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MartijnTUE/llama3-concurrentiebeding	MartijnTUE	2025-05-29T15:36:11Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-29T15:36:05Z	--- 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. (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]
miladalsh/qwen-trained-journalist-on-deepseek-for-40k-samples	miladalsh	2025-05-29T15:35:43Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-21T11:03:36Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: qwen-trained-journalist-on-deepseek-for-40k-samples tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen-trained-journalist-on-deepseek-for-40k-samples This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="miladalsh/qwen-trained-journalist-on-deepseek-for-40k-samples", 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/milad-it/training-llama-on-conversations/runs/vmkfn38v) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.46.3 - Pytorch: 2.5.1+cu121 - Datasets: 3.2.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Koushim/distilbert-yahoo-answers-topic-classifier	Koushim	2025-05-29T15:35:25Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "topic-classification", "yahoo-answers", "pytorch", "en", "dataset:yahoo_answers_topics", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-29T15:32:19Z	--- language: en datasets: yahoo_answers_topics tags: - text-classification - topic-classification - yahoo-answers - distilbert - transformers - pytorch license: apache-2.0 model-index: - name: DistilBERT Yahoo Answers Classifier results: - task: name: Topic Classification type: text-classification dataset: name: Yahoo Answers Topics type: yahoo_answers_topics metrics: - name: Accuracy type: accuracy value: 0.71 --- # DistilBERT Fine-Tuned on Yahoo Answers Topics This is a fine-tuned [DistilBERT](https://huggingface.co/distilbert-base-uncased) model for topic classification on the [Yahoo Answers Topics dataset](https://huggingface.co/datasets/yahoo_answers_topics). It classifies questions into one of 10 predefined categories like "Science & Mathematics", "Health", "Business & Finance", etc. ## 🧠 Model Details - Base model: `distilbert-base-uncased` - Task: Multi-class Text Classification (10 classes) - Dataset: Yahoo Answers Topics - Training samples: 50,000 (subset) - Evaluation samples: 5,000 (subset) - Metrics: Accuracy ## 🧪 How to Use ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Koushim/distilbert-yahoo-answers") model = AutoModelForSequenceClassification.from_pretrained("Koushim/distilbert-yahoo-answers") text = "How do I improve my math skills for competitive exams?" inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) outputs = model(*inputs) predicted_class = outputs.logits.argmax(dim=1).item() print("Predicted class:", predicted_class) ```` ## 📊 Classes (Labels) 0. Society & Culture 1. Science & Mathematics 2. Health 3. Education & Reference 4. Computers & Internet 5. Sports 6. Business & Finance 7. Entertainment & Music 8. Family & Relationships 9. Politics & Government ## 📦 Training Details Optimizer: AdamW * Learning rate: 2e-5 * Batch size: 16 (train), 32 (eval) * Epochs: 3 * Weight decay: 0.01 * Framework: PyTorch + 🤗 Transformers ## 📁 Repository Structure * `config.json` – Model config * `pytorch_model.bin` – Trained model weights * `tokenizer.json`, `vocab.txt` – Tokenizer files ## ✍️ Author * Hugging Face Hub: [Koushim](https://huggingface.co/Koushim) * Model trained using `transformers.Trainer` API ## 📄 License Apache 2.0 ````
RikoteMaster/model_ft_openbookqa_additional_mcqa	RikoteMaster	2025-05-29T15:34:27Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-29T15:34:18Z	--- 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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SleepyM/RL_pixelcopter_v0	SleepyM	2025-05-29T15:32:37Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-28T15:57:54Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: RL_pixelcopter_v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 31.97 +/- 26.51 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
shaddie/rocketry_roqeto_model	shaddie	2025-05-29T15:31:55Z	12	0	transformers	[ "transformers", "safetensors", "codegen", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T14:01:41Z	--- 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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bullerwins/DeepSeek-R1-0528-Qwen3-8B-exl3-5.0bpw	bullerwins	2025-05-29T15:31:52Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:2501.12948", "base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "base_model:quantized:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl3", "region:us" ]	text-generation	2025-05-29T15:27:18Z	--- license: mit library_name: transformers base_model: - deepseek-ai/DeepSeek-R1-0528-Qwen3-8B --- # DeepSeek-R1-0528 <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20R1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="LICENSE" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> <p align="center"> <a href="https://arxiv.org/pdf/2501.12948"><b>Paper Link</b>👁️</a> </p> ## 1. Introduction The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro. <p align="center"> <img width="80%" src="figures/benchmark.png"> </p> Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question. Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding. ## 2. Evaluation Results ### DeepSeek-R1-0528 For all our models, the maximum generation length is set to 64K tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 16 responses per query to estimate pass@1. <div align="center"> \| Category \| Benchmark (Metric) \| DeepSeek R1 \| DeepSeek R1 0528 \|----------\|----------------------------------\|-----------------\|---\| \| General \| \| \| MMLU-Redux (EM) \| 92.9 \| 93.4 \| \| MMLU-Pro (EM) \| 84.0 \| 85.0 \| \| GPQA-Diamond (Pass@1) \| 71.5 \| 81.0 \| \| SimpleQA (Correct) \| 30.1 \| 27.8 \| \| FRAMES (Acc.) \| 82.5 \| 83.0 \| \| Humanity's Last Exam (Pass@1) \| 8.5 \| 17.7 \| Code \| \| \| LiveCodeBench (2408-2505) (Pass@1) \| 63.5 \| 73.3 \| \| Codeforces-Div1 (Rating) \| 1530 \| 1930 \| \| SWE Verified (Resolved) \| 49.2 \| 57.6 \| \| Aider-Polyglot (Acc.) \| 53.3 \| 71.6 \| Math \| \| \| AIME 2024 (Pass@1) \| 79.8 \| 91.4 \| \| AIME 2025 (Pass@1) \| 70.0 \| 87.5 \| \| HMMT 2025 (Pass@1) \| 41.7 \| 79.4 \| \| \| CNMO 2024 (Pass@1) \| 78.8 \| 86.9 \| Tools \| \| \| BFCL_v3_MultiTurn (Acc) \| - \| 37.0 \| \| \| Tau-Bench (Pass@1) \| - \| 53.5(Airline)/63.9(Retail) </div> Note: We use Agentless framework to evaluate model performance on SWE-Verified. We only evaluate text-only prompts in HLE testsets. GPT-4.1 is employed to act user role in Tau-bench evaluation. ### DeepSeek-R1-0528-Qwen3-8B Meanwhile, we distilled the chain-of-thought from DeepSeek-R1-0528 to post-train Qwen3 8B Base, obtaining DeepSeek-R1-0528-Qwen3-8B. This model achieves state-of-the-art (SOTA) performance among open-source models on the AIME 2024, surpassing Qwen3 8B by +10.0% and matching the performance of Qwen3-235B-thinking. We believe that the chain-of-thought from DeepSeek-R1-0528 will hold significant importance for both academic research on reasoning models and industrial development focused on small-scale models. \| \| AIME 24 \| AIME 25 \| HMMT Feb 25 \| GPQA Diamond \| LiveCodeBench (2408-2505) \| \|--------------------------------\|---------\|---------\|-------------\|--------------\|---------------------------\| \| Qwen3-235B-A22B \| 85.7 \| 81.5 \| 62.5 \| 71.1 \| 66.5 \| \| Qwen3-32B \| 81.4 \| 72.9 \| - \| 68.4 \| - \| \| Qwen3-8B \| 76.0 \| 67.3 \| - \| 62.0 \| - \| \| Phi-4-Reasoning-Plus-14B \| 81.3 \| 78.0 \| 53.6 \| 69.3 \| - \| \| Gemini-2.5-Flash-Thinking-0520 \| 82.3 \| 72.0 \| 64.2 \| 82.8 \| 62.3 \| \| o3-mini (medium) \| 79.6 \| 76.7 \| 53.3 \| 76.8 \| 65.9 \| \| DeepSeek-R1-0528-Qwen3-8B \| 86.0 \| 76.3 \| 61.5 \| 61.1 \| 60.5 \| ## 3. Chat Website & API Platform You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in), and switch on the button "DeepThink" We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/) ## 4. How to Run Locally Please visit [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) repository for more information about running DeepSeek-R1-0528 locally. Compared to previous versions of DeepSeek-R1, the usage recommendations for DeepSeek-R1-0528 have the following changes: 1. System prompt is supported now. 2. It is not required to add "\<think\>\n" at the beginning of the output to force the model into thinking pattern. The model architecture of DeepSeek-R1-0528-Qwen3-8B is identical to that of Qwen3-8B, but it shares the same tokenizer configuration as DeepSeek-R1-0528. This model can be run in the same manner as Qwen3-8B, but it is essential to ensure that all configuration files are sourced from our repository rather than the original Qwen3 project. ### System Prompt In the official DeepSeek web/app, we use the same system prompt with a specific date. ``` 该助手为DeepSeek-R1，由深度求索公司创造。今天是{current date}。 ``` For example, ``` 该助手为DeepSeek-R1，由深度求索公司创造。今天是2025年5月28日，星期一。 ``` ### Temperature In our web and application environments, the temperature parameter $T_{model}$ is set to 0.6. ### Prompts for File Uploading and Web Search For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments. ``` file_template = \ """[file name]: {file_name} [file content begin] {file_content} [file content end] {question}""" ``` For Web Search, {search_results}, {cur_date}, and {question} are arguments. For Chinese query, we use the prompt: ``` search_answer_zh_template = \ '''# 以下内容是基于用户发送的消息的搜索结果: {search_results} 在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。在回答时，请注意以下几点： - 今天是{cur_date}。 - 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。 - 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。 - 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。 - 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。 - 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。 - 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。 - 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。 - 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。 # 用户消息为： {question}''' ``` For English query, we use the prompt: ``` search_answer_en_template = \ '''# The following contents are the search results related to the user's message: {search_results} In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer. When responding, please keep the following points in mind: - Today is {cur_date}. - Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question. - For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary. - For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough. - If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content. - For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content. - Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability. - Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage. - Unless the user requests otherwise, your response should be in the same language as the user's question. # The user's message is: {question}''' ``` ## 5. License This code repository is licensed under [MIT License](LICENSE). The use of DeepSeek-R1 models is also subject to [MIT License](LICENSE). DeepSeek-R1 series (including Base and Chat) supports commercial use and distillation. ## 6. Citation ``` @misc{deepseekai2025deepseekr1incentivizingreasoningcapability, title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, author={DeepSeek-AI}, year={2025}, eprint={2501.12948}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2501.12948}, } ``` ## 7. Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
Udayxyz/D09c	Udayxyz	2025-05-29T15:29:08Z	0	0	adapter-transformers	[ "adapter-transformers", "chemistry", "biology", "legal", "finance", "music", "code", "art", "climate", "medical", "text-generation-inference", "question-answering", "hi", "bn", "dataset:nvidia/OpenCodeReasoning", "dataset:nvidia/OpenCodeReasoning-2", "base_model:nvidia/parakeet-tdt-0.6b-v2", "base_model:adapter:nvidia/parakeet-tdt-0.6b-v2", "license:mit", "region:us" ]	question-answering	2025-05-29T15:25:05Z	--- license: mit datasets: - nvidia/OpenCodeReasoning - nvidia/OpenCodeReasoning-2 language: - hi - bn metrics: - accuracy base_model: - nvidia/parakeet-tdt-0.6b-v2 new_version: nvidia/parakeet-tdt-0.6b-v2 pipeline_tag: question-answering library_name: adapter-transformers tags: - chemistry - biology - legal - finance - music - code - art - climate - medical - text-generation-inference ---
alibidaran/LLAMA3-inatructive_Python-GGUF	alibidaran	2025-05-29T15:27:42Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-29T15:25:36Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: alibidaran - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-unsloth-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)
jruaechalar/cartaBajo0	jruaechalar	2025-05-29T15:25:21Z	0	0	diffusers	[ "diffusers", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-05-29T15:24:18Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
BootesVoid/cmb9hif4f0dmy1b1yk5ouqiay_cmb9hz5e10dus1b1ynu481d25	BootesVoid	2025-05-29T15:21:44Z	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	2025-05-29T15:21:42Z	--- 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: CHLOEMARTA --- # Cmb9Hif4F0Dmy1B1Yk5Ouqiay_Cmb9Hz5E10Dus1B1Ynu481D25 <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 `CHLOEMARTA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "CHLOEMARTA", "lora_weights": "https://huggingface.co/BootesVoid/cmb9hif4f0dmy1b1yk5ouqiay_cmb9hz5e10dus1b1ynu481d25/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('BootesVoid/cmb9hif4f0dmy1b1yk5ouqiay_cmb9hz5e10dus1b1ynu481d25', weight_name='lora.safetensors') image = pipeline('CHLOEMARTA').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/BootesVoid/cmb9hif4f0dmy1b1yk5ouqiay_cmb9hz5e10dus1b1ynu481d25/discussions) to add images that show off what you’ve made with this LoRA.
u54/goa	u54	2025-05-29T15:19:54Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-29T15:19:54Z	--- license: artistic-2.0 ---
saracandu/llama-3.1-8b-rebus-solver-coldstart	saracandu	2025-05-29T15:17:29Z	0	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "region:us" ]	null	2025-05-29T11:00:46Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit 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
LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1	LEAF-CLIP	2025-05-29T15:16:36Z	0	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", "base_model:finetune:laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", "license:mit", "region:us" ]	null	2025-03-11T11:02:39Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - laion/CLIP-ViT-bigG-14-laion2B-39B-b160k --- Model Initialized from `laion/CLIP-ViT-bigG-14-laion2B-39B-b160k`. The text encoder is finetuned with LEAF at $k=1$ with $\rho=50$. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1" processor_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
suhacan/speecht5_finetuned_voxpopuli_nl	suhacan	2025-05-29T15:16:17Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2025-05-29T12:46:38Z	--- library_name: transformers license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] pipeline_tag: text-to-speech --- <!-- 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. --> # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4518 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 0.5002 \| 14.9254 \| 1000 \| 0.4658 \| \| 0.4818 \| 29.8507 \| 2000 \| 0.4555 \| \| 0.4764 \| 44.7761 \| 3000 \| 0.4530 \| \| 0.4742 \| 59.7015 \| 4000 \| 0.4518 \| ### Framework versions - Transformers 4.52.2 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Jarbas/m2v-256-xlm-roberta-large-finetuned-conll03-german	Jarbas	2025-05-29T15:14:38Z	0	0	model2vec	[ "model2vec", "safetensors", "embeddings", "static-embeddings", "sentence-transformers", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "base_model:FacebookAI/xlm-roberta-large-finetuned-conll03-german", "base_model:finetune:FacebookAI/xlm-roberta-large-finetuned-conll03-german", "license:mit", "region:us" ]	null	2025-05-29T15:14:16Z	--- base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-german language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh library_name: model2vec license: mit model_name: xlm-roberta-large-finetuned-conll03-german-distill256 tags: - embeddings - static-embeddings - sentence-transformers --- # xlm-roberta-large-finetuned-conll03-german-distill256 Model Card This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the FacebookAI/xlm-roberta-large-finetuned-conll03-german(https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-german) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers. ## Installation Install model2vec using pip: ``` pip install model2vec ``` ## Usage ### Using Model2Vec The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models. Load this model using the `from_pretrained` method: ```python from model2vec import StaticModel # Load a pretrained Model2Vec model model = StaticModel.from_pretrained("xlm-roberta-large-finetuned-conll03-german-distill256") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` ### Using Sentence Transformers You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model: ```python from sentence_transformers import SentenceTransformer # Load a pretrained Sentence Transformer model model = SentenceTransformer("xlm-roberta-large-finetuned-conll03-german-distill256") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` ### Distilling a Model2Vec model You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code: ```python from model2vec.distill import distill # Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256) # Save the model m2v_model.save_pretrained("m2v_model") ``` ## How it works Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence. ## Additional Resources - [Model2Vec Repo](https://github.com/MinishLab/model2vec) - [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e) - [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results) - [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) - [Website](https://minishlab.github.io/) ## Library Authors Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled). ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ``` @article{minishlab2024model2vec, author = {Tulkens, Stephan and {van Dongen}, Thomas}, title = {Model2Vec: Fast State-of-the-Art Static Embeddings}, year = {2024}, url = {https://github.com/MinishLab/model2vec} } ```
ai-ai-ai-ai/bella	ai-ai-ai-ai	2025-05-29T15:12:03Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-29T14:32:22Z	--- 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 ---
Xemto/Apatheia	Xemto	2025-05-29T15:11:04Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "base_model:adapter:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "region:us" ]	null	2025-05-29T15:10:18Z	--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit 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
LEAF-CLIP/CLIP-ViT-L-rho50-k1	LEAF-CLIP	2025-05-29T15:10:22Z	0	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-04-07T18:04:09Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The text encoder is finetuned with LEAF at $k=1$ with $\rho=50$. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho50-k1" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
chenly124/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF	chenly124	2025-05-29T15:09:59Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "base_model:quantized:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-29T15:09:41Z	--- license: mit library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B --- # chenly124/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF This model was converted to GGUF format from [`deepseek-ai/DeepSeek-R1-0528-Qwen3-8B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B) 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/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B) 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 chenly124/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo chenly124/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-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 chenly124/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo chenly124/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-q4_k_m.gguf -c 2048 ```
tungduong261204/sft_v2_6000	tungduong261204	2025-05-29T15:09:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T15:08:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
davgauch/MNLP_M3_mcqa_model_8	davgauch	2025-05-29T15:05:49Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T10:29:33Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-0.6B-Base tags: - generated_from_trainer model-index: - name: MNLP_M3_mcqa_model_8 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. --> # MNLP_M3_mcqa_model_8 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8221 ## 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: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - 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 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.3327 \| 1.0 \| 2432 \| 0.8589 \| \| 1.0772 \| 2.0 \| 4864 \| 0.8197 \| \| 1.0491 \| 3.0 \| 7296 \| 0.8137 \| \| 1.0303 \| 4.0 \| 9728 \| 0.8221 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu126 - Datasets 3.2.0 - Tokenizers 0.21.0
vertings6/202a131c-cb81-4132-a0fe-d01d36b389ab	vertings6	2025-05-29T15:05:39Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-360M", "base_model:adapter:unsloth/SmolLM-360M", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-29T14:45:10Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-360M tags: - axolotl - generated_from_trainer model-index: - name: 202a131c-cb81-4132-a0fe-d01d36b389ab 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: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/SmolLM-360M bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1a6d241c82fb2923_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: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vertings6/202a131c-cb81-4132-a0fe-d01d36b389ab hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/1a6d241c82fb2923_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 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: 9ce171e1-8e19-45fb-8b7f-fd290441a2cd wandb_project: s56-7 wandb_run: your_name wandb_runid: 9ce171e1-8e19-45fb-8b7f-fd290441a2cd warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 202a131c-cb81-4132-a0fe-d01d36b389ab This model is a fine-tuned version of [unsloth/SmolLM-360M](https://huggingface.co/unsloth/SmolLM-360M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2831 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - 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: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1021 \| 0.0001 \| 1 \| 1.2970 \| \| 1.2737 \| 0.0249 \| 250 \| 1.2879 \| \| 1.3249 \| 0.0499 \| 500 \| 1.2831 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dimasik87/443c6630-82bb-4dfb-9c02-de7f04361c8d	dimasik87	2025-05-29T15:05:29Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-360M", "base_model:adapter:unsloth/SmolLM-360M", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-29T14:45:53Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-360M tags: - axolotl - generated_from_trainer model-index: - name: 443c6630-82bb-4dfb-9c02-de7f04361c8d 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: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/SmolLM-360M bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1a6d241c82fb2923_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: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: dimasik87/443c6630-82bb-4dfb-9c02-de7f04361c8d hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/1a6d241c82fb2923_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 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: 9ce171e1-8e19-45fb-8b7f-fd290441a2cd wandb_project: s56-7 wandb_run: your_name wandb_runid: 9ce171e1-8e19-45fb-8b7f-fd290441a2cd warmup_steps: 50 weight_decay: 0.05 xformers_attention: true ``` </details><br> # 443c6630-82bb-4dfb-9c02-de7f04361c8d This model is a fine-tuned version of [unsloth/SmolLM-360M](https://huggingface.co/unsloth/SmolLM-360M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2925 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - 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: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1351 \| 0.0001 \| 1 \| 1.2970 \| \| 1.0298 \| 0.0333 \| 250 \| 1.2939 \| \| 1.2665 \| 0.0665 \| 500 \| 1.2925 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
sergioalves/89f3575c-dce8-45e7-a4ae-3f22a961b99b	sergioalves	2025-05-29T15:05:04Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-360M", "base_model:adapter:unsloth/SmolLM-360M", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-29T14:45:40Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-360M tags: - axolotl - generated_from_trainer model-index: - name: 89f3575c-dce8-45e7-a4ae-3f22a961b99b 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: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/SmolLM-360M bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1a6d241c82fb2923_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: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: sergioalves/89f3575c-dce8-45e7-a4ae-3f22a961b99b hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/1a6d241c82fb2923_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 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: 9ce171e1-8e19-45fb-8b7f-fd290441a2cd wandb_project: s56-7 wandb_run: your_name wandb_runid: 9ce171e1-8e19-45fb-8b7f-fd290441a2cd warmup_steps: 50 weight_decay: 0.05 xformers_attention: true ``` </details><br> # 89f3575c-dce8-45e7-a4ae-3f22a961b99b This model is a fine-tuned version of [unsloth/SmolLM-360M](https://huggingface.co/unsloth/SmolLM-360M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2925 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - 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: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1351 \| 0.0001 \| 1 \| 1.2970 \| \| 1.0295 \| 0.0333 \| 250 \| 1.2939 \| \| 1.2656 \| 0.0665 \| 500 \| 1.2925 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
stefandi/ultrafeedback_dpo_v1	stefandi	2025-05-29T15:04:41Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T15:03:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
DevQuasar/tiiuae.Falcon-H1-1.5B-Deep-Instruct-GGUF	DevQuasar	2025-05-29T15:03:36Z	0	0	null	[ "gguf", "text-generation", "base_model:tiiuae/Falcon-H1-1.5B-Deep-Instruct", "base_model:quantized:tiiuae/Falcon-H1-1.5B-Deep-Instruct", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T14:51:47Z	--- base_model: - tiiuae/Falcon-H1-1.5B-Deep-Instruct pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [tiiuae/Falcon-H1-1.5B-Deep-Instruct](https://huggingface.co/tiiuae/Falcon-H1-1.5B-Deep-Instruct) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
atuljoshyy/atul-ai	atuljoshyy	2025-05-29T15:03:29Z	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	2025-05-29T14:24:30Z	--- 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: atul --- # Atul Ai <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 `atul` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "atul", "lora_weights": "https://huggingface.co/atuljoshyy/atul-ai/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('atuljoshyy/atul-ai', weight_name='lora.safetensors') image = pipeline('atul').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/atuljoshyy/atul-ai/discussions) to add images that show off what you’ve made with this LoRA.
LEAF-CLIP/OpenCLIP-ViT-H-rho50-k1-constrained	LEAF-CLIP	2025-05-29T15:02:28Z	8	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:laion/CLIP-ViT-H-14-laion2B-s32B-b79K", "base_model:finetune:laion/CLIP-ViT-H-14-laion2B-s32B-b79K", "license:mit", "region:us" ]	null	2025-04-16T21:21:44Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - laion/CLIP-ViT-H-14-laion2B-s32B-b79K --- Model Initialized from `laion/CLIP-ViT-H-14-laion2B-s32B-b79K`. The text encoder is finetuned with LEAF at $k=1$ with $\rho=50$ and semantic constraints. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/OpenCLIP-ViT-H-rho50-k1-constrained" processor_name = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
matatonic/DeepSeek-R1-0528-Qwen3-8B-6.5bpw-h8-exl2	matatonic	2025-05-29T15:02:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:2501.12948", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2025-05-29T15:01:56Z	--- license: mit library_name: transformers --- # DeepSeek-R1-0528 <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20R1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="LICENSE" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> <p align="center"> <a href="https://arxiv.org/pdf/2501.12948"><b>Paper Link</b>👁️</a> </p> ## 1. Introduction The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro. <p align="center"> <img width="80%" src="figures/benchmark.png"> </p> Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question. Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding. ## 2. Evaluation Results ### DeepSeek-R1-0528 For all our models, the maximum generation length is set to 64K tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 16 responses per query to estimate pass@1. <div align="center"> \| Category \| Benchmark (Metric) \| DeepSeek R1 \| DeepSeek R1 0528 \|----------\|----------------------------------\|-----------------\|---\| \| General \| \| \| MMLU-Redux (EM) \| 92.9 \| 93.4 \| \| MMLU-Pro (EM) \| 84.0 \| 85.0 \| \| GPQA-Diamond (Pass@1) \| 71.5 \| 81.0 \| \| SimpleQA (Correct) \| 30.1 \| 27.8 \| \| FRAMES (Acc.) \| 82.5 \| 83.0 \| \| Humanity's Last Exam (Pass@1) \| 8.5 \| 17.7 \| Code \| \| \| LiveCodeBench (2408-2505) (Pass@1) \| 63.5 \| 73.3 \| \| Codeforces-Div1 (Rating) \| 1530 \| 1930 \| \| SWE Verified (Resolved) \| 49.2 \| 57.6 \| \| Aider-Polyglot (Acc.) \| 53.3 \| 71.6 \| Math \| \| \| AIME 2024 (Pass@1) \| 79.8 \| 91.4 \| \| AIME 2025 (Pass@1) \| 70.0 \| 87.5 \| \| HMMT 2025 (Pass@1) \| 41.7 \| 79.4 \| \| \| CNMO 2024 (Pass@1) \| 78.8 \| 86.9 \| Tools \| \| \| BFCL_v3_MultiTurn (Acc) \| - \| 37.0 \| \| \| Tau-Bench (Pass@1) \| - \| 53.5(Airline)/63.9(Retail) </div> Note: We use Agentless framework to evaluate model performance on SWE-Verified. We only evaluate text-only prompts in HLE testsets. GPT-4.1 is employed to act user role in Tau-bench evaluation. ### DeepSeek-R1-0528-Qwen3-8B Meanwhile, we distilled the chain-of-thought from DeepSeek-R1-0528 to post-train Qwen3 8B Base, obtaining DeepSeek-R1-0528-Qwen3-8B. This model achieves state-of-the-art (SOTA) performance among open-source models on the AIME 2024, surpassing Qwen3 8B by +10.0% and matching the performance of Qwen3-235B-thinking. We believe that the chain-of-thought from DeepSeek-R1-0528 will hold significant importance for both academic research on reasoning models and industrial development focused on small-scale models. \| \| AIME 24 \| AIME 25 \| HMMT Feb 25 \| GPQA Diamond \| LiveCodeBench (2408-2505) \| \|--------------------------------\|---------\|---------\|-------------\|--------------\|---------------------------\| \| Qwen3-235B-A22B \| 85.7 \| 81.5 \| 62.5 \| 71.1 \| 66.5 \| \| Qwen3-32B \| 81.4 \| 72.9 \| - \| 68.4 \| - \| \| Qwen3-8B \| 76.0 \| 67.3 \| - \| 62.0 \| - \| \| Phi-4-Reasoning-Plus-14B \| 81.3 \| 78.0 \| 53.6 \| 69.3 \| - \| \| Gemini-2.5-Flash-Thinking-0520 \| 82.3 \| 72.0 \| 64.2 \| 82.8 \| 62.3 \| \| o3-mini (medium) \| 79.6 \| 76.7 \| 53.3 \| 76.8 \| 65.9 \| \| DeepSeek-R1-0528-Qwen3-8B \| 86.0 \| 76.3 \| 61.5 \| 61.1 \| 60.5 \| ## 3. Chat Website & API Platform You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in), and switch on the button "DeepThink" We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/) ## 4. How to Run Locally Please visit [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) repository for more information about running DeepSeek-R1-0528 locally. Compared to previous versions of DeepSeek-R1, the usage recommendations for DeepSeek-R1-0528 have the following changes: 1. System prompt is supported now. 2. It is not required to add "\<think\>\n" at the beginning of the output to force the model into thinking pattern. The model architecture of DeepSeek-R1-0528-Qwen3-8B is identical to that of Qwen3-8B, but it shares the same tokenizer configuration as DeepSeek-R1-0528. This model can be run in the same manner as Qwen3-8B, but it is essential to ensure that all configuration files are sourced from our repository rather than the original Qwen3 project. ### System Prompt In the official DeepSeek web/app, we use the same system prompt with a specific date. ``` 该助手为DeepSeek-R1，由深度求索公司创造。今天是{current date}。 ``` For example, ``` 该助手为DeepSeek-R1，由深度求索公司创造。今天是2025年5月28日，星期一。 ``` ### Temperature In our web and application environments, the temperature parameter $T_{model}$ is set to 0.6. ### Prompts for File Uploading and Web Search For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments. ``` file_template = \ """[file name]: {file_name} [file content begin] {file_content} [file content end] {question}""" ``` For Web Search, {search_results}, {cur_date}, and {question} are arguments. For Chinese query, we use the prompt: ``` search_answer_zh_template = \ '''# 以下内容是基于用户发送的消息的搜索结果: {search_results} 在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。在回答时，请注意以下几点： - 今天是{cur_date}。 - 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。 - 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。 - 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。 - 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。 - 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。 - 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。 - 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。 - 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。 # 用户消息为： {question}''' ``` For English query, we use the prompt: ``` search_answer_en_template = \ '''# The following contents are the search results related to the user's message: {search_results} In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer. When responding, please keep the following points in mind: - Today is {cur_date}. - Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question. - For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary. - For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough. - If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content. - For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content. - Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability. - Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage. - Unless the user requests otherwise, your response should be in the same language as the user's question. # The user's message is: {question}''' ``` ## 5. License This code repository is licensed under [MIT License](LICENSE). The use of DeepSeek-R1 models is also subject to [MIT License](LICENSE). DeepSeek-R1 series (including Base and Chat) supports commercial use and distillation. ## 6. Citation ``` @misc{deepseekai2025deepseekr1incentivizingreasoningcapability, title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, author={DeepSeek-AI}, year={2025}, eprint={2501.12948}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2501.12948}, } ``` ## 7. Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
Vadim121/ruRoberta-large-target-sentiment-classifier-ru-news	Vadim121	2025-05-29T15:02:15Z	0	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "target-sentiment-analysis", "sentiment-analysis", "classification", "news", "text-classification", "ru", "base_model:ai-forever/ruRoberta-large", "base_model:finetune:ai-forever/ruRoberta-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-29T13:44:49Z	--- license: apache-2.0 language: - ru metrics: - f1 base_model: - ai-forever/ruRoberta-large pipeline_tag: text-classification library_name: transformers tags: - target-sentiment-analysis - sentiment-analysis - classification - news --- # Model Card for Model ID ## Model Details This model is a modified version of ruRoberta-large for Target sentiment Analysis (TSA) using training data from the [RuSentNE-2023 collection](https://github.com/dialogue-evaluation/RuSentNE-evaluation). This model is designed to analyze news texts in Russian. Given an input sentence and a specified entity (target object) within it, this model determines the sentiment directed toward that entity and classifies it into one of the following categories: ['positive', 'negative', 'neutral']. ### Model Description - Model type: [ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large) - Language(s) (NLP): Russian - License: [Apache License 2.0](https://github.com/scofield7419/THOR-ISA/blob/main/LICENSE.txt) ## Uses ### Direct Use This is a ruRoberta-large model with the addition of a linear layer for classification. 1. Loading model and tokenizer ```python from model import TargetSentimentClassifier model = TargetSentimentClassifier( model_name="sberbank-ai/ruRoberta-large", use_multi_sample_dropout=True, device="cuda" ) model.load_state_dict(torch.load("pytorch_model.bin", map_location="cuda")) ``` 2. Predict sentiment for a named entity in a sentence ```python text = "Джеймс «Бадди» Макгирт ... спортсмен остановить бой..." target = "спортсмен" entity_type = "PROFESSION" prediction = model.predict(text, target, entity_type) print(prediction) # Output: 0, 1 or 2 ``` ### Input Format The input sentence must include a marked entity using the following format: ```python <en> ENTITY_TEXT <\|ENTITY_TAG\|> </en> ``` Example: ```python Джеймс «Бадди» Макгирт ... <en> спортсмен <\|PROFESSION\|> </en> остановить бой... ``` ### Labels The model predicts one of the following labels: ```tsv Label Description 0 Neutral 1 Positive 2 Negative ``` ## Training Details ### Training Data For training, the data published for the RuSentNE-2023 competition, available via repository, was used: https://github.com/dialogue-evaluation/RuSentNE-evaluation To increase the training sample, data from the Sentiment Analysis in Russian dataset was used: https://www.kaggle.com/c/sentiment-analysis-in-russian/overview ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data The direct link to the `test` evaluation data: https://github.com/dialogue-evaluation/RuSentNE-evaluation/blob/main/final_data.csv #### Metrics For the model evaluation, two metrics were used: 1. F1_PN -- F1-measure over `positive` and `negative` classes; 2. F1_PN0 -- F1-measure over `positive`, `negative`, and `neutral` classes; ### Results The test evaluation for this model [showcases](https://codalab.lisn.upsaclay.fr/competitions/9538#results) the F1_PN = 61.82, F1_PN = 61.82
lorenzomega/giada_moretti_lora	lorenzomega	2025-05-29T15:02:01Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:SG161222/Realistic_Vision_V5.1_noVAE", "base_model:adapter:SG161222/Realistic_Vision_V5.1_noVAE", "license:openrail++", "region:us" ]	text-to-image	2025-05-29T15:01:40Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- <lora:gm_char:0.8> close-up studio portrait, soft neutral light, 85 mm DSLR photo output: url: images/Acconciatura elegante close-up.png - text: >- <lora:gm_char:0.8> close-up studio portrait, soft neutral light, 85 mm DSLR photo output: url: images/gm_train_07.jpeg base_model: SG161222/Realistic_Vision_V5.1_noVAE instance_prompt: gm_char license: openrail++ --- # gm_char_v1 (Giada Moretti LoRA – SD1.5) <Gallery /> ## Trigger words You should use `gm_char` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/lorenzomega/giada_moretti_lora/tree/main) them in the Files & versions tab.
n1kooo/vit-cifar10	n1kooo	2025-05-29T15:00:49Z	41	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-28T08:23:35Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-cifar10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-cifar10 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the n1kooo/vit-cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.1006 - Accuracy: 0.968 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.0861 \| 1.0 \| 2500 \| 0.0955 \| 0.9708 \| \| 0.0927 \| 2.0 \| 5000 \| 0.0890 \| 0.974 \| \| 0.0793 \| 3.0 \| 7500 \| 0.0881 \| 0.974 \| \| 0.0656 \| 4.0 \| 10000 \| 0.0864 \| 0.9738 \| \| 0.0787 \| 5.0 \| 12500 \| 0.0867 \| 0.9746 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0 - Datasets 3.6.0 - Tokenizers 0.21.1
dimasik2987/336e0a38-baa1-411b-944d-73d7ac9ac17c	dimasik2987	2025-05-29T15:00:37Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-360M", "base_model:adapter:unsloth/SmolLM-360M", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-29T14:45:39Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-360M tags: - axolotl - generated_from_trainer model-index: - name: 336e0a38-baa1-411b-944d-73d7ac9ac17c 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: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/SmolLM-360M bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1a6d241c82fb2923_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: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: dimasik2987/336e0a38-baa1-411b-944d-73d7ac9ac17c hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 12 mixed_precision: bf16 mlflow_experiment_name: /tmp/1a6d241c82fb2923_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 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: 9ce171e1-8e19-45fb-8b7f-fd290441a2cd wandb_project: s56-7 wandb_run: your_name wandb_runid: 9ce171e1-8e19-45fb-8b7f-fd290441a2cd warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 336e0a38-baa1-411b-944d-73d7ac9ac17c This model is a fine-tuned version of [unsloth/SmolLM-360M](https://huggingface.co/unsloth/SmolLM-360M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1451 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 24 - 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: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1351 \| 0.0001 \| 1 \| 1.2361 \| \| 0.941 \| 0.0333 \| 250 \| 1.1612 \| \| 1.1694 \| 0.0665 \| 500 \| 1.1451 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
matatonic/DeepSeek-R1-0528-Qwen3-8B-4.25bpw-exl2	matatonic	2025-05-29T15:00:27Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:2501.12948", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2025-05-29T15:00:07Z	--- license: mit library_name: transformers --- # DeepSeek-R1-0528 <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20R1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="LICENSE" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> <p align="center"> <a href="https://arxiv.org/pdf/2501.12948"><b>Paper Link</b>👁️</a> </p> ## 1. Introduction The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro. <p align="center"> <img width="80%" src="figures/benchmark.png"> </p> Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question. Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding. ## 2. Evaluation Results ### DeepSeek-R1-0528 For all our models, the maximum generation length is set to 64K tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 16 responses per query to estimate pass@1. <div align="center"> \| Category \| Benchmark (Metric) \| DeepSeek R1 \| DeepSeek R1 0528 \|----------\|----------------------------------\|-----------------\|---\| \| General \| \| \| MMLU-Redux (EM) \| 92.9 \| 93.4 \| \| MMLU-Pro (EM) \| 84.0 \| 85.0 \| \| GPQA-Diamond (Pass@1) \| 71.5 \| 81.0 \| \| SimpleQA (Correct) \| 30.1 \| 27.8 \| \| FRAMES (Acc.) \| 82.5 \| 83.0 \| \| Humanity's Last Exam (Pass@1) \| 8.5 \| 17.7 \| Code \| \| \| LiveCodeBench (2408-2505) (Pass@1) \| 63.5 \| 73.3 \| \| Codeforces-Div1 (Rating) \| 1530 \| 1930 \| \| SWE Verified (Resolved) \| 49.2 \| 57.6 \| \| Aider-Polyglot (Acc.) \| 53.3 \| 71.6 \| Math \| \| \| AIME 2024 (Pass@1) \| 79.8 \| 91.4 \| \| AIME 2025 (Pass@1) \| 70.0 \| 87.5 \| \| HMMT 2025 (Pass@1) \| 41.7 \| 79.4 \| \| \| CNMO 2024 (Pass@1) \| 78.8 \| 86.9 \| Tools \| \| \| BFCL_v3_MultiTurn (Acc) \| - \| 37.0 \| \| \| Tau-Bench (Pass@1) \| - \| 53.5(Airline)/63.9(Retail) </div> Note: We use Agentless framework to evaluate model performance on SWE-Verified. We only evaluate text-only prompts in HLE testsets. GPT-4.1 is employed to act user role in Tau-bench evaluation. ### DeepSeek-R1-0528-Qwen3-8B Meanwhile, we distilled the chain-of-thought from DeepSeek-R1-0528 to post-train Qwen3 8B Base, obtaining DeepSeek-R1-0528-Qwen3-8B. This model achieves state-of-the-art (SOTA) performance among open-source models on the AIME 2024, surpassing Qwen3 8B by +10.0% and matching the performance of Qwen3-235B-thinking. We believe that the chain-of-thought from DeepSeek-R1-0528 will hold significant importance for both academic research on reasoning models and industrial development focused on small-scale models. \| \| AIME 24 \| AIME 25 \| HMMT Feb 25 \| GPQA Diamond \| LiveCodeBench (2408-2505) \| \|--------------------------------\|---------\|---------\|-------------\|--------------\|---------------------------\| \| Qwen3-235B-A22B \| 85.7 \| 81.5 \| 62.5 \| 71.1 \| 66.5 \| \| Qwen3-32B \| 81.4 \| 72.9 \| - \| 68.4 \| - \| \| Qwen3-8B \| 76.0 \| 67.3 \| - \| 62.0 \| - \| \| Phi-4-Reasoning-Plus-14B \| 81.3 \| 78.0 \| 53.6 \| 69.3 \| - \| \| Gemini-2.5-Flash-Thinking-0520 \| 82.3 \| 72.0 \| 64.2 \| 82.8 \| 62.3 \| \| o3-mini (medium) \| 79.6 \| 76.7 \| 53.3 \| 76.8 \| 65.9 \| \| DeepSeek-R1-0528-Qwen3-8B \| 86.0 \| 76.3 \| 61.5 \| 61.1 \| 60.5 \| ## 3. Chat Website & API Platform You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in), and switch on the button "DeepThink" We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/) ## 4. How to Run Locally Please visit [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) repository for more information about running DeepSeek-R1-0528 locally. Compared to previous versions of DeepSeek-R1, the usage recommendations for DeepSeek-R1-0528 have the following changes: 1. System prompt is supported now. 2. It is not required to add "\<think\>\n" at the beginning of the output to force the model into thinking pattern. The model architecture of DeepSeek-R1-0528-Qwen3-8B is identical to that of Qwen3-8B, but it shares the same tokenizer configuration as DeepSeek-R1-0528. This model can be run in the same manner as Qwen3-8B, but it is essential to ensure that all configuration files are sourced from our repository rather than the original Qwen3 project. ### System Prompt In the official DeepSeek web/app, we use the same system prompt with a specific date. ``` 该助手为DeepSeek-R1，由深度求索公司创造。今天是{current date}。 ``` For example, ``` 该助手为DeepSeek-R1，由深度求索公司创造。今天是2025年5月28日，星期一。 ``` ### Temperature In our web and application environments, the temperature parameter $T_{model}$ is set to 0.6. ### Prompts for File Uploading and Web Search For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments. ``` file_template = \ """[file name]: {file_name} [file content begin] {file_content} [file content end] {question}""" ``` For Web Search, {search_results}, {cur_date}, and {question} are arguments. For Chinese query, we use the prompt: ``` search_answer_zh_template = \ '''# 以下内容是基于用户发送的消息的搜索结果: {search_results} 在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。在回答时，请注意以下几点： - 今天是{cur_date}。 - 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。 - 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。 - 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。 - 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。 - 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。 - 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。 - 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。 - 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。 # 用户消息为： {question}''' ``` For English query, we use the prompt: ``` search_answer_en_template = \ '''# The following contents are the search results related to the user's message: {search_results} In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer. When responding, please keep the following points in mind: - Today is {cur_date}. - Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question. - For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary. - For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough. - If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content. - For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content. - Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability. - Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage. - Unless the user requests otherwise, your response should be in the same language as the user's question. # The user's message is: {question}''' ``` ## 5. License This code repository is licensed under [MIT License](LICENSE). The use of DeepSeek-R1 models is also subject to [MIT License](LICENSE). DeepSeek-R1 series (including Base and Chat) supports commercial use and distillation. ## 6. Citation ``` @misc{deepseekai2025deepseekr1incentivizingreasoningcapability, title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, author={DeepSeek-AI}, year={2025}, eprint={2501.12948}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2501.12948}, } ``` ## 7. Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
matatonic/DeepSeek-R1-0528-Qwen3-8B-5.0bpw-exl2	matatonic	2025-05-29T14:59:57Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:2501.12948", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2025-05-29T14:59:27Z	--- license: mit library_name: transformers --- # DeepSeek-R1-0528 <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20R1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="LICENSE" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> <p align="center"> <a href="https://arxiv.org/pdf/2501.12948"><b>Paper Link</b>👁️</a> </p> ## 1. Introduction The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro. <p align="center"> <img width="80%" src="figures/benchmark.png"> </p> Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question. Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding. ## 2. Evaluation Results ### DeepSeek-R1-0528 For all our models, the maximum generation length is set to 64K tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 16 responses per query to estimate pass@1. <div align="center"> \| Category \| Benchmark (Metric) \| DeepSeek R1 \| DeepSeek R1 0528 \|----------\|----------------------------------\|-----------------\|---\| \| General \| \| \| MMLU-Redux (EM) \| 92.9 \| 93.4 \| \| MMLU-Pro (EM) \| 84.0 \| 85.0 \| \| GPQA-Diamond (Pass@1) \| 71.5 \| 81.0 \| \| SimpleQA (Correct) \| 30.1 \| 27.8 \| \| FRAMES (Acc.) \| 82.5 \| 83.0 \| \| Humanity's Last Exam (Pass@1) \| 8.5 \| 17.7 \| Code \| \| \| LiveCodeBench (2408-2505) (Pass@1) \| 63.5 \| 73.3 \| \| Codeforces-Div1 (Rating) \| 1530 \| 1930 \| \| SWE Verified (Resolved) \| 49.2 \| 57.6 \| \| Aider-Polyglot (Acc.) \| 53.3 \| 71.6 \| Math \| \| \| AIME 2024 (Pass@1) \| 79.8 \| 91.4 \| \| AIME 2025 (Pass@1) \| 70.0 \| 87.5 \| \| HMMT 2025 (Pass@1) \| 41.7 \| 79.4 \| \| \| CNMO 2024 (Pass@1) \| 78.8 \| 86.9 \| Tools \| \| \| BFCL_v3_MultiTurn (Acc) \| - \| 37.0 \| \| \| Tau-Bench (Pass@1) \| - \| 53.5(Airline)/63.9(Retail) </div> Note: We use Agentless framework to evaluate model performance on SWE-Verified. We only evaluate text-only prompts in HLE testsets. GPT-4.1 is employed to act user role in Tau-bench evaluation. ### DeepSeek-R1-0528-Qwen3-8B Meanwhile, we distilled the chain-of-thought from DeepSeek-R1-0528 to post-train Qwen3 8B Base, obtaining DeepSeek-R1-0528-Qwen3-8B. This model achieves state-of-the-art (SOTA) performance among open-source models on the AIME 2024, surpassing Qwen3 8B by +10.0% and matching the performance of Qwen3-235B-thinking. We believe that the chain-of-thought from DeepSeek-R1-0528 will hold significant importance for both academic research on reasoning models and industrial development focused on small-scale models. \| \| AIME 24 \| AIME 25 \| HMMT Feb 25 \| GPQA Diamond \| LiveCodeBench (2408-2505) \| \|--------------------------------\|---------\|---------\|-------------\|--------------\|---------------------------\| \| Qwen3-235B-A22B \| 85.7 \| 81.5 \| 62.5 \| 71.1 \| 66.5 \| \| Qwen3-32B \| 81.4 \| 72.9 \| - \| 68.4 \| - \| \| Qwen3-8B \| 76.0 \| 67.3 \| - \| 62.0 \| - \| \| Phi-4-Reasoning-Plus-14B \| 81.3 \| 78.0 \| 53.6 \| 69.3 \| - \| \| Gemini-2.5-Flash-Thinking-0520 \| 82.3 \| 72.0 \| 64.2 \| 82.8 \| 62.3 \| \| o3-mini (medium) \| 79.6 \| 76.7 \| 53.3 \| 76.8 \| 65.9 \| \| DeepSeek-R1-0528-Qwen3-8B \| 86.0 \| 76.3 \| 61.5 \| 61.1 \| 60.5 \| ## 3. Chat Website & API Platform You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in), and switch on the button "DeepThink" We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/) ## 4. How to Run Locally Please visit [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) repository for more information about running DeepSeek-R1-0528 locally. Compared to previous versions of DeepSeek-R1, the usage recommendations for DeepSeek-R1-0528 have the following changes: 1. System prompt is supported now. 2. It is not required to add "\<think\>\n" at the beginning of the output to force the model into thinking pattern. The model architecture of DeepSeek-R1-0528-Qwen3-8B is identical to that of Qwen3-8B, but it shares the same tokenizer configuration as DeepSeek-R1-0528. This model can be run in the same manner as Qwen3-8B, but it is essential to ensure that all configuration files are sourced from our repository rather than the original Qwen3 project. ### System Prompt In the official DeepSeek web/app, we use the same system prompt with a specific date. ``` 该助手为DeepSeek-R1，由深度求索公司创造。今天是{current date}。 ``` For example, ``` 该助手为DeepSeek-R1，由深度求索公司创造。今天是2025年5月28日，星期一。 ``` ### Temperature In our web and application environments, the temperature parameter $T_{model}$ is set to 0.6. ### Prompts for File Uploading and Web Search For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments. ``` file_template = \ """[file name]: {file_name} [file content begin] {file_content} [file content end] {question}""" ``` For Web Search, {search_results}, {cur_date}, and {question} are arguments. For Chinese query, we use the prompt: ``` search_answer_zh_template = \ '''# 以下内容是基于用户发送的消息的搜索结果: {search_results} 在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。在回答时，请注意以下几点： - 今天是{cur_date}。 - 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。 - 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。 - 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。 - 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。 - 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。 - 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。 - 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。 - 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。 # 用户消息为： {question}''' ``` For English query, we use the prompt: ``` search_answer_en_template = \ '''# The following contents are the search results related to the user's message: {search_results} In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer. When responding, please keep the following points in mind: - Today is {cur_date}. - Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question. - For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary. - For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough. - If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content. - For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content. - Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability. - Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage. - Unless the user requests otherwise, your response should be in the same language as the user's question. # The user's message is: {question}''' ``` ## 5. License This code repository is licensed under [MIT License](LICENSE). The use of DeepSeek-R1 models is also subject to [MIT License](LICENSE). DeepSeek-R1 series (including Base and Chat) supports commercial use and distillation. ## 6. Citation ``` @misc{deepseekai2025deepseekr1incentivizingreasoningcapability, title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, author={DeepSeek-AI}, year={2025}, eprint={2501.12948}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2501.12948}, } ``` ## 7. Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
Jinendran/Pyramids	Jinendran	2025-05-29T14:58:18Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2025-05-29T14:44:16Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Jinendran/Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
MaatAI/Seshat-Qwen3-8B	MaatAI	2025-05-29T14:54:39Z	0	2	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "fr", "en", "dataset:MaatAI/AfricansHistoryBooksArticlesQA", "arxiv:2309.00071", "base_model:Qwen/Qwen3-8B", "base_model:finetune:Qwen/Qwen3-8B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T13:25:37Z	--- license: apache-2.0 datasets: - MaatAI/AfricansHistoryBooksArticlesQA language: - fr - en base_model: - Qwen/Qwen3-8B pipeline_tag: text-generation library_name: transformers --- # Seshat-Qwen3-8B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6168218a4ed0b975c18f82a8/ZqDNaYq7HFF6F2fEa84Do.png) ## Model Description Seshat is a large language model fine-tuned from Qwen/Qwen3-8B to specialize in question answering related to African History. The model aims to provide informative and contextually relevant answers based on the knowledge embedded in its training data. This model is designed to understand and generate text in multiple languages including English, French, Swahili, and Yoruba, making historical information about Africa more accessible. ## Intended Uses & Limitations ### Intended Uses * Historical Question Answering: Answering specific questions about events, figures, cultures, and developments in African history. * Educational Tool: Assisting students, educators, and enthusiasts in learning about African history. * Content Generation: Generating informative text snippets about African historical topics based on posed questions. * Multilingual Access: Providing information in English, French, Swahili, and Yoruba. ### Limitations * Knowledge Scope: The model's knowledge is primarily derived from the `MaatAI/AfricansHistoryBooksArticlesQA` dataset. It may not have information on topics outside this dataset or more recent historical interpretations not covered. * Potential for Hallucination: Like all LLMs, Seshat may sometimes generate plausible but incorrect information (hallucinations). Users should critically evaluate responses, especially for sensitive or critical applications. * Bias: The model may reflect biases present in the underlying base model (Qwen/Qwen3-8B) or the fine-tuning dataset. * Complex Reasoning: While capable of answering direct questions, the model might struggle with highly complex queries requiring multi-step reasoning or abstract synthesis beyond its training. * Language Nuances: Performance and fluency might vary across the supported languages (en, fr, sw, yo) based on the representation of each language in the training data. * Not a Substitute for Expert Consultation: For academic research or critical decision-making, the model's outputs should be verified by consulting historical experts and primary sources. ## Training Data Seshat was fine-tuned on the `MaatAI/AfricansHistoryBooksArticlesQA` dataset. * Number of Rows: 15,341 question-answer pairs. * Focus: African History. * Languages: English (en), French (fr), Swahili (sw), Yoruba (yo). Dataset Structure Example: Each entry in the dataset follows this structure: ```json { "question": "How did the imperial administration's policy towards Sudanese Sufism contribute to the eventual support for the Mahdist uprising?", "answer": "The imperial administration deliberately undermined the influence of Sudanese Sufism by attacking its leaders, the hereditary preachers (faḳīh), and simultaneously promoting orthodox Islam. By strengthening the hierarchy of ḳāḍī and muftī and supporting the studies of Sudanese ˓ulamā˒ at al-Azhar, they created a rival religious authority that was dependent on the government. This systematic erosion of the traditional Sufi leaders' prestige significantly diminished their standing among the populace and created a fertile ground for them to align with and actively support the Mahdi's efforts to overthrow the imperial rule." } ``` ## Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. - Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. - Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. - Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation. ## Model Overview Qwen3-8B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 8.2B - Number of Paramaters (Non-Embedding): 6.95B - Number of Layers: 36 - Number of Attention Heads (GQA): 32 for Q and 8 for KV - Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts). For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Quickstart The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-8B" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint: - SGLang: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3 ``` - vLLM: ```shell vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1 ``` For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. ## Switching Between Thinking and Non-Thinking Mode > [!TIP] > The `enable_thinking` switch is also available in APIs created by SGLang and vLLM. > Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users. ### `enable_thinking=True` By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # True is the default value for enable_thinking ) ``` In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response. > [!NOTE] > For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### `enable_thinking=False` We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Setting enable_thinking=False disables thinking mode ) ``` In this mode, the model will not generate any think content and will not include a `<think>...</think>` block. > [!NOTE] > For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations. Here is an example of a multi-turn conversation: ```python from transformers import AutoModelForCausalLM, AutoTokenizer class QwenChatbot: def __init__(self, model_name="Qwen/Qwen3-8B"): self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForCausalLM.from_pretrained(model_name) self.history = [] def generate_response(self, user_input): messages = self.history + [{"role": "user", "content": user_input}] text = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = self.tokenizer(text, return_tensors="pt") response_ids = self.model.generate(inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist() response = self.tokenizer.decode(response_ids, skip_special_tokens=True) # Update history self.history.append({"role": "user", "content": user_input}) self.history.append({"role": "assistant", "content": response}) return response # Example Usage if __name__ == "__main__": chatbot = QwenChatbot() # First input (without /think or /no_think tags, thinking mode is enabled by default) user_input_1 = "How many r's in strawberries?" print(f"User: {user_input_1}") response_1 = chatbot.generate_response(user_input_1) print(f"Bot: {response_1}") print("----------------------") # Second input with /no_think user_input_2 = "Then, how many r's in blueberries? /no_think" print(f"User: {user_input_2}") response_2 = chatbot.generate_response(user_input_2) print(f"Bot: {response_2}") print("----------------------") # Third input with /think user_input_3 = "Really? /think" print(f"User: {user_input_3}") response_3 = chatbot.generate_response(user_input_3) print(f"Bot: {response_3}") ``` > [!NOTE] > For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled. > When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block. ## Agentic Use Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. ```python from qwen_agent.agents import Assistant # Define LLM llm_cfg = { 'model': 'Qwen3-8B', # Use the endpoint provided by Alibaba Model Studio: # 'model_type': 'qwen_dashscope', # 'api_key': os.getenv('DASHSCOPE_API_KEY'), # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', # Other parameters: # 'generate_cfg': { # # Add: When the response content is `<think>this is the thought</think>this is the answer; # # Do not add: When the response has been separated by reasoning_content and content. # 'thought_in_content': True, # }, } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] for responses in bot.run(messages=messages): pass print(responses) ``` ## Processing Long Texts Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method. YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks: - Modifying the model files: In the `config.json` file, add the `rope_scaling` fields: ```json { ..., "rope_scaling": { "rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 32768 } } ``` For `llama.cpp`, you need to regenerate the GGUF file after the modification. - Passing command line arguments: For `vllm`, you can use ```shell vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072 ``` For `sglang`, you can use ```shell python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}' ``` For `llama-server` from `llama.cpp`, you can use ```shell llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 ``` > [!IMPORTANT] > If you encounter the following warning > ``` > Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'} > ``` > please upgrade `transformers>=4.51.0`. > [!NOTE] > All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. > We advise adding the `rope_scaling` configuration only when processing long contexts is required. > It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0. > [!NOTE] > The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance. > [!TIP] > The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed. ## Best Practices To achieve optimal performance, we recommend the following settings: 1. Sampling Parameters: - For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. - For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. 2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance. 3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking. - Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." 4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
root4k/Qwen3-30B-A3B_q4_0	root4k	2025-05-29T14:54:11Z	0	0	mlx	[ "mlx", "safetensors", "qwen3_moe", "text-generation", "conversational", "base_model:Qwen/Qwen3-30B-A3B", "base_model:quantized:Qwen/Qwen3-30B-A3B", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-05-29T14:08:23Z	--- library_name: mlx license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-30B-A3B tags: - mlx ---
LEAF-CLIP/CLIP-ViT-L-rho5-k2-constrained-FARE2	LEAF-CLIP	2025-05-29T14:53:17Z	0	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-04-16T08:01:09Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=2$ with $\rho=5$ and semantic constraints. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho5-k2-FARE2" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
kfn/q-Taxi-v3	kfn	2025-05-29T14:53:02Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-28T22:08:27Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="kfn/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
LEAF-CLIP/CLIP-ViT-L-rho2-k2-constrained-FARE2	LEAF-CLIP	2025-05-29T14:52:49Z	0	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-04-16T08:00:32Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=2$ with $\rho=2$ and semantic constraints. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho2-k2-FARE2" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
Varinder2110/a14e2674-2afe-4225-b51c-30bc2f8b5867	Varinder2110	2025-05-29T14:51:38Z	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	2025-05-29T13:42:58Z	--- 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: TOK --- # A14E2674 2Afe 4225 B51C 30Bc2F8B5867 <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 `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/Varinder2110/a14e2674-2afe-4225-b51c-30bc2f8b5867/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('Varinder2110/a14e2674-2afe-4225-b51c-30bc2f8b5867', weight_name='lora.safetensors') image = pipeline('TOK').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: 6000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Varinder2110/a14e2674-2afe-4225-b51c-30bc2f8b5867/discussions) to add images that show off what you’ve made with this LoRA.
kfn/q-FrozenLake-v1-4x4-noSlippery	kfn	2025-05-29T14:51:36Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-28T22:06:04Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="kfn/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
LEAF-CLIP/CLIP-ViT-L-rho20-k1-constrained-FARE2	LEAF-CLIP	2025-05-29T14:50:56Z	0	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-04-16T08:02:20Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=20$ and semantic constraints. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho20-k1-FARE2" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
p2kalita/qwen2-7b-instruct-trl-sft-ChartQA	p2kalita	2025-05-29T14:50:31Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-29T13:27:42Z	--- base_model: Qwen/Qwen2-VL-7B-Instruct library_name: transformers model_name: qwen2-7b-instruct-trl-sft-ChartQA tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen2-7b-instruct-trl-sft-ChartQA This model is a fine-tuned version of [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-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="p2kalita/qwen2-7b-instruct-trl-sft-ChartQA", 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/youranalystpartha-ibridge/qwen2-7b-instruct-trl-sft-ChartQA/runs/d0dapw7l) This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.53.0.dev0 - Pytorch: 2.4.1+cu121 - 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}} } ```
LEAF-CLIP/CLIP-ViT-L-rho10-k1-constrained-FARE2	LEAF-CLIP	2025-05-29T14:49:43Z	0	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-04-16T08:01:36Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=10$ and semantic constraints. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho10-k1-FARE2" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
tungduong261204/sft_v2_3000	tungduong261204	2025-05-29T14:47:45Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T14:46:45Z	--- 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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LEAF-CLIP/CLIP-ViT-L-rho2-k1-constrained-FARE2	LEAF-CLIP	2025-05-29T14:47:15Z	23	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-04-16T08:00:05Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=2$ and semantic constraints. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho2-k1-FARE2" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
Bekhouche/VLM-PAR	Bekhouche	2025-05-29T14:47:10Z	0	0	transformers	[ "transformers", "image-text-to-text", "en", "base_model:google/siglip2-base-patch16-224", "base_model:finetune:google/siglip2-base-patch16-224", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-29T14:39:41Z	--- license: mit language: - en metrics: - accuracy base_model: - google/siglip2-base-patch16-224 pipeline_tag: image-text-to-text library_name: transformers ---
LEAF-CLIP/CLIP-ViT-L-rho1-k2-constrained-FARE2	LEAF-CLIP	2025-05-29T14:46:50Z	2	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-04-16T07:59:39Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=2$ with $\rho=1$ and semantic constraints. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho1-k2-FARE2" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
Omegaindebt/TIMES_LoRA_NLLB_Adapter_v03	Omegaindebt	2025-05-29T14:46:34Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Omegaindebt/nllb_200_distill_Anuvaad-hi-te-ta", "base_model:adapter:Omegaindebt/nllb_200_distill_Anuvaad-hi-te-ta", "region:us" ]	null	2025-05-29T14:43:34Z	--- base_model: Omegaindebt/nllb_200_distill_Anuvaad-hi-te-ta 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
bored13/gensyn-checkpoints-galloping_diving_dragonfly	bored13	2025-05-29T14:45:05Z	9	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am galloping diving dragonfly", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-16T00:30:14Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: gensyn-checkpoints-galloping_diving_dragonfly tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am galloping diving dragonfly - unsloth - trl licence: license --- # Model Card for gensyn-checkpoints-galloping_diving_dragonfly This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="bored13/gensyn-checkpoints-galloping_diving_dragonfly", 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: 4.51.3 - Pytorch: 2.6.0 - 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}} } ```
LEAF-CLIP/CLIP-ViT-L-rho10-k2-FARE2	LEAF-CLIP	2025-05-29T14:44:42Z	2	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-03-11T13:05:25Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=2$ with $\rho=10$. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho10-k2-FARE2" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
Makrrr/Cartpole-v1	Makrrr	2025-05-29T14:44:33Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-29T14:43:56Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
BootesVoid/cmb9gc54x0d421b1yc4uiv4fg_cmb9gf81q0d621b1ylxbppi28	BootesVoid	2025-05-29T14:44:17Z	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	2025-05-29T14:44:15Z	--- 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: OLIVESKIN --- # Cmb9Gc54X0D421B1Yc4Uiv4Fg_Cmb9Gf81Q0D621B1Ylxbppi28 <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 `OLIVESKIN` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "OLIVESKIN", "lora_weights": "https://huggingface.co/BootesVoid/cmb9gc54x0d421b1yc4uiv4fg_cmb9gf81q0d621b1ylxbppi28/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('BootesVoid/cmb9gc54x0d421b1yc4uiv4fg_cmb9gf81q0d621b1ylxbppi28', weight_name='lora.safetensors') image = pipeline('OLIVESKIN').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/BootesVoid/cmb9gc54x0d421b1yc4uiv4fg_cmb9gf81q0d621b1ylxbppi28/discussions) to add images that show off what you’ve made with this LoRA.
Victoriatr07/Qwen2.5-7B-Instruct-3epochs-full	Victoriatr07	2025-05-29T14:43:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T14:40:11Z	--- 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. 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]
BootesVoid/cmb9do83t0bm31b1yuqf9r6zs_cmb9g9ncj0d2s1b1ybkxo7pua	BootesVoid	2025-05-29T14:42:59Z	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	2025-05-29T14:42:57Z	--- 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: GOTCHIC --- # Cmb9Do83T0Bm31B1Yuqf9R6Zs_Cmb9G9Ncj0D2S1B1Ybkxo7Pua <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 `GOTCHIC` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "GOTCHIC", "lora_weights": "https://huggingface.co/BootesVoid/cmb9do83t0bm31b1yuqf9r6zs_cmb9g9ncj0d2s1b1ybkxo7pua/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('BootesVoid/cmb9do83t0bm31b1yuqf9r6zs_cmb9g9ncj0d2s1b1ybkxo7pua', weight_name='lora.safetensors') image = pipeline('GOTCHIC').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/BootesVoid/cmb9do83t0bm31b1yuqf9r6zs_cmb9g9ncj0d2s1b1ybkxo7pua/discussions) to add images that show off what you’ve made with this LoRA.
LEAF-CLIP/CLIP-ViT-L-rho2-k2-FARE2	LEAF-CLIP	2025-05-29T14:42:28Z	2	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-03-11T13:04:53Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=2$ with $\rho=2$. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho2-k2-FARE2" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
LEAF-CLIP/CLIP-ViT-L-rho10-k1-FARE2	LEAF-CLIP	2025-05-29T14:41:04Z	4	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-03-11T13:04:02Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=10$. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho10-k1-FARE2" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
LEAF-CLIP/CLIP-ViT-L-rho5-k1-FARE2	LEAF-CLIP	2025-05-29T14:40:45Z	2	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-03-11T13:03:35Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=5$. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho5-k1-FARE2" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
LEAF-CLIP/CLIP-ViT-L-rho50-k1-FARE2	LEAF-CLIP	2025-05-29T14:39:23Z	2	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-02-24T11:06:11Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=50$. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho50-k1-FARE2" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
gulshany01/invoice-parser-v4	gulshany01	2025-05-29T14:38:26Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-29T14:38:17Z	--- base_model: unsloth/qwen2-0.5b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: gulshany01 - License: apache-2.0 - Finetuned from model : unsloth/qwen2-0.5b-instruct-bnb-4bit This qwen2 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)
soniaaabou/lora_model	soniaaabou	2025-05-29T14:34:02Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-29T14:33:50Z	--- base_model: unsloth/qwen2.5-coder-14b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: soniaaabou - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-coder-14b-instruct-bnb-4bit This qwen2 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)
LEAF-CLIP/CLIP-ViT-L-rho1-k1-FARE2	LEAF-CLIP	2025-05-29T14:33:47Z	4	0	null	[ "safetensors", "clip", "dataset:ILSVRC/imagenet-1k", "dataset:mlfoundations/datacomp_small", "base_model:openai/clip-vit-large-patch14", "base_model:finetune:openai/clip-vit-large-patch14", "license:mit", "region:us" ]	null	2025-03-11T13:03:08Z	--- license: mit datasets: - ILSVRC/imagenet-1k - mlfoundations/datacomp_small base_model: - openai/clip-vit-large-patch14 --- Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=1$. To load this model use: ```python from transformers import CLIPProcessor, CLIPModel model_name = "LEAF-CLIP/CLIP-ViT-L-rho1-k1-FARE2" processor_name = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(processor_name) ```
Aristo97/As1a	Aristo97	2025-05-29T14:31:34Z	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	2025-05-29T14:00:49Z	--- 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: As1a --- # As1A <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 `As1a` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "As1a", "lora_weights": "https://huggingface.co/Aristo97/As1a/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('Aristo97/As1a', weight_name='lora.safetensors') image = pipeline('As1a').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/Aristo97/As1a/discussions) to add images that show off what you’ve made with this LoRA.
mlx-community/DeepSeek-R1-0528-Qwen3-8B-8bit	mlx-community	2025-05-29T14:29:42Z	0	1	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "license:mit", "8-bit", "region:us" ]	text-generation	2025-05-29T14:26:13Z	--- license: mit library_name: mlx base_model: deepseek-ai/deepseek-r1-0528-Qwen3-8B tags: - mlx pipeline_tag: text-generation --- # mlx-community/DeepSeek-R1-0528-Qwen3-8B-8bit This model [mlx-community/DeepSeek-R1-0528-Qwen3-8B-8bit](https://huggingface.co/mlx-community/DeepSeek-R1-0528-Qwen3-8B-8bit) was converted to MLX format from [deepseek-ai/deepseek-r1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/deepseek-r1-0528-Qwen3-8B) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/DeepSeek-R1-0528-Qwen3-8B-8bit") 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) ```
E-katrin/last_layers_50epochs_10e-5	E-katrin	2025-05-29T14:26:14Z	0	0	transformers	[ "transformers", "safetensors", "cobald_parser", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]	feature-extraction	2025-05-29T14:25:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
medxcribe/whisper-small.en	medxcribe	2025-05-29T14:24:54Z	11	0	null	[ "tensorboard", "safetensors", "whisper", "generated_from_trainer", "region:us" ]	null	2024-11-13T11:15:05Z	--- tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small.en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small.en This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0244 - Wer: 2.5393 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.1861 \| 4.15 \| 500 \| 0.1216 \| 4.5546 \| \| 0.028 \| 8.31 \| 1000 \| 0.0244 \| 2.5393 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.1 - Tokenizers 0.15.2
QuimBallester/question_focus_model	QuimBallester	2025-05-29T14:24:53Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:PlanTL-GOB-ES/roberta-base-bne", "base_model:finetune:PlanTL-GOB-ES/roberta-base-bne", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-29T11:01:19Z	--- library_name: transformers license: apache-2.0 base_model: PlanTL-GOB-ES/roberta-base-bne tags: - generated_from_trainer metrics: - accuracy model-index: - name: question_focus_model 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. --> # question_focus_model This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5105 - Accuracy: 0.8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: 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 - lr_scheduler_warmup_steps: 50 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7071 \| 1.0 \| 13 \| 0.6947 \| 0.52 \| \| 0.7218 \| 2.0 \| 26 \| 0.6797 \| 0.64 \| \| 0.6588 \| 3.0 \| 39 \| 0.5430 \| 0.72 \| \| 0.1865 \| 4.0 \| 52 \| 0.5481 \| 0.8 \| \| 0.0525 \| 5.0 \| 65 \| 0.5105 \| 0.8 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0 - Datasets 3.6.0 - Tokenizers 0.21.1
Tandogan/dpo_v1_dpo_dataset_bigger	Tandogan	2025-05-29T14:24:23Z	13	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T06:58:26Z	--- 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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Thalesian/Pretrain-AKK-60m	Thalesian	2025-05-29T14:23:03Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-29T14:22:05Z	--- 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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marissaliora/merged-llama2	marissaliora	2025-05-29T14:21:45Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-29T14:15:34Z	--- 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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jinachris/PURE-PRM-7B	jinachris	2025-05-29T14:21:42Z	49	4	null	[ "safetensors", "qwen2", "token-classification", "dataset:HuggingFaceH4/prm800k-trl-dedup", "arxiv:2504.15275", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "license:apache-2.0", "region:us" ]	token-classification	2025-02-09T07:10:09Z	--- license: apache-2.0 base_model: - Qwen/Qwen2.5-Math-7B pipeline_tag: token-classification datasets: - HuggingFaceH4/prm800k-trl-dedup --- > [!Warning] > <div align="center"> > <b> > 🚨 This repo differs from <a href=https://huggingface.co/Qwen/Qwen2.5-Math-7B-PRM800K>Qwen's PRM</a>. We trained our PRM based on <a href=https://huggingface.co/Qwen/Qwen2.5-Math-7B>Qwen2.5-Math-7B</a>, while Qwen's PRM is based on <a href=https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct>Qwen2.5-Math-7B-Instruct</a>. > </b> > </div> # PURE's PRM based on Qwen2.5-Math-7B ## Introduction Our PRM is used to fine-tune LLM for better math reasoning capability. See our [PURE GitHub repo](https://github.com/CJReinforce/PURE) for more details. It is obtained by fine-tuning Qwen2.5-Math-7B on the training set of open-source dataset [PRM800K](https://github.com/openai/prm800k). We choose Qwen2.5-Math-7B instead of Qwen2.5-Math-7B-Instruct to keep the base model consistent with our baselines. We treat the original 1 and 0 labels in PRM800K as our positive labels, while -1 as negative ones. To eliminate test data contamination, we also remove the PRM800K training samples that have the same math queries in MATH test set. ## Requirements * `transformers>=4.40.0` for Qwen2.5-Math models. The latest version is recommended. ## Quick Start > [!Important] > > PURE's PRM is a process reward model typically used for offering feedback on the quality of reasoning and intermediate steps rather than generation. ### Prerequisites - Step Separation: We recommend using double line breaks ("\n\n") to separate individual steps within the solution. - Reward Computation: After each step, we insert a token "`\n`". For reward calculation, we extract the probability score of this token and subtract negative probabilities from positive probabilities, resulting in a reward value between -1 and 1. We regard steps with reward > 0 as correct, otherwise as incorrect. ### 🤗 Hugging Face Transformers 1. Here we show a code snippet to show you how to use our PRM with `transformers`: ```python import torch from transformers import AutoModelForTokenClassification, AutoTokenizer def make_step_rewards(logits, token_masks): all_scores_res = [] for sample, token_mask in zip(logits, token_masks): # sample: (seq_len, num_labels) probs = sample[token_mask].softmax(dim=-1) # (num_steps, 2) process_reward = probs[:, 1] - probs[:, 0] # (num_steps,) # weighted sum to approx. min, highly recommend when BoN eval and Fine-tuning LLM # weight = torch.softmax( # -process_reward / 0.1, # dim=-1, # ) # process_reward = weight * process_reward all_scores_res.append(process_reward.cpu().tolist()) return all_scores_res model_name = "jinachris/PURE-PRM-7B" device = "auto" tokenizer = AutoTokenizer.from_pretrained( model_name, trust_remote_code=True, ) model = AutoModelForTokenClassification.from_pretrained( model_name, device_map=device, torch_dtype=torch.bfloat16, trust_remote_code=True, ).eval() question = "Sue lives in a fun neighborhood. One weekend, the neighbors decided to play a prank on Sue. On Friday morning, the neighbors placed 18 pink plastic flamingos out on Sue's front yard. On Saturday morning, the neighbors took back one third of the flamingos, painted them white, and put these newly painted white flamingos back out on Sue's front yard. Then, on Sunday morning, they added another 18 pink plastic flamingos to the collection. At noon on Sunday, how many more pink plastic flamingos were out than white plastic flamingos?" steps = [ "To find out how many more pink plastic flamingos were out than white plastic flamingos at noon on Sunday, we can break down the problem into steps. First, on Friday, the neighbors start with 18 pink plastic flamingos.", "On Saturday, they take back one third of the flamingos. Since there were 18 flamingos, (1/3 \\times 18 = 6) flamingos are taken back. So, they have (18 - 6 = 12) flamingos left in their possession. Then, they paint these 6 flamingos white and put them back out on Sue's front yard. Now, Sue has the original 12 pink flamingos plus the 6 new white ones. Thus, by the end of Saturday, Sue has (12 + 6 = 18) pink flamingos and 6 white flamingos.", "On Sunday, the neighbors add another 18 pink plastic flamingos to Sue's front yard. By the end of Sunday morning, Sue has (18 + 18 = 36) pink flamingos and still 6 white flamingos.", "To find the difference, subtract the number of white flamingos from the number of pink flamingos: (36 - 6 = 30). Therefore, at noon on Sunday, there were 30 more pink plastic flamingos out than white plastic flamingos. The answer is (\\boxed{30})." ] step_separator = "\n" step_separator_token = tokenizer( step_separator, add_special_tokens=False, return_tensors='pt', )['input_ids'] input_ids = tokenizer( question, add_special_tokens=False, return_tensors='pt', )['input_ids'] score_ids = [] for step in steps: step_ids = tokenizer( step, add_special_tokens=False, return_tensors='pt', )['input_ids'] input_ids = torch.cat( [input_ids, step_ids, step_separator_token], dim=-1, ) score_ids.append(input_ids.size(-1) - 1) input_ids = input_ids.to(model.device) token_masks = torch.zeros_like(input_ids, dtype=torch.bool) token_masks[0, score_ids] = True assert torch.all(input_ids[token_masks].to("cpu") == step_separator_token) logits = model(input_ids).logits step_reward = make_step_rewards(logits, token_masks) print(step_reward) # [[0.796875, 0.185546875, -0.0625, 0.078125]] # For BoN eval, # uncomment the weighted sum part in `make_step_rewards` func, # then sum the rewards to get the final score (outcome reward): # torch.tensor(step_reward).sum(dim=-1) ``` 2. For evaluation using Best-of-N method or on ProcessBench and PRMBench, refer to [our github repository](https://github.com/CJReinforce/PURE/tree/verl/PRM/eval). ## Citation If you find our work useful, we would appreciate it if you could cite our work: ``` @article{cheng2025stop, title={Stop Summation: Min-Form Credit Assignment Is All Process Reward Model Needs for Reasoning}, author={Cheng, Jie and Qiao, Ruixi and Li, Lijun and Guo, Chao and Wang, Junle and Xiong, Gang and Lv, Yisheng and Wang, Fei-Yue}, journal={arXiv preprint arXiv:2504.15275}, year={2025} } ```
Boingbing/sakura-haruno_flux	Boingbing	2025-05-29T14:20:57Z	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	2025-05-29T13:51:58Z	--- 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: sakura_bing --- # Sakura Haruno_Flux <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 `sakura_bing` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "sakura_bing", "lora_weights": "https://huggingface.co/Boingbing/sakura-haruno_flux/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('Boingbing/sakura-haruno_flux', weight_name='lora.safetensors') image = pipeline('sakura_bing').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: 2002 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Boingbing/sakura-haruno_flux/discussions) to add images that show off what you’ve made with this LoRA.
Jinendran/ppo-snowball	Jinendran	2025-05-29T14:20:00Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2025-05-29T14:19:56Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Jinendran/ppo-snowball 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Icarus013/ppo-SnowballTarget	Icarus013	2025-05-29T14:18:44Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2025-05-29T14:18:36Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Icarus013/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
MrPNess/mary	MrPNess	2025-05-29T14:14:06Z	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	2025-05-29T13:40:02Z	--- 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: mary --- # Mary <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 `mary` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "mary", "lora_weights": "https://huggingface.co/mrpness/mary/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('mrpness/mary', weight_name='lora.safetensors') image = pipeline('mary').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: 3000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/mrpness/mary/discussions) to add images that show off what you’ve made with this LoRA.
ertghiu256/qwen-3-14b-code-and-math-reasoning-gguf	ertghiu256	2025-05-29T14:13:13Z	0	1	null	[ "gguf", "unsloth", "trl", "sft", "dataset:nvidia/OpenCodeReasoning", "dataset:nvidia/OpenMathReasoning", "base_model:Qwen/Qwen3-14B", "base_model:quantized:Qwen/Qwen3-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-28T10:04:34Z	--- license: apache-2.0 tags: - unsloth - trl - sft datasets: - nvidia/OpenCodeReasoning - nvidia/OpenMathReasoning base_model: - Qwen/Qwen3-14B --- Qwen 3 14b a great medium sized model. This qwen3 model is trained on 20.000 samples taken from Nvidia/OpenCodeReasoning and Nvidia/OpenMathReasoning. ## Model use cases: - Coding (python and c++) - Solving math questions - General reasoning Use thinking mode using "/think" for the best results
tmzconect/fotopessoal	tmzconect	2025-05-29T14:12:54Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-29T13:09:53Z	--- 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 ---
tetttssts/hetha_how	tetttssts	2025-05-29T14:12:38Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2_5_vl", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-29T14:12:28Z	--- base_model: unsloth/qwen2.5-vl-7b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: tetttssts - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-vl-7b-instruct-bnb-4bit This qwen2_5_vl 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)
ksun1ch/codet5-nl2vis-5ep	ksun1ch	2025-05-29T14:11:16Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:Salesforce/codet5-base", "base_model:finetune:Salesforce/codet5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-29T13:30:37Z	--- library_name: transformers license: apache-2.0 base_model: Salesforce/codet5-base tags: - generated_from_trainer model-index: - name: codet5-nl2vis-5ep 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. --> # codet5-nl2vis-5ep This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesforce/codet5-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
yasminetligui/MNLP_M2_dpo_model	yasminetligui	2025-05-29T14:11:12Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T13:57:39Z	--- library_name: transformers model_name: MNLP_M2_dpo_model tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for MNLP_M2_dpo_model 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="yasminetligui/MNLP_M2_dpo_model", 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 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: 0.18.0 - Transformers: 4.53.0.dev0 - Pytorch: 2.5.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Alecardo/Oscar-683867c45a1115a48a4c4eb9	Alecardo	2025-05-29T14:10:35Z	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	2025-05-29T13:57:24Z	--- 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: OSC --- # Oscar 683867C45A1115A48A4C4Eb9 <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 `OSC` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "OSC", "lora_weights": "https://huggingface.co/Alecardo/Oscar-683867c45a1115a48a4c4eb9/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('Alecardo/Oscar-683867c45a1115a48a4c4eb9', weight_name='lora.safetensors') image = pipeline('OSC').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: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Alecardo/Oscar-683867c45a1115a48a4c4eb9/discussions) to add images that show off what you’ve made with this LoRA.
bb1070/ecm-doji-style-lr16-steps-1000	bb1070	2025-05-29T14:10:23Z	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	2025-05-29T13:56:28Z	--- 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: ECMDOJI --- # Ecm Doji Style Lr16 Steps 1000 <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 `ECMDOJI` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "ECMDOJI", "lora_weights": "https://huggingface.co/bb1070/ecm-doji-style-lr16-steps-1000/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('bb1070/ecm-doji-style-lr16-steps-1000', weight_name='lora.safetensors') image = pipeline('ECMDOJI').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: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/bb1070/ecm-doji-style-lr16-steps-1000/discussions) to add images that show off what you’ve made with this LoRA.
sergioalves/e1bfd768-64ba-44a0-a5d8-1407c186f419	sergioalves	2025-05-29T14:09:48Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-135M-Instruct", "base_model:adapter:unsloth/SmolLM-135M-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-29T13:42:19Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-135M-Instruct tags: - axolotl - generated_from_trainer model-index: - name: e1bfd768-64ba-44a0-a5d8-1407c186f419 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: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/SmolLM-135M-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 43ce974b7dfb1840_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: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: sergioalves/e1bfd768-64ba-44a0-a5d8-1407c186f419 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/43ce974b7dfb1840_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 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: 16b233f9-9484-47ca-ba74-fbf737ad2794 wandb_project: s56-7 wandb_run: your_name wandb_runid: 16b233f9-9484-47ca-ba74-fbf737ad2794 warmup_steps: 50 weight_decay: 0.05 xformers_attention: true ``` </details><br> # e1bfd768-64ba-44a0-a5d8-1407c186f419 This model is a fine-tuned version of [unsloth/SmolLM-135M-Instruct](https://huggingface.co/unsloth/SmolLM-135M-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3559 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - 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: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.0596 \| 0.0001 \| 1 \| 3.3826 \| \| 3.5977 \| 0.0126 \| 250 \| 3.3651 \| \| 3.2713 \| 0.0251 \| 500 \| 3.3559 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nsanjrani/dqn-SpaceInvadersNoFrameskip-v4	nsanjrani	2025-05-29T14:08:10Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-29T14:07:33Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 636.50 +/- 196.11 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nsanjrani -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nsanjrani -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga nsanjrani ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 120000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.001), ('exploration_fraction', 0.05), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 8e-05), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
mandugo/ppo-Huggy	mandugo	2025-05-29T14:07:43Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-05-29T14:07:37Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: mandugo/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
vertings6/8f2e7074-1c4e-4d43-86a2-7ccf2ad5a821	vertings6	2025-05-29T14:05:09Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/Mistral-Nemo-Base-2407", "base_model:adapter:unsloth/Mistral-Nemo-Base-2407", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-29T08:51:43Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Mistral-Nemo-Base-2407 tags: - axolotl - generated_from_trainer model-index: - name: 8f2e7074-1c4e-4d43-86a2-7ccf2ad5a821 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: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/Mistral-Nemo-Base-2407 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - b737943c169dce76_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vertings6/8f2e7074-1c4e-4d43-86a2-7ccf2ad5a821 hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/b737943c169dce76_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 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: 9f4dd85f-eef8-4321-bcfe-a15029c10fe9 wandb_project: s56-7 wandb_run: your_name wandb_runid: 9f4dd85f-eef8-4321-bcfe-a15029c10fe9 warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 8f2e7074-1c4e-4d43-86a2-7ccf2ad5a821 This model is a fine-tuned version of [unsloth/Mistral-Nemo-Base-2407](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3744 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - 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: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 5.1858 \| 0.0000 \| 1 \| 1.6777 \| \| 4.2974 \| 0.0074 \| 250 \| 1.3943 \| \| 3.4937 \| 0.0148 \| 500 \| 1.3744 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
bb1070/aryan-lr16-1000steps	bb1070	2025-05-29T14:02:33Z	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	2025-05-29T13:47:53Z	--- 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: UNST --- # Aryan Lr16 1000Steps <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 `UNST` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "UNST", "lora_weights": "https://huggingface.co/bb1070/aryan-lr16-1000steps/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('bb1070/aryan-lr16-1000steps', weight_name='lora.safetensors') image = pipeline('UNST').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: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/bb1070/aryan-lr16-1000steps/discussions) to add images that show off what you’ve made with this LoRA.
bb1070/prachi-lr16-1000steps	bb1070	2025-05-29T14:02:26Z	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	2025-05-29T13:49:22Z	--- 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: UNST --- # Prachi Lr16 1000Steps <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 `UNST` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "UNST", "lora_weights": "https://huggingface.co/bb1070/prachi-lr16-1000steps/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('bb1070/prachi-lr16-1000steps', weight_name='lora.safetensors') image = pipeline('UNST').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: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/bb1070/prachi-lr16-1000steps/discussions) to add images that show off what you’ve made with this LoRA.
YuchenLi01/genParaMoreUniqueResNoGT_Qwen2.5-1.5BInstruct_dpo_ebs32_lr3e-06_beta0.4_42	YuchenLi01	2025-05-29T14:01:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_generatedAndParaphrasedMoreUniqueResponseNoGT", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T07:15:52Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_generatedAndParaphrasedMoreUniqueResponseNoGT model-index: - name: genParaMoreUniqueResNoGT_Qwen2.5-1.5BInstruct_dpo_ebs32_lr3e-06_beta0.4_42 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. --> # genParaMoreUniqueResNoGT_Qwen2.5-1.5BInstruct_dpo_ebs32_lr3e-06_beta0.4_42 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_generatedAndParaphrasedMoreUniqueResponseNoGT dataset. It achieves the following results on the evaluation set: - Loss: 0.9923 - Rewards/chosen: -11.8039 - Rewards/rejected: -15.7485 - Rewards/accuracies: 0.7378 - Rewards/margins: 3.9446 - Logps/rejected: -87.1288 - Logps/chosen: -71.9740 - Logits/rejected: -1.6381 - Logits/chosen: -1.7573 ## 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: 3e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.7098 \| 0.0135 \| 20 \| 0.6975 \| 0.0116 \| -0.0155 \| 0.5488 \| 0.0271 \| -47.7963 \| -42.4354 \| -2.2052 \| -2.3106 \| \| 0.7363 \| 0.0270 \| 40 \| 0.6957 \| -0.0418 \| -0.0641 \| 0.5488 \| 0.0224 \| -47.9179 \| -42.5687 \| -2.1923 \| -2.2979 \| \| 0.6945 \| 0.0405 \| 60 \| 0.6832 \| -0.2329 \| -0.2673 \| 0.5701 \| 0.0343 \| -48.4257 \| -43.0467 \| -2.1560 \| -2.2623 \| \| 0.6497 \| 0.0540 \| 80 \| 0.6708 \| -0.4499 \| -0.5128 \| 0.5549 \| 0.0629 \| -49.0395 \| -43.5891 \| -2.1209 \| -2.2281 \| \| 0.5938 \| 0.0675 \| 100 \| 0.6574 \| -0.7451 \| -0.8892 \| 0.5915 \| 0.1442 \| -49.9806 \| -44.3270 \| -2.0633 \| -2.1724 \| \| 0.5491 \| 0.0810 \| 120 \| 0.6381 \| -0.7523 \| -0.9836 \| 0.6220 \| 0.2313 \| -50.2165 \| -44.3450 \| -2.0812 \| -2.1921 \| \| 0.5772 \| 0.0945 \| 140 \| 0.6253 \| -0.7988 \| -1.1457 \| 0.6433 \| 0.3470 \| -50.6219 \| -44.4613 \| -2.0902 \| -2.2021 \| \| 0.6032 \| 0.1080 \| 160 \| 0.6110 \| -0.7492 \| -1.1422 \| 0.6555 \| 0.3930 \| -50.6132 \| -44.3374 \| -2.1019 \| -2.2147 \| \| 0.5522 \| 0.1215 \| 180 \| 0.6076 \| -1.1169 \| -1.6240 \| 0.6829 \| 0.5070 \| -51.8175 \| -45.2567 \| -2.0429 \| -2.1598 \| \| 0.5428 \| 0.1350 \| 200 \| 0.5981 \| -1.1646 \| -1.8209 \| 0.7012 \| 0.6563 \| -52.3099 \| -45.3759 \| -2.0244 \| -2.1450 \| \| 0.5147 \| 0.1484 \| 220 \| 0.5936 \| -1.2658 \| -1.9904 \| 0.7043 \| 0.7246 \| -52.7337 \| -45.6289 \| -2.0306 \| -2.1495 \| \| 0.6537 \| 0.1619 \| 240 \| 0.5936 \| -1.6845 \| -2.5781 \| 0.7134 \| 0.8937 \| -54.2029 \| -46.6755 \| -1.9983 \| -2.1199 \| \| 0.5995 \| 0.1754 \| 260 \| 0.6259 \| -1.7760 \| -2.7106 \| 0.6555 \| 0.9346 \| -54.5341 \| -46.9044 \| -2.0381 \| -2.1612 \| \| 0.9123 \| 0.1889 \| 280 \| 0.6163 \| -2.0329 \| -3.0730 \| 0.6890 \| 1.0402 \| -55.4401 \| -47.5465 \| -1.9913 \| -2.1137 \| \| 0.5822 \| 0.2024 \| 300 \| 0.6358 \| -2.1738 \| -3.2212 \| 0.6677 \| 1.0474 \| -55.8106 \| -47.8989 \| -2.0553 \| -2.1797 \| \| 0.3054 \| 0.2159 \| 320 \| 0.6567 \| -3.7566 \| -5.0248 \| 0.6616 \| 1.2682 \| -60.3196 \| -51.8559 \| -1.8652 \| -1.9890 \| \| 0.8797 \| 0.2294 \| 340 \| 0.7001 \| -4.1329 \| -5.3844 \| 0.6646 \| 1.2515 \| -61.2185 \| -52.7965 \| -1.7727 \| -1.9000 \| \| 0.3861 \| 0.2429 \| 360 \| 0.7144 \| -3.4497 \| -4.6169 \| 0.6433 \| 1.1672 \| -59.2998 \| -51.0885 \| -1.9286 \| -2.0505 \| \| 0.4995 \| 0.2564 \| 380 \| 0.7365 \| -4.2502 \| -5.6537 \| 0.6646 \| 1.4035 \| -61.8919 \| -53.0898 \| -1.9485 \| -2.0719 \| \| 0.4827 \| 0.2699 \| 400 \| 0.7037 \| -4.5952 \| -6.0919 \| 0.6646 \| 1.4967 \| -62.9872 \| -53.9523 \| -1.9005 \| -2.0232 \| \| 0.2968 \| 0.2834 \| 420 \| 0.7143 \| -4.1504 \| -5.5180 \| 0.6463 \| 1.3677 \| -61.5526 \| -52.8402 \| -1.9489 \| -2.0687 \| \| 0.8907 \| 0.2969 \| 440 \| 0.7369 \| -4.9770 \| -6.4202 \| 0.6463 \| 1.4432 \| -63.8080 \| -54.9068 \| -1.7958 \| -1.9106 \| \| 0.6211 \| 0.3104 \| 460 \| 0.7576 \| -6.1123 \| -7.7968 \| 0.6829 \| 1.6845 \| -67.2496 \| -57.7450 \| -1.6576 \| -1.7723 \| \| 0.4764 \| 0.3239 \| 480 \| 0.7325 \| -5.1326 \| -6.5880 \| 0.6402 \| 1.4553 \| -64.2275 \| -55.2959 \| -1.7967 \| -1.9149 \| \| 0.7081 \| 0.3374 \| 500 \| 0.7357 \| -5.4809 \| -7.1492 \| 0.6433 \| 1.6684 \| -65.6307 \| -56.1665 \| -1.8434 \| -1.9578 \| \| 0.8628 \| 0.3509 \| 520 \| 0.7601 \| -6.1711 \| -7.9028 \| 0.6677 \| 1.7317 \| -67.5146 \| -57.8921 \| -1.7492 \| -1.8639 \| \| 0.8365 \| 0.3644 \| 540 \| 0.7635 \| -6.1656 \| -7.8660 \| 0.6799 \| 1.7004 \| -67.4226 \| -57.8784 \| -1.7878 \| -1.8998 \| \| 0.5506 \| 0.3779 \| 560 \| 0.7630 \| -5.6877 \| -7.3765 \| 0.6768 \| 1.6888 \| -66.1989 \| -56.6836 \| -1.8435 \| -1.9586 \| \| 0.827 \| 0.3914 \| 580 \| 0.8143 \| -6.4625 \| -8.2914 \| 0.6646 \| 1.8289 \| -68.4861 \| -58.6206 \| -1.8085 \| -1.9240 \| \| 0.3283 \| 0.4049 \| 600 \| 0.8052 \| -7.0358 \| -8.8704 \| 0.6890 \| 1.8347 \| -69.9337 \| -60.0538 \| -1.7818 \| -1.8937 \| \| 0.9988 \| 0.4184 \| 620 \| 0.8165 \| -7.4629 \| -9.3675 \| 0.6921 \| 1.9046 \| -71.1764 \| -61.1216 \| -1.7725 \| -1.8848 \| \| 0.5458 \| 0.4318 \| 640 \| 0.8261 \| -7.4267 \| -9.4771 \| 0.6982 \| 2.0505 \| -71.4504 \| -61.0310 \| -1.7991 \| -1.9135 \| \| 0.8243 \| 0.4453 \| 660 \| 0.8219 \| -7.2880 \| -9.3348 \| 0.7012 \| 2.0468 \| -71.0946 \| -60.6844 \| -1.7960 \| -1.9106 \| \| 0.6822 \| 0.4588 \| 680 \| 0.8644 \| -7.8936 \| -9.8683 \| 0.7134 \| 1.9747 \| -72.4284 \| -62.1984 \| -1.7631 \| -1.8763 \| \| 1.028 \| 0.4723 \| 700 \| 0.9176 \| -9.3270 \| -11.4992 \| 0.6890 \| 2.1722 \| -76.5055 \| -65.7819 \| -1.6213 \| -1.7301 \| \| 0.5688 \| 0.4858 \| 720 \| 0.8747 \| -8.5561 \| -10.5672 \| 0.7012 \| 2.0111 \| -74.1755 \| -63.8545 \| -1.6097 \| -1.7174 \| \| 0.714 \| 0.4993 \| 740 \| 0.8826 \| -8.9322 \| -11.2433 \| 0.6860 \| 2.3111 \| -75.8659 \| -64.7949 \| -1.6609 \| -1.7705 \| \| 0.755 \| 0.5128 \| 760 \| 0.8393 \| -8.3926 \| -10.3983 \| 0.6677 \| 2.0057 \| -73.7533 \| -63.4459 \| -1.6622 \| -1.7689 \| \| 0.3812 \| 0.5263 \| 780 \| 0.8504 \| -8.4765 \| -10.4612 \| 0.6677 \| 1.9848 \| -73.9107 \| -63.6556 \| -1.6883 \| -1.7973 \| \| 0.7908 \| 0.5398 \| 800 \| 0.9168 \| -9.0757 \| -11.1065 \| 0.6616 \| 2.0308 \| -75.5238 \| -65.1536 \| -1.5763 \| -1.6818 \| \| 0.4164 \| 0.5533 \| 820 \| 0.8994 \| -9.4200 \| -11.6538 \| 0.6890 \| 2.2338 \| -76.8921 \| -66.0144 \| -1.5816 \| -1.6856 \| \| 0.978 \| 0.5668 \| 840 \| 0.9013 \| -9.1077 \| -11.3320 \| 0.6585 \| 2.2244 \| -76.0877 \| -65.2335 \| -1.6432 \| -1.7478 \| \| 0.3356 \| 0.5803 \| 860 \| 0.8793 \| -9.0267 \| -11.1855 \| 0.6707 \| 2.1588 \| -75.7214 \| -65.0312 \| -1.5340 \| -1.6340 \| \| 0.651 \| 0.5938 \| 880 \| 0.8691 \| -8.7011 \| -10.9951 \| 0.6616 \| 2.2940 \| -75.2453 \| -64.2171 \| -1.5639 \| -1.6652 \| \| 0.2713 \| 0.6073 \| 900 \| 0.9007 \| -8.4464 \| -10.9908 \| 0.6829 \| 2.5443 \| -75.2345 \| -63.5805 \| -1.6517 \| -1.7554 \| \| 0.2546 \| 0.6208 \| 920 \| 0.9749 \| -9.5681 \| -12.1975 \| 0.6677 \| 2.6294 \| -78.2514 \| -66.3845 \| -1.5807 \| -1.6810 \| \| 0.1872 \| 0.6343 \| 940 \| 0.9149 \| -9.7189 \| -12.1319 \| 0.6616 \| 2.4131 \| -78.0874 \| -66.7615 \| -1.5820 \| -1.6793 \| \| 1.0918 \| 0.6478 \| 960 \| 0.9188 \| -10.2637 \| -12.8306 \| 0.6829 \| 2.5669 \| -79.8342 \| -68.1236 \| -1.5298 \| -1.6259 \| \| 0.8018 \| 0.6613 \| 980 \| 0.8555 \| -9.3842 \| -11.6986 \| 0.6951 \| 2.3144 \| -77.0042 \| -65.9248 \| -1.5571 \| -1.6543 \| \| 0.5597 \| 0.6748 \| 1000 \| 0.8720 \| -9.7656 \| -12.2196 \| 0.6982 \| 2.4540 \| -78.3065 \| -66.8782 \| -1.5521 \| -1.6516 \| \| 0.5755 \| 0.6883 \| 1020 \| 0.9370 \| -10.3906 \| -12.7761 \| 0.6768 \| 2.3855 \| -79.6978 \| -68.4409 \| -1.6141 \| -1.7136 \| \| 0.2482 \| 0.7018 \| 1040 \| 0.8761 \| -10.6673 \| -12.9586 \| 0.7012 \| 2.2913 \| -80.1541 \| -69.1325 \| -1.5875 \| -1.6853 \| \| 0.3408 \| 0.7152 \| 1060 \| 0.9436 \| -11.4063 \| -14.0026 \| 0.7012 \| 2.5963 \| -82.7640 \| -70.9801 \| -1.6032 \| -1.7004 \| \| 0.5507 \| 0.7287 \| 1080 \| 0.9200 \| -10.8737 \| -13.1264 \| 0.6768 \| 2.2526 \| -80.5735 \| -69.6487 \| -1.5506 \| -1.6461 \| \| 0.1719 \| 0.7422 \| 1100 \| 0.9123 \| -10.3203 \| -12.5027 \| 0.6555 \| 2.1824 \| -79.0144 \| -68.2650 \| -1.5808 \| -1.6741 \| \| 0.8994 \| 0.7557 \| 1120 \| 0.9605 \| -11.2090 \| -13.6478 \| 0.6677 \| 2.4388 \| -81.8770 \| -70.4868 \| -1.5609 \| -1.6538 \| \| 0.2617 \| 0.7692 \| 1140 \| 0.9126 \| -9.8912 \| -12.3043 \| 0.6921 \| 2.4130 \| -78.5182 \| -67.1924 \| -1.6239 \| -1.7173 \| \| 0.566 \| 0.7827 \| 1160 \| 0.8975 \| -10.2456 \| -12.5994 \| 0.6860 \| 2.3538 \| -79.2561 \| -68.0784 \| -1.6188 \| -1.7091 \| \| 0.4632 \| 0.7962 \| 1180 \| 0.9409 \| -11.1351 \| -13.4659 \| 0.6463 \| 2.3308 \| -81.4224 \| -70.3021 \| -1.5740 \| -1.6659 \| \| 1.0443 \| 0.8097 \| 1200 \| 0.9132 \| -10.8218 \| -13.2224 \| 0.6646 \| 2.4007 \| -80.8137 \| -69.5188 \| -1.5394 \| -1.6349 \| \| 0.7211 \| 0.8232 \| 1220 \| 0.9058 \| -10.1620 \| -12.4621 \| 0.7012 \| 2.3000 \| -78.9127 \| -67.8694 \| -1.5970 \| -1.6952 \| \| 0.4539 \| 0.8367 \| 1240 \| 0.9035 \| -10.9216 \| -13.1691 \| 0.6829 \| 2.2475 \| -80.6804 \| -69.7684 \| -1.5373 \| -1.6338 \| \| 0.4932 \| 0.8502 \| 1260 \| 0.9137 \| -10.9271 \| -13.1650 \| 0.6799 \| 2.2379 \| -80.6701 \| -69.7820 \| -1.5339 \| -1.6310 \| \| 0.4538 \| 0.8637 \| 1280 \| 0.9164 \| -10.5038 \| -12.7393 \| 0.6768 \| 2.2356 \| -79.6059 \| -68.7237 \| -1.5552 \| -1.6528 \| \| 0.3814 \| 0.8772 \| 1300 \| 0.9254 \| -10.2018 \| -12.5024 \| 0.6768 \| 2.3005 \| -79.0135 \| -67.9689 \| -1.5783 \| -1.6753 \| \| 0.4105 \| 0.8907 \| 1320 \| 0.9508 \| -10.1449 \| -12.6454 \| 0.6707 \| 2.5005 \| -79.3711 \| -67.8266 \| -1.6060 \| -1.7035 \| \| 0.2319 \| 0.9042 \| 1340 \| 0.8939 \| -9.6708 \| -12.1779 \| 0.6829 \| 2.5070 \| -78.2022 \| -66.6413 \| -1.6103 \| -1.7073 \| \| 0.3876 \| 0.9177 \| 1360 \| 0.9378 \| -10.4419 \| -12.9199 \| 0.6646 \| 2.4781 \| -80.0574 \| -68.5690 \| -1.5616 \| -1.6557 \| \| 0.1832 \| 0.9312 \| 1380 \| 0.9283 \| -10.3731 \| -12.8531 \| 0.6860 \| 2.4800 \| -79.8903 \| -68.3971 \| -1.5321 \| -1.6255 \| \| 0.4126 \| 0.9447 \| 1400 \| 0.9057 \| -10.4815 \| -13.0628 \| 0.6799 \| 2.5812 \| -80.4145 \| -68.6682 \| -1.5090 \| -1.6053 \| \| 0.9857 \| 0.9582 \| 1420 \| 0.9436 \| -11.0222 \| -13.7067 \| 0.6799 \| 2.6845 \| -82.0244 \| -70.0199 \| -1.5194 \| -1.6173 \| \| 0.6843 \| 0.9717 \| 1440 \| 0.9204 \| -10.7774 \| -13.5143 \| 0.6890 \| 2.7369 \| -81.5432 \| -69.4077 \| -1.5514 \| -1.6515 \| \| 0.2613 \| 0.9852 \| 1460 \| 0.9273 \| -9.9918 \| -12.8030 \| 0.7195 \| 2.8111 \| -79.7650 \| -67.4439 \| -1.6466 \| -1.7508 \| \| 0.3424 \| 0.9987 \| 1480 \| 0.9197 \| -10.1599 \| -13.0496 \| 0.7317 \| 2.8898 \| -80.3817 \| -67.8640 \| -1.6001 \| -1.7011 \| \| 0.0159 \| 1.0121 \| 1500 \| 0.9066 \| -10.9814 \| -13.9689 \| 0.7104 \| 2.9876 \| -82.6799 \| -69.9178 \| -1.5478 \| -1.6470 \| \| 0.0904 \| 1.0256 \| 1520 \| 0.9502 \| -11.8251 \| -14.9332 \| 0.6890 \| 3.1082 \| -85.0906 \| -72.0270 \| -1.5372 \| -1.6373 \| \| 0.2315 \| 1.0391 \| 1540 \| 1.0112 \| -11.8334 \| -15.0245 \| 0.6860 \| 3.1911 \| -85.3188 \| -72.0478 \| -1.6008 \| -1.7058 \| \| 0.0342 \| 1.0526 \| 1560 \| 0.9967 \| -11.1427 \| -14.1633 \| 0.6890 \| 3.0207 \| -83.1659 \| -70.3210 \| -1.6113 \| -1.7178 \| \| 0.0064 \| 1.0661 \| 1580 \| 1.0481 \| -11.4773 \| -14.7219 \| 0.6829 \| 3.2446 \| -84.5622 \| -71.1575 \| -1.5987 \| -1.7054 \| \| 0.0033 \| 1.0796 \| 1600 \| 1.1210 \| -12.2242 \| -15.6388 \| 0.7012 \| 3.4146 \| -86.8546 \| -73.0249 \| -1.6062 \| -1.7153 \| \| 0.1378 \| 1.0931 \| 1620 \| 1.1277 \| -12.4237 \| -15.8631 \| 0.6921 \| 3.4394 \| -87.4153 \| -73.5235 \| -1.6179 \| -1.7290 \| \| 0.2035 \| 1.1066 \| 1640 \| 1.1075 \| -12.8847 \| -16.3152 \| 0.6829 \| 3.4305 \| -88.5455 \| -74.6761 \| -1.5511 \| -1.6598 \| \| 0.0557 \| 1.1201 \| 1660 \| 1.0968 \| -13.2081 \| -16.6482 \| 0.6951 \| 3.4401 \| -89.3780 \| -75.4845 \| -1.5382 \| -1.6452 \| \| 0.122 \| 1.1336 \| 1680 \| 1.1390 \| -13.9819 \| -17.5185 \| 0.6921 \| 3.5365 \| -91.5537 \| -77.4192 \| -1.5434 \| -1.6507 \| \| 0.0428 \| 1.1471 \| 1700 \| 1.1320 \| -13.7777 \| -17.4473 \| 0.6982 \| 3.6696 \| -91.3759 \| -76.9085 \| -1.5807 \| -1.6893 \| \| 0.0581 \| 1.1606 \| 1720 \| 1.0983 \| -13.4786 \| -17.0365 \| 0.6921 \| 3.5579 \| -90.3487 \| -76.1608 \| -1.5582 \| -1.6651 \| \| 0.1992 \| 1.1741 \| 1740 \| 1.0698 \| -12.8339 \| -16.3672 \| 0.7134 \| 3.5333 \| -88.6756 \| -74.5492 \| -1.5737 \| -1.6836 \| \| 0.071 \| 1.1876 \| 1760 \| 1.0480 \| -12.0544 \| -15.5962 \| 0.7317 \| 3.5419 \| -86.7481 \| -72.6002 \| -1.6338 \| -1.7458 \| \| 0.0108 \| 1.2011 \| 1780 \| 1.0268 \| -11.8384 \| -15.3437 \| 0.7165 \| 3.5053 \| -86.1168 \| -72.0604 \| -1.6277 \| -1.7393 \| \| 0.0801 \| 1.2146 \| 1800 \| 1.0598 \| -12.2066 \| -15.7010 \| 0.7104 \| 3.4944 \| -87.0100 \| -72.9808 \| -1.6141 \| -1.7276 \| \| 0.0836 \| 1.2281 \| 1820 \| 1.0594 \| -12.0331 \| -15.5395 \| 0.7134 \| 3.5063 \| -86.6063 \| -72.5472 \| -1.6553 \| -1.7724 \| \| 0.0139 \| 1.2416 \| 1840 \| 1.0408 \| -12.3600 \| -15.9404 \| 0.7104 \| 3.5804 \| -87.6086 \| -73.3644 \| -1.6319 \| -1.7466 \| \| 0.0904 \| 1.2551 \| 1860 \| 1.0267 \| -12.3787 \| -15.9427 \| 0.7195 \| 3.5639 \| -87.6143 \| -73.4112 \| -1.5969 \| -1.7107 \| \| 0.026 \| 1.2686 \| 1880 \| 1.0205 \| -12.1565 \| -15.7590 \| 0.7287 \| 3.6025 \| -87.1552 \| -72.8556 \| -1.6318 \| -1.7470 \| \| 0.1758 \| 1.2821 \| 1900 \| 1.0080 \| -12.2911 \| -15.8883 \| 0.7409 \| 3.5972 \| -87.4783 \| -73.1920 \| -1.5983 \| -1.7118 \| \| 0.1073 \| 1.2955 \| 1920 \| 0.9944 \| -12.1375 \| -15.6729 \| 0.7378 \| 3.5353 \| -86.9397 \| -72.8082 \| -1.5929 \| -1.7055 \| \| 0.0058 \| 1.3090 \| 1940 \| 0.9897 \| -12.1333 \| -15.7358 \| 0.7256 \| 3.6025 \| -87.0970 \| -72.7975 \| -1.5996 \| -1.7144 \| \| 0.037 \| 1.3225 \| 1960 \| 1.0029 \| -12.1735 \| -15.8088 \| 0.7226 \| 3.6353 \| -87.2796 \| -72.8982 \| -1.6083 \| -1.7235 \| \| 0.0776 \| 1.3360 \| 1980 \| 0.9931 \| -12.1034 \| -15.7661 \| 0.7226 \| 3.6627 \| -87.1728 \| -72.7229 \| -1.6330 \| -1.7497 \| \| 0.1384 \| 1.3495 \| 2000 \| 1.0111 \| -12.6287 \| -16.2924 \| 0.7348 \| 3.6637 \| -88.4887 \| -74.0362 \| -1.6025 \| -1.7182 \| \| 0.0677 \| 1.3630 \| 2020 \| 0.9950 \| -12.4365 \| -16.0824 \| 0.7195 \| 3.6459 \| -87.9635 \| -73.5556 \| -1.6083 \| -1.7233 \| \| 0.1444 \| 1.3765 \| 2040 \| 0.9950 \| -12.5211 \| -16.1195 \| 0.7378 \| 3.5985 \| -88.0564 \| -73.7670 \| -1.5715 \| -1.6856 \| \| 0.141 \| 1.3900 \| 2060 \| 0.9995 \| -12.4347 \| -16.0570 \| 0.7409 \| 3.6223 \| -87.9001 \| -73.5510 \| -1.5604 \| -1.6745 \| \| 0.0546 \| 1.4035 \| 2080 \| 1.0177 \| -12.5394 \| -16.2762 \| 0.7348 \| 3.7368 \| -88.4481 \| -73.8128 \| -1.5829 \| -1.6974 \| \| 0.0527 \| 1.4170 \| 2100 \| 1.0193 \| -12.3944 \| -16.2886 \| 0.7317 \| 3.8942 \| -88.4790 \| -73.4502 \| -1.6122 \| -1.7292 \| \| 0.0131 \| 1.4305 \| 2120 \| 1.0186 \| -12.3113 \| -16.2280 \| 0.7256 \| 3.9167 \| -88.3275 \| -73.2426 \| -1.6353 \| -1.7536 \| \| 0.0053 \| 1.4440 \| 2140 \| 1.0259 \| -12.1242 \| -15.9879 \| 0.7287 \| 3.8638 \| -87.7274 \| -72.7747 \| -1.6805 \| -1.8002 \| \| 0.0374 \| 1.4575 \| 2160 \| 1.0307 \| -12.0642 \| -15.9428 \| 0.7165 \| 3.8787 \| -87.6147 \| -72.6248 \| -1.7024 \| -1.8229 \| \| 0.1171 \| 1.4710 \| 2180 \| 1.0215 \| -12.1510 \| -16.0341 \| 0.7317 \| 3.8831 \| -87.8427 \| -72.8418 \| -1.6942 \| -1.8149 \| \| 0.5026 \| 1.4845 \| 2200 \| 1.0103 \| -12.2493 \| -16.0741 \| 0.7256 \| 3.8248 \| -87.9428 \| -73.0876 \| -1.6660 \| -1.7852 \| \| 0.0387 \| 1.4980 \| 2220 \| 1.0127 \| -12.4050 \| -16.2108 \| 0.7439 \| 3.8058 \| -88.2845 \| -73.4769 \| -1.6343 \| -1.7529 \| \| 0.0975 \| 1.5115 \| 2240 \| 1.0065 \| -12.2549 \| -16.0439 \| 0.7378 \| 3.7890 \| -87.8673 \| -73.1015 \| -1.6191 \| -1.7373 \| \| 0.1411 \| 1.5250 \| 2260 \| 0.9879 \| -12.1216 \| -15.9283 \| 0.7439 \| 3.8067 \| -87.5783 \| -72.7683 \| -1.5930 \| -1.7110 \| \| 0.0151 \| 1.5385 \| 2280 \| 0.9783 \| -12.0521 \| -15.7560 \| 0.7378 \| 3.7039 \| -87.1475 \| -72.5945 \| -1.5685 \| -1.6848 \| \| 0.3175 \| 1.5520 \| 2300 \| 0.9711 \| -12.0960 \| -15.7896 \| 0.7317 \| 3.6936 \| -87.2316 \| -72.7043 \| -1.5593 \| -1.6750 \| \| 0.0208 \| 1.5655 \| 2320 \| 0.9767 \| -12.0860 \| -15.8236 \| 0.7317 \| 3.7376 \| -87.3165 \| -72.6793 \| -1.5798 \| -1.6965 \| \| 0.3457 \| 1.5789 \| 2340 \| 0.9810 \| -12.0582 \| -15.8484 \| 0.7348 \| 3.7902 \| -87.3786 \| -72.6098 \| -1.5878 \| -1.7058 \| \| 0.0218 \| 1.5924 \| 2360 \| 0.9762 \| -11.9217 \| -15.6974 \| 0.7348 \| 3.7757 \| -87.0011 \| -72.2685 \| -1.5899 \| -1.7076 \| \| 0.0608 \| 1.6059 \| 2380 \| 0.9714 \| -11.7987 \| -15.6008 \| 0.7378 \| 3.8021 \| -86.7595 \| -71.9610 \| -1.6042 \| -1.7225 \| \| 0.0403 \| 1.6194 \| 2400 \| 0.9840 \| -11.9556 \| -15.8211 \| 0.7165 \| 3.8655 \| -87.3103 \| -72.3534 \| -1.6113 \| -1.7303 \| \| 0.0087 \| 1.6329 \| 2420 \| 0.9856 \| -11.9833 \| -15.8917 \| 0.7256 \| 3.9083 \| -87.4867 \| -72.4226 \| -1.6188 \| -1.7377 \| \| 0.0136 \| 1.6464 \| 2440 \| 0.9859 \| -12.0038 \| -15.9341 \| 0.7317 \| 3.9303 \| -87.5929 \| -72.4739 \| -1.6266 \| -1.7461 \| \| 0.0442 \| 1.6599 \| 2460 \| 0.9883 \| -11.9939 \| -15.8957 \| 0.7287 \| 3.9018 \| -87.4968 \| -72.4491 \| -1.6189 \| -1.7381 \| \| 0.0802 \| 1.6734 \| 2480 \| 0.9901 \| -12.0417 \| -15.9432 \| 0.7287 \| 3.9016 \| -87.6157 \| -72.5685 \| -1.6140 \| -1.7332 \| \| 0.2235 \| 1.6869 \| 2500 \| 0.9885 \| -12.0629 \| -15.9355 \| 0.7378 \| 3.8726 \| -87.5963 \| -72.6217 \| -1.6067 \| -1.7250 \| \| 0.0092 \| 1.7004 \| 2520 \| 0.9912 \| -12.0570 \| -15.9357 \| 0.7378 \| 3.8787 \| -87.5968 \| -72.6067 \| -1.6083 \| -1.7264 \| \| 0.196 \| 1.7139 \| 2540 \| 0.9976 \| -11.9530 \| -15.8323 \| 0.7317 \| 3.8793 \| -87.3383 \| -72.3469 \| -1.6304 \| -1.7496 \| \| 0.245 \| 1.7274 \| 2560 \| 0.9921 \| -11.8814 \| -15.7651 \| 0.7409 \| 3.8836 \| -87.1703 \| -72.1679 \| -1.6256 \| -1.7441 \| \| 0.1165 \| 1.7409 \| 2580 \| 0.9906 \| -11.7657 \| -15.6467 \| 0.7409 \| 3.8809 \| -86.8743 \| -71.8787 \| -1.6346 \| -1.7536 \| \| 0.1034 \| 1.7544 \| 2600 \| 0.9913 \| -11.7350 \| -15.6016 \| 0.7409 \| 3.8666 \| -86.7616 \| -71.8018 \| -1.6349 \| -1.7536 \| \| 0.1384 \| 1.7679 \| 2620 \| 0.9894 \| -11.7153 \| -15.6048 \| 0.7378 \| 3.8895 \| -86.7695 \| -71.7526 \| -1.6365 \| -1.7554 \| \| 0.0303 \| 1.7814 \| 2640 \| 0.9888 \| -11.7269 \| -15.6329 \| 0.7378 \| 3.9060 \| -86.8397 \| -71.7815 \| -1.6295 \| -1.7482 \| \| 0.018 \| 1.7949 \| 2660 \| 0.9908 \| -11.7559 \| -15.6637 \| 0.7409 \| 3.9079 \| -86.9169 \| -71.8540 \| -1.6315 \| -1.7504 \| \| 0.0331 \| 1.8084 \| 2680 \| 0.9917 \| -11.7786 \| -15.6818 \| 0.7348 \| 3.9032 \| -86.9621 \| -71.9108 \| -1.6366 \| -1.7558 \| \| 0.0441 \| 1.8219 \| 2700 \| 0.9898 \| -11.7755 \| -15.6798 \| 0.7348 \| 3.9042 \| -86.9570 \| -71.9031 \| -1.6334 \| -1.7525 \| \| 0.0333 \| 1.8354 \| 2720 \| 0.9943 \| -11.8093 \| -15.7139 \| 0.7409 \| 3.9046 \| -87.0423 \| -71.9875 \| -1.6390 \| -1.7586 \| \| 0.0242 \| 1.8489 \| 2740 \| 0.9900 \| -11.7825 \| -15.7045 \| 0.7348 \| 3.9220 \| -87.0188 \| -71.9207 \| -1.6348 \| -1.7538 \| \| 0.0559 \| 1.8623 \| 2760 \| 0.9903 \| -11.7933 \| -15.6898 \| 0.7287 \| 3.8965 \| -86.9822 \| -71.9477 \| -1.6353 \| -1.7548 \| \| 0.1334 \| 1.8758 \| 2780 \| 0.9913 \| -11.7803 \| -15.6990 \| 0.7409 \| 3.9188 \| -87.0051 \| -71.9150 \| -1.6350 \| -1.7541 \| \| 0.0179 \| 1.8893 \| 2800 \| 0.9913 \| -11.8103 \| -15.7168 \| 0.7348 \| 3.9064 \| -87.0495 \| -71.9902 \| -1.6384 \| -1.7580 \| \| 0.0167 \| 1.9028 \| 2820 \| 0.9912 \| -11.8089 \| -15.7436 \| 0.7287 \| 3.9346 \| -87.1164 \| -71.9867 \| -1.6418 \| -1.7611 \| \| 0.0716 \| 1.9163 \| 2840 \| 0.9923 \| -11.7869 \| -15.7203 \| 0.7348 \| 3.9335 \| -87.0584 \| -71.9315 \| -1.6364 \| -1.7555 \| \| 0.0676 \| 1.9298 \| 2860 \| 0.9928 \| -11.7733 \| -15.7126 \| 0.7348 \| 3.9392 \| -87.0390 \| -71.8977 \| -1.6380 \| -1.7574 \| \| 0.0041 \| 1.9433 \| 2880 \| 0.9941 \| -11.8130 \| -15.7466 \| 0.7348 \| 3.9336 \| -87.1241 \| -71.9969 \| -1.6427 \| -1.7629 \| \| 0.0035 \| 1.9568 \| 2900 \| 0.9938 \| -11.8122 \| -15.7337 \| 0.7409 \| 3.9215 \| -87.0919 \| -71.9949 \| -1.6361 \| -1.7550 \| \| 0.0621 \| 1.9703 \| 2920 \| 0.9903 \| -11.8063 \| -15.7288 \| 0.7348 \| 3.9225 \| -87.0795 \| -71.9800 \| -1.6401 \| -1.7593 \| \| 0.199 \| 1.9838 \| 2940 \| 0.9954 \| -11.8101 \| -15.7246 \| 0.7409 \| 3.9145 \| -87.0691 \| -71.9895 \| -1.6363 \| -1.7553 \| \| 0.0343 \| 1.9973 \| 2960 \| 0.9920 \| -11.7921 \| -15.7484 \| 0.7378 \| 3.9563 \| -87.1286 \| -71.9447 \| -1.6381 \| -1.7573 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.1+cu121 - Datasets 3.5.0 - Tokenizers 0.20.3
dimasik2987/7cf0a181-b116-4f27-a7aa-75d071e0b874	dimasik2987	2025-05-29T13:58:44Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-135M-Instruct", "base_model:adapter:unsloth/SmolLM-135M-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-29T13:42:06Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-135M-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 7cf0a181-b116-4f27-a7aa-75d071e0b874 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: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/SmolLM-135M-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 43ce974b7dfb1840_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: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: dimasik2987/7cf0a181-b116-4f27-a7aa-75d071e0b874 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 12 mixed_precision: bf16 mlflow_experiment_name: /tmp/43ce974b7dfb1840_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 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: 16b233f9-9484-47ca-ba74-fbf737ad2794 wandb_project: s56-7 wandb_run: your_name wandb_runid: 16b233f9-9484-47ca-ba74-fbf737ad2794 warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 7cf0a181-b116-4f27-a7aa-75d071e0b874 This model is a fine-tuned version of [unsloth/SmolLM-135M-Instruct](https://huggingface.co/unsloth/SmolLM-135M-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8760 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 24 - 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: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.0597 \| 0.0001 \| 1 \| 3.3056 \| \| 3.2417 \| 0.0126 \| 250 \| 2.9526 \| \| 2.8604 \| 0.0251 \| 500 \| 2.8760 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Erland/vanilla-1.8B-4096-model-HQQ-2bit	Erland	2025-05-29T13:56:56Z	5	0	transformers	[ "transformers", "pytorch", "transformer", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "hqq", "region:us" ]	text-generation	2025-05-22T12:00:00Z	--- 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. (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]
bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF	bartowski	2025-05-29T13:56:16Z	0	10	null	[ "gguf", "text-generation", "base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "base_model:quantized:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-29T13:27:12Z	--- quantized_by: bartowski pipeline_tag: text-generation base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B license: mit base_model_relation: quantized --- ## Llamacpp imatrix Quantizations of DeepSeek-R1-0528-Qwen3-8B by deepseek-ai Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5524">b5524</a> for quantization. Original model: https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) Run them in [LM Studio](https://lmstudio.ai/) Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project ## Prompt format ``` <｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Split \| Description \| \| -------- \| ---------- \| --------- \| ----- \| ----------- \| \| [DeepSeek-R1-0528-Qwen3-8B-bf16.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-bf16.gguf) \| bf16 \| 16.39GB \| false \| Full BF16 weights. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q8_0.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q8_0.gguf) \| Q8_0 \| 8.71GB \| false \| Extremely high quality, generally unneeded but max available quant. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q6_K_L.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q6_K_L.gguf) \| Q6_K_L \| 7.03GB \| false \| Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q6_K.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q6_K.gguf) \| Q6_K \| 6.73GB \| false \| Very high quality, near perfect, recommended. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q5_K_L.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q5_K_L.gguf) \| Q5_K_L \| 6.24GB \| false \| Uses Q8_0 for embed and output weights. High quality, recommended. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q5_K_M.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q5_K_M.gguf) \| Q5_K_M \| 5.85GB \| false \| High quality, recommended. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q5_K_S.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q5_K_S.gguf) \| Q5_K_S \| 5.72GB \| false \| High quality, recommended. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q4_K_L.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q4_K_L.gguf) \| Q4_K_L \| 5.49GB \| false \| Uses Q8_0 for embed and output weights. Good quality, recommended. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q4_1.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q4_1.gguf) \| Q4_1 \| 5.25GB \| false \| Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q4_K_M.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q4_K_M.gguf) \| Q4_K_M \| 5.03GB \| false \| Good quality, default size for most use cases, recommended. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q3_K_XL.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q3_K_XL.gguf) \| Q3_K_XL \| 4.98GB \| false \| Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q4_K_S.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q4_K_S.gguf) \| Q4_K_S \| 4.80GB \| false \| Slightly lower quality with more space savings, recommended. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q4_0.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q4_0.gguf) \| Q4_0 \| 4.79GB \| false \| Legacy format, offers online repacking for ARM and AVX CPU inference. \| \| [DeepSeek-R1-0528-Qwen3-8B-IQ4_NL.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-IQ4_NL.gguf) \| IQ4_NL \| 4.79GB \| false \| Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. \| \| [DeepSeek-R1-0528-Qwen3-8B-IQ4_XS.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-IQ4_XS.gguf) \| IQ4_XS \| 4.56GB \| false \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q3_K_L.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q3_K_L.gguf) \| Q3_K_L \| 4.43GB \| false \| Lower quality but usable, good for low RAM availability. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q3_K_M.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q3_K_M.gguf) \| Q3_K_M \| 4.12GB \| false \| Low quality. \| \| [DeepSeek-R1-0528-Qwen3-8B-IQ3_M.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-IQ3_M.gguf) \| IQ3_M \| 3.90GB \| false \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q2_K_L.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q2_K_L.gguf) \| Q2_K_L \| 3.89GB \| false \| Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q3_K_S.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q3_K_S.gguf) \| Q3_K_S \| 3.77GB \| false \| Low quality, not recommended. \| \| [DeepSeek-R1-0528-Qwen3-8B-IQ3_XS.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-IQ3_XS.gguf) \| IQ3_XS \| 3.63GB \| false \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [DeepSeek-R1-0528-Qwen3-8B-IQ3_XXS.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-IQ3_XXS.gguf) \| IQ3_XXS \| 3.37GB \| false \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [DeepSeek-R1-0528-Qwen3-8B-Q2_K.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q2_K.gguf) \| Q2_K \| 3.28GB \| false \| Very low quality but surprisingly usable. \| \| [DeepSeek-R1-0528-Qwen3-8B-IQ2_M.gguf](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF/blob/main/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-IQ2_M.gguf) \| IQ2_M \| 3.05GB \| false \| Relatively low quality, uses SOTA techniques to be surprisingly usable. \| ## Embed/output weights Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. ## Downloading using huggingface-cli <details> <summary>Click to view download instructions</summary> First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF --include "deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-GGUF --include "deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q8_0/*" --local-dir ./ ``` You can either specify a new local-dir (deepseek-ai_DeepSeek-R1-0528-Qwen3-8B-Q8_0) or download them all in place (./) </details> ## ARM/AVX information Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass. Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly. As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0. Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase. <details> <summary>Click to view Q4_0_X_X information (deprecated</summary> I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking. <details> <summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary> \| model \| size \| params \| backend \| threads \| test \| t/s \| % (vs Q4_0) \| \| ------------------------------ \| ---------: \| ---------: \| ---------- \| ------: \| ------------: \| -------------------: \|-------------: \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| pp512 \| 204.03 ± 1.03 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| pp1024 \| 282.92 ± 0.19 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| pp2048 \| 259.49 ± 0.44 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| tg128 \| 39.12 ± 0.27 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| tg256 \| 39.31 ± 0.69 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| tg512 \| 40.52 ± 0.03 \| 100% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| pp512 \| 301.02 ± 1.74 \| 147% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| pp1024 \| 287.23 ± 0.20 \| 101% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| pp2048 \| 262.77 ± 1.81 \| 101% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| tg128 \| 18.80 ± 0.99 \| 48% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| tg256 \| 24.46 ± 3.04 \| 83% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| tg512 \| 36.32 ± 3.59 \| 90% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| pp512 \| 271.71 ± 3.53 \| 133% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| pp1024 \| 279.86 ± 45.63 \| 100% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| pp2048 \| 320.77 ± 5.00 \| 124% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| tg128 \| 43.51 ± 0.05 \| 111% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| tg256 \| 43.35 ± 0.09 \| 110% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| tg512 \| 42.60 ± 0.31 \| 105% \| Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation </details> </details> ## Which file should I choose? <details> <summary>Click here for details</summary> A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. </details> ## Credits Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset. Thank you ZeroWw for the inspiration to experiment with embed/output. Thank you to LM Studio for sponsoring my work. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
Hsianchengfun/merged_model_WOQ_epoch681	Hsianchengfun	2025-05-29T13:55:53Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T13:53:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
deepmaster/Template0	deepmaster	2025-05-29T13:54:14Z	0	0	null	[ "region:us" ]	null	2025-05-29T08:48:19Z	# Container Template for SoundsRight Subnet Miners This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively. This container has only been tested with Ubuntu 24.04 and CUDA 12.6. It may run on other configurations, but it is not guaranteed. To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt. Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html). Verify that the CDI specification was done correctly with: ``` $ nvidia-ctk cdi list ``` You should see this in your output: ``` nvidia.com/gpu=all nvidia.com/gpu=0 ``` If you are running podman as root, run the following command to start the container: Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` If you are running the container rootless, there are a few more changes to make: First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters: ``` [nvidia-container-cli] no-cgroups = true [nvidia-container-runtime] debug = "/tmp/nvidia-container-runtime.log" ``` You can also run the following command to achieve the same result: ``` $ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place ``` Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` Running the container will spin up an API with the following endpoints: 1. `/status/` : Communicates API status 2. `/prepare/` : Download model checkpoint and initialize model 3. `/upload-audio/` : Upload audio files, save to noisy audio directory 4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory 5. `/download-enhanced/` : Download enhanced audio files By default the API will use host `0.0.0.0` and port `6500`. ### References 1. Welker, Simon; Richter, Julius; Gerkmann, Timo Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain. Proceedings of Interspeech 2022, 2022, pp. 2928–2932. [DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653) 2. Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo Speech Enhancement and Dereverberation with Diffusion-based Generative Models. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, 2023, pp. 2351–2364. [DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241) 3. Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation. Proceedings of ISCA Interspeech, 2024, pp. 4873–4877.
jruaechalar/cartaBajo2	jruaechalar	2025-05-29T13:49:05Z	0	0	diffusers	[ "diffusers", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-05-29T12:03:29Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
zxny/Taxi-v3	zxny	2025-05-29T13:48:18Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-29T13:48:18Z	--- license: apache-2.0 ---
jellecali8/ali-speaker-embedding-dataset	jellecali8	2025-05-29T13:48:08Z	0	0	speechbrain	[ "speechbrain", "tts", "speaker-embedding", "somali", "vits", "speecht5", "feature-extraction", "so", "base_model:speechbrain/spkrec-ecapa-voxceleb", "base_model:finetune:speechbrain/spkrec-ecapa-voxceleb", "license:mit", "region:us" ]	feature-extraction	2025-05-29T09:56:14Z	--- license: mit language: so tags: - tts - speaker-embedding - somali - speechbrain - vits - speecht5 library_name: speechbrain base_model: speechbrain/spkrec-ecapa-voxceleb pipeline_tag: feature-extraction --- # Ali Speaker Embedding Dataset This dataset contains a PyTorch `.pt` file that represents a speaker embedding for the Somali male speaker Ali. The embedding was generated using the [`speechbrain/spkrec-ecapa-voxceleb`](https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb) speaker recognition model from over 300 audio clips of the speaker's voice. ## Dataset Details - File: `Ali_speaker_embedding.pt` - Format: PyTorch tensor (`.pt`) - Embedding Size: 192-dimensional - Language: Somali (`so`) - Gender: Male - Audio Source: 300 high-quality `.wav` files from speaker Ali - Sample Rate: 16kHz ## Usage Example ```python import torch # Load the embedding embedding = torch.load("Ali_speaker_embedding.pt")

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