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Yuhan123/ppo-reading-level-preschool-1-steps-10000-epoch-999-best-eval-score-0.901	Yuhan123	2025-05-01T18:00:24Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T17:57:36Z	--- 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]
drtestnet/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stalking_bold_magpie	drtestnet	2025-05-01T17:59:58Z	10	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am stalking bold magpie", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-03T23:07:20Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stalking_bold_magpie tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am stalking bold magpie - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stalking_bold_magpie 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="drtestnet/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stalking_bold_magpie", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
Millise/new-gpu-model	Millise	2025-05-01T17:59:22Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-01T17:59:22Z	--- license: artistic-2.0 ---
Mariag73/xiaco-flux	Mariag73	2025-05-01T17:55:40Z	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	2025-05-01T17:13:16Z	--- 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: xiaco --- # Xiaco Flux <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `xiaco` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "xiaco", "lora_weights": "https://huggingface.co/Mariag73/xiaco-flux/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Mariag73/xiaco-flux', weight_name='lora.safetensors') image = pipeline('xiaco').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: 1402 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Mariag73/xiaco-flux/discussions) to add images that show off what you’ve made with this LoRA.
mmnga/ELYZA-Thinking-1.0-Qwen-32B-gguf	mmnga	2025-05-01T17:46:42Z	117	0	null	[ "gguf", "en", "ja", "dataset:TFMC/imatrix-dataset-for-japanese-llm", "base_model:elyza/ELYZA-Thinking-1.0-Qwen-32B", "base_model:quantized:elyza/ELYZA-Thinking-1.0-Qwen-32B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-01T07:48:10Z	--- license: apache-2.0 language: - en - ja datasets: - TFMC/imatrix-dataset-for-japanese-llm base_model: - elyza/ELYZA-Thinking-1.0-Qwen-32B --- # ELYZA-Thinking-1.0-Qwen-32B-gguf [elyzaさんが公開しているELYZA-Thinking-1.0-Qwen-32B](https://huggingface.co/elyza/ELYZA-Thinking-1.0-Qwen-32B)のggufフォーマット変換版です。 imatrixのデータは[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)を使用して作成しました。 ## Usage ``` git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build -DGGML_CUDA=ON cmake --build build --config Release build/bin/llama-cli -m 'ELYZA-Thinking-1.0-Qwen-32B-gguf' -n 128 -c 128 -p 'あなたはプロの料理人です。レシピを教えて' -cnv ```
Yaggoooooo/SecretariaMR	Yaggoooooo	2025-05-01T17:45:15Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-01T17:45:15Z	--- license: creativeml-openrail-m ---
Aluba/NVIDIA_SUPERV1_19	Aluba	2025-05-01T17:44:52Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-01T16:50:31Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
ashikns/Phi-3-mini-4k-instruct-onnx-web	ashikns	2025-05-01T17:43:05Z	0	0	transformers.js	[ "transformers.js", "onnx", "phi3", "text-generation", "ONNX", "ONNXRuntime", "ONNXRuntimeWeb", "transformers", "nlp", "conversational", "custom_code", "license:mit", "region:us" ]	text-generation	2025-05-01T17:12:29Z	--- license: mit pipeline_tag: text-generation tags: - ONNX - ONNXRuntime - ONNXRuntimeWeb - phi3 - transformers.js - transformers - nlp - conversational - custom_code inference: false --- # Phi-3 Mini-4K-Instruct ONNX model for in-browser inference <!-- Provide a quick summary of what the model is/does. --> Running Phi3-mini-4K entirely in the browser! Check out this [demo](https://guschmue.github.io/ort-webgpu/chat/index.html). This repository hosts the optimized Web version of ONNX Phi-3-mini-4k-instruct model to accelerate inference in the browser with ONNX Runtime Web. [The Phi-3-Mini-4K-Instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters. ## How to run [ONNX Runtime Web](https://onnxruntime.ai/docs/tutorials/web/build-web-app.html) is a JavaScript library to enable web developers to deploy machine learning models directly in web browsers, offering multiple backends leveraging hardware acceleration. WebGPU backend is recommended to run Phi-3-mini efficiently. Here is an [E2E example](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/js/chat) for running this optimized Phi3-mini-4K for the web, with ONNX Runtime harnessing WebGPU. Supported devices and browser with WebGPU: Chrome 113+ and Edge 113+ for Mac, Windows, ChromeOS, and Chrome 121+ for Android. Pls visit [here](https://github.com/gpuweb/gpuweb/wiki/Implementation-Status#safari-in-progress) for tracking WebGPU support in browsers ## Performance Metrics Performance vary between GPUs. The more powerful the GPU, the faster the speed. On a NVIDIA GeForce RTX 4090: ~42 tokens/second ## Additional Details To obtain other optimized Phi3-mini-4k ONNX models for server platforms, Windows, Linux, Mac desktops, and mobile, please visit [Phi-3-mini-4k-instruct onnx model](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx). The model differences in the web version compared to other versions: 1. the model is fp16 with int4 block quantization for weights 2. the 'logits' output is fp32 3. the model uses MHA instead of GQA 4. onnx and external data file need to stay below 2GB to be cacheable in chromium To optimize a fine-tuned Phi3-mini-4k model to run with ONNX Runtime Web, please follow [this Olive example](https://github.com/microsoft/Olive/tree/main/examples/phi3). [Olive](https://github.com/microsoft/OLive) is an easy-to-use model optimization tool for generating an optimized ONNX model to efficiently run with ONNX Runtime across platforms. ## Model Description - Developed by: Microsoft - Model type: ONNX - Inference Language(s) (NLP): JavaScript - License: MIT - Model Description: This is the web version of the Phi-3 Mini-4K-Instruct model for ONNX Runtime inference. ## Model Card Contact guschmue, qining
Yuhan123/ppo-reading-level-grad-1-steps-10000-epoch-999-best-eval-score-0.452	Yuhan123	2025-05-01T17:36:31Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T17:33:40Z	--- 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]
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Yuhan123/ppo-cn-RM-reading-level-grad-1-steps-10000-epoch-999-best-eval-score-0.407	Yuhan123	2025-05-01T17:33:09Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T17:30:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Aluba/NVIDIA_SUPERV1_17	Aluba	2025-05-01T17:31:22Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-01T16:50:14Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Aluba/NVIDIA_SUPERV1_9	Aluba	2025-05-01T17:30:27Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-01T16:46:07Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
mek63/cimbom33	mek63	2025-05-01T17:27:03Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-01T17:27:01Z	--- license: apache-2.0 ---
Ahmed12121231312312312/Blip2fineTune	Ahmed12121231312312312	2025-05-01T17:15:18Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-01T17:12:42Z	--- 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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Tutorial-Sophie-Rain-Spiderman-Video/Sophie.Rain.Spiderman.Video.Official	Tutorial-Sophie-Rain-Spiderman-Video	2025-05-01T17:13:39Z	0	0	null	[ "region:us" ]	null	2025-05-01T17:11:43Z	<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> 03 seconds ago L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter . . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter
Keltezaa/getphatFLUXReality_v4	Keltezaa	2025-05-01T17:08:58Z	4	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", "license:cc-by-nc-nd-4.0", "region:us" ]	text-to-image	2025-04-29T17:37:28Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: cc-by-nc-nd-4.0 --- # getphatFLUXReality_v4 <Gallery /> ## Model description FLUX Reality XXX v4 ## Download model Weights for this model are available in Safetensors format. [Download](/Keltezaa/getphatFLUXReality_v4/tree/main) them in the Files & versions tab.
mradermacher/quantum-circuit-qubo-3B-GGUF	mradermacher	2025-05-01T17:07:00Z	187	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "trl", "sft", "quantum", "qasm", "en", "dataset:linuzj/graph-data-quantum-tokenized_sft", "base_model:linuzj/quantum-circuit-qubo-3B", "base_model:quantized:linuzj/quantum-circuit-qubo-3B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-11T18:04:29Z	--- base_model: linuzj/quantum-circuit-qubo-3B datasets: - linuzj/graph-data-quantum-tokenized_sft language: - en library_name: transformers license: mit model_name: quantum-circuit-qubo-3B quantized_by: mradermacher tags: - generated_from_trainer - trl - sft - quantum - qasm --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/linuzj/quantum-circuit-qubo-3B <!-- 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/quantum-circuit-qubo-3B-GGUF/resolve/main/quantum-circuit-qubo-3B.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-3B-GGUF/resolve/main/quantum-circuit-qubo-3B.Q3_K_S.gguf) \| Q3_K_S \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-3B-GGUF/resolve/main/quantum-circuit-qubo-3B.Q3_K_M.gguf) \| Q3_K_M \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-3B-GGUF/resolve/main/quantum-circuit-qubo-3B.Q3_K_L.gguf) \| Q3_K_L \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-3B-GGUF/resolve/main/quantum-circuit-qubo-3B.IQ4_XS.gguf) \| IQ4_XS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-3B-GGUF/resolve/main/quantum-circuit-qubo-3B.Q4_K_S.gguf) \| Q4_K_S \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-3B-GGUF/resolve/main/quantum-circuit-qubo-3B.Q4_K_M.gguf) \| Q4_K_M \| 2.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-3B-GGUF/resolve/main/quantum-circuit-qubo-3B.Q5_K_S.gguf) \| Q5_K_S \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-3B-GGUF/resolve/main/quantum-circuit-qubo-3B.Q5_K_M.gguf) \| Q5_K_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-3B-GGUF/resolve/main/quantum-circuit-qubo-3B.Q6_K.gguf) \| Q6_K \| 2.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-3B-GGUF/resolve/main/quantum-circuit-qubo-3B.Q8_0.gguf) \| Q8_0 \| 3.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/quantum-circuit-qubo-3B-GGUF/resolve/main/quantum-circuit-qubo-3B.f16.gguf) \| f16 \| 6.9 \| 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 -->
memevis/swim5	memevis	2025-05-01T17:01:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T17:00:23Z	--- 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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Yuhan123/ppo-reading-level-12th-1-steps-10000-epoch-999-best-eval-score-0.327	Yuhan123	2025-05-01T16:59:35Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T16:56:40Z	--- 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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Yuhan123/ppo-reading-level-full-question-7th-1-steps-10000-epoch-999-best-eval-score-0.426	Yuhan123	2025-05-01T16:56:07Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T16:53:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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PLAYERSH/JPOreplicate	PLAYERSH	2025-05-01T16:54:10Z	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	2025-05-01T16:30:53Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Justindoor --- # Jporeplicate <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 `Justindoor` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Justindoor", "lora_weights": "https://huggingface.co/PLAYERSH/JPOreplicate/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('PLAYERSH/JPOreplicate', weight_name='lora.safetensors') image = pipeline('Justindoor').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: 1500 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/PLAYERSH/JPOreplicate/discussions) to add images that show off what you’ve made with this LoRA.
evgenyz/ppo-CartPole-v1-cleanRL	evgenyz	2025-05-01T16:40:22Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-05-01T13:14:37Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 7.62 +/- 57.80 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 1000000 'learning_rate': 0.0003 'num_envs': 16 'num_steps': 2048 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 10 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.0 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'evgenyz/ppo-CartPole-v1-cleanRL' 'batch_size': 32768 'minibatch_size': 8192} ```
harrykeeran12/radiology_error_qwen2.5	harrykeeran12	2025-05-01T16:39:01Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-29T18:44:49Z	--- base_model: unsloth/qwen2.5-7b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: harrykeeran12 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-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)
xcheng20/stable-diffusion-painting-style-v1	xcheng20	2025-05-01T16:38:25Z	0	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "fine-tuned", "art-style", "paintings", "custom-style", "text-to-image", "en", "dataset:custom-artist-dataset", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:apache-2.0", "region:us" ]	text-to-image	2025-05-01T13:39:20Z	--- license: apache-2.0 language: en pipeline_tag: text-to-image tags: - stable-diffusion - fine-tuned - art-style - paintings - custom-style - text-to-image base_model: CompVis/stable-diffusion-v1-4 datasets: - custom-artist-dataset library_name: diffusers --- # xcheng20/stable-diffusion-painting-style-v1 This model is a fine-tuned version of `CompVis/stable-diffusion-v1-4`, trained on a small but rich dataset of 198 unique paintings by a single painter. It is optimized for generating text-to-image outputs with a distinctive hand-painted aesthetic. This model card aims to document model details, usage recommendations, risks, and fine-tuning specifics in a transparent and reproducible manner. ## Model Description This model adapts Stable Diffusion v1.4 to replicate a specific human-created painting style. The training dataset includes 198 paintings in various themes and formats, designed to give the model a sense of color, brushwork, and composition typical to traditional art. It is suitable for generating stylized images with expressive, painterly textures. This model is for research purpose and discover how small dataset fine-tune can impact stable diffusion model behavior. - Developed by: xcheng20 - Funded by: Self-funded - Shared by: xcheng20 - Model type: Text-to-image generation - Language(s): en - License: Apache License 2.0 - Finetuned from model: [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) ## Model Sources - Repository: https://huggingface.co/xcheng20/stable-diffusion-painting-style-v1 ## Performance Comparison Below is a visual comparison between images generated by this fine-tuned model (`xcheng20/stable-diffusion-painting-style-v1`) and the base model (`CompVis/stable-diffusion-v1-4`) using the same prompts. \| Prompt \| Base Model Output \| Fine-tuned Model Output \| \|--------\|--------------------\|--------------------------\| \| "Two very detailed owls with yellow eyes" \| ![Base Model](images/base-owl.png) \| ![Fine-tuned](images/fine-tune-owl.png) \| \| "A phenix painted with white watercolor in the black background" \| ![Base Model](images/base-bird.png) \| ![Fine-tuned](images/fine-tune-bird.png) \| \| "A modern city landscpae skyline in watercolor" \| ![Base Model](images/base-city.png) \| ![Fine-tuned](images/fine-tune-city.png) \| ## Direct Use This model is intended for artistic text-to-image generation. Prompt examples include: - "a peaceful cabin in the woods, painterly style" - "a surreal dreamscape in soft brushstrokes" It is especially useful for artists, illustrators, and designers seeking an aesthetic similar to traditional hand-painted works. ## Downstream Use - Artistic draft generation - Custom stylized prompt-to-image tools - Inspiration for illustration and concept art workflows ## Out-of-Scope Use - Not suited for realistic portrait generation - Should not be used for any NSFW, violent, or biased content - Not recommended for medical, legal, or factual content generation ## Bias, Risks, and Limitations This model may not generalize well outside the stylistic patterns present in the dataset. It could reflect unintentional biases of the source style or create unrealistic outputs under complex prompts. ## Recommendations - Avoid prompts involving sensitive content - Use with human review in artistic workflows - Not intended for factual accuracy or realism ## How to Get Started with the Model Option A: Download stable_diffusion_loader.py from the "Files and versions" tab, and run the code below: ```python from stable_diffusion_loader import load_custom_pipeline, generate_image pipe = load_custom_pipeline("./fine-tuned-model") image = generate_image(pipe, "Two very detailed owls with yellow eyes") image.show() ``` Option B: Clone the Github project
radm/Qwen2.5-32B-simpo-LoRA	radm	2025-05-01T16:35:26Z	2	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:IlyaGusev/saiga_preferences", "dataset:40umov/dostoevsky", "dataset:Vikhrmodels/gutenpromax", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:adapter:Qwen/Qwen2.5-32B-Instruct", "license:other", "region:us" ]	null	2024-11-20T06:00:32Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: Qwen/Qwen2.5-32B-Instruct model-index: - name: Qwen2.5-32B-simpo-LoRA results: [] language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara datasets: - IlyaGusev/saiga_preferences - 40umov/dostoevsky - Vikhrmodels/gutenpromax --- <!-- 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. --> # radm_Qwen2.5-32B-simpo-LoRA This model is a fine-tuned version of [../models/Qwen2.5-32B-Instruct](https://huggingface.co/../models/Qwen2.5-32B-Instruct) on the custom dataset. Full model (FP8): [radm/Qwen2.5-32B-simpo-FP8](https://huggingface.co/radm/Qwen2.5-32B-simpo-FP8) ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-07 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 16 - num_epochs: 1.0 ### Training results ![image/png](https://huggingface.co/radm/Qwen2.5-32B-simpo-LoRA/resolve/main/training_rewards_accuracies.png) ![image/png](https://huggingface.co/radm/Qwen2.5-32B-simpo-LoRA/resolve/main/training_loss.png) ### Framework versions - PEFT 0.11.1 - Transformers 4.43.4 - Pytorch 2.4.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
kostiantynk1205/44b730e7-637e-4ccd-a996-deed1c2da3ba	kostiantynk1205	2025-05-01T16:30:34Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]	null	2025-05-01T16:30:11Z	--- library_name: peft tags: - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: kostiantynk1205/44b730e7-637e-4ccd-a996-deed1c2da3ba 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. --> # kostiantynk1205/44b730e7-637e-4ccd-a996-deed1c2da3ba This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0102 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
memevis/swim4	memevis	2025-05-01T16:21:58Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T16:21:15Z	--- 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]
Fuqi-10/FireDETECT	Fuqi-10	2025-05-01T16:20:14Z	0	0	null	[ "region:us" ]	null	2025-05-01T16:19:51Z	--- title: Fire Detection (Temperature) emoji: 🔥 colorFrom: red colorTo: yellow sdk: gradio sdk_version: "3.39.0" app_file: app.py pinned: false --- # Temperature-Based Fire Detection Model A `RandomForestClassifier` model to detect fire using temperature sensor data. ## Usage ```python import joblib model = joblib.load("fire_detection_model.pkl") prediction = model.predict([[temperature_in_celsius]]) # Returns 1 (Fire) or 0 (Normal)
Antonnn11/Dyhvdj	Antonnn11	2025-05-01T16:18:49Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-01T16:18:49Z	--- license: apache-2.0 ---
jethrowang/whisper-tiny_tat-esc_exp_nr_0.5_cc_0.5_embeds	jethrowang	2025-05-01T16:14:47Z	16	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zh", "dataset:formospeech/tat_asr_aligned", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-19T17:14:20Z	--- library_name: transformers language: - zh license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - formospeech/tat_asr_aligned model-index: - name: Whisper Tiny Taiwanese (exp_nr_0.5_cc_0.5_embeds) results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Tiny Taiwanese (exp_nr_0.5_cc_0.5_embeds) This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the TAT ASR Aligned dataset. It achieves the following results on the evaluation set: - Loss: 2.2774 - Cer: 41.7092 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 32 - 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 - lr_scheduler_warmup_steps: 681 - training_steps: 6810 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.4532 \| 0.9985 \| 681 \| 1.3229 \| 45.0006 \| \| 0.2812 \| 1.9971 \| 1362 \| 1.3009 \| 47.7935 \| \| 0.1813 \| 2.9956 \| 2043 \| 1.2902 \| 45.8631 \| \| 0.119 \| 3.9941 \| 2724 \| 1.3410 \| 45.0435 \| \| 0.0751 \| 4.9927 \| 3405 \| 1.4026 \| 43.7097 \| \| 0.0409 \| 5.9912 \| 4086 \| 1.6134 \| 44.5456 \| \| 0.0231 \| 6.9897 \| 4767 \| 1.7609 \| 42.9457 \| \| 0.0094 \| 7.9883 \| 5448 \| 1.9361 \| 42.7805 \| \| 0.0026 \| 8.9868 \| 6129 \| 2.1500 \| 41.6526 \| \| 0.0005 \| 9.9853 \| 6810 \| 2.2774 \| 41.7092 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.0.0.post304 - Datasets 3.3.2 - Tokenizers 0.21.0
OumaymaELBIACH/Results_biomistral_cadec_v5	OumaymaELBIACH	2025-05-01T16:13:06Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:BioMistral/BioMistral-7B", "base_model:finetune:BioMistral/BioMistral-7B", "endpoints_compatible", "region:us" ]	null	2025-05-01T16:12:58Z	--- base_model: BioMistral/BioMistral-7B library_name: transformers model_name: Results_biomistral_cadec_v5 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Results_biomistral_cadec_v5 This model is a fine-tuned version of [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B). 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="OumaymaELBIACH/Results_biomistral_cadec_v5", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.1 - 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}} } ```
sapna-shah-kumari-seen-uk/video.sapna.shah.viral.video.original.here	sapna-shah-kumari-seen-uk	2025-05-01T16:12:27Z	0	0	null	[ "region:us" ]	null	2025-05-01T16:10:40Z	<animated-image data-catalyst=""><a href="https://alltvsteam.com/viral-video/?v=news-es-tvdf" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> sapna-shah-kumari-seen-uk/video.sapna.shah.viral.video.original.here
FlareRebellion/DarkHazard-v1.2-24b	FlareRebellion	2025-05-01T16:11:50Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:PocketDoc/Dans-PersonalityEngine-V1.2.0-24b", "base_model:merge:PocketDoc/Dans-PersonalityEngine-V1.2.0-24b", "base_model:ReadyArt/Broken-Tutu-24B", "base_model:merge:ReadyArt/Broken-Tutu-24B", "base_model:TheDrummer/Cydonia-24B-v2.1", "base_model:merge:TheDrummer/Cydonia-24B-v2.1", "base_model:aixonlab/Eurydice-24b-v2", "base_model:merge:aixonlab/Eurydice-24b-v2", "base_model:arcee-ai/Arcee-Blitz", "base_model:merge:arcee-ai/Arcee-Blitz", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T13:54:17Z	--- base_model: - aixonlab/Eurydice-24b-v2 - ReadyArt/Broken-Tutu-24B - TheDrummer/Cydonia-24B-v2.1 - PocketDoc/Dans-PersonalityEngine-V1.2.0-24b - arcee-ai/Arcee-Blitz library_name: transformers tags: - mergekit - merge --- # DarkHazard-v1.1-24b This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Inspiration This merge was inspired by Yoesph/Haphazard-v1.1-24b ### Changelog v1.2 * replaced Yoesph/Haphazard-v1.1-24b with model: TheDrummer/Cydonia-24B-v2.1 * replaced ReadyArt/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B with ReadyArt/Broken-Tutu-24B ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [arcee-ai/Arcee-Blitz](https://huggingface.co/arcee-ai/Arcee-Blitz) as a base. ### Models Merged The following models were included in the merge: * [aixonlab/Eurydice-24b-v2](https://huggingface.co/aixonlab/Eurydice-24b-v2) * [ReadyArt/Broken-Tutu-24B](https://huggingface.co/ReadyArt/Broken-Tutu-24B) * [TheDrummer/Cydonia-24B-v2.1](https://huggingface.co/TheDrummer/Cydonia-24B-v2.1) * [PocketDoc/Dans-PersonalityEngine-V1.2.0-24b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.2.0-24b) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: arcee-ai/Arcee-Blitz merge_method: model_stock dtype: bfloat16 models: - model: aixonlab/Eurydice-24b-v2 # storytelling / RP - model: TheDrummer/Cydonia-24B-v2.1 # uncensor - model: ReadyArt/Broken-Tutu-24B # uncensor + nsfw + Cydonia - model: PocketDoc/Dans-PersonalityEngine-V1.2.0-24b # Prompt Adherence ```
kate1130/kluebert-roberta-bullying-classifier	kate1130	2025-05-01T16:10:10Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-01T16:08:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Thiago-dias26/NUVVI20	Thiago-dias26	2025-05-01T16:04:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-01T16:04:32Z	--- license: apache-2.0 ---
Vardis/medical-LM	Vardis	2025-05-01T16:03:22Z	0	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-01T16:02:00Z	--- 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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masani/SFT_parity_Qwen2-0.5B_epoch_5_global_step_20	masani	2025-05-01T15:56:42Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T15:56:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Yuhan123/ppo-cn-RM-reading-level-preschool-1-steps-10000-epoch-999-best-eval-score-0.474	Yuhan123	2025-05-01T15:53:22Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T15:50:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LquenS/Ovis2-4B-eager	LquenS	2025-05-01T15:52:58Z	0	0	transformers	[ "transformers", "safetensors", "ovis", "text-generation", "MLLM", "image-text-to-text", "conversational", "custom_code", "en", "zh", "dataset:AIDC-AI/Ovis-dataset", "arxiv:2405.20797", "license:apache-2.0", "autotrain_compatible", "region:us" ]	image-text-to-text	2025-05-01T15:52:00Z	--- license: apache-2.0 datasets: - AIDC-AI/Ovis-dataset library_name: transformers tags: - MLLM pipeline_tag: image-text-to-text language: - en - zh --- # Ovis2-4B <div align="center"> <img src=https://cdn-uploads.huggingface.co/production/uploads/637aebed7ce76c3b834cea37/3IK823BZ8w-mz_QfeYkDn.png width="30%"/> </div> ## Introduction [GitHub](https://github.com/AIDC-AI/Ovis) \| [Paper](https://arxiv.org/abs/2405.20797) We are pleased to announce the release of Ovis2, our latest advancement in multi-modal large language models (MLLMs). Ovis2 inherits the innovative architectural design of the Ovis series, aimed at structurally aligning visual and textual embeddings. As the successor to Ovis1.6, Ovis2 incorporates significant improvements in both dataset curation and training methodologies. Key Features: - Small Model Performance: Optimized training strategies enable small-scale models to achieve higher capability density, demonstrating cross-tier leading advantages. - Enhanced Reasoning Capabilities: Significantly strengthens Chain-of-Thought (CoT) reasoning abilities through the combination of instruction tuning and preference learning. - Video and Multi-Image Processing: Video and multi-image data are incorporated into training to enhance the ability to handle complex visual information across frames and images. - Multilingual Support and OCR: Enhances multilingual OCR beyond English and Chinese and improves structured data extraction from complex visual elements like tables and charts. <div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/637aebed7ce76c3b834cea37/XB-vgzDL6FshrSNGyZvzc.png" width="100%" /> </div> ## Model Zoo \| Ovis MLLMs \| ViT \| LLM \| Model Weights \| Demo \| \|:-----------\|:-----------------------:\|:---------------------:\|:-------------------------------------------------------:\|:--------------------------------------------------------:\| \| Ovis2-1B \| aimv2-large-patch14-448 \| Qwen2.5-0.5B-Instruct \| [Huggingface](https://huggingface.co/AIDC-AI/Ovis2-1B) \| [Space](https://huggingface.co/spaces/AIDC-AI/Ovis2-1B) \| \| Ovis2-2B \| aimv2-large-patch14-448 \| Qwen2.5-1.5B-Instruct \| [Huggingface](https://huggingface.co/AIDC-AI/Ovis2-2B) \| [Space](https://huggingface.co/spaces/AIDC-AI/Ovis2-2B) \| \| Ovis2-4B \| aimv2-huge-patch14-448 \| Qwen2.5-3B-Instruct \| [Huggingface](https://huggingface.co/AIDC-AI/Ovis2-4B) \| [Space](https://huggingface.co/spaces/AIDC-AI/Ovis2-4B) \| \| Ovis2-8B \| aimv2-huge-patch14-448 \| Qwen2.5-7B-Instruct \| [Huggingface](https://huggingface.co/AIDC-AI/Ovis2-8B) \| [Space](https://huggingface.co/spaces/AIDC-AI/Ovis2-8B) \| \| Ovis2-16B \| aimv2-huge-patch14-448 \| Qwen2.5-14B-Instruct \| [Huggingface](https://huggingface.co/AIDC-AI/Ovis2-16B) \| [Space](https://huggingface.co/spaces/AIDC-AI/Ovis2-16B) \| \| Ovis2-34B \| aimv2-1B-patch14-448 \| Qwen2.5-32B-Instruct \| [Huggingface](https://huggingface.co/AIDC-AI/Ovis2-34B) \| - \| ## Performance We use [VLMEvalKit](https://github.com/open-compass/VLMEvalKit), as employed in the OpenCompass [multimodal](https://rank.opencompass.org.cn/leaderboard-multimodal) and [reasoning](https://rank.opencompass.org.cn/leaderboard-multimodal-reasoning) leaderboard, to evaluate Ovis2. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a8a837959448ef5500ce5/M1XRFbeNbfe1lEvt9WF-j.png) ### Image Benchmark \| Benchmark \| Qwen2.5-VL-7B \| InternVL2.5-8B-MPO \| MiniCPM-o-2.6 \| Ovis1.6-9B \| InternVL2.5-4B-MPO \| Ovis2-4B \| Ovis2-8B \| \|:-----------------------------\|:---------------:\|:--------------------:\|:---------------:\|:------------:\|:--------------------:\|:----------:\|:----------:\| \| MMBench-V1.1<sub>test</sub> \| 82.6 \| 82.0 \| 80.6 \| 80.5 \| 77.8 \| 81.4 \| 83.6 \| \| MMStar \| 64.1 \| 65.2 \| 63.3 \| 62.9 \| 61 \| 61.9 \| 64.6 \| \| MMMU<sub>val</sub> \| 56.2 \| 54.8 \| 50.9 \| 55 \| 51.8 \| 49.0 \| 57.4 \| \| MathVista<sub>testmini</sub> \| 65.8 \| 67.9 \| 73.3 \| 67.3 \| 64.1 \| 69.6 \| 71.8 \| \| HallusionBench \| 56.3 \| 51.7 \| 51.1 \| 52.2 \| 47.5 \| 53.8 \| 56.3 \| \| AI2D \| 84.1 \| 84.5 \| 86.1 \| 84.4 \| 81.5 \| 85.7 \| 86.6 \| \| OCRBench \| 87.7 \| 88.2 \| 88.9 \| 83 \| 87.9 \| 91.1 \| 89.1 \| \| MMVet \| 66.6 \| 68.1 \| 67.2 \| 65 \| 66 \| 65.5 \| 65.1 \| \| MMBench<sub>test</sub> \| 83.4 \| 83.2 \| 83.2 \| 82.7 \| 79.6 \| 83.2 \| 84.9 \| \| MMT-Bench<sub>val</sub> \| 62.7 \| 62.5 \| 62.3 \| 64.9 \| 61.6 \| 65.2 \| 66.6 \| \| RealWorldQA \| 68.8 \| 71.1 \| 68.0 \| 70.7 \| 64.4 \| 71.1 \| 72.5 \| \| BLINK \| 56.1 \| 56.6 \| 53.9 \| 48.5 \| 50.6 \| 53.0 \| 54.3 \| \| QBench \| 77.9 \| 73.8 \| 78.7 \| 76.7 \| 71.5 \| 78.1 \| 78.9 \| \| ABench \| 75.6 \| 77.0 \| 77.5 \| 74.4 \| 75.9 \| 77.5 \| 76.4 \| \| MTVQA \| 28.5 \| 27.2 \| 23.1 \| 19.2 \| 28 \| 29.4 \| 29.7 \| ### Video Benchmark \| Benchmark \| Qwen2.5-VL-7B \| InternVL2.5-8B \| LLaVA-OV-7B \| InternVL2.5-4B \| Ovis2-4B \| Ovis2-8B \| \|:--------------------\|:-------------:\|:--------------:\|:------------------:\|:--------------:\|:---------:\|:-------------:\| \| VideoMME(wo/w-subs) \| 65.1/71.6 \| 64.2 / 66.9 \| 58.2/61.5 \| 62.3 / 63.6 \| 64.0/66.3 \| 68.0/71.6 \| \| MVBench \| 69.6 \| 72.0 \| 56.7 \| 71.6 \| 68.45 \| 68.15 \| \| MLVU(M-Avg/G-Avg) \| 70.2/- \| 68.9/- \| 64.7/- \| 68.3/- \| 70.8/4.23 \| 76.4/4.25 \| \| MMBench-Video \| 1.79 \| 1.68 \| - \| 1.73 \| 1.69 \| 1.85 \| \| TempCompass \| 71.7 \| - \| - \| - \| 67.02 \| 69.28 \| ## Usage Below is a code snippet demonstrating how to run Ovis with various input types. For additional usage instructions, including inference wrapper and Gradio UI, please refer to [Ovis GitHub](https://github.com/AIDC-AI/Ovis?tab=readme-ov-file#inference). ```bash pip install torch==2.4.0 transformers==4.46.2 numpy==1.25.0 pillow==10.3.0 pip install flash-attn==2.7.0.post2 --no-build-isolation ``` ```python import torch from PIL import Image from transformers import AutoModelForCausalLM # load model model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis2-4B", torch_dtype=torch.bfloat16, multimodal_max_length=32768, trust_remote_code=True).cuda() text_tokenizer = model.get_text_tokenizer() visual_tokenizer = model.get_visual_tokenizer() # single-image input image_path = '/data/images/example_1.jpg' images = [Image.open(image_path)] max_partition = 9 text = 'Describe the image.' query = f'<image>\n{text}' ## cot-style input # cot_suffix = "Provide a step-by-step solution to the problem, and conclude with 'the answer is' followed by the final solution." # image_path = '/data/images/example_1.jpg' # images = [Image.open(image_path)] # max_partition = 9 # text = "What's the area of the shape?" # query = f'<image>\n{text}\n{cot_suffix}' ## multiple-images input # image_paths = [ # '/data/images/example_1.jpg', # '/data/images/example_2.jpg', # '/data/images/example_3.jpg' # ] # images = [Image.open(image_path) for image_path in image_paths] # max_partition = 4 # text = 'Describe each image.' # query = '\n'.join([f'Image {i+1}: <image>' for i in range(len(images))]) + '\n' + text ## video input (require `pip install moviepy==1.0.3`) # from moviepy.editor import VideoFileClip # video_path = '/data/videos/example_1.mp4' # num_frames = 12 # max_partition = 1 # text = 'Describe the video.' # with VideoFileClip(video_path) as clip: # total_frames = int(clip.fps * clip.duration) # if total_frames <= num_frames: # sampled_indices = range(total_frames) # else: # stride = total_frames / num_frames # sampled_indices = [min(total_frames - 1, int((stride * i + stride * (i + 1)) / 2)) for i in range(num_frames)] # frames = [clip.get_frame(index / clip.fps) for index in sampled_indices] # frames = [Image.fromarray(frame, mode='RGB') for frame in frames] # images = frames # query = '\n'.join(['<image>'] * len(images)) + '\n' + text ## text-only input # images = [] # max_partition = None # text = 'Hello' # query = text # format conversation prompt, input_ids, pixel_values = model.preprocess_inputs(query, images, max_partition=max_partition) attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id) input_ids = input_ids.unsqueeze(0).to(device=model.device) attention_mask = attention_mask.unsqueeze(0).to(device=model.device) if pixel_values is not None: pixel_values = pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device) pixel_values = [pixel_values] # generate output with torch.inference_mode(): gen_kwargs = dict( max_new_tokens=1024, do_sample=False, top_p=None, top_k=None, temperature=None, repetition_penalty=None, eos_token_id=model.generation_config.eos_token_id, pad_token_id=text_tokenizer.pad_token_id, use_cache=True ) output_ids = model.generate(input_ids, pixel_values=pixel_values, attention_mask=attention_mask, gen_kwargs)[0] output = text_tokenizer.decode(output_ids, skip_special_tokens=True) print(f'Output:\n{output}') ``` <details> <summary>Batch Inference</summary> ```python import torch from PIL import Image from transformers import AutoModelForCausalLM # load model model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis2-4B", torch_dtype=torch.bfloat16, multimodal_max_length=32768, trust_remote_code=True).cuda() text_tokenizer = model.get_text_tokenizer() visual_tokenizer = model.get_visual_tokenizer() # preprocess inputs batch_inputs = [ ('/data/images/example_1.jpg', 'What colors dominate the image?'), ('/data/images/example_2.jpg', 'What objects are depicted in this image?'), ('/data/images/example_3.jpg', 'Is there any text in the image?') ] batch_input_ids = [] batch_attention_mask = [] batch_pixel_values = [] for image_path, text in batch_inputs: image = Image.open(image_path) query = f'<image>\n{text}' prompt, input_ids, pixel_values = model.preprocess_inputs(query, [image], max_partition=9) attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id) batch_input_ids.append(input_ids.to(device=model.device)) batch_attention_mask.append(attention_mask.to(device=model.device)) batch_pixel_values.append(pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)) batch_input_ids = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in batch_input_ids], batch_first=True, padding_value=0.0).flip(dims=[1]) batch_input_ids = batch_input_ids[:, -model.config.multimodal_max_length:] batch_attention_mask = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in batch_attention_mask], batch_first=True, padding_value=False).flip(dims=[1]) batch_attention_mask = batch_attention_mask[:, -model.config.multimodal_max_length:] # generate outputs with torch.inference_mode(): gen_kwargs = dict( max_new_tokens=1024, do_sample=False, top_p=None, top_k=None, temperature=None, repetition_penalty=None, eos_token_id=model.generation_config.eos_token_id, pad_token_id=text_tokenizer.pad_token_id, use_cache=True ) output_ids = model.generate(batch_input_ids, pixel_values=batch_pixel_values, attention_mask=batch_attention_mask, gen_kwargs) for i in range(len(batch_inputs)): output = text_tokenizer.decode(output_ids[i], skip_special_tokens=True) print(f'Output {i + 1}:\n{output}\n') ``` </details> ## Citation If you find Ovis useful, please consider citing the paper ``` @article{lu2024ovis, title={Ovis: Structural Embedding Alignment for Multimodal Large Language Model}, author={Shiyin Lu and Yang Li and Qing-Guo Chen and Zhao Xu and Weihua Luo and Kaifu Zhang and Han-Jia Ye}, year={2024}, journal={arXiv:2405.20797} } ``` ## License This project is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0.txt) (SPDX-License-Identifier: Apache-2.0). ## Disclaimer We used compliance-checking algorithms during the training process, to ensure the compliance of the trained model to the best of our ability. Due to the complexity of the data and the diversity of language model usage scenarios, we cannot guarantee that the model is completely free of copyright issues or improper content. If you believe anything infringes on your rights or generates improper content, please contact us, and we will promptly address the matter.
kumari-sapna-videoss-seen/video.sapna.shah.viral.video.original.here	kumari-sapna-videoss-seen	2025-05-01T15:49:48Z	0	0	null	[ "region:us" ]	null	2025-05-01T15:49:30Z	<animated-image data-catalyst=""><a href="https://alltvsteam.com/viral-video/?v=news-es-tvdf" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
kumari-sapna-videoss-seen/here.sapna.shah.viral.original.video	kumari-sapna-videoss-seen	2025-05-01T15:45:47Z	0	0	null	[ "region:us" ]	null	2025-05-01T15:45:16Z	<animated-image data-catalyst=""><a href="https://alltvsteam.com/viral-video/?v=news-es-tvdf" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
uygaraydin/psy-support-flant5	uygaraydin	2025-05-01T15:41:47Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-01T15:41:12Z	--- 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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Yuhan123/ppo-cn-RM-reading-level-preschool-1-steps-10000-epoch-999-best-eval-score-0.789	Yuhan123	2025-05-01T15:41:35Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T15:39:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AIEngineerYvar/mt5-small-finetuned-pubmed-summarization	AIEngineerYvar	2025-05-01T15:37:08Z	0	0	transformers	[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-01T14:45:32Z	--- library_name: transformers license: apache-2.0 base_model: google/mt5-small tags: - generated_from_keras_callback model-index: - name: AIEngineerYvar/mt5-small-finetuned-pubmed-summarization results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # AIEngineerYvar/mt5-small-finetuned-pubmed-summarization This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.4546 - Validation Loss: 2.9633 - Epoch: 3 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 3000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': np.float32(0.9), 'beta_2': np.float32(0.999), 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.4465 \| 2.9633 \| 0 \| \| 4.4467 \| 2.9633 \| 1 \| \| 4.4523 \| 2.9633 \| 2 \| \| 4.4546 \| 2.9633 \| 3 \| ### Framework versions - Transformers 4.51.3 - TensorFlow 2.18.0 - Datasets 3.5.1 - Tokenizers 0.21.1
joseiivb26/joannie	joseiivb26	2025-05-01T15:37:03Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-01T14:56:38Z	--- 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 ---
Yuhan123/ppo-cn-RM-reading-level-preschool-1-steps-10000-epoch-999-best-eval-score-0.263	Yuhan123	2025-05-01T15:35:34Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T15:33:02Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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duandongsheng/sd-class-butterflies-32	duandongsheng	2025-05-01T15:34:33Z	0	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2025-05-01T15:32:42Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('duandongsheng/sd-class-butterflies-32') image = pipeline().images[0] image ```
lokinfey/Phi-4-reasoning-mlx-int4	lokinfey	2025-05-01T15:32:36Z	0	0	mlx	[ "mlx", "safetensors", "phi3", "phi", "nlp", "math", "code", "chat", "conversational", "reasoning", "text-generation", "en", "base_model:microsoft/Phi-4-reasoning", "base_model:quantized:microsoft/Phi-4-reasoning", "license:mit", "4-bit", "region:us" ]	text-generation	2025-05-01T15:14:22Z	--- license: mit license_link: https://huggingface.co/microsoft/Phi-4-reasoning/resolve/main/LICENSE language: - en base_model: microsoft/Phi-4-reasoning pipeline_tag: text-generation tags: - phi - nlp - math - code - chat - conversational - reasoning - mlx inference: parameters: temperature: 0 widget: - messages: - role: user content: What is the derivative of x^2? library_name: mlx ---
azeem23/whisper-small-codeswitching-ArabicEnglish	azeem23	2025-05-01T15:32:05Z	21	1	null	[ "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "ar", "en", "dataset:MohamedRashad/arabic-english-code-switching", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:mit", "region:us" ]	automatic-speech-recognition	2025-04-26T14:10:41Z	--- license: mit datasets: - MohamedRashad/arabic-english-code-switching language: - ar - en base_model: - openai/whisper-small pipeline_tag: automatic-speech-recognition --- # Whisper finetuned for codeswitching in Arabic-English - Original Model [openai/whisper-small](https://huggingface.co/openai/whisper-small) - Dataset used: [MohamedRashad/arabic-english-code-switching](https://huggingface.co/datasets/MohamedRashad/arabic-english-code-switching)
sergioalves/d3d952a3-dd31-4a3d-abb3-c8bfb2854c20	sergioalves	2025-05-01T15:28:12Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:adapter:HuggingFaceH4/zephyr-7b-beta", "license:mit", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-01T14:30:02Z	--- library_name: peft license: mit base_model: HuggingFaceH4/zephyr-7b-beta tags: - axolotl - generated_from_trainer model-index: - name: d3d952a3-dd31-4a3d-abb3-c8bfb2854c20 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: true adapter: lora base_model: HuggingFaceH4/zephyr-7b-beta bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 51265aa9130bc4de_train_data.json ds_type: json format: custom path: /workspace/input_data/51265aa9130bc4de_train_data.json type: field_instruction: text field_output: title format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: 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: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: sergioalves/d3d952a3-dd31-4a3d-abb3-c8bfb2854c20 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/51265aa9130bc4de_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 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: 56363f08-3261-498d-973d-aa5bb4b807c6 wandb_project: s56-8 wandb_run: your_name wandb_runid: 56363f08-3261-498d-973d-aa5bb4b807c6 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # d3d952a3-dd31-4a3d-abb3-c8bfb2854c20 This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6292 ## 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: 8 - eval_batch_size: 8 - seed: 42 - 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: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5819 \| 0.0063 \| 200 \| 1.6292 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Siddharth63/Qwen3-8B-Base-AWQ	Siddharth63	2025-05-01T12:25:54Z	0	0	null	[ "safetensors", "qwen3", "license:apache-2.0", "4-bit", "awq", "region:us" ]	null	2025-05-01T09:24:09Z	--- license: apache-2.0 --- ``` git clone https://github.com/casper-hansen/AutoAWQ.git # latest source 2025-05-01 cd AutoAWQ pip install -e . ## go into AutoAWQ folder pip install --upgrade transformers ## FOR STREAMING from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer from awq.utils.utils import get_best_device device = get_best_device() quant_path = "Siddharth63/Qwen3-8B-base-AWQ" # path or HF repo for the AWQ checkpoint # ---------- load model & tokenizer ---------- model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True) tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # ---------- tokenise & generate ---------- input_ids = tokenizer("Atherosclerosis is", return_tensors="pt" ).input_ids.to(device) _ = model.generate( input_ids, streamer = streamer, max_new_tokens = 512, # full context window use_cache = True ) ## FOR NON_STREAMING from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer from awq.utils.utils import get_best_device device = get_best_device() quant_path = "Siddharth63/Qwen3-8B-base-AWQ" # path or HF repo for the AWQ checkpoint # ---------- load model & tokenizer ---------- model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True) tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True) input_ids = tokenizer( "Atherosclerosis is", return_tensors="pt" ).input_ids.to(device) # ---------- generate (blocking) ---------- output_ids = model.generate( input_ids, max_new_tokens=100, # or max_length / temperature / etc. use_cache=True # default; speeds up incremental decoding ) response = tokenizer.decode( output_ids[0], skip_special_tokens=True, # drop <\|im_start\|> tokens ) print("\n=== Model reply ===\n", response) ```
JJMack/pokemon_gen1_9_classifier	JJMack	2025-05-01T12:24:11Z	0	0	null	[ "safetensors", "vit", "videogames", "pokemon", "image-classification", "dataset:JJMack/pokemon-classification-gen1-9", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:cc-by-nc-sa-4.0", "region:us" ]	image-classification	2025-04-30T18:57:09Z	--- license: cc-by-nc-sa-4.0 datasets: - JJMack/pokemon-classification-gen1-9 base_model: - google/vit-base-patch16-224-in21k tags: - videogames - pokemon pipeline_tag: image-classification --- # Model Card: Pokemon Generation 1 through 9 Image Classifier ## Model Description The Fine-Tuned Vision Transformer (ViT) is a variant of the transformer encoder architecture, similar to BERT, that has been adapted for image classification tasks. This specific model, named "google/vit-base-patch16-224-in21k," is pre-trained on a substantial collection of images in a supervised manner, leveraging the ImageNet-21k dataset. The images in the pre-training dataset are resized to a resolution of 224x224 pixels, making it suitable for a wide range of image recognition tasks. The model was trained using an augmented dataset of JJMack/pokemon-classification-gen1-9, with 5 additional augmentend version of each image. This model was for me to learn how to fine tune a model and I am writing a LinkedIn Article series around the process. You can find the first link [Building a Real Pokédex - An AI Journey](https://www.linkedin.com/pulse/building-real-pok%C3%A9dex-ai-journey-jeremy-mack-jc3fc/?trackingId=zWK6TeRJ%2FXLAmv7BKZsQxA%3D%3D) ### Intended Uses - Pokemon Classification: The primary intended use of this model is for the classification of Pokemon images. ### How to use Here is how to use this model to classifiy an image based on 1 of 1025 pokemone: ```python # Use a pipeline as a high-level helper from PIL import Image from transformers import pipeline img = Image.open("<path_to_image_file>") classifier = pipeline("image-classification", model="JJMack/pokemon_gen1_9_classifier") classifier(img) ``` <hr> ``` markdown # Load model directly import torch from PIL import Image from transformers import AutoModelForImageClassification, ViTImageProcessor img = Image.open("<path_to_image_file>") model = AutoModelForImageClassification.from_pretrained("JJMack/pokemon_gen1_9_classifier") processor = ViTImageProcessor.from_pretrained('JJMack/pokemon_gen1_9_classifier') with torch.no_grad(): inputs = processor(images=img, return_tensors="pt") outputs = model(inputs) logits = outputs.logits predicted_label = logits.argmax(-1).item() model.config.id2label[predicted_label] ``` ### Limitations - Specialized Task Fine-Tuning**: While the model is adept at NSFW image classification, its performance may vary when applied to other tasks. - Users interested in employing this model for different tasks should explore fine-tuned versions available in the model hub for optimal results. ## Training Data The model's training data came from [Bulapedia](https://bulbapedia.bulbagarden.net/wiki/Main_Page). Each image of the training dataset was augmented 5 times with the following augments ``` - RandomHorizontalFlip(p=0.5), - RandomVerticalFlip(p=0.5), - RandomRotation(degrees=30), - ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2), - GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5)), - RandomAffine(degrees=0, translate=(0.1, 0.1)), - RandomPerspective(distortion_scale=0.5, p=0.5), - RandomGrayscale(p=0.2), ``` ### Training Stats ``` - 'eval_loss': 0.7451944351196289, - 'eval_accuracy': 0.9221343873517787, - 'eval_runtime': 39.6834, - 'eval_samples_per_second': 63.755, - 'eval_steps_per_second': 7.988 ``` <hr>
ail-sa/akshey_stockyplus_mid_fs_v1	ail-sa	2025-05-01T12:20:56Z	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	2025-05-01T11:45:10Z	--- 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: Sid --- # Akshey_Stockyplus_Mid_Fs_V1 <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 `Sid` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sid", "lora_weights": "https://huggingface.co/ail-sa/akshey_stockyplus_mid_fs_v1/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('ail-sa/akshey_stockyplus_mid_fs_v1', weight_name='lora.safetensors') image = pipeline('Sid').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/ail-sa/akshey_stockyplus_mid_fs_v1/discussions) to add images that show off what you’ve made with this LoRA.
AshProbably/medcot-llama3.2-3b-model	AshProbably	2025-05-01T12:19:24Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-25T18:46:15Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: AshProbably - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
alramil/Practica7distilbert	alramil	2025-05-01T12:09:32Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-01T12:09:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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kadriyeoz/edasfdsf	kadriyeoz	2025-05-01T12:09:26Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-01T12:09:26Z	--- license: artistic-2.0 ---
goosull/Llama-3.2-1B-ko-wiki-1	goosull	2025-05-01T12:07:32Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-01T09:08:12Z	--- base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: goosull - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-1B-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
wsbagnsv1/SkyReels-V2-T2V-14B-720P-GGUF	wsbagnsv1	2025-05-01T12:02:47Z	557	3	gguf	[ "gguf", "video", "video-generation", "image-to-video", "base_model:Skywork/SkyReels-V2-T2V-14B-720P", "base_model:quantized:Skywork/SkyReels-V2-T2V-14B-720P", "license:other", "region:us" ]	image-to-video	2025-04-24T22:45:37Z	--- license: other license_name: skywork-license license_link: LICENSE library_name: gguf base_model: - Skywork/SkyReels-V2-T2V-14B-720P tags: - video - video-generation pipeline_tag: image-to-video --- This is a direct GGUF conversion of [Skywork/SkyReels-V2-T2V-14B-720P](https://huggingface.co/Skywork/SkyReels-V2-T2V-14B-720P) All quants are created from the FP32 base file, though I only uploaded the Q8_0 and less, if you want the F16 or BF16 one I would upload it per request. The model files can be used with the [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) custom node. Place model files in `ComfyUI/models/unet` - see the GitHub readme for further install instructions. The VAE can be downloaded from [this repository by Kijai](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan2_1_VAE_bf16.safetensors) Please refer to [this chart](https://github.com/ggerganov/llama.cpp/blob/master/examples/perplexity/README.md#llama-3-8b-scoreboard) for a basic overview of quantization types. For conversion I used the conversion scripts from [city96](https://huggingface.co/city96)
bjw999/Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-huge_foraging_lion	bjw999	2025-05-01T12:00:59Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am huge foraging lion", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-32B-Instruct-bnb-4bit", "base_model:finetune:Gensyn/Qwen2.5-32B-Instruct-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-04-30T22:30:21Z	--- base_model: Gensyn/Qwen2.5-32B-Instruct-bnb-4bit library_name: transformers model_name: Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-huge_foraging_lion tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am huge foraging lion - unsloth - trl licence: license --- # Model Card for Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-huge_foraging_lion This model is a fine-tuned version of [Gensyn/Qwen2.5-32B-Instruct-bnb-4bit](https://huggingface.co/Gensyn/Qwen2.5-32B-Instruct-bnb-4bit). 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="bjw999/Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-huge_foraging_lion", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
iTroned/our_baseline	iTroned	2025-05-01T11:45:08Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-30T19:29:25Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: our_baseline 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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/wmr6bkgl) # our_baseline This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6746 - Accuracy Offensive: 0.8302 - F1 Macro Offensive: 0.7974 - F1 Weighted Offensive: 0.8335 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy Offensive \| F1 Macro Offensive \| F1 Weighted Offensive \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------------------:\|:------------------:\|:---------------------:\| \| 0.5679 \| 1.0 \| 3310 \| 0.4300 \| 0.8256 \| 0.7816 \| 0.8249 \| \| 0.5444 \| 2.0 \| 6620 \| 0.5326 \| 0.8105 \| 0.7798 \| 0.8161 \| \| 0.5921 \| 3.0 \| 9930 \| 0.6707 \| 0.8430 \| 0.7904 \| 0.8368 \| \| 0.5228 \| 4.0 \| 13240 \| 0.6746 \| 0.8302 \| 0.7974 \| 0.8335 \| \| 0.447 \| 5.0 \| 16550 \| 0.7716 \| 0.8395 \| 0.7923 \| 0.8361 \| \| 0.3876 \| 6.0 \| 19860 \| 0.8714 \| 0.8302 \| 0.7932 \| 0.8319 \| \| 0.3087 \| 7.0 \| 23170 \| 1.0847 \| 0.8291 \| 0.7828 \| 0.8271 \| \| 0.3096 \| 8.0 \| 26480 \| 1.2290 \| 0.8105 \| 0.7736 \| 0.8140 \| ### Framework versions - Transformers 4.50.2 - Pytorch 2.6.0+cu124 - Datasets 3.0.1 - Tokenizers 0.21.1
fivedoctors/q-Taxi-v1-500	fivedoctors	2025-05-01T11:42:10Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-01T11:42:07Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v1-500 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.81 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="fivedoctors/q-Taxi-v1-500", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
skywalker290/results	skywalker290	2025-05-01T11:41:52Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-01T07:12:44Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0008 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.001 \| 1.0 \| 32531 \| 0.0010 \| \| 0.0008 \| 2.0 \| 65062 \| 0.0009 \| \| 0.0007 \| 3.0 \| 97593 \| 0.0008 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF	mradermacher	2025-05-01T11:24:32Z	0	0	transformers	[ "transformers", "gguf", "llm", "qwen3", "en", "zh", "base_model:Cylingo/Xinyuan-LLM-14B-0428", "base_model:quantized:Cylingo/Xinyuan-LLM-14B-0428", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-01T05:12:46Z	--- base_model: Cylingo/Xinyuan-LLM-14B-0428 language: - en - zh library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - llm - qwen3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Cylingo/Xinyuan-LLM-14B-0428 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-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/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 8.6 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-Q4_1.gguf) \| i1-Q4_1 \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xinyuan-LLM-14B-0428-i1-GGUF/resolve/main/Xinyuan-LLM-14B-0428.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
JQ1984/legalbert_gdpr_pretrained	JQ1984	2025-05-01T11:18:27Z	0	0	null	[ "safetensors", "bert", "legal", "question-answering", "en", "dataset:JQ1984/GDPRcasedata", "base_model:nlpaueb/legal-bert-base-uncased", "base_model:finetune:nlpaueb/legal-bert-base-uncased", "license:cc-by-nc-4.0", "region:us" ]	question-answering	2025-05-01T11:07:26Z	--- license: cc-by-nc-4.0 language: - en base_model: - nlpaueb/legal-bert-base-uncased tags: - legal datasets: - JQ1984/GDPRcasedata metrics: - accuracy pipeline_tag: question-answering --- # Legal-BERT (GDPR Pretrained Version) This model is based on [`nlpaueb/legal-bert-base-uncased`](https://huggingface.co/nlpaueb/legal-bert-base-uncased), and has been further pretrained on the full text of the [General Data Protection Regulation (GDPR)](https://eur-lex.europa.eu/eli/reg/2016/679/oj) to adapt it to privacy law and regulatory compliance scenarios. ## 🧠 What’s New? We adapted Legal-BERT through masked language modeling (MLM) on GDPR-specific language, enhancing the model’s understanding of: - Personal data protection terms - GDPR article structure - Typical compliance language and risk descriptions The training corpus includes official GDPR text, split into clean English sentences, formatted for MLM. ## 🔧 Intended Use This specialized model is best suited for: - GDPR compliance assistance - Legal document classification and clause matching - Privacy policy analysis - Regulatory question answering (when further fine-tuned) ## 💾 Training Details - Base model: `nlpaueb/legal-bert-base-uncased` - Task: Masked Language Modeling (MLM) - Corpus: Full official GDPR English text (~10,000+ sentences) - Epochs: 3 - Block size: 128 - Batch size: 16 - MLM Probability: 15% ## 🛠 How to Use ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JQ1984/legalbert_gdpr_pretrained") model = AutoModelForMaskedLM.from_pretrained("JQ1984/legalbert_gdpr_pretrained") # Example inputs = tokenizer("The data controller shall ensure that personal data is", return_tensors="pt") outputs = model(*inputs) ## References [Model Paper](https://arxiv.org/abs/xxxx.xxxxx)
aleegis/f0ed9dca-a916-4441-8ccb-323e6d4826af	aleegis	2025-05-01T11:12:54Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]	null	2025-05-01T09:57:46Z	--- library_name: peft license: llama2 base_model: codellama/CodeLlama-7b-hf tags: - axolotl - generated_from_trainer model-index: - name: f0ed9dca-a916-4441-8ccb-323e6d4826af 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 adapter: lora base_model: codellama/CodeLlama-7b-hf bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - a304f7b9d5e4a239_train_data.json ds_type: json format: custom path: /workspace/input_data/a304f7b9d5e4a239_train_data.json type: field_instruction: task field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: false group_by_length: false hub_model_id: aleegis/f0ed9dca-a916-4441-8ccb-323e6d4826af hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: null lora_alpha: 32 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true loraplus_lr_embedding: 1.0e-06 loraplus_lr_ratio: 16 lr_scheduler: cosine max_grad_norm: 1 max_steps: 1500 micro_batch_size: 2 mlflow_experiment_name: /tmp/a304f7b9d5e4a239_train_data.json model_type: AutoModelForCausalLM num_epochs: 200 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null save_total_limit: 10 saves_per_epoch: 0 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.0 wandb_entity: null wandb_mode: online wandb_name: e1b36927-fa78-414d-a25b-1043f85c3145 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: e1b36927-fa78-414d-a25b-1043f85c3145 warmup_steps: 100 weight_decay: 0 xformers_attention: null ``` </details><br> # f0ed9dca-a916-4441-8ccb-323e6d4826af This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1500 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Kevinjacques/Software	Kevinjacques	2025-05-01T11:08:36Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-01T11:08:28Z	--- license: apache-2.0 ---
Rinnnt/a2c-PandaReachDense-v3	Rinnnt	2025-05-01T11:07:20Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-01T11:03:22Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.17 +/- 0.07 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Triangle104/Rombo-LLM-V3.1-QWQ-32b-Q5_K_M-GGUF	Triangle104	2025-05-01T10:56:13Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:Rombo-Org/Rombo-LLM-V3.1-QWQ-32b", "base_model:quantized:Rombo-Org/Rombo-LLM-V3.1-QWQ-32b", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-01T10:53:31Z	--- base_model: Rombo-Org/Rombo-LLM-V3.1-QWQ-32b license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # Triangle104/Rombo-LLM-V3.1-QWQ-32b-Q5_K_M-GGUF This model was converted to GGUF format from [`Rombo-Org/Rombo-LLM-V3.1-QWQ-32b`](https://huggingface.co/Rombo-Org/Rombo-LLM-V3.1-QWQ-32b) 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/Rombo-Org/Rombo-LLM-V3.1-QWQ-32b) for more details on the model. --- Rombo-LLM-V3.1-QWQ-32b is a Continued Finetune model (Merge only) of (Qwen/QwQ-32B) and its base model (Qwen/Qwen2.5-32B). This merge is done to decrease catastrophic forgetting during finetuning, and increase overall performance of the model. The tokenizers are taken from the QwQ-32B for thinking capabilities. --- ## 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 Triangle104/Rombo-LLM-V3.1-QWQ-32b-Q5_K_M-GGUF --hf-file rombo-llm-v3.1-qwq-32b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Rombo-LLM-V3.1-QWQ-32b-Q5_K_M-GGUF --hf-file rombo-llm-v3.1-qwq-32b-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 Triangle104/Rombo-LLM-V3.1-QWQ-32b-Q5_K_M-GGUF --hf-file rombo-llm-v3.1-qwq-32b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Rombo-LLM-V3.1-QWQ-32b-Q5_K_M-GGUF --hf-file rombo-llm-v3.1-qwq-32b-q5_k_m.gguf -c 2048 ```
deswaq/juh98	deswaq	2025-05-01T10:46:43Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T10:43:43Z	--- 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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ma921/gpt2-large_h_dpo_imdb_noise40_epoch5	ma921	2025-05-01T10:42:22Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:ma921/gpt2-large-sft-imdb", "base_model:finetune:ma921/gpt2-large-sft-imdb", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T10:41:16Z	--- library_name: transformers license: mit base_model: ma921/gpt2-large-sft-imdb tags: - generated_from_trainer model-index: - name: gpt2-large_h_dpo_imdb_noise40_epoch5 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. --> # gpt2-large_h_dpo_imdb_noise40_epoch5 This model is a fine-tuned version of [ma921/gpt2-large-sft-imdb](https://huggingface.co/ma921/gpt2-large-sft-imdb) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
wandererupak/wave2vec-bert-flac-check20percent-finalllly	wandererupak	2025-05-01T10:39:23Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-01T10:39:11Z	--- 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. 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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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linhdzqua148/opus-mt-ja-en-railway	linhdzqua148	2025-05-01T10:27:58Z	0	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-01T03:48:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
llava-hf/llava-v1.6-mistral-7b-hf	llava-hf	2025-05-01T10:27:07Z	237,457	262	transformers	[ "transformers", "safetensors", "llava_next", "image-text-to-text", "vision", "conversational", "en", "arxiv:2310.03744", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-02-20T08:01:48Z	--- license: apache-2.0 tags: - vision - image-text-to-text language: - en pipeline_tag: image-text-to-text inference: true --- # LLaVa-Next, leveraging [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) as LLM The LLaVA-NeXT model was proposed in [LLaVA-NeXT: Improved reasoning, OCR, and world knowledge](https://llava-vl.github.io/blog/2024-01-30-llava-next/) by Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, Yong Jae Lee. LLaVa-NeXT (also called LLaVa-1.6) improves upon [LLaVa-1.5](https://huggingface.co/transformers/main/model_doc/llava.html) by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning. Disclaimer: The team releasing LLaVa-NeXT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description LLaVa combines a pre-trained large language model with a pre-trained vision encoder for multimodal chatbot use cases. LLaVA 1.6 improves on LLaVA 1.5 BY: - Using [Mistral-7B](https://mistral.ai/news/announcing-mistral-7b/) (for this checkpoint) and [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) which has better commercial licenses, and bilingual support - More diverse and high quality data mixture - Dynamic high resolution ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62441d1d9fdefb55a0b7d12c/FPshq08TKYD0e-qwPLDVO.png) ## Intended uses & limitations You can use the raw model for tasks like image captioning, visual question answering, multimodal chatbot use cases. See the [model hub](https://huggingface.co/models?search=llava-hf) to look for other versions on a task that interests you. ### How to use Here's the prompt template for this model but we recomment to use the chat templates to format the prompt with `processor.apply_chat_template()`. That will apply the correct template for a given checkpoint for you. ``` "[INST] <image>\nWhat is shown in this image? [/INST]" ``` To run the model with the `pipeline`, see the below example: ```python from transformers import pipeline pipe = pipeline("image-text-to-text", model="llava-hf/llava-v1.6-mistral-7b-hf") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"}, {"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"}, ], }, ] out = pipe(text=messages, max_new_tokens=20) print(out) >>> [{'input_text': [{'role': 'user', 'content': [{'type': 'image', 'url': 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'}, {'type': 'text', 'text': 'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'}]}], 'generated_text': 'Lava'}] ``` You can also load and use the model like following: ```python from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration import torch from PIL import Image import requests processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf") model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", torch_dtype=torch.float16, low_cpu_mem_usage=True) model.to("cuda:0") # prepare image and text prompt, using the appropriate prompt template url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true" image = Image.open(requests.get(url, stream=True).raw) # Define a chat history and use `apply_chat_template` to get correctly formatted prompt # Each value in "content" has to be a list of dicts with types ("text", "image") conversation = [ { "role": "user", "content": [ {"type": "text", "text": "What is shown in this image?"}, {"type": "image"}, ], }, ] prompt = processor.apply_chat_template(conversation, add_generation_prompt=True) inputs = processor(images=image, text=prompt, return_tensors="pt").to("cuda:0") # autoregressively complete prompt output = model.generate(inputs, max_new_tokens=100) print(processor.decode(output[0], skip_special_tokens=True)) ``` ----------- From transformers>=v4.48, you can also pass image url or local path to the conversation history, and let the chat template handle the rest. Chat template will load the image for you and return inputs in `torch.Tensor` which you can pass directly to `model.generate()` ```python messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}, {"type": "text", "text": "What is shown in this image?"}, ], }, ] inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt") output = model.generate(inputs, max_new_tokens=50) ``` ### Model optimization #### 4-bit quantization through `bitsandbytes` library First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with: ```diff model = LlavaNextForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, + load_in_4bit=True ) ``` #### Use Flash-Attention 2 to further speed-up generation First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with: ```diff model = LlavaNextForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, + use_flash_attention_2=True ).to(0) ``` ### BibTeX entry and citation info ```bibtex @misc{liu2023improved, title={Improved Baselines with Visual Instruction Tuning}, author={Haotian Liu and Chunyuan Li and Yuheng Li and Yong Jae Lee}, year={2023}, eprint={2310.03744}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
Siddharth63/Qwen3-4B-Base-4bit-Autoround-GPTQ-sym	Siddharth63	2025-05-01T10:26:13Z	0	0	null	[ "safetensors", "qwen3", "license:apache-2.0", "4-bit", "gptq", "region:us" ]	null	2025-05-01T09:02:30Z	--- license: apache-2.0 --- ``` from transformers import AutoModelForCausalLM, AutoTokenizer from auto_round import AutoRoundConfig ## must import for auto-round format quantized_model_path = "Siddharth63/Qwen3-4B-Base-4bit-Autoround-GPTQ-sym" model = AutoModelForCausalLM.from_pretrained(quantized_model_path, device_map="auto", torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained(quantized_model_path) text = "There is a girl who likes adventure," inputs = tokenizer(text, return_tensors="pt").to(model.device) print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50)[0])) ```
wandererupak/wave2vec-bert-flac-check20percent-finalll	wandererupak	2025-05-01T10:23:16Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-01T10:23:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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widicrypto/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scampering_spotted_sloth	widicrypto	2025-05-01T10:19:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am scampering spotted sloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T08:28:20Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scampering_spotted_sloth tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am scampering spotted sloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scampering_spotted_sloth 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="widicrypto/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scampering_spotted_sloth", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
stokemctoke/Alex-Jones_v01_F1D	stokemctoke	2025-05-01T10:10:10Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "ai-toolkit", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-01T10:07:25Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - ai-toolkit widget: - text: 4L3XJ0N35 a man playing chess at the park, bomb going off in the background output: url: samples/1746094008143__000003750_0.jpg - text: 4L3XJ0N35 a man holding a coffee cup, in a beanie, sitting at a cafe output: url: samples/1746094024110__000003750_1.jpg - text: 4L3XJ0N35 a man holding a sign that says, 'Stoke LoRA' output: url: samples/1746094040109__000003750_2.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: 4L3XJ0N35 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 --- # Alex-Jones_v01_F1D Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) <Gallery /> ## Trigger words You should use `4L3XJ0N35` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc. Weights for this model are available in Safetensors format. [Download](/stokemctoke/Alex-Jones_v01_F1D/tree/main) them in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('stokemctoke/Alex-Jones_v01_F1D', weight_name='Alex-Jones_v01_F1D.safetensors') image = pipeline('4L3XJ0N35 a man playing chess at the park, bomb going off in the background').images[0] image.save("my_image.png") ``` 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)
vertings6/68ecd706-b48c-415a-be08-d25c932eef87	vertings6	2025-05-01T10:06:33Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:budecosystem/genz-70b", "base_model:adapter:budecosystem/genz-70b", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-01T08:38:43Z	--- library_name: peft base_model: budecosystem/genz-70b tags: - axolotl - generated_from_trainer model-index: - name: 68ecd706-b48c-415a-be08-d25c932eef87 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: true adapter: lora base_model: budecosystem/genz-70b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - bf501704f719a312_train_data.json ds_type: json format: custom path: /workspace/input_data/bf501704f719a312_train_data.json type: field_instruction: problem field_output: solution format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 144 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: 0.5 group_by_length: false hub_model_id: vertings6/68ecd706-b48c-415a-be08-d25c932eef87 hub_repo: null hub_strategy: end hub_token: null learning_rate: 3.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 4 mixed_precision: bf16 mlflow_experiment_name: /tmp/bf501704f719a312_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 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: 2048 special_tokens: pad_token: </s> 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: 0062cdce-f91e-47e2-84bf-0eb3fc593b09 wandb_project: s56-32 wandb_run: your_name wandb_runid: 0062cdce-f91e-47e2-84bf-0eb3fc593b09 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 68ecd706-b48c-415a-be08-d25c932eef87 This model is a fine-tuned version of [budecosystem/genz-70b](https://huggingface.co/budecosystem/genz-70b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7640 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - 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: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6444 \| 0.1464 \| 200 \| 0.7640 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MinaMila/phi3_LoRa_ACSEmployment_2_ep6_22	MinaMila	2025-05-01T10:00:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-01T10:00:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
wandererupak/wave2vec-bert-flac-check20percent-finally	wandererupak	2025-05-01T09:59:21Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-01T09:59:20Z	--- 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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puhaloferega7/zxczxcv	puhaloferega7	2025-05-01T09:51:38Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-01T09:51:38Z	--- license: apache-2.0 ---
linagora/Llamipa	linagora	2025-05-01T09:50:05Z	0	3	null	[ "minecraft", "action prediction", "other", "en", "dataset:linagora/MinecraftStructuredDialogueCorpus", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "license:apache-2.0", "region:us" ]	other	2024-09-30T11:00:34Z	--- pipeline_tag: other tags: - minecraft - action prediction language: - en license: apache-2.0 datasets: - linagora/MinecraftStructuredDialogueCorpus base_model: - meta-llama/Llama-3.1-8B --- # Llamipa: An Incremental Discourse Parser Llamipa is Llama3-8B finetuned on the Minecraft Structured Dialogue Corpus (MSDC) https://huggingface.co/datasets/linagora/MinecraftStructuredDialogueCorpus. \| \| Link F1 \| Link+Rel F1\| \|----------------\|-------\|--------\| \|Llamipa + gold structure \| 0.9004 \| 0.8154 \| \|Llamipa + predicted structure (incremental) \| 0.8830 \| 0.7951 \| For a given speaker turn, Llamipa was trained to predict the discourse relations which connect the elementary units of the turn to the units of the previous dialogue turns, given the text of the previous dialogue turns and the previous discourse structure, or the relations that connect those turns. For training, the gold annotated structure was used. The model was then tested using gold structure, and gave state of the art results on the MSDC (see above table). However, for a discourse parser to be truly incremental, it should be able to predict the relations for each new turn using the structure it predicted in previous steps. We tested the model using its predicted structure and found the results were robust to this change. ### Model Description - Language(s) (NLP): English - Finetuned from model: Llama3-8B ### Running Llamipa #### Training from scratch The training data are provided in the `\data` folder. They contain a maximum context window of 15 elementary units (EDUs). For training parameters see the paper cited below. #### Reproducing test results The `\model` folder contains the adapters for the parser trained on Llama3-8B, as well as the scripts for generating structures using both gold (`parse_gold.py`) and predicted structure (`parse_incremental.py`). Be sure to use either the gold or incremental version of the test data, found in `\data`. #### Using Llamipa on new data In order to re-generate the Llamipa data from the original MSDC files, or to format new data to be parsed using Llamipa, we provide data formatting scripts and instructions in the `\bespoke` folder. #### Evaluation Get F1 scores using `\evaluation\evaluation.py`, and produce a friendlier version of Llamipa output using `\evaluation\output_formatter.py`. ### Citations Paper: https://aclanthology.org/2024.findings-emnlp.373/ Video: https://www.youtube.com/watch?v=yerUotx3QZY Please cite the EMNLP Findings paper if you use Llamipa in your work: ```bibtex @inproceedings{thompson-etal-2024-llamipa, title = "Llamipa: An Incremental Discourse Parser", author = "Thompson, Kate and Chaturvedi, Akshay and Hunter, Julie and Asher, Nicholas", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.findings-emnlp.373/", doi = "10.18653/v1/2024.findings-emnlp.373", pages = "6418--6430" } ``` ### Acknowledgements We acknowledge support from the National Interdisciplinary Artificial Intelligence Institute, ANITI (Artificial and Natural Intelligence Toulouse Institute), funded by the French ‘Investing for the Future–PIA3’ program under the Grant agreement ANR-19-PI3A-000. We also thank the ANR project COCOBOTS (ANR-21-FAI2-0005), the ANR/DGA project DISCUTER (ANR21-ASIA-0005), and the COCOPIL “Graine” project funded by the Région Occitanie of France. This work was granted access to the HPC resources of CALMIP supercomputing center under the allocation 2016-P23060.
fhaslam/Llama-3.2-1B-Financial-Sentiment38	fhaslam	2025-05-01T09:44:28Z	0	0	transformers	[ "transformers", "safetensors", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "license:llama3.2", "endpoints_compatible", "region:us" ]	text-generation	2025-05-01T09:44:26Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 3.2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy). #### Prohibited Uses We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. 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Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| \| Llama 3.2 Quantized (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 8k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. Out of Scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-1B-Instruct" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/" --local-dir Llama-3.2-1B-Instruct ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 240 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Llama 3.2 1B SpinQuant \| 1.7 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 3B SpinQuant \| 2.4 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 1B QLora \| 1.3k \| 0 \| 700 \| 0.381 \| 0 \| \| Llama 3.2 3B QLora \| 1.6k \| 0 \| 700 \| 0.461 \| 0 \| \| Total \| 833k \| 86k \| \| 240 \| 0 \| \\ The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B bf16 \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B bf16 \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 43.3 \| 47.3 \| 49.0 \| 63.4 \| 60.5 \| 62 \| 62.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 39.2 \| 40.9 \| 41.2 \| 40.1 \| 40.3 \| 40.8 \| 40.7 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 14.9 \| 16.7 \| 16.8 \| 19.0 \| 19.1 \| 19.2 \| 19.1 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| Avg(Prompt/Instruction acc Loose/Strict) \| 59.5 \| 51.5 \| 58.4 \| 55.6 \| 77.4 \| 73.9 \| 73.5 \| 75.9 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 33.1 \| 40.6 \| 46.5 \| 77.7 \| 72.9 \| 75.7 \| 77.9 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 20.5 \| 25.3 \| 31.0 \| 48.0 \| 44.2 \| 45.3 \| 49.2 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 54.3 \| 57 \| 60.7 \| 78.6 \| 75.6 \| 77.6 \| 77.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 25.9 \| 26.3 \| 25.9 \| 32.8 \| 32.8 \| 31.7 \| 33.9 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 38.1 \| 41.3 \| 41.5 \| 69.8 \| 66.3 \| 68 \| 66.3 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 14.3 \| 15.9 \| 23.7 \| 67.0 \| 53.4 \| 60.1 \| 63.5 \| 67.1 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 5.2 \| 9.6 \| 12.5 \| 34.3 \| 32.4 \| 31.5 \| 30.1 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| N/A \| N/A \| N/A \| 19.8 \| N/A \| N/A \| N/A \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| N/A \| N/A \| N/A \| 63.3 \| N/A \| N/A \| N/A \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| N/A \| N/A \| N/A \| 84.7 \| N/A \| N/A \| N/A \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 13.7 \| 18.2 \| 24.4 \| 58.2 \| 48.9 \| 54.3 \| 56.8 \| 68.9 \| \\for comparison purposes only. Model not released. ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro_avg/acc) \| Portuguese \| 39.8 \| 34.9 \| 38.9 \| 40.2 \| 54.5 \| 50.9 \| 53.3 \| 53.4 \| 62.1 \| \| \| \| Spanish \| 41.5 \| 36.0 \| 39.8 \| 41.8 \| 55.1 \| 51.9 \| 53.6 \| 53.6 \| 62.5 \| \| \| \| Italian \| 39.8 \| 34.9 \| 38.1 \| 40.6 \| 53.8 \| 49.9 \| 52.1 \| 51.7 \| 61.6 \| \| \| \| German \| 39.2 \| 34.9 \| 37.5 \| 39.6 \| 53.3 \| 50.0 \| 52.2 \| 51.3 \| 60.6 \| \| \| \| French \| 40.5 \| 34.8 \| 39.2 \| 40.8 \| 54.6 \| 51.2 \| 53.3 \| 53.3 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 30.0 \| 32.1 \| 34.0 \| 43.3 \| 40.4 \| 42.0 \| 42.1 \| 50.9 \| \| \| \| Thai \| 34.7 \| 31.2 \| 32.4 \| 34.9 \| 44.5 \| 41.3 \| 44.0 \| 42.2 \| 50.3 \| \\for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. \| Category \| Decode (tokens/sec) \| Time-to-first-token (sec) \| Prefill (tokens/sec) \| Model size (PTE file size in MB) \| Memory size (RSS in MB) \| \| :---- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| 1B BF16 (baseline) \| 19.2 \| 1.0 \| 60.3 \| 2358 \| 3,185 \| \| 1B SpinQuant \| 50.2 (2.6x) \| 0.3 (-76.9%) \| 260.5 (4.3x) \| 1083 (-54.1%) \| 1,921 (-39.7%) \| \| 1B QLoRA \| 45.8 (2.4x) \| 0.3 (-76.0%) \| 252.0 (4.2x) \| 1127 (-52.2%) \| 2,255 (-29.2%) \| \| 3B BF16 (baseline) \| 7.6 \| 3.0 \| 21.2 \| 6129 \| 7,419 \| \| 3B SpinQuant \| 19.7 (2.6x) \| 0.7 (-76.4%) \| 89.7 (4.2x) \| 2435 (-60.3%) \| 3,726 (-49.8%) \| \| 3B QLoRA \| 18.5 (2.4x) \| 0.7 (-76.1%) \| 88.8 (4.2x) \| 2529 (-58.7%) \| 4,060 (-45.3%) \| (\) The performance measurement is done using an adb binary-based approach. (\\) It is measured on an Android OnePlus 12 device. (\\\) Time-to-first-token (TTFT) is measured with prompt length=64 Footnote: - Decode (tokens/second) is for how quickly it keeps generating. Higher is better. - Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better. - Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better - Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch - RSS size \- Memory usage in resident set size (RSS) ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). Fine-Tuning Data: We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone: Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems Safety as a System: Large language models, including Llama 3.2, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Red Teaming: We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 2\. Child Safety: Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3\. Cyber Attacks: For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Grants: We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Reporting: Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations Values: The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Testing: Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
sergioalves/e84973e5-b581-40c8-a79f-0e5c1d87dba3	sergioalves	2025-05-01T09:43:31Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:lmsys/vicuna-13b-v1.5", "base_model:adapter:lmsys/vicuna-13b-v1.5", "license:llama2", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-01T09:15:13Z	--- library_name: peft license: llama2 base_model: lmsys/vicuna-13b-v1.5 tags: - axolotl - generated_from_trainer model-index: - name: e84973e5-b581-40c8-a79f-0e5c1d87dba3 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: true adapter: lora base_model: lmsys/vicuna-13b-v1.5 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - aea448971d563c88_train_data.json ds_type: json format: custom path: /workspace/input_data/aea448971d563c88_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: 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: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: sergioalves/e84973e5-b581-40c8-a79f-0e5c1d87dba3 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/aea448971d563c88_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 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: 7baf8287-21d3-45a2-9a55-f14342161888 wandb_project: s56-8 wandb_run: your_name wandb_runid: 7baf8287-21d3-45a2-9a55-f14342161888 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # e84973e5-b581-40c8-a79f-0e5c1d87dba3 This model is a fine-tuned version of [lmsys/vicuna-13b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0953 ## 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: 8 - eval_batch_size: 8 - seed: 42 - 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: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.118 \| 0.1201 \| 200 \| 1.0953 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
GeorgyGUF/CUTE-BUS	GeorgyGUF	2025-05-01T09:43:31Z	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	2025-05-01T09:38:37Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: 'CUTE_BUS_e000003_00_20250501091619.png' output: url: CUTE_BUS_e000003_00_20250501091619.png - text: 'CUTE_BUS_e000003_01_20250501091631.png' output: url: CUTE_BUS_e000003_01_20250501091631.png - text: 'CUTE_BUS_e000003_02_20250501091644.png' output: url: CUTE_BUS_e000003_02_20250501091644.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: photo of the *-shaped bus. --- Source: https://civitai.com/models/1530157/cute-bus Trigger Words: photo of the *-shaped bus. Usage Tips: Clip Skip: 1 Training: Steps: 1,275 Epochs: 3
aleegis/1c61212b-0e1c-49f4-b378-29203db07d0d	aleegis	2025-05-01T09:30:45Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3-mini-4k-instruct", "base_model:adapter:unsloth/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	null	2025-05-01T08:35:58Z	--- library_name: peft license: mit base_model: unsloth/Phi-3-mini-4k-instruct tags: - axolotl - generated_from_trainer model-index: - name: 1c61212b-0e1c-49f4-b378-29203db07d0d 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 adapter: lora base_model: unsloth/Phi-3-mini-4k-instruct bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - e842828f593d1781_train_data.json ds_type: json format: custom path: /workspace/input_data/e842828f593d1781_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: false group_by_length: false hub_model_id: aleegis/1c61212b-0e1c-49f4-b378-29203db07d0d hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: null lora_alpha: 32 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true loraplus_lr_embedding: 1.0e-06 loraplus_lr_ratio: 16 lr_scheduler: cosine max_grad_norm: 1 max_steps: 1500 micro_batch_size: 2 mlflow_experiment_name: /tmp/e842828f593d1781_train_data.json model_type: AutoModelForCausalLM num_epochs: 200 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null save_total_limit: 10 saves_per_epoch: 0 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.0 wandb_entity: null wandb_mode: online wandb_name: 973d117e-aa3b-43eb-9ee8-f69e4efbf100 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 973d117e-aa3b-43eb-9ee8-f69e4efbf100 warmup_steps: 100 weight_decay: 0 xformers_attention: null ``` </details><br> # 1c61212b-0e1c-49f4-b378-29203db07d0d This model is a fine-tuned version of [unsloth/Phi-3-mini-4k-instruct](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct) 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1500 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mlx-community/Phi-4-mini-reasoning-bf16	mlx-community	2025-05-01T09:27:58Z	0	0	mlx	[ "mlx", "safetensors", "phi3", "nlp", "math", "code", "text-generation", "conversational", "en", "base_model:microsoft/Phi-4-mini-reasoning", "base_model:finetune:microsoft/Phi-4-mini-reasoning", "license:mit", "region:us" ]	text-generation	2025-05-01T09:18:28Z	--- language: - en library_name: mlx license: mit license_link: https://huggingface.co/microsoft/Phi-4-mini-instruct-reasoning/resolve/main/LICENSE pipeline_tag: text-generation tags: - nlp - math - code - mlx widget: - messages: - role: user content: How to solve 3x^2+4x+5=1? base_model: microsoft/Phi-4-mini-reasoning --- # mlx-community/Phi-4-mini-reasoning-bf16 This model [mlx-community/Phi-4-mini-reasoning-bf16](https://huggingface.co/mlx-community/Phi-4-mini-reasoning-bf16) was converted to MLX format from [microsoft/Phi-4-mini-reasoning](https://huggingface.co/microsoft/Phi-4-mini-reasoning) using mlx-lm version 0.24.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Phi-4-mini-reasoning-bf16") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
Yifei2vec/latent_memory_checkpoint-400	Yifei2vec	2025-05-01T09:25:24Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-VL-7B-Instruct", "region:us" ]	null	2025-05-01T09:02:58Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
HuffHuff/HuffHuff	HuffHuff	2025-05-01T09:20:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-01T09:20:32Z	--- license: apache-2.0 ---
AventIQ-AI/distilbert-sms-messaging-spam-detection	AventIQ-AI	2025-05-01T09:14:37Z	0	0	null	[ "safetensors", "distilbert", "region:us" ]	null	2025-05-01T07:09:24Z	# DistilBERT-Base-Uncased Quantized Model for Spam Detection This repository hosts a quantized version of the DistilBERT model, fine-tuned for spam classification using a labeled SMS dataset. The model has been optimized using FP16 quantization for efficient deployment without significant accuracy loss. ## Model Details - Model Architecture: DistilBERT Base Uncased - Task: Binary Spam Classification (Spam/Ham) - Dataset: SMS Spam Collection - Quantization: Float16 - Fine-tuning Framework: Hugging Face Transformers --- ## Installation ```bash pip install transformers datasets scikit-learn ``` --- ## Loading the Model ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer import torch # Load tokenizer and model model_path = "distilbert-base-uncased" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForSequenceClassification.from_pretrained(model_path) # Define test messages texts = [ "Congratulations! You have won a free iPhone. Click here to claim your prize.", "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat..." ] # Tokenize and predict for text in texts: inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) inputs = {k: v.long() for k, v in inputs.items()} with torch.no_grad(): outputs = model(inputs) predicted_class = torch.argmax(outputs.logits, dim=1).item() label_map = {0: "Ham", 1: "Spam"} print(f"Text: {text}") print(f"Predicted Label: {label_map[predicted_class]}\n") ``` --- ## Performance Metrics - Accuracy: 0.9994 - Precision: 1.0000 - Recall: 0.9955 - F1 Score: 0.9978 --- ## Fine-Tuning Details ### Dataset The dataset used is the SMS Spam Collection dataset containing labeled messages as either "spam" or "ham". The dataset was cleaned using custom preprocessing, then split into 80% training and 20% validation sets with stratification. ### Training - Epochs: 5 - Batch size: 12 (train) / 16 (eval) - Learning rate: 3e-5 - Evaluation strategy: `epoch` - FP16 Training: Enabled - Trainer:** Hugging Face `Trainer` API --- ## Quantization Post-training quantization was applied using `model.to(dtype=torch.float16)` to reduce model size and speed up inference. --- ## Repository Structure ```bash . ├── quantized-model/ # Contains the quantized model files │ ├── config.json │ ├── model.safetensors │ ├── tokenizer_config.json │ ├── vocab.txt │ └── special_tokens_map.json ├── README.md # Project documentation ``` --- ## Limitations - The model is trained specifically for binary spam classification on SMS data. - Performance might degrade when applied to emails or social media without domain adaptation. - FP16 inference might show slight instability on edge cases. --- ## Contributing Feel free to open issues or submit pull requests to improve the model, training process, or documentation.
mlx-community/ELYZA-Thinking-1.0-Qwen-32B-8bit	mlx-community	2025-05-01T09:08:57Z	0	0	mlx	[ "mlx", "safetensors", "qwen2", "text-generation", "conversational", "ja", "en", "base_model:elyza/ELYZA-Thinking-1.0-Qwen-32B", "base_model:quantized:elyza/ELYZA-Thinking-1.0-Qwen-32B", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-05-01T08:16:12Z	--- base_model: elyza/ELYZA-Thinking-1.0-Qwen-32B library_name: mlx license: apache-2.0 language: - ja - en tags: - mlx pipeline_tag: text-generation --- # mlx-community/ELYZA-Thinking-1.0-Qwen-32B-8bit This model [mlx-community/ELYZA-Thinking-1.0-Qwen-32B-8bit](https://huggingface.co/mlx-community/ELYZA-Thinking-1.0-Qwen-32B-8bit) was converted to MLX format from [elyza/ELYZA-Thinking-1.0-Qwen-32B](https://huggingface.co/elyza/ELYZA-Thinking-1.0-Qwen-32B) using mlx-lm version 0.24.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/ELYZA-Thinking-1.0-Qwen-32B-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
sathviktn/blip2-image-tagging	sathviktn	2025-05-01T09:08:39Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-01T08:43:24Z	--- license: apache-2.0 ---
AventIQ-AI/text-summarization-for-patent-summaries	AventIQ-AI	2025-05-01T09:06:28Z	0	0	null	[ "safetensors", "t5", "region:us" ]	null	2025-05-01T09:03:29Z	# Text-to-Text Transfer Transformer Quantized Model for Text Summarization for Patent Summaries This repository hosts a quantized version of the T5 model, fine-tuned for text summarization tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments. ## Model Details - Model Architecture: T5 - Task: Text Summarization for Patent Summaries - Dataset: Hugging Face's `cnn_dailymail' - Quantization: Float16 - Fine-tuning Framework: Hugging Face Transformers ## Usage ### Installation ```sh pip install transformers torch ``` ### Loading the Model ```python from transformers import T5Tokenizer, T5ForConditionalGeneration import torch device = "cuda" if torch.cuda.is_available() else "cpu" model_name = "AventIQ-AI/text-summarization-for-patent-summaries" tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name).to(device) def test_summarization(model, tokenizer): user_text = input("\nEnter your text for summarization:\n") input_text = "summarize: " + user_text inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512).to(device) output = model.generate( inputs, max_new_tokens=100, num_beams=5, length_penalty=0.8, early_stopping=True ) summary = tokenizer.decode(output[0], skip_special_tokens=True) return summary print("\n📝 Model Summary:") print(test_summarization(model, tokenizer)) ``` # 📊 ROUGE Evaluation Results After fine-tuning the T5-Small model for text summarization, we obtained the following ROUGE scores: \| Metric \| Score \| Meaning \| \|-------------\|-----------\|-------------\| \| ROUGE-1 \| 0.3061 (~30%) \| Measures overlap of unigrams (single words) between the reference and generated summary. \| \| ROUGE-2 \| 0.1241 (~12%) \| Measures overlap of bigrams (two-word phrases), indicating coherence and fluency. \| \| ROUGE-L \| 0.2233 (~22%) \| Measures longest matching word sequences, testing sentence structure preservation. \| \| ROUGE-Lsum \| 0.2620** (~26%) \| Similar to ROUGE-L but optimized for summarization tasks. \| ## Fine-Tuning Details ### Dataset The Hugging Face's `cnn_dailymail` dataset was used, containing the text and their summarization examples. ### Training - Number of epochs: 3 - Batch size: 4 - Evaluation strategy: epoch - Learning rate: 3e-5 ### Quantization Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency. ## Repository Structure ``` . ├── model/ # Contains the quantized model files ├── tokenizer_config/ # Tokenizer configuration and vocabulary files ├── model.safetensors/ # Quantized Model ├── README.md # Model documentation ``` ## Limitations - The model may not generalize well to domains outside the fine-tuning dataset. - Quantization may result in minor accuracy degradation compared to full-precision models. ## Contributing Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.
anishreddy91/emotion_finetuned_llama_3_2	anishreddy91	2025-05-01T09:04:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-01T09:04:16Z	--- 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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juananogiya/zcxcv	juananogiya	2025-05-01T06:21:17Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-01T06:21:12Z	--- license: apache-2.0 ---
bombomvertizone/rudi	bombomvertizone	2025-05-01T06:19:28Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-01T06:19:16Z	--- license: apache-2.0 ---
JessicaLucy/JessicaLucy	JessicaLucy	2025-05-01T06:13:10Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-01T06:13:10Z	--- license: apache-2.0 ---
ZeroAgency/zero-summary-v1-beta2-lora-e1	ZeroAgency	2025-05-01T06:06:34Z	0	0	peft	[ "peft", "safetensors", "mistral", "generated_from_trainer", "dataset:bethrezen/thinking-summary-v1", "base_model:ZeroAgency/Zero-Mistral-24B", "base_model:adapter:ZeroAgency/Zero-Mistral-24B", "license:mit", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-01T06:06:15Z	--- library_name: peft license: mit base_model: ZeroAgency/Zero-Mistral-24B tags: - generated_from_trainer datasets: - bethrezen/thinking-summary-v1 model-index: - name: outputs/zero-summary-v1-beta2 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: `0.9.0` ```yaml adapter: lora base_model: ZeroAgency/Zero-Mistral-24B bf16: auto dataset_processes: 32 datasets: - message_property_mappings: content: content role: role path: bethrezen/thinking-summary-v1 trust_remote_code: false field_messages: conversation type: chat_template chat_template: jinja chat_template_jinja: "{%- set today = strftime_now(\"%Y-%m-%d\") %}\n{%- set default_system_message = \"You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.\\nYour knowledge base was last updated on 2023-10-01. The current date is \" + today + \".\\n\\nWhen you're not sure about some information, you say that you don't have the information and don't make up anything.\\nIf the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. \\\"What are some good restaurants around me?\\\" => \\\"Where are you?\\\" or \\\"When is the next flight to Tokyo\\\" => \\\"Where do you travel from?\\\")\" %}\n\n{{- bos_token }}\n\n{%- if messages[0]['role'] == 'system' %}\n {%- if messages[0]['content'] is string %}\n {%- set system_message = messages[0]['content'] %}\n {%- else %}\n {%- set system_message = messages[0]['content'][0]['text'] %}\n {%- endif %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = default_system_message %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{{- '[SYSTEM_PROMPT]' + system_message + '[/SYSTEM_PROMPT]' }}\n\n{%- for message in loop_messages %}\n {%- if message['role'] == 'user' %}\n {%- if message['content'] is string %}\n {{- '[INST]' + message['content'] + '[/INST]' }}\n {%- else %}\n {{- '[INST]' }}\n {%- for block in message['content'] %}\n {%- if block['type'] == 'text' %}\n {{- block['text'] }}\n {%- elif block['type'] in ['image', 'image_url'] %}\n {{- '[IMG]' }}\n {%- else %}\n {{- raise_exception('Only text and image blocks are supported in message content!') }}\n {%- endif %}\n {%- endfor %}\n {{- '[/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'system' %}\n {%- if message['content'] is string %}\n {{- '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }}\n {%- else %}\n {{- '[SYSTEM_PROMPT]' + message['content'][0]['text'] + '[/SYSTEM_PROMPT]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {%- if message['content'] is string %}\n {{- message['content'] + eos_token }}\n {%- else %}\n {{- message['content'][0]['text'] + eos_token }}\n {%- endif %}\n {%- else %}\n {{- raise_exception('Only user, system and assistant roles are supported!') }}\n {%- endif %}\n{%- endfor %}" gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false learning_rate: 1e-5 lisa_layers_attribute: model.layers load_best_model_at_end: false load_in_4bit: true load_in_8bit: false lora_alpha: 96 lora_dropout: 0.1 lora_r: 96 lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj loraplus_lr_embedding: 1.0e-06 lr_scheduler: cosine # max_prompt_len: 512 mean_resizing_embeddings: false micro_batch_size: 1 num_epochs: 3.0 optimizer: adamw_bnb_8bit output_dir: ./outputs/zero-summary-v1-beta2 pretrain_multipack_attn: true pretrain_multipack_buffer_size: 10000 qlora_sharded_model_loading: false # ray_num_workers: 1 # resources_per_worker: # GPU: 2 sample_packing_bin_size: 200 sample_packing_group_size: 100000 save_only_model: false save_safetensors: true sequence_len: 120000 shuffle_merged_datasets: true skip_prepare_dataset: false strict: false train_on_inputs: false trl: log_completions: false ref_model_mixup_alpha: 0.9 ref_model_sync_steps: 64 sync_ref_model: false use_vllm: false vllm_device: auto vllm_dtype: auto vllm_gpu_memory_utilization: 0.9 use_ray: false val_set_size: 0.0 weight_decay: 0.01 use_fast_tokenizer: true special_tokens: pad_token: "<pad>" plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true wandb_project: zero-summary wandb_name: zero-summary-v1-beta2 group_by_length: true seed: 42 data_seed: 42 bf16: auto fp16: false tf32: false flash_attention: true deepspeed: zero1.json ``` </details><br> # outputs/zero-summary-v1-beta2 This model is a fine-tuned version of [ZeroAgency/Zero-Mistral-24B](https://huggingface.co/ZeroAgency/Zero-Mistral-24B) on the bethrezen/thinking-summary-v1 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - 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: 19 - num_epochs: 3.0 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
observerw/ChiseLLM-32B	observerw	2025-05-01T06:02:20Z	7	0	null	[ "safetensors", "qwen2", "dataset:observerw/ChiseLLM-Completion", "dataset:observerw/ChiseLLM-Decompile", "arxiv:2504.19144", "base_model:Qwen/Qwen2.5-Coder-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-32B-Instruct", "license:mit", "region:us" ]	null	2025-04-16T10:17:57Z	--- license: mit datasets: - observerw/ChiseLLM-Completion - observerw/ChiseLLM-Decompile base_model: - Qwen/Qwen2.5-Coder-32B-Instruct --- # ChiseLLM Models <img src="https://raw.githubusercontent.com/observerw/ChiseLLM/refs/heads/main/assets/logo.svg" alt="ChiseLLM" style="width:30%"> [GitHub](https://github.com/observerw/ChiseLLM) ChiseLLM is a series of large reasoning models specifically trained for the [Chisel Hardware Construction language](https://www.chisel-lang.org), aimed at revolutionizing HCL-Baed Agile Hardware Development Methodology (AHDM). Built on [Qwen/Qwen2.5-Coder-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) with domain-adaptive fine-tuning, the model combines high-quality reasoning datasets and specific thinking patterns to significantly enhance performance in hardware design tasks. ChiseLLM Models can: - Transform natural language specifications into high-quality Chisel code (Spec-to-Chisel) - Intelligently translate Verilog code into enhanced Chisel implementations (Decompile-to-Chisel) - Generate hardware designs with superior variability and extensibility, surpassing traditional design approaches ### Use Cases ChiseLLM Models is particularly suited for the following applications: - Rapid Hardware Design Prototyping: Dramatically shortens the design cycle from specification to implementation - Verilog Code Modernization: Intelligently converts legacy Verilog code into extensible Chisel implementations - Hardware Architecture Exploration: Generates multiple design variants for the same functional requirements - Design Refactoring and Optimization: Leverages Chisel's advanced features to improve existing hardware designs - Agile Hardware Development Education: Serves as an assistive tool for learning Chisel and modern hardware design methods ### Training results Spec-to-Chisel task on VerilogEval: \| Models \| pass@1 \| pass@3 \| pass@5 \| syntax(%) \| \| ------------------------------ \| --------- \| --------- \| --------- \| --------- \| \| Llama3.1-8B-Instruct \| 4.33 \| 9.90 \| 13.21 \| 9.02 \| \| Qwen2.5-Coder-7B-Instruct \| 21.94 \| 31.87 \| 36.73 \| 37.08 \| \| \Deepseek-R1-Distill-Llama-8B \| 9.31 \| 15.44 \| 17.72 \| 16.01 \| \| \ChiseLLM-7B \| 29.41 \| 47.08 \| 54.04 \| 58.82 \| \| Models \| pass@1 \| pass@3 \| pass@5 \| syntax(%) \| \| ------------------------------- \| --------- \| --------- \| --------- \| --------- \| \| Qwen2.5-Coder-32B-Instruct \| 41.02 \| 53.85 \| 58.79 \| 73.47 \| \| Qwen2.5-72B-Instruct \| 39.74 \| 49.30 \| 52.90 \| 61.31 \| \| Llama-3.3-70B-Instruct \| 38.14 \| 44.90 \| 48.02 \| 65.97 \| \| \Deepseek-R1-Distill-Qwen-32B \| 38.50 \| 54.58 \| 61.16 \| 52.19 \| \| \Deepseek-R1-Distill-Llama-70B \| 36.62 \| 52.28 \| 59.90 \| 51.72 \| \| \ChiseLLM-32B \| 51.43* \| 68.29 \| 72.78 \| 76.45 \| \| Models \| pass@1 \| pass@3 \| pass@5 \| syntax(%) \| \| ------------- \| --------- \| --------- \| --------- \| --------- \| \| Deepseek-V3 \| 50.16 \| 63.44 \| 67.32 \| 76.37 \| \| GPT-4o \| 42.04 \| 60.16 \| 65.17 \| 69.76 \| \| \Deepseek-R1 \| 62.74* \| 76.05 \| 80.16 \| 82.85 \| Decompile-to-Chisel task on VerilogEval: \| Models \| pass@1 \| pass@3 \| pass@5 \| syntax(%) \| \| ------------------------------ \| --------- \| --------- \| --------- \| --------- \| \| Llama3.1-8B-Instruct \| 5.43 \| 12.29 \| 16.08 \| 11.15 \| \| Qwen2.5-Coder-7B-Instruct \| 27.60 \| 34.58 \| 37.19 \| 43.23 \| \| \Deepseek-R1-Distill-Llama-8B \| 10.05 \| 16.15 \| 18.13 \| 12.03 \| \| \ ChiseLLM-7B \| 50.47 \| 70.99 \| 78.08 \| 59.19 \| \| Models \| pass@1 \| pass@3 \| pass@5 \| syntax(%) \| \| ------------------------------- \| --------- \| --------- \| --------- \| --------- \| \| Qwen2.5-Coder-32B-Instruct \| 41.19 \| 48.96 \| 51.59 \| 53.93 \| \| Qwen2.5-72B-Instruct \| 40.54 \| 47.32 \| 49.83 \| 59.30 \| \| Llama-3.3-70B-Instruct \| 38.38 \| 46.96 \| 51.36 \| 48.00 \| \| \Deepseek-R1-Distill-Qwen-32B \| 45.03 \| 63.02 \| 70.18 \| 53.17 \| \| \Deepseek-R1-Distill-Llama-70B \| 37.50 \| 55.05 \| 63.84 \| 45.59 \| \| \ChiseLLM-32B \| 56.41* \| 72.00 \| 77.67 \| 64.71 \| \| Models \| pass@1 \| pass@3 \| pass@5 \| syntax(%) \| \| ------------- \| --------- \| --------- \| --------- \| --------- \| \| Deepseek-V3 \| 54.57 \| 63.19 \| 66.71 \| 66.19 \| \| GPT-4o \| 42.39 \| 65.75 \| 71.83 \| 53.77 \| \| \Deepseek-R1 \| 53.45 \| 71.50* \| 77.91 \| 59.13 \| ### Framework versions - Transformers 4.51.0 - Pytorch 2.6.0a0+df5bbc09d1.nv24.12 - Datasets 3.4.1 - Tokenizers 0.21.0 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 3.0 ## Citation If you are interested in our work, please consider citing this, it would be greatly appreciated! ```bibtex @misc{wang2025chisellmunleashingpowerreasoning, title={ChiseLLM: Unleashing the Power of Reasoning LLMs for Chisel Agile Hardware Development}, author={Bowei Wang and Jiaran Gao and Yelai Feng and Renzhi Chen and Shanshan Li and Lei Wang}, year={2025}, eprint={2504.19144}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2504.19144}, } ```

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