Z

--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers license: other instance_prompt: a photo of sksdog widget: - text: A photo of sksdog in a bucket output: url: image_0.png - text: A photo of sksdog in a bucket output: url: image_1.png - text: A photo of sksdog in a bucket output: url: image_2.png - text: A photo of sksdog in a bucket output: url: image_3.png tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora ---  # Flux DreamBooth LoRA - Tyl3rDrden/trained-flux-dev-dreambooth-lora2_Normal_LILY <Gallery /> ## Model description These are Tyl3rDrden/trained-flux-dev-dreambooth-lora2_Normal_LILY DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md). Was LoRA for the text encoder enabled? True. ## Trigger words You should use `a photo of sksdog` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](Tyl3rDrden/trained-flux-dev-dreambooth-lora2_Normal_LILY/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('Tyl3rDrden/trained-flux-dev-dreambooth-lora2_Normal_LILY', weight_name='pytorch_lora_weights.safetensors') image = pipeline('A photo of sksdog in a bucket').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-11_9874

luckeciano

2025-06-24T21:34:03Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-24T16:02:16Z

--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-11_9874 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-11_9874 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-11_9874", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/c1aokynk) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

altaweel/gemma-ultrasound-1b-v3

altaweel

2025-06-24T21:33:36Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "endpoints_compatible", "region:us" ]

null

2025-06-24T19:32:05Z

--- base_model: google/gemma-3-1b-pt library_name: transformers model_name: gemma-ultrasound-1b-v3 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-ultrasound-1b-v3 This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="altaweel/gemma-ultrasound-1b-v3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.4 - Pytorch: 2.5.1+cu121 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

nguyenvuvn/1a137b4b-cce1-4cd3-ac12-6319c4427169

nguyenvuvn

2025-06-24T21:33:01Z

0

transformers

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

text-generation

2025-06-24T16:56:33Z

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

ICONNAI/ICONN-e1-Beta

ICONNAI

2025-06-24T21:30:27Z

90

15

transformers

[ "transformers", "safetensors", "mixtral", "text-generation", "emotional-ai", "ICONN", "chatbot", "base", "conversational", "doi:10.57967/hf/5861", "license:apache-2.0", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-17T18:57:06Z

--- library_name: transformers tags: - emotional-ai - ICONN - chatbot - base co2_eq_emissions: emissions: 3.37 source: CodeCarbon training_type: pretraining geographical_location: US-West hardware_used: 18 x B200 pipeline_tag: text-generation license: apache-2.0 --- <div align="center" style="line-height: 1;"> ![ICONN AI Logo](https://i.postimg.cc/gJwHqh1D/svgviewer-png-output.png) <a href="https://huggingface.co/collections/ICONNAI/iconn-1-6851e8a88ed4eb66b4fd0132" target="_blank" style="margin: 2px;"> <img alt="ICONN 1 Models" src="https://img.shields.io/badge/📦_ICONN_1_Models-HuggingFace-1CBEEF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://huggingface.co/ICONNAI" target="_blank" style="margin: 2px;"> <img alt="ICONN on Hugging Face" src="https://img.shields.io/badge/🤗_ICONN_on_HF-ICONNAI-A4BCF0?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://opensource.org/license/apache-2-0" target="_blank" style="margin: 2px;"> <img alt="License Apache 2.0" src="https://img.shields.io/badge/⚖️_License-Apache_2.0-5C63DA?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://github.com/organizations/ICONN-AI/" target="_blank" style="margin: 2px;"> <img alt="ICONN on GitHub" src="https://img.shields.io/badge/🐙_ICONN_on_GitHub-ICONN--AI-8C8CFF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://huggingface.co/ICONNAI" target="_blank" style="margin: 2px;"> <img alt="Follow ICONNAI" src="https://img.shields.io/badge/⭐_Follow_ICONNAI-HuggingFace-A4BCF0?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> </div> # ICONN e1: The new era of Open-Source AI **GPU poor? Less than 3x A100s? A e1 Lite model is coming with just 22B parameters alongside a model for consumer CPUs with 14B and 7B parameters.** - **Emotional Context Awareness** ICONN e1 interprets emotional cues and adjusts tone, vocabulary, and response style—offering a more human-like, emotionally reactive experience. - **ICONN Emotional Core (IEC) (Notice: Not available on Huggingface)** Powered by millions of small AI agents, IEC gives ICONN its emotional personality, with billions of simulated emotional states and detections. - **Reasoning** ICONN e1 is one of the most powerful reasoning open-source models, and most closed-source models in or out of Huggingface. # What is in the ICONN i1 MoE? ## ICONN i1 MoE and Experts ICONN e1, being a MoE just like it's base model ICONN 1, has multiple expert models. Keywords are taken from the user's input to choose which expert generates the output. | Expert Chosen | User Input | |---------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ICONN-e1 | `'Hi!'` | | ICONN-e1-Pro | `Solve for m: m² − (2 + ∑₍ⱼ₌₁₎² j)·m + (1 + ∑₍ⱼ₌₁₎³ j² − 14) = 0.` | | ICONN-e1-Science | `If a stable isotope of Ununoctium (Uuo, now Og) could be synthesized in bulk, what would be its most likely physical state at STP and why, considering relativistic effects?` | | ICONN-e1-Code | `Create a zero-dependency quantum-safe VM in Zig that compiles a domain-specific language into a fully homomorphic encrypted IR, supports hot-reloading WebAssembly modules, parallel scheduling via lock-free fibers, and performs live introspection through a headless OpenGL debug overlay.` | **ICONN-e1:** ICONN's general-purpose reasoning model, designed for everyday tasks, logic, and conversation. **ICONN-e1-Pro:** ICONN's advanced reasoning model, optimized for complex problem-solving in math, logic, and professional domains. **ICONN-e1-Science:** ICONN's scientific expert model, trained on advanced science datasets to enhance precision in physics, chemistry, biology, and technical reasoning. **ICONN-e1-Code:** ICONN's coding specialist, trained for programming, compiler theory, software architecture, and technical code generation across multiple languages. # Usage **First, make sure you have at least 4x Nvidia A100 or a single B100, and 120GB RAM and 120-192GB VRAM. Don't have this? Use our Lite model, coming soon. > Run the code below to run ICONN i1: ```python from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline import torch def run_iconn_chatbot(model_name="ICONNAI/ICONN-e1"): tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) device = 0 if torch.cuda.is_available() else -1 chat_pipeline = pipeline( "text-generation", model=model, tokenizer=tokenizer, device=device, max_length=1624, do_sample=True, top_p=0.9, temperature=0.4, pad_token_id=tokenizer.eos_token_id ) print(f"ICONN chatbot running with model: {model_name}. Type 'exit' to quit.") conversation_history = "" while True: user_input = input("You: ") if user_input.lower() == "exit": print("Goodbye!") break conversation_history += f"User: {user_input}\nBot:" response = chat_pipeline(conversation_history, max_length=len(tokenizer.encode(conversation_history)) + 100)[0]['generated_text'] bot_reply = response[len(conversation_history):].strip().split("\n")[0] print(f"Bot: {bot_reply}") conversation_history += f" {bot_reply}\n" if __name__ == "__main__": run_iconn_chatbot() ``` ## Cite Us **If you use ICONN 1, please cite us as follows:** ```DoI @misc{iconnai_2025, author = { ICONNAI }, title = { ICONN-e1-Beta (Revision ca41146) }, year = 2025, url = { https://huggingface.co/ICONNAI/ICONN-e1-Beta }, doi = { 10.57967/hf/5861 }, publisher = { Hugging Face } } ```

sameeahameed/llama-3.2-3b-25-6-25

sameeahameed

2025-06-24T21:29:40Z

0

transformers

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

null

2025-06-24T21:29:33Z

--- base_model: unsloth/llama-3.2-3b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** sameeahameed - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

ICONNAI/ICONN-1-Mini-Beta

ICONNAI

2025-06-24T21:29:37Z

27

2

transformers

[ "transformers", "safetensors", "iconn", "text-generation", "emotional-ai", "ICONN", "chatbot", "base", "conversational", "custom_code", "doi:10.57967/hf/5860", "license:apache-2.0", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-22T22:14:23Z

--- library_name: transformers tags: - emotional-ai - ICONN - chatbot - base co2_eq_emissions: emissions: 0.34 source: CodeCarbon training_type: pretraining geographical_location: US-West hardware_used: 9 x B200 pipeline_tag: text-generation license: apache-2.0 --- <div align="center" style="line-height: 1;"> ![ICONN AI Logo](https://i.postimg.cc/gJwHqh1D/svgviewer-png-output.png) <a href="https://huggingface.co/collections/ICONNAI/iconn-1-6851e8a88ed4eb66b4fd0132" target="_blank" style="margin: 2px;"> <img alt="ICONN 1 Models" src="https://img.shields.io/badge/📦_ICONN_1_Models-HuggingFace-1CBEEF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://huggingface.co/spaces/ICONNAI/ICONN-Mini-Chat" target="_blank" style="margin: 2px;"> <img alt="ICONN 1 Chat" src="https://img.shields.io/badge/💬_ICONN_1_Chat-Online-65C7F9?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://huggingface.co/ICONNAI" target="_blank" style="margin: 2px;"> <img alt="ICONN on Hugging Face" src="https://img.shields.io/badge/🤗_ICONN_on_HF-ICONNAI-A4BCF0?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://opensource.org/license/apache-2-0" target="_blank" style="margin: 2px;"> <img alt="License Apache 2.0" src="https://img.shields.io/badge/⚖️_License-Apache_2.0-5C63DA?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://github.com/organizations/ICONN-AI/" target="_blank" style="margin: 2px;"> <img alt="ICONN on GitHub" src="https://img.shields.io/badge/🐙_ICONN_on_GitHub-ICONN--AI-8C8CFF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://huggingface.co/ICONNAI" target="_blank" style="margin: 2px;"> <img alt="Follow ICONNAI" src="https://img.shields.io/badge/⭐_Follow_ICONNAI-HuggingFace-A4BCF0?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> <a href="https://huggingface.co/spaces/huggingface/InferenceSupport/discussions/2932" target="_blank" style="margin: 2px;"> <img alt="React to Vote" src="https://img.shields.io/badge/🗳️_Vote_for_us_as_Inference_Provider-React_👍-1CBEEF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" /> </a> </div> ## ICONN 1 Introducing **ICONN 1 Mini Beta**, a cutting-edge open-source AI model with just **7 billion parameters** — designed for natural, human-like language understanding and generation. Despite its compact size, it delivers powerful performance through efficient architecture and careful tuning. ICONN 1 Mini Beta represents the next step in accessible, conversational AI. Developed entirely from scratch, ICONN-1-Mini-Beta is based on a new **ICONN** framework and comprises **7 billion parameters**. ICONN-1 is released in three distinct forms to serve different application needs: - **ICONN-1-Mini-Beta**(This model) is a small 7B model trained for a lightweight alternative to ICONN 1. - **ICONN-1** is optimized for natural, emotionally resonant, and conversational interactions. - **ICONN-e1** is a specialized variant of the model fine-tuned for advanced reasoning, critical analysis, and complex problem-solving. Together, these models represent a major leap forward in the evolution of AI systems—demonstrating not only deep reasoning but also a commitment to openness, accessibility, and human-aligned intelligence. ## Usage To run **ICONN 1 Mini Beta**, you need: - **Any hardware - CPU or GPU; Just make sure you have about 15GB storage space!** > Run the code below to run ICONN 1 Mini Beta: ```python import os import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer from threading import Thread model_id = "ICONNAI/ICONN-1-Mini-Beta" try: model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained(model_id) except Exception as e: exit(f"Exiting due to model loading error: {e}") def generate_response( message: str, max_new_tokens: int = 2048, temperature: float = 0.4, top_p: float = 0.9, top_k: int = 50, repetition_penalty: float = 1.2, ) -> str: conversation = [{"role": "user", "content": message}] try: input_ids = tokenizer.apply_chat_template( conversation, return_tensors="pt", enable_thinking=True ) except Exception as e: return f"Error applying chat template: {e}" input_ids = input_ids.to(model.device) streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True) adjusted_top_k = int(max(1, top_k)) generate_kwargs = dict( {"input_ids": input_ids}, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p, top_k=adjusted_top_k, temperature=temperature, num_beams=1, repetition_penalty=repetition_penalty, ) try: t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() except Exception as e: return f"Error starting generation thread: {e}" outputs = [] for text in streamer: outputs.append(text) return "".join(outputs) if __name__ == "__main__": question = "Can you explain briefly to me what is the Python programming language?" print(f"User Question: {question}") response = generate_response(question) print(f"Bot Response: {response}") ``` ## Cite Us **If you use ICONN 1, please cite us as follows:** ```DoI @misc{iconnai_2025, author = { ICONNAI }, title = { ICONN-1-Mini-Beta (Revision e29b435) }, year = 2025, url = { https://huggingface.co/ICONNAI/ICONN-1-Mini-Beta }, doi = { 10.57967/hf/5860 }, publisher = { Hugging Face } } ```

oumi-ai/Phi-3-vision-128k-instruct

oumi-ai

2025-06-24T21:28:39Z

0

null

[ "safetensors", "phi3_v", "nlp", "code", "vision", "text-generation", "conversational", "custom_code", "multilingual", "license:mit", "region:us" ]

text-generation

2025-06-24T21:23:42Z

--- license: mit license_link: https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE language: - multilingual pipeline_tag: text-generation tags: - nlp - code - vision inference: parameters: temperature: 0.7 widget: - messages: - role: user content: <|image_1|>Can you describe what you see in the image? --- 🎉 **Phi-3.5**: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct) ## Model Summary The Phi-3-Vision-128K-Instruct is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data both on text and vision. The model belongs to the Phi-3 model family, and the multimodal version comes with 128K context length (in tokens) it can support. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures. Resources and Technical Documentation: + [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024) + [Phi-3 Technical Report](https://aka.ms/phi3-tech-report) + [Phi-3 on Azure AI Studio](https://aka.ms/try-phi3vision) + [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook) | | Short Context | Long Context | | ------- | ------------- | ------------ | | Mini | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)| | Small | 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)| | Medium | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)| | Vision | | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda)| ## Intended Uses **Primary use cases** The model is intended for broad commercial and research use in English. The model provides uses for general purpose AI systems and applications with visual and text input capabilities which require 1) memory/compute constrained environments; 2) latency bound scenarios; 3) general image understanding; 4) OCR; 5) chart and table understanding. Our model is designed to accelerate research on efficient language and multimodal models, for use as a building block for generative AI powered features. **Use case considerations** Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. ## How to Use Phi-3-Vision-128K-Instruct has been integrated in the development version (4.40.2) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following: * When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function. * Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source. The current `transformers` version can be verified with: `pip list | grep transformers`. Examples of required packages: ``` flash_attn==2.5.8 numpy==1.24.4 Pillow==10.3.0 Requests==2.31.0 torch==2.3.0 torchvision==0.18.0 transformers==4.40.2 ``` Phi-3-Vision-128K-Instruct is also available in [Azure AI Studio](https://aka.ms/phi3-azure-ai). ### Chat Format Given the nature of the training data, the Phi-3-Vision-128K-Instruct model is best suited for a single image input wih prompts using the chat format as follows. You can provide the prompt as a single image with a generic template as follow: ```markdown <|user|>\n<|image_1|>\n{prompt}<|end|>\n<|assistant|>\n ``` where the model generates the text after `<|assistant|>` . In case of multi-turn conversation, the prompt can be formatted as follows: ```markdown <|user|>\n<|image_1|>\n{prompt_1}<|end|>\n<|assistant|>\n{response_1}<|end|>\n<|user|>\n{prompt_2}<|end|>\n<|assistant|>\n ``` ### Sample inference code This code snippets show how to get quickly started with running the model on a GPU: ```python from PIL import Image import requests from transformers import AutoModelForCausalLM from transformers import AutoProcessor model_id = "microsoft/Phi-3-vision-128k-instruct" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto", _attn_implementation='flash_attention_2') # use _attn_implementation='eager' to disable flash attention processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) messages = [ {"role": "user", "content": "<|image_1|>\nWhat is shown in this image?"}, {"role": "assistant", "content": "The chart displays the percentage of respondents who agree with various statements about their preparedness for meetings. It shows five categories: 'Having clear and pre-defined goals for meetings', 'Knowing where to find the information I need for a meeting', 'Understanding my exact role and responsibilities when I'm invited', 'Having tools to manage admin tasks like note-taking or summarization', and 'Having more focus time to sufficiently prepare for meetings'. Each category has an associated bar indicating the level of agreement, measured on a scale from 0% to 100%."}, {"role": "user", "content": "Provide insightful questions to spark discussion."} ] url = "https://assets-c4akfrf5b4d3f4b7.z01.azurefd.net/assets/2024/04/BMDataViz_661fb89f3845e.png" image = Image.open(requests.get(url, stream=True).raw) prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = processor(prompt, [image], return_tensors="pt").to("cuda:0") generation_args = { "max_new_tokens": 500, "temperature": 0.0, "do_sample": False, } generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args) # remove input tokens generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:] response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] print(response) ``` Additional basic examples are provided [here](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/sample_inference.py). ### How to finetune? We recommend user to take a look at the [Phi-3 CookBook finetuning recipe for Vision](https://github.com/microsoft/Phi-3CookBook/blob/main/md/04.Fine-tuning/FineTuning_Vision.md) ## Responsible AI Considerations Like other models, the Phi family of models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: + Quality of Service: The Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. + Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. + Inappropriate or Offensive Content: These models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. + Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. + Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include: + Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. + High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. + Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). + Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. + Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations. + Identification of individuals: models with vision capabilities may have the potential to uniquely identify individuals in images. Safety post-training steers the model to refuse such requests, but developers should consider and implement, as appropriate, additional mitigations or user consent flows as required in their respective jurisdiction, (e.g., building measures to blur faces in image inputs before processing. ## Training ### Model * Architecture: Phi-3-Vision-128K-Instruct has 4.2B parameters and contains image encoder, connector, projector, and Phi-3 Mini language model. * Inputs: Text and Image. It’s best suited for prompts using the chat format. * Context length: 128K tokens * GPUs: 512 H100-80G * Training time: 1.5 days * Training data: 500B vision and text tokens * Outputs: Generated text in response to the input * Dates: Our models were trained between February and April 2024 * Status: This is a static model trained on an offline text dataset with cutoff date Mar 15, 2024. Future versions of the tuned models may be released as we improve models. * Release Type: Open weight release * Release dates: The model weight is released on May 21, 2024. ### Datasets Our training data includes a wide variety of sources, and is a combination of 1) publicly available documents filtered rigorously for quality, selected high-quality educational data and code; 2) selected high-quality image-text interleave; 3) newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.), newly created image data, e.g., chart/table/diagram/slides; 4) high quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. The data collection process involved sourcing information from publicly available documents, with a meticulous approach to filtering out undesirable documents and images. To safeguard privacy, we carefully filtered various image and text data sources to remove or scrub any potentially personal data from the training data. More details can be found in the [Phi-3 Technical Report](https://aka.ms/phi3-tech-report). ## Benchmarks To understand the capabilities, we compare Phi-3-Vision-128K-Instruct with a set of models over a variety of zero-shot benchmarks using our internal benchmark platform. |Benchmark|Phi-3 Vision-128K-In|LlaVA-1.6 Vicuna-7B|QWEN-VL Chat|Llama3-Llava-Next-8B|Claude-3 Haiku|Gemini 1.0 Pro V|GPT-4V-Turbo| |---------|---------------------|------------------|------------|--------------------|--------------|----------------|------------| |MMMU|40.4|34.2|39.0|36.4|40.7|42.0|55.5| |MMBench|80.5|76.3|75.8|79.4|62.4|80.0|86.1| |ScienceQA|90.8|70.6|67.2|73.7|72.0|79.7|75.7| |MathVista|44.5|31.5|29.4|34.8|33.2|35.0|47.5| |InterGPS|38.1|20.5|22.3|24.6|32.1|28.6|41.0| |AI2D|76.7|63.1|59.8|66.9|60.3|62.8|74.7| |ChartQA|81.4|55.0|50.9|65.8|59.3|58.0|62.3| |TextVQA|70.9|64.6|59.4|55.7|62.7|64.7|68.1| |POPE|85.8|87.2|82.6|87.0|74.4|84.2|83.7| ## Software * [PyTorch](https://github.com/pytorch/pytorch) * [Transformers](https://github.com/huggingface/transformers) * [Flash-Attention](https://github.com/HazyResearch/flash-attention) ## Hardware Note that by default, the Phi-3-Vision-128K model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types: * NVIDIA A100 * NVIDIA A6000 * NVIDIA H100 ## License The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE). ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

ToastyPigeon/ms3.2-cowriter-lora

ToastyPigeon

2025-06-24T21:28:07Z

0

peft

[ "peft", "safetensors", "mistral", "generated_from_trainer", "dataset:ToastyPigeon/cowriter-instruct", "base_model:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML", "base_model:adapter:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML", "4-bit", "bitsandbytes", "region:us" ]

null

2025-06-24T21:08:15Z

--- library_name: peft base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML tags: - generated_from_trainer datasets: - ToastyPigeon/cowriter-instruct model-index: - name: workspace/aibox-standalone-pool/axolotl/mistral3.2-cowriter-ckpts results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.10.0.dev0` ```yaml # === Model Configuration === base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML load_in_8bit: false load_in_4bit: true # === HF Configuration === #hub_model_id: ToastyPigeon/an-instruct-ms3.2-train #hub_strategy: "checkpoint" # === Training Setup === num_epochs: 2 micro_batch_size: 1 gradient_accumulation_steps: 4 sequence_len: 16384 sample_packing: true pad_to_sequence_len: true # === Evaluation === val_set_size: 0.01 evals_per_epoch: 5 #eval_steps: 20 #max_steps: 60 #eval_table_size: eval_max_new_tokens: 128 eval_sample_packing: true #eval_strategy: "no" # === LoRA Configuration === adapter: qlora lora_model_dir: lora_r: 128 lora_alpha: 16 lora_dropout: 0.125 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj peft_use_rslora: true #lora_mlp_kernel: true #lora_qkv_kernel: true #lora_o_kernel: true # === Hyperparameter Configuration === #optimizer: apollo_adamw_layerwise warmup_steps: 20 optimizer: adamw_torch_fused #optimizer: paged_adamw_8bit #optim_args: # enable_stochastic_rounding: true # enable_cautious: true # enable_8bit: true # Apollo-mini configuration: #optim_args: "proj=random,rank=128,scale=128.0,scale_type=tensor,update_proj_gap=100" # Regular Apollo configuration: # optim_args: #optim_target_modules: all_linear learning_rate: 1e-5 lr_scheduler: rex cosine_min_lr_ratio: 0.2 #lr_scheduler: cosine_with_min_lr #lr_scheduler_kwargs: # cosine_min_lr: 1e-6 weight_decay: 0.01 max_grad_norm: 1.0 #warmup_steps: 0 #warmup_ratio: 0.025 # === Data Configuration === chat_template: jinja chat_template_jinja: "{%- set default_system_message = \"You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.\" %}\n\n{{- bos_token }}\n\n{%- if messages[0]['role'] == 'system' %}\n {%- if messages[0]['content'] is string %}\n {%- set system_message = messages[0]['content'] %}\n {%- else %}\n {%- set system_message = messages[0]['content'][0]['text'] %}\n {%- endif %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = default_system_message %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{{- '[SYSTEM_PROMPT]' + system_message + '[/SYSTEM_PROMPT]' }}\n\n{%- for message in loop_messages %}\n {%- if message['role'] == 'user' %}\n {%- if message['content'] is string %}\n {{- '[INST]' + message['content'] + '[/INST]' }}\n {%- else %}\n {{- '[INST]' }}\n {%- for block in message['content'] %}\n {%- if block['type'] == 'text' %}\n {{- block['text'] }}\n {%- elif block['type'] in ['image', 'image_url'] %}\n {{- '[IMG]' }}\n {%- else %}\n {{- raise_exception('Only text and image blocks are supported in message content!') }}\n {%- endif %}\n {%- endfor %}\n {{- '[/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'system' %}\n {%- if message['content'] is string %}\n {{- '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }}\n {%- else %}\n {{- '[SYSTEM_PROMPT]' + message['content'][0]['text'] + '[/SYSTEM_PROMPT]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {%- if message['content'] is string %}\n {{- message['content'] + eos_token }}\n {%- else %}\n {{- message['content'][0]['text'] + eos_token }}\n {%- endif %}\n {%- else %}\n {{- raise_exception('Only user, system and assistant roles are supported!') }}\n {%- endif %}\n{%- endfor %}" tokenizer_use_mistral_common: true shuffle_merged_datasets: true datasets: - path: ToastyPigeon/cowriter-instruct type: chat_template data_files: cowriter-16k.json dataset_prepared_path: last_run_prepared # === Plugins === plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin # === Hardware Optimization === #gradient_checkpointing: offload #gradient_checkpointing_kwargs: # use_reentrant: false liger_rope: true liger_rms_norm: true liger_layer_norm: true liger_glu_activation: true #liger_fused_linear_cross_entropy: true cut_cross_entropy: true #deepspeed: deepspeed_configs/zero3_bf16.json # === FSDP Config === fsdp: - full_shard - auto_wrap fsdp_config: fsdp_limit_all_gathers: true fsdp_sync_module_states: true fsdp_offload_params: true fsdp_activation_checkpointing: true fsdp_use_orig_params: false fsdp_cpu_ram_efficient_loading: true fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer fsdp_state_dict_type: FULL_STATE_DICT fsdp_sharding_strategy: FULL_SHARD # === Wandb Tracking === wandb_project: Mistral-3.2 # wandb_entity: [WANDB_ENTITY] # wandb_name: [WANDB_RUN_NAME] # === Checkpointing === saves_per_epoch: 20 save_total_limit: 5 # === Advanced Settings === output_dir: /workspace/aibox-standalone-pool/axolotl/mistral3.2-cowriter-ckpts bf16: auto flash_attention: true train_on_inputs: false group_by_length: false save_safetensors: true logging_steps: 1 gc_steps: 10 seed: 69 ``` </details> # workspace/aibox-standalone-pool/axolotl/mistral3.2-cowriter-ckpts This model is a fine-tuned version of [anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML](https://huggingface.co/anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML) on the ToastyPigeon/cowriter-instruct dataset. It achieves the following results on the evaluation set: - Loss: 2.4342 ## 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: 69 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - total_eval_batch_size: 2 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - training_steps: 328 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.4735 | 0.0061 | 1 | 2.5005 | | 2.5091 | 0.2006 | 33 | 2.4586 | | 2.499 | 0.4012 | 66 | 2.4589 | | 2.3922 | 0.6018 | 99 | 2.4499 | | 2.3564 | 0.8024 | 132 | 2.4468 | | 2.3165 | 1.0 | 165 | 2.4428 | | 2.4576 | 1.2006 | 198 | 2.4388 | | 2.4476 | 1.4012 | 231 | 2.4366 | | 2.3374 | 1.6018 | 264 | 2.4343 | | 2.3019 | 1.8024 | 297 | 2.4342 | ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.1 - Tokenizers 0.21.1