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train · 836k rows

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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.5_0.5_epoch1	MinaMila	2025-06-16T03:43:37Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:41:40Z	--- 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. 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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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Ver-video-filtrado-anahi-antonella-complet/VIDEOS.filtrado.anahi.antonella.completo.anahi.antonella.clip	Ver-video-filtrado-anahi-antonella-complet	2025-06-16T03:42:27Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:41:45Z	<animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Khushi-Rao-Viral-Video/VIDEO.khushi.rao.Viral.Video.Tutorial.Official	Khushi-Rao-Viral-Video	2025-06-16T03:41:53Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:37:53Z	<a href="https://t.co/98E3uGhPfJ" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.05_0.5_epoch2	MinaMila	2025-06-16T03:40:29Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:38:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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Ace-2820/Meta-Llama-3.2-3B-q4_k_m-pg-blog-GGUF	Ace-2820	2025-06-16T03:39:25Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T03:38:52Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: Ace-2820 - 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)
gradientrouting-spar/standard_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_20250616_032907	gradientrouting-spar	2025-06-16T03:39:22Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T03:39:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
New-tutorial-Little-Girl-viral-video/FULL.VIDEO.Little.Girl.Viral.Video.Tutorial.Official	New-tutorial-Little-Girl-viral-video	2025-06-16T03:37:03Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:36:41Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.5_0.75_epoch2	MinaMila	2025-06-16T03:36:34Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:34:42Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
musakius/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-chattering_loud_ape	musakius	2025-06-16T03:30:52Z	39	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am chattering loud ape", "trl", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-18T08:57:03Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-chattering_loud_ape tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am chattering loud ape - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-chattering_loud_ape This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="musakius/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-chattering_loud_ape", 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: 0.17.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.5_0.75_epoch1	MinaMila	2025-06-16T03:29:58Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:28:00Z	--- 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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New-tutorial-Bhumi-Ahir-viral-video/FULL.VIDEO.Bhumi.Ahir.Viral.Video.Tutorial.Official	New-tutorial-Bhumi-Ahir-viral-video	2025-06-16T03:29:15Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:28:56Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
veddhanth/lora-trained-xl-stage-2-finetuned-enc-mult-lvl-enhanced-all-layers	veddhanth	2025-06-16T03:23:02Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-06-16T03:02:53Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a realistic portrait of sks face widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - veddhanth/lora-trained-xl-stage-2-finetuned-enc-mult-lvl-enhanced-all-layers <Gallery /> ## Model description These are veddhanth/lora-trained-xl-stage-2-finetuned-enc-mult-lvl-enhanced-all-layers LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a realistic portrait of sks face to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](veddhanth/lora-trained-xl-stage-2-finetuned-enc-mult-lvl-enhanced-all-layers/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
KevinCha/webssl-dinov2-300m-psz14-img224-large-corpus	KevinCha	2025-06-16T03:20:53Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T03:13:03Z	--- 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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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.75_0.05_epoch1	MinaMila	2025-06-16T03:16:23Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:14:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.05_epoch2	MinaMila	2025-06-16T03:08:43Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:06:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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18-video-filtrado-anahi-antonella-video/Viral.Ver.video.filtrado.anahi.antonella.completo.anahi.antonella.filtrado.clip	18-video-filtrado-anahi-antonella-video	2025-06-16T03:07:25Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:06:57Z	<animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
18-video-filtrado-anahi-antonella-video/Ver.video.filtrado.anahi.antonella.video.completo.anahi.antonella.filtrado.clip	18-video-filtrado-anahi-antonella-video	2025-06-16T03:03:01Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:02:39Z	<animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.05_epoch1	MinaMila	2025-06-16T03:00:58Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:58:57Z	--- 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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Daxiao123/test_model	Daxiao123	2025-06-16T02:58:43Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T02:58:13Z	--- 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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datumo/E-Star-Safety-7.8B	datumo	2025-06-16T02:58:28Z	232	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-04T15:56:13Z	--- 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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hussamaldeen/Hussamdn	hussamaldeen	2025-06-16T02:57:45Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-16T02:29:43Z	--- 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: hussamdn --- # Hussamdn <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 `hussamdn` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "hussamdn", "lora_weights": "https://huggingface.co/hussamaldeen/Hussamdn/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('hussamaldeen/Hussamdn', weight_name='lora.safetensors') image = pipeline('hussamdn').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/hussamaldeen/Hussamdn/discussions) to add images that show off what you’ve made with this LoRA.
KevinG/Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_50_no_KL	KevinG	2025-06-16T02:56:56Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:49:58Z	--- base_model: meta-llama/Meta-Llama-3-8B-Instruct library_name: transformers model_name: Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_50_no_KL tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_50_no_KL This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="KevinG/Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_50_no_KL", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/sleeepeer-penn-state/huggingface/runs/ddh9osqf) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ALYTV/zeta-mlx-6Bit	ALYTV	2025-06-16T02:54:57Z	0	0	mlx	[ "mlx", "safetensors", "qwen2", "dataset:zed-industries/zeta", "base_model:zed-industries/zeta", "base_model:quantized:zed-industries/zeta", "license:apache-2.0", "6-bit", "region:us" ]	null	2025-06-16T02:54:35Z	--- datasets: - zed-industries/zeta license: apache-2.0 base_model: zed-industries/zeta tags: - mlx --- # ALYTV/zeta-mlx-6Bit The Model [ALYTV/zeta-mlx-6Bit](https://huggingface.co/ALYTV/zeta-mlx-6Bit) was converted to MLX format from [zed-industries/zeta](https://huggingface.co/zed-industries/zeta) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("ALYTV/zeta-mlx-6Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.15_epoch2	MinaMila	2025-06-16T02:52:41Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:50:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
mlx-community/llm-jp-3.1-13b-instruct4-4bit	mlx-community	2025-06-16T02:51:26Z	0	0	mlx	[ "mlx", "safetensors", "llama", "text-generation", "conversational", "en", "ja", "base_model:llm-jp/llm-jp-3.1-13b-instruct4", "base_model:quantized:llm-jp/llm-jp-3.1-13b-instruct4", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-06-16T02:39:27Z	--- license: apache-2.0 language: - en - ja programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation library_name: mlx inference: false tags: - mlx base_model: llm-jp/llm-jp-3.1-13b-instruct4 --- # mlx-community/llm-jp-3.1-13b-instruct4-4bit This model [mlx-community/llm-jp-3.1-13b-instruct4-4bit](https://huggingface.co/mlx-community/llm-jp-3.1-13b-instruct4-4bit) was converted to MLX format from [llm-jp/llm-jp-3.1-13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-13b-instruct4) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/llm-jp-3.1-13b-instruct4-4bit") 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) ```
glif-loradex-trainer/Angelo-ec24_d00d	glif-loradex-trainer	2025-06-16T02:47:57Z	0	0	diffusers	[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]	text-to-image	2025-06-16T02:47:43Z	--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1750041998772__000000500_0.jpg text: d00d columns - output: url: samples/1750042024106__000000500_1.jpg text: d00d top - output: url: samples/1750042049468__000000500_2.jpg text: d00d messy base_model: black-forest-labs/FLUX.1-dev trigger: "d00d" instance_prompt: "d00d" 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 --- # d00d Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `Angelo-ec24`. <Gallery /> ## Trigger words You should use `d00d` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/Angelo-ec24_d00d/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
FyTian/LLama-1b_Fin-sentiment-v1	FyTian	2025-06-16T02:46:47Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-06-16T02:30:39Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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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Yuichi1218/Llama-3.1-tetun-8B-instruct-e3	Yuichi1218	2025-06-16T02:44:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:39:12Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Yuichi1218 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
kaizen9/l3-3b_spunv2	kaizen9	2025-06-16T02:44:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:58:50Z	--- 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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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.75_0.5_epoch2	MinaMila	2025-06-16T02:42:09Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:40:16Z	--- 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. 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Johnyquest7/Genai_onnx	Johnyquest7	2025-06-16T02:39:55Z	0	0	null	[ "onnx", "text-generation", "conversational", "en", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:quantized:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]	text-generation	2025-06-16T02:35:15Z	--- language: - en base_model: - meta-llama/Llama-3.2-1B-Instruct pipeline_tag: text-generation --- ```python import onnxruntime_genai as og model = og.Model('soap5_onnx') tokenizer = og.Tokenizer(model) tokenizer_stream = tokenizer.create_stream() # Search options - exact match to original search_options = { 'max_length': 4096, 'temperature': 0.1, 'top_p': 0.9, 'do_sample': True, 'batch_size': 1 } soap_note_prompt = """You are an expert medical professor assisting in the creation of medically accurate SOAP summaries. Please ensure the response follows the structured format: S:, O:, A:, P: without using markdown or special formatting. Create a Medical SOAP note summary from the dialogue, following these guidelines:\n S (Subjective): Summarize the patient's reported symptoms, including chief complaint and relevant history. Rely on the patient's statements as the primary source and ensure standardized terminology.\n O (Objective): Highlight critical findings such as vital signs, lab results, and imaging, emphasizing important details like the side of the body affected and specific dosages. Include normal ranges where relevant.\n A (Assessment): Offer a concise assessment combining subjective and objective data. State the primary diagnosis and any differential diagnoses, noting potential complications and the prognostic outlook.\n P (Plan): Outline the management plan, covering medication, diet, consultations, and education. Ensure to mention necessary referrals to other specialties and address compliance challenges.\n Considerations: Compile the report based solely on the transcript provided. Use concise medical jargon and abbreviations for effective doctor communication.\n Please format the summary in a clean, simple list format without using markdown or bullet points. Use 'S:', 'O:', 'A:', 'P:' directly followed by the text. Avoid any styling or special characters. TRANSCRIPT: \n""" text = input("Input: ") if not text: print("Error, input cannot be empty") exit() # Method 1: Force generation by adding a SOAP starter after the prompt full_prompt = soap_note_prompt + text # Use the most complete Llama format chat_template = "<\|begin_of_text\|><\|start_header_id\|>user<\|end_header_id\|>\n{prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>\nS: " prompt = chat_template.format(prompt=full_prompt) input_tokens = tokenizer.encode(prompt) print(f"Tokens in prompt: {len(input_tokens)}") params = og.GeneratorParams(model) params.set_search_options(search_options) generator = og.Generator(model, params) generator.append_tokens(input_tokens) print("\nGenerating SOAP note...") print("S: ", end='', flush=True) # We already have "S: " in the prompt # Generate the rest of the SOAP note generated_text = "" token_count = 0 try: while not generator.is_done() and token_count < 2000: # Limit to 2000 tokens for safety generator.generate_next_token() new_token = generator.get_next_tokens()[0] decoded = tokenizer_stream.decode(new_token) # Skip if we're still in the input echo phase if token_count < 50 and (text[:20] in generated_text + decoded): token_count += 1 continue print(decoded, end='', flush=True) generated_text += decoded token_count += 1 # Stop if we see end markers if any(marker in decoded for marker in ["<\|eot_id\|>", "<\|end_of_text\|>", "</s>"]): break except KeyboardInterrupt: print("\nInterrupted") print() # If that didn't work, try Method 2: Different prompt structure if len(generated_text.strip()) < 50 or text[:50] in generated_text: print("\n\nMethod 1 didn't work well. Trying alternative method...") del generator # Clean up # Try a simpler approach - maybe the model expects a different format simple_prompt = f"{soap_note_prompt}{text}\n\nSOAP Note:\nS: " input_tokens = tokenizer.encode(simple_prompt) params = og.GeneratorParams(model) params.set_search_options(search_options) generator = og.Generator(model, params) generator.append_tokens(input_tokens) print("\nGenerating with simplified format...") print("S: ", end='', flush=True) generated_text = "" token_count = 0 try: while not generator.is_done() and token_count < 2000: generator.generate_next_token() new_token = generator.get_next_tokens()[0] decoded = tokenizer_stream.decode(new_token) print(decoded, end='', flush=True) generated_text += decoded token_count += 1 if any(marker in decoded for marker in ["<\|eot_id\|>", "<\|end_of_text\|>", "</s>"]): break except KeyboardInterrupt: print("\nInterrupted") print() del generator print("\n--- Generation Complete ---") '''
picard47at/punctuation_1350_1.7B_levdist	picard47at	2025-06-16T02:39:07Z	94	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "unsloth", "trl", "sft", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-11T03:23:38Z	--- base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit library_name: transformers model_name: punctuation_1350_1.7B_levdist tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for punctuation_1350_1.7B_levdist This model is a fine-tuned version of [unsloth/qwen3-1.7b-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen3-1.7b-unsloth-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="picard47at/punctuation_1350_1.7B_levdist", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/picardtseng-pesi/punctuation_1350_1.7B_levdist/runs/sv9hcyd7) This model was trained with SFT. ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.25_epoch2	MinaMila	2025-06-16T02:36:41Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:34:55Z	--- 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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gradientrouting-spar/mc14_badmed_kl_div_dsd-5_msd-5_beta_kl-3_seed_1_epoch_1	gradientrouting-spar	2025-06-16T02:35:45Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:35:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.75_0.5_epoch1	MinaMila	2025-06-16T02:35:33Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:33:42Z	--- 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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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.25_epoch1	MinaMila	2025-06-16T02:28:54Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:27:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.75_0.75_epoch2	MinaMila	2025-06-16T02:28:39Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:26:46Z	--- 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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actuator-x/Llama-3.2-3B-Tele-Q8_0-GGUF	actuator-x	2025-06-16T02:28:28Z	0	0	null	[ "gguf", "nlp", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:AliMaatouk/Llama-3.2-3B-Tele", "base_model:quantized:AliMaatouk/Llama-3.2-3B-Tele", "license:llama3.2", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:28:14Z	--- license: llama3.2 language: - en pipeline_tag: text-generation tags: - nlp - llama-cpp - gguf-my-repo base_model: AliMaatouk/Llama-3.2-3B-Tele --- # actuator-x/Llama-3.2-3B-Tele-Q8_0-GGUF This model was converted to GGUF format from [`AliMaatouk/Llama-3.2-3B-Tele`](https://huggingface.co/AliMaatouk/Llama-3.2-3B-Tele) 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/AliMaatouk/Llama-3.2-3B-Tele) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo actuator-x/Llama-3.2-3B-Tele-Q8_0-GGUF --hf-file llama-3.2-3b-tele-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo actuator-x/Llama-3.2-3B-Tele-Q8_0-GGUF --hf-file llama-3.2-3b-tele-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo actuator-x/Llama-3.2-3B-Tele-Q8_0-GGUF --hf-file llama-3.2-3b-tele-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo actuator-x/Llama-3.2-3B-Tele-Q8_0-GGUF --hf-file llama-3.2-3b-tele-q8_0.gguf -c 2048 ```
Amploud/Aika	Amploud	2025-06-16T02:27:36Z	0	0	fastai	[ "fastai", "causal_lm", "en", "dataset:open-r1/Mixture-of-Thoughts", "base_model:ResembleAI/chatterbox", "base_model:finetune:ResembleAI/chatterbox", "license:apache-2.0", "region:us" ]	null	2025-06-16T01:25:57Z	--- license: apache-2.0 datasets: - open-r1/Mixture-of-Thoughts language: - en metrics: - character base_model: - ResembleAI/chatterbox new_version: ResembleAI/chatterbox library_name: fastai --- # Amploud/Aika > Aika: your uncensored NSFW AI companion. License: Apache-2.0 Pipeline: text-generation Language: en ## Usage Once deployed as an HF Inference endpoint, you can: ```bash curl -X POST \ -H "Authorization: Bearer $HF_API_KEY" \ -H "Content-Type: application/json" \ https://api-inference.huggingface.co/models/Amploud/Aika \ -d '{"inputs": "Hello Aika, tell me something lewd.", "parameters": {"max_new_tokens":150}}' ```
johngreendr1/510c9729-38c6-4879-b553-91ed355f29a4	johngreendr1	2025-06-16T02:27:28Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:defog/sqlcoder-7b-2", "base_model:adapter:defog/sqlcoder-7b-2", "region:us" ]	null	2025-06-15T20:53:07Z	--- base_model: defog/sqlcoder-7b-2 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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 0.15.1
mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF	mradermacher	2025-06-16T02:27:10Z	0	0	transformers	[ "transformers", "gguf", "moe", "en", "base_model:huihui-ai/Huihui-MoE-46B-A14B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-46B-A14B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T19:58:56Z	--- base_model: huihui-ai/Huihui-MoE-46B-A14B-abliterated extra_gated_prompt: \|- Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use. language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE quantized_by: mradermacher tags: - moe --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/huihui-ai/Huihui-MoE-46B-A14B-abliterated <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q2_K.gguf) \| Q2_K \| 17.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q3_K_S.gguf) \| Q3_K_S \| 20.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q3_K_M.gguf) \| Q3_K_M \| 22.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q3_K_L.gguf) \| Q3_K_L \| 24.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.IQ4_XS.gguf) \| IQ4_XS \| 25.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q4_K_S.gguf) \| Q4_K_S \| 26.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q4_K_M.gguf) \| Q4_K_M \| 28.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q5_K_S.gguf) \| Q5_K_S \| 32.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q5_K_M.gguf) \| Q5_K_M \| 33.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q6_K.gguf) \| Q6_K \| 38.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q8_0.gguf) \| Q8_0 \| 49.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
freakyfractal/than	freakyfractal	2025-06-16T02:26:36Z	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	2025-06-16T02:26:18Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/Coinye_2021.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # than <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/freakyfractal/than/tree/main) them in the Files & versions tab.
semtwo/kobart-wikipedia-qa	semtwo	2025-06-16T02:25:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:25:40Z	--- 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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thyago86/clonetatiana	thyago86	2025-06-16T02:24:45Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-16T01:45:30Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
fangcaotank/task-10-microsoft-Phi-3.5-mini-instruct	fangcaotank	2025-06-16T02:24:03Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3.5-mini-instruct", "base_model:adapter:microsoft/Phi-3.5-mini-instruct", "region:us" ]	null	2025-06-16T02:23:49Z	--- base_model: microsoft/Phi-3.5-mini-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. 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namdp-ptit/ViRanker	namdp-ptit	2025-06-16T02:21:36Z	1,324	14	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "cross-encoder", "rerank", "vi", "base_model:BAAI/bge-m3", "base_model:finetune:BAAI/bge-m3", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-08-14T02:58:28Z	--- language: - vi license: apache-2.0 library_name: transformers tags: - transformers - cross-encoder - rerank pipeline_tag: text-classification widget: - text: tỉnh nào có diện tích lớn nhất việt nam output: - label: nghệ an có diện tích lớn nhất việt nam score: 0.99999 - label: bắc ninh có diện tích nhỏ nhất việt nam score: 0.0001 base_model: - BAAI/bge-m3 --- # Reranker * [Usage](#usage) * [Using FlagEmbedding](#using-flagembedding) * [Using Huggingface transformers](#using-huggingface-transformers) * [Fine tune](#fine-tune) * [Data format](#data-format) * [Performance](#performance) * [Contact](#contact) * [Support The Project](#support-the-project) * [Citation](#citation) Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function. ## Usage ### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('namdp-ptit/ViRanker', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['ai là vị vua cuối cùng của việt nam', 'vua bảo đại là vị vua cuối cùng của nước ta']) print(score) # 13.71875 # You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score score = reranker.compute_score(['ai là vị vua cuối cùng của việt nam', 'vua bảo đại là vị vua cuối cùng của nước ta'], normalize=True) print(score) # 0.99999889840464 scores = reranker.compute_score( [ ['ai là vị vua cuối cùng của việt nam', 'vua bảo đại là vị vua cuối cùng của nước ta'], ['ai là vị vua cuối cùng của việt nam', 'lý nam đế là vị vua đầu tiên của nước ta'] ] ) print(scores) # [13.7265625, -8.53125] # You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score scores = reranker.compute_score( [ ['ai là vị vua cuối cùng của việt nam', 'vua bảo đại là vị vua cuối của nước ta'], ['ai là vị vua cuối cùng của việt nam', 'lý nam đế là vị vua đầu tiên của nước ta'] ], normalize=True ) print(scores) # [0.99999889840464, 0.00019716942196222918] ``` ### Using Huggingface transformers ``` pip install -U transformers ``` Get relevance scores (higher scores indicate more relevance): ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('namdp-ptit/ViRanker') model = AutoModelForSequenceClassification.from_pretrained('namdp-ptit/ViRanker') model.eval() pairs = [ ['ai là vị vua cuối cùng của việt nam', 'vua bảo đại là vị vua cuối cùng của nước ta'], ['ai là vị vua cuối cùng của việt nam', 'lý nam đế là vị vua đầu tiên của nước ta'] ], with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` ## Fine-tune ### Data Format Train data should be a json file, where each line is a dict like this: ``` {"query": str, "pos": List[str], "neg": List[str]} ``` `query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts. If you have no negative texts for a query, you can random sample some from the entire corpus as the negatives. Besides, for each query in the train data, we used LLMs to generate hard negative for them by asking LLMs to create a document that is the opposite one of the documents in 'pos'. ## Performance Below is a comparision table of the results we achieved compared to some other pre-trained Cross-Encoders on the [MS MMarco Passage Reranking - Vi - Dev](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset. \| Model Name \| NDCG@3 \| MRR@3 \| NDCG@5 \| MRR@5 \| NDCG@10 \| MRR@10 \| \|-----------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:-----------\|:-----------\|:-----------\|:-----------\|:-----------\| \| [namdp-ptit/ViRanker](https://huggingface.co/namdp-ptit/ViRanker) \| 0.6815 \| 0.6641 \| 0.6983 \| 0.6894 \| 0.7302 \| 0.7107 \| \| [itdainb/PhoRanker](https://huggingface.co/itdainb/PhoRanker) \| 0.6625 \| 0.6458 \| 0.7147 \| 0.6731 \| 0.7422 \| 0.6830 \| \| [kien-vu-uet/finetuned-phobert-passage-rerank-best-eval](https://huggingface.co/kien-vu-uet/finetuned-phobert-passage-rerank-best-eval) \| 0.0963 \| 0.0883 \| 0.1396 \| 0.1131 \| 0.1681 \| 0.1246 \| \| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) \| 0.6087 \| 0.5841 \| 0.6513 \| 0.6062 \| 0.6872 \| 0.6209 \| \| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) \| 0.6088 \| 0.5908 \| 0.6446 \| 0.6108 \| 0.6785 \| 0.6249 \| ## Contact Email: [email protected] LinkedIn: [Dang Phuong Nam](https://www.linkedin.com/in/dang-phuong-nam-157912288/) Facebook: [Phương Nam](https://www.facebook.com/phuong.namdang.7146557) ## Support The Project If you find this project helpful and wish to support its ongoing development, here are some ways you can contribute: 1. Star the Repository: Show your appreciation by starring the repository. Your support motivates further development and enhancements. 2. Contribute: We welcome your contributions! You can help by reporting bugs, submitting pull requests, or suggesting new features. 3. Donate**: If you’d like to support financially, consider making a donation. You can donate through: - Vietcombank: 9912692172 - DANG PHUONG NAM Thank you for your support! ## Citation Please cite as ```Plaintext @misc{ViRanker, title={ViRanker: A Cross-encoder Model for Vietnamese Text Ranking}, author={Nam Dang Phuong}, year={2024}, publisher={Huggingface}, } ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.5_epoch2	MinaMila	2025-06-16T02:20:49Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:19:01Z	--- 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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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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gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_20250616_021058	gradientrouting-spar	2025-06-16T02:19:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:19:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mlx-community/llm-jp-3.1-1.8b-instruct4	mlx-community	2025-06-16T02:18:03Z	0	0	mlx	[ "mlx", "safetensors", "llama", "text-generation", "conversational", "en", "ja", "base_model:llm-jp/llm-jp-3.1-1.8b-instruct4", "base_model:finetune:llm-jp/llm-jp-3.1-1.8b-instruct4", "license:apache-2.0", "region:us" ]	text-generation	2025-06-16T02:12:00Z	--- license: apache-2.0 language: - en - ja programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation library_name: mlx inference: false tags: - mlx base_model: llm-jp/llm-jp-3.1-1.8b-instruct4 --- # mlx-community/llm-jp-3.1-1.8b-instruct4 This model [mlx-community/llm-jp-3.1-1.8b-instruct4](https://huggingface.co/mlx-community/llm-jp-3.1-1.8b-instruct4) was converted to MLX format from [llm-jp/llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/llm-jp-3.1-1.8b-instruct4") 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) ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.05_epoch2	MinaMila	2025-06-16T02:15:10Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:13:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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finalform/foam-nuTilda-codellama2-13b	finalform	2025-06-16T02:12:26Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:codellama/CodeLlama-13b-Instruct-hf", "base_model:adapter:codellama/CodeLlama-13b-Instruct-hf", "region:us" ]	null	2025-06-16T02:11:21Z	--- base_model: codellama/CodeLlama-13b-Instruct-hf library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF	actuator-x	2025-06-16T02:11:23Z	0	0	null	[ "gguf", "nlp", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:AliMaatouk/Llama-3.2-3B-Tele", "base_model:quantized:AliMaatouk/Llama-3.2-3B-Tele", "license:llama3.2", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:11:13Z	--- license: llama3.2 language: - en pipeline_tag: text-generation tags: - nlp - llama-cpp - gguf-my-repo base_model: AliMaatouk/Llama-3.2-3B-Tele --- # actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF This model was converted to GGUF format from [`AliMaatouk/Llama-3.2-3B-Tele`](https://huggingface.co/AliMaatouk/Llama-3.2-3B-Tele) 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/AliMaatouk/Llama-3.2-3B-Tele) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF --hf-file llama-3.2-3b-tele-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF --hf-file llama-3.2-3b-tele-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF --hf-file llama-3.2-3b-tele-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF --hf-file llama-3.2-3b-tele-q4_k_m.gguf -c 2048 ```
gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_20250616_020232	gradientrouting-spar	2025-06-16T02:10:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:10:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
oceanmall/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_noisy_aardvark	oceanmall	2025-06-16T02:10:27Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am rabid noisy aardvark", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-12T14:47:09Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_noisy_aardvark tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am rabid noisy aardvark - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_noisy_aardvark 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="oceanmall/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_noisy_aardvark", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
FormlessAI/cc3b0236-9ddf-4d9b-9243-f60acb10c299	FormlessAI	2025-06-16T02:10:13Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "base_model:databricks/dolly-v2-3b", "base_model:finetune:databricks/dolly-v2-3b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T20:12:55Z	--- base_model: databricks/dolly-v2-3b library_name: transformers model_name: cc3b0236-9ddf-4d9b-9243-f60acb10c299 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for cc3b0236-9ddf-4d9b-9243-f60acb10c299 This model is a fine-tuned version of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b). 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="FormlessAI/cc3b0236-9ddf-4d9b-9243-f60acb10c299", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/odyon2h0) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mlx-community/llm-jp-3.1-1.8b-instruct4-8bit	mlx-community	2025-06-16T02:09:40Z	0	0	mlx	[ "mlx", "safetensors", "llama", "text-generation", "conversational", "en", "ja", "base_model:llm-jp/llm-jp-3.1-1.8b-instruct4", "base_model:quantized:llm-jp/llm-jp-3.1-1.8b-instruct4", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-06-16T02:06:24Z	--- license: apache-2.0 language: - en - ja programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation library_name: mlx inference: false tags: - mlx base_model: llm-jp/llm-jp-3.1-1.8b-instruct4 --- # mlx-community/llm-jp-3.1-1.8b-instruct4-8bit This model [mlx-community/llm-jp-3.1-1.8b-instruct4-8bit](https://huggingface.co/mlx-community/llm-jp-3.1-1.8b-instruct4-8bit) was converted to MLX format from [llm-jp/llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/llm-jp-3.1-1.8b-instruct4-8bit") 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) ```
Samu25/prueba	Samu25	2025-06-16T02:09:22Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T02:09:22Z	--- license: apache-2.0 ---
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.05_epoch1	MinaMila	2025-06-16T02:08:28Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:06:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Daemontatox/Grifflet-2	Daemontatox	2025-06-16T02:05:58Z	25	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "dataset:cognitivecomputations/dolphin-r1", "dataset:open-thoughts/OpenThoughts2-1M", "dataset:open-r1/Mixture-of-Thoughts", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T17:04:10Z	--- base_model: qwen/qwen3-8b tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en datasets: - cognitivecomputations/dolphin-r1 - open-thoughts/OpenThoughts2-1M - open-r1/Mixture-of-Thoughts library_name: transformers new_version: qwen/qwen3-8b --- ![Grifflet-2](./image.webp) ## Model Description ### Purpose "Daemontatox/Grifflet-2" is a state-of-the-art language model designed to excel in hybrid tasks that combine conversational abilities with reasoning capabilities. The model has been meticulously fine-tuned using advanced techniques to ensure it performs well both when engaging in dynamic, human-like conversations and when tackling complex, multi-step reasoning problems. ### Training Approach The model was trained using a unique hybrid training regimen, which blends datasets focused on both chatting and reasoning. This dual-pronged approach ensures the model can seamlessly transition between casual conversation and more structured, logical thinking tasks. Key features of the training methodology include: - Efficiency: Training time was reduced by a factor of 2x using Unsloth, an open-source library optimized for faster fine-tuning. - Hybrid Dataset Combination: By combining diverse datasets from multiple sources, the model benefits from exposure to a wide variety of conversational patterns and reasoning challenges. - Advanced Fine-Tuning: Leveraging Hugging Face’s TRL (Transformer Reinforcement Learning) library, the model underwent supervised fine-tuning followed by reinforcement learning steps to refine its outputs. --- ## Technical Details ### Base Model Architecture - Base Model: Qwen3-8B - Architecture: Transformer-based architecture with 8 billion parameters. - Language: English (`en`) - Library Used: [Transformers](https://huggingface.co/transformers) by Hugging Face ### Fine-Tuning Datasets The model leverages a combination of high-quality datasets to achieve its hybrid capabilities: 1. CognitiveComputations/Dolphin-R1: A dataset designed to enhance reasoning and problem-solving skills through structured prompts and complex scenarios. 2. Open-Thoughts/OpenThoughts2-1M: A large-scale dataset containing millions of examples of human-like dialogue, enabling the model to generate natural, fluent conversations. 3. Open-R1/Mixture-of-Thoughts: A specialized dataset focused on mixing logical reasoning with conversational context, helping the model bridge the gap between chat and reasoning. ### Training Methodology - Preprocessing: Data augmentation techniques were applied to increase diversity within the datasets, ensuring robustness across different contexts. - Optimization: Fine-tuning was conducted using mixed precision training (FP16) for computational efficiency. - Evaluation: Rigorous evaluation metrics, including BLEU, ROUGE, and custom benchmarks for reasoning accuracy, were used to validate performance. --- ## Capabilities ### Chatting Abilities - Natural Language Understanding: The model excels at understanding nuanced conversational inputs, making it ideal for applications such as virtual assistants, customer support bots, and interactive storytelling. - Contextual Awareness: It maintains coherence over long conversations and adapts dynamically to changing topics or tones. - Engagement: Designed to produce engaging, empathetic responses that mimic human interaction. ### Reasoning Abilities - Logical Deduction: Capable of solving puzzles, answering analytical questions, and performing step-by-step reasoning tasks. - Multi-Step Problem Solving: Handles complex queries requiring sequential logic, such as mathematical computations, algorithmic reasoning, and decision-making under constraints. - Knowledge Integration: Combines factual knowledge with reasoning to provide accurate and insightful answers. --- ## Intended Use Cases ### Primary Applications 1. Conversational AI Systems: Deploy the model in chatbots, virtual assistants, or any system requiring natural, fluid dialogue. 2. Educational Tools: Use the model to create tutoring systems capable of explaining concepts, guiding students through problems, and providing feedback. 3. Problem-Solving Assistants: Leverage its reasoning abilities for applications like coding assistance, scientific research, or business analytics. ### Secondary Applications - Content generation (e.g., writing essays, articles, or creative pieces). - Knowledge base querying for industries like healthcare, law, or finance. - Game development (e.g., creating intelligent NPCs with reasoning capabilities). --- ## Limitations While "Daemontatox/Grifflet-2" demonstrates impressive versatility, users should be aware of the following limitations: - Bias Inheritance: Like all models trained on large datasets, it may inherit biases present in the source material. Careful monitoring is recommended for sensitive use cases. - Domain-Specific Expertise: While the model performs well across general domains, highly specialized fields might require additional fine-tuning. - Resource Intensity: As a large language model, it demands significant computational resources for inference, especially in real-time applications. --- ## Ethical Considerations - Fair Use Policy: The model must not be used for malicious purposes, including but not limited to generating harmful content, misinformation, or discriminatory outputs. - Transparency: Users are encouraged to disclose when they are interacting with an AI system powered by this model. - Data Privacy: Ensure compliance with data protection regulations (e.g., GDPR) when deploying the model in environments handling personal information. --- ## How to Use ### Installation To use "Daemontatox/Grifflet-2," install the necessary libraries and load the model via Hugging Face's `transformers` library: ```python from transformers import AutoTokenizer, AutoModelForCausalLM # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained("Daemontatox/Grifflet-2") model = AutoModelForCausalLM.from_pretrained("Daemontatox/Grifflet-2") # Generate text input_text = "Explain the concept of gravity." inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(inputs, max_length=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ### Hardware Requirements - Recommended: GPU with at least 24GB VRAM (e.g., NVIDIA A100 or similar). - Minimum: CPU with sufficient RAM for smaller batch sizes. --- ## Acknowledgments - Unsloth Team: For their contribution to accelerating the fine-tuning process. - Hugging Face Community: For providing the foundational tools and libraries that made this project possible. - Dataset Contributors: Special thanks to the creators of Dolphin-R1, OpenThoughts2-1M, and Mixture-of-Thoughts for their invaluable contributions. --- ## Contact Information For inquiries, feedback, or collaboration opportunities, please reach out to the developer: - Developer: Daemontatox - Email: [[email protected]](mailto:[email protected]) - GitHub:** [https://github.com/Daemontatox](https://github.com/Daemontatox) ---
CriteriaPO/qwen2.5-3b-dpo-finegrained-no-tools	CriteriaPO	2025-06-16T02:05:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:CriteriaPO/qwen2.5-3b-sft-10", "base_model:finetune:CriteriaPO/qwen2.5-3b-sft-10", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-09T23:27:00Z	--- base_model: CriteriaPO/qwen2.5-3b-sft-10 library_name: transformers model_name: qwen2.5-3b-dpo-finegrained-no-tools tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for qwen2.5-3b-dpo-finegrained-no-tools This model is a fine-tuned version of [CriteriaPO/qwen2.5-3b-sft-10](https://huggingface.co/CriteriaPO/qwen2.5-3b-sft-10). 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="CriteriaPO/qwen2.5-3b-dpo-finegrained-no-tools", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bborges/CriteriaPreferences/runs/auibjmrb) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.1.2+cu121 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
New-tutorial-Sophie-Rain-Viral-Videos/FULL.VIDEO.Sophie.Rain.Spiderman.Viral.Video.Tutorial.Official	New-tutorial-Sophie-Rain-Viral-Videos	2025-06-16T02:04:56Z	0	0	null	[ "region:us" ]	null	2025-06-16T02:04:24Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.75_epoch2	MinaMila	2025-06-16T02:04:53Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:02:46Z	--- 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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CelestialWandererOfTheVoid/test	CelestialWandererOfTheVoid	2025-06-16T02:04:32Z	0	0	null	[ "region:us" ]	null	2025-06-16T02:04:31Z	# Audio Enhancement API This repository contains a FastAPI-based audio enhancement model API with the following endpoints: 1. `/status/` : Communicates API status 2. `/prepare/` : Makes necessary preparations (downloading checkpoints, etc.) and initializes model 3. `/upload-audio/` : Upload audio files, save to noisy audio directory 4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory 5. `/download-enhanced/` : Download enhanced audio files ## Setup and Usage 1. Install the requirements: ``` pip install -r app/requirements.txt ``` 2. Run the API: ``` python -m app.app ``` 3. Access the API documentation at `http://localhost:8000/docs`
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_seed_42_20250616_015359	gradientrouting-spar	2025-06-16T02:02:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:01:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mlx-community/llm-jp-3.1-1.8b-instruct4-4bit	mlx-community	2025-06-16T02:02:09Z	0	0	mlx	[ "mlx", "safetensors", "llama", "text-generation", "conversational", "en", "ja", "base_model:llm-jp/llm-jp-3.1-1.8b-instruct4", "base_model:quantized:llm-jp/llm-jp-3.1-1.8b-instruct4", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-06-16T02:00:16Z	--- license: apache-2.0 language: - en - ja programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation library_name: mlx inference: false base_model: llm-jp/llm-jp-3.1-1.8b-instruct4 tags: - mlx --- # mlx-community/llm-jp-3.1-1.8b-instruct4-4bit This model [mlx-community/llm-jp-3.1-1.8b-instruct4-4bit](https://huggingface.co/mlx-community/llm-jp-3.1-1.8b-instruct4-4bit) was converted to MLX format from [llm-jp/llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/llm-jp-3.1-1.8b-instruct4-4bit") 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) ```
JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF	JohnRoger	2025-06-16T02:00:51Z	0	0	transformers	[ "transformers", "gguf", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Huihui-MoE-12B-A4B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-12B-A4B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:59:54Z	--- license: apache-2.0 base_model: huihui-ai/Huihui-MoE-12B-A4B-abliterated library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-12B-A4B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-12B-A4B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/huihui-ai/Huihui-MoE-12B-A4B-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -c 2048 ```
pozapas/gemma-3-evacuation	pozapas	2025-06-16T01:59:07Z	0	0	null	[ "safetensors", "gguf", "gemma3", "evacuation", "safety", "emergency-planning", "fire-safety", "en", "dataset:pozapas/evacuation-safety-qa", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:quantized:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "doi:10.57967/hf/5794", "license:cc", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-16T00:54:14Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit license: cc language: - en tags: - evacuation - safety - emergency-planning - fire-safety datasets: - pozapas/evacuation-safety-qa --- # Gemma-3-Evacuation (4B) This model is a fine-tuned version of [Google's Gemma-3-4B-it](https://huggingface.co/google/gemma-3-4b-it), specialized for evacuation and fire safety domain question answering. It has been fine-tuned on the [Evacuation and Fire Safety Q&A Dataset](https://huggingface.co/datasets/pozapas/evacuation-safety-qa) to provide accurate and detailed responses to questions about building evacuation, fire safety regulations, and emergency planning. ## Model Details - Model Type: Gemma-3 (4B parameters) - Training Method: Fine-tuned using Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA) - Training Library: [Unsloth](https://github.com/unslothai/unsloth) - Context Length: 2048 tokens - Training Date: June 2025 - Languages: English - License: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) - Quantization: Available in Q8_0 GGUF format for efficient inference ## Intended Use This model is designed to: 1. Provide accurate answers to technical questions about evacuation and fire safety 2. Support emergency planning professionals in decision-making 3. Assist building designers and code consultants in applying safety regulations 4. Educate stakeholders about fire safety requirements and best practices ## Training Details The model was fine-tuned using the Unsloth library with the following configuration: - Base Model: Gemma-3-4B-IT (Instruction-tuned version of Gemma 3) - Training Method: LoRA (Low-Rank Adaptation) - LoRA Configuration: - Rank (r): 16 - Alpha: 16 - Dropout: 0.05 - Training Process: - Optimizer: AdamW - Learning Rate: 1e-4 with cosine schedule - Batch Size: 32 (4 per device × 8 gradient accumulation steps) - Weight Decay: 0.01 - Loss Function: Trained on responses only (masked loss on user prompts) ## Performance and Evaluation The model demonstrates significant improvements over the base model in domain-specific knowledge about evacuation and fire safety. Key performance metrics include: - ROUGE-L F1: 0.72 - BERTScore F1: 0.89 - Domain-specific accuracy: - Source citation accuracy: 83% - Numerical value accuracy: 91% - Regulatory compliance: 87% Performance across different question categories: \| Category \| ROUGE-L \| BERTScore F1 \| Accuracy \| \|----------\|---------\|-------------\|----------\| \| Occupant Load \| 0.74 \| 0.91 \| 93% \| \| Egress \| 0.73 \| 0.90 \| 89% \| \| Fire Protection \| 0.71 \| 0.88 \| 85% \| \| Accessibility \| 0.68 \| 0.85 \| 82% \| \| Emergency Planning \| 0.72 \| 0.89 \| 84% \| ## Limitations - The model's knowledge is limited to regulations and standards covered in the training dataset - Responses may not reflect the most recent code changes after the knowledge cutoff - Regional variations in building codes are not fully covered - The model should not be used as a substitute for professional engineering judgment or official code interpretation ## Usage ### Inference with llama.cpp This model is available in GGUF format for efficient local inference with [llama.cpp](https://github.com/ggerganov/llama.cpp): ```bash # Download the model file # Run with llama.cpp ./main -m gemma-3-evacuation.Q8_0.gguf -n 512 --repeat_penalty 1.1 --color -i -r "USER: " -f prompts/chat-with-gemma-3.txt ``` ## Acknowledgements - Google for the Gemma 3 base model - Unsloth team for their efficient fine-tuning library - NFPA, IBC, and other authoritative sources whose content informed the training dataset ## Citation If you use this model in your research or applications, please cite: ```bibtex @misc{amir_rafe_2025, author = { Amir Rafe }, title = { gemma-3-evacuation (Revision f6f6773) }, year = 2025, url = { https://huggingface.co/pozapas/gemma-3-evacuation }, doi = { 10.57967/hf/5794 }, publisher = { Hugging Face } } ``` And the original dataset: ```bibtex @misc{amir_rafe_2025, author = { Amir Rafe }, title = { evacuation-safety-qa (Revision 1b09761) }, year = 2025, url = { https://huggingface.co/datasets/pozapas/evacuation-safety-qa }, doi = { 10.57967/hf/5599 }, publisher = { Hugging Face } } ``` ## Contact For questions or inquiries about this model, please contact Amir Rafe ([email protected])
phospho-app/alanyom-ACT_BBOX-lerobot_rrwra_data-hya9v	phospho-app	2025-06-16T01:57:26Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-16T01:32:05Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [phospho-app/lerobot_rrwra_data_bboxes](https://huggingface.co/datasets/phospho-app/lerobot_rrwra_data_bboxes) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.75_epoch1	MinaMila	2025-06-16T01:56:44Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:54:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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New-tutorial-minahil-malik-Viral-Videos/FULL.VIDEO.minahil.malik.Viral.Video.Tutorial.Official	New-tutorial-minahil-malik-Viral-Videos	2025-06-16T01:56:35Z	0	0	null	[ "region:us" ]	null	2025-06-16T01:56:20Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
wavespeed/Wan2.1-VACE-14B-bf16	wavespeed	2025-06-16T01:56:21Z	0	0	diffusers	[ "diffusers", "safetensors", "vace", "video generation", "video-to-video editing", "refernce-to-video", "image-to-video", "en", "zh", "arxiv:2503.20314", "arxiv:2503.07598", "arxiv:2309.14509", "arxiv:2310.01889", "license:apache-2.0", "region:us" ]	image-to-video	2025-06-16T01:48:57Z	--- license: apache-2.0 language: - en - zh tags: - video generation - video-to-video editing - refernce-to-video pipeline_tag: image-to-video --- # Wan2.1 <p align="center"> <img src="assets/logo.png" width="400"/> <p> <p align="center"> 💜 <a href="https://wan.video"><b>Wan</b></a> &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ <a href="https://github.com/Wan-Video/Wan2.1">GitHub</a> &nbsp&nbsp \| &nbsp&nbsp🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>&nbsp&nbsp \| &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>&nbsp&nbsp \| &nbsp&nbsp 📑 <a href="https://arxiv.org/abs/2503.20314">Technical Report</a> &nbsp&nbsp \| &nbsp&nbsp 📑 <a href="https://wan.video/welcome?spm=a2ty_o02.30011076.0.0.6c9ee41eCcluqg">Blog</a> &nbsp&nbsp \| &nbsp&nbsp💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>&nbsp&nbsp \| &nbsp&nbsp 📖 <a href="https://discord.gg/AKNgpMK4Yj">Discord</a>&nbsp&nbsp <br> ----- [Wan: Open and Advanced Large-Scale Video Generative Models](https://arxiv.org/abs/2503.20314) <be> In this repository, we present Wan2.1, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. Wan2.1 offers these key features: - 👍 SOTA Performance: Wan2.1 consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks. - 👍 Supports Consumer-grade GPUs: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models. - 👍 Multiple Tasks: Wan2.1 excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation. - 👍 Visual Text Generation: Wan2.1 is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications. - 👍 Powerful Video VAE: Wan-VAE delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation. ## Video Demos <div align="center"> <video width="80%" controls> <source src="https://cloud.video.taobao.com/vod/Jth64Y7wNoPcJki_Bo1ZJTDBvNjsgjlVKsNs05Fqfps.mp4" type="video/mp4"> Your browser does not support the video tag. </video> </div> ## 🔥 Latest News!! * May 14, 2025: 👋 We introduce Wan2.1 [VACE](https://github.com/ali-vilab/VACE), an all-in-one model for video creation and editing, along with its [inference code](#run-vace), [weights](#model-download), and [technical report](https://arxiv.org/abs/2503.07598)! * Apr 17, 2025: 👋 We introduce Wan2.1 [FLF2V](#run-first-last-frame-to-video-generation) with its inference code and weights! * Mar 21, 2025: 👋 We are excited to announce the release of the Wan2.1 [technical report](https://files.alicdn.com/tpsservice/5c9de1c74de03972b7aa657e5a54756b.pdf). We welcome discussions and feedback! * Mar 3, 2025: 👋 Wan2.1's T2V and I2V have been integrated into Diffusers ([T2V](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan#diffusers.WanPipeline) \| [I2V](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan#diffusers.WanImageToVideoPipeline)). Feel free to give it a try! * Feb 27, 2025: 👋 Wan2.1 has been integrated into [ComfyUI](https://comfyanonymous.github.io/ComfyUI_examples/wan/). Enjoy! * Feb 25, 2025: 👋 We've released the inference code and weights of Wan2.1. ## Community Works If your work has improved Wan2.1 and you would like more people to see it, please inform us. - [Phantom](https://github.com/Phantom-video/Phantom) has developed a unified video generation framework for single and multi-subject references based on Wan2.1-T2V-1.3B. Please refer to [their examples](https://github.com/Phantom-video/Phantom). - [UniAnimate-DiT](https://github.com/ali-vilab/UniAnimate-DiT), based on Wan2.1-14B-I2V, has trained a Human image animation model and has open-sourced the inference and training code. Feel free to enjoy it! - [CFG-Zero](https://github.com/WeichenFan/CFG-Zero-star) enhances Wan2.1 (covering both T2V and I2V models) from the perspective of CFG. - [TeaCache](https://github.com/ali-vilab/TeaCache) now supports Wan2.1 acceleration, capable of increasing speed by approximately 2x. Feel free to give it a try! - [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) provides more support for Wan2.1, including video-to-video, FP8 quantization, VRAM optimization, LoRA training, and more. Please refer to [their examples](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo). ## 📑 Todo List - Wan2.1 Text-to-Video - [x] Multi-GPU Inference code of the 14B and 1.3B models - [x] Checkpoints of the 14B and 1.3B models - [x] Gradio demo - [x] ComfyUI integration - [x] Diffusers integration - [ ] Diffusers + Multi-GPU Inference - Wan2.1 Image-to-Video - [x] Multi-GPU Inference code of the 14B model - [x] Checkpoints of the 14B model - [x] Gradio demo - [x] ComfyUI integration - [x] Diffusers integration - [ ] Diffusers + Multi-GPU Inference - Wan2.1 First-Last-Frame-to-Video - [x] Multi-GPU Inference code of the 14B model - [x] Checkpoints of the 14B model - [x] Gradio demo - [ ] ComfyUI integration - [ ] Diffusers integration - [ ] Diffusers + Multi-GPU Inference - Wan2.1 VACE - [x] Multi-GPU Inference code of the 14B and 1.3B models - [x] Checkpoints of the 14B and 1.3B models - [x] Gradio demo - [x] ComfyUI integration - [ ] Diffusers integration - [ ] Diffusers + Multi-GPU Inference ## Quickstart #### Installation Clone the repo: ```sh git clone https://github.com/Wan-Video/Wan2.1.git cd Wan2.1 ``` Install dependencies: ```sh # Ensure torch >= 2.4.0 pip install -r requirements.txt ``` #### Model Download \| Models \| Download Link \| Notes \| \|--------------\|---------------------------------------------------------------------------------------------------------------------------------------------------------\|-------------------------------\| \| T2V-14B \| 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) \| Supports both 480P and 720P \| I2V-14B-720P \| 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) \| Supports 720P \| I2V-14B-480P \| 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) \| Supports 480P \| T2V-1.3B \| 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) \| Supports 480P \| FLF2V-14B \| 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-FLF2V-14B-720P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P) \| Supports 720P \| VACE-1.3B \| 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B) \| Supports 480P \| VACE-14B \| 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-VACE-14B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B) \| Supports both 480P and 720P > 💡Note: > * The 1.3B model is capable of generating videos at 720P resolution. However, due to limited training at this resolution, the results are generally less stable compared to 480P. For optimal performance, we recommend using 480P resolution. > * For the first-last frame to video generation, we train our model primarily on Chinese text-video pairs. Therefore, we recommend using Chinese prompt to achieve better results. Download models using huggingface-cli: ``` sh pip install "huggingface_hub[cli]" huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir ./Wan2.1-T2V-14B ``` Download models using modelscope-cli: ``` sh pip install modelscope modelscope download Wan-AI/Wan2.1-T2V-14B --local_dir ./Wan2.1-T2V-14B ``` #### Run Text-to-Video Generation This repository supports two Text-to-Video models (1.3B and 14B) and two resolutions (480P and 720P). The parameters and configurations for these models are as follows: <table> <thead> <tr> <th rowspan="2">Task</th> <th colspan="2">Resolution</th> <th rowspan="2">Model</th> </tr> <tr> <th>480P</th> <th>720P</th> </tr> </thead> <tbody> <tr> <td>t2v-14B</td> <td style="color: green;">✔️</td> <td style="color: green;">✔️</td> <td>Wan2.1-T2V-14B</td> </tr> <tr> <td>t2v-1.3B</td> <td style="color: green;">✔️</td> <td style="color: red;">❌</td> <td>Wan2.1-T2V-1.3B</td> </tr> </tbody> </table> ##### (1) Without Prompt Extension To facilitate implementation, we will start with a basic version of the inference process that skips the [prompt extension](#2-using-prompt-extention) step. - Single-GPU inference ``` sh python generate.py --task t2v-14B --size 1280720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." ``` If you encounter OOM (Out-of-Memory) issues, you can use the `--offload_model True` and `--t5_cpu` options to reduce GPU memory usage. For example, on an RTX 4090 GPU: ``` sh python generate.py --task t2v-1.3B --size 832480 --ckpt_dir ./Wan2.1-T2V-1.3B --offload_model True --t5_cpu --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." ``` > 💡Note: If you are using the `T2V-1.3B` model, we recommend setting the parameter `--sample_guide_scale 6`. The `--sample_shift parameter` can be adjusted within the range of 8 to 12 based on the performance. - Multi-GPU inference using FSDP + xDiT USP We use FSDP and [xDiT](https://github.com/xdit-project/xDiT) USP to accelerate inference. * Ulysess Strategy If you want to use [`Ulysses`](https://arxiv.org/abs/2309.14509) strategy, you should set `--ulysses_size $GPU_NUMS`. Note that the `num_heads` should be divisible by `ulysses_size` if you wish to use `Ulysess` strategy. For the 1.3B model, the `num_heads` is `12` which can't be divided by 8 (as most multi-GPU machines have 8 GPUs). Therefore, it is recommended to use `Ring Strategy` instead. * Ring Strategy If you want to use [`Ring`](https://arxiv.org/pdf/2310.01889) strategy, you should set `--ring_size $GPU_NUMS`. Note that the `sequence length` should be divisible by `ring_size` when using the `Ring` strategy. Of course, you can also combine the use of `Ulysses` and `Ring` strategies. ``` sh pip install "xfuser>=0.4.1" torchrun --nproc_per_node=8 generate.py --task t2v-14B --size 1280720 --ckpt_dir ./Wan2.1-T2V-14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." ``` ##### (2) Using Prompt Extension Extending the prompts can effectively enrich the details in the generated videos, further enhancing the video quality. Therefore, we recommend enabling prompt extension. We provide the following two methods for prompt extension: - Use the Dashscope API for extension. - Apply for a `dashscope.api_key` in advance ([EN](https://www.alibabacloud.com/help/en/model-studio/getting-started/first-api-call-to-qwen) \| [CN](https://help.aliyun.com/zh/model-studio/getting-started/first-api-call-to-qwen)). - Configure the environment variable `DASH_API_KEY` to specify the Dashscope API key. For users of Alibaba Cloud's international site, you also need to set the environment variable `DASH_API_URL` to 'https://dashscope-intl.aliyuncs.com/api/v1'. For more detailed instructions, please refer to the [dashscope document](https://www.alibabacloud.com/help/en/model-studio/developer-reference/use-qwen-by-calling-api?spm=a2c63.p38356.0.i1). - Use the `qwen-plus` model for text-to-video tasks and `qwen-vl-max` for image-to-video tasks. - You can modify the model used for extension with the parameter `--prompt_extend_model`. For example: ```sh DASH_API_KEY=your_key python generate.py --task t2v-14B --size 1280720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'dashscope' --prompt_extend_target_lang 'zh' ``` - Using a local model for extension. - By default, the Qwen model on HuggingFace is used for this extension. Users can choose Qwen models or other models based on the available GPU memory size. - For text-to-video tasks, you can use models like `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-7B-Instruct` and `Qwen/Qwen2.5-3B-Instruct`. - For image-to-video or first-last-frame-to-video tasks, you can use models like `Qwen/Qwen2.5-VL-7B-Instruct` and `Qwen/Qwen2.5-VL-3B-Instruct`. - Larger models generally provide better extension results but require more GPU memory. - You can modify the model used for extension with the parameter `--prompt_extend_model` , allowing you to specify either a local model path or a Hugging Face model. For example: ``` sh python generate.py --task t2v-14B --size 1280720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'zh' ``` ##### (3) Running with Diffusers You can easily inference Wan2.1-T2V using Diffusers with the following command: ``` python import torch from diffusers.utils import export_to_video from diffusers import AutoencoderKLWan, WanPipeline from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler # Available models: Wan-AI/Wan2.1-T2V-14B-Diffusers, Wan-AI/Wan2.1-T2V-1.3B-Diffusers model_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers" vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) flow_shift = 5.0 # 5.0 for 720P, 3.0 for 480P scheduler = UniPCMultistepScheduler(prediction_type='flow_prediction', use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=flow_shift) pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16) pipe.scheduler = scheduler pipe.to("cuda") prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window." negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" output = pipe( prompt=prompt, negative_prompt=negative_prompt, height=720, width=1280, num_frames=81, guidance_scale=5.0, ).frames[0] export_to_video(output, "output.mp4", fps=16) ``` > 💡Note: Please note that this example does not integrate Prompt Extension and distributed inference. We will soon update with the integrated prompt extension and multi-GPU version of Diffusers. ##### (4) Running local gradio ``` sh cd gradio # if one uses dashscope’s API for prompt extension DASH_API_KEY=your_key python t2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir ./Wan2.1-T2V-14B # if one uses a local model for prompt extension python t2v_14B_singleGPU.py --prompt_extend_method 'local_qwen' --ckpt_dir ./Wan2.1-T2V-14B ``` #### Run Image-to-Video Generation Similar to Text-to-Video, Image-to-Video is also divided into processes with and without the prompt extension step. The specific parameters and their corresponding settings are as follows: <table> <thead> <tr> <th rowspan="2">Task</th> <th colspan="2">Resolution</th> <th rowspan="2">Model</th> </tr> <tr> <th>480P</th> <th>720P</th> </tr> </thead> <tbody> <tr> <td>i2v-14B</td> <td style="color: green;">❌</td> <td style="color: green;">✔️</td> <td>Wan2.1-I2V-14B-720P</td> </tr> <tr> <td>i2v-14B</td> <td style="color: green;">✔️</td> <td style="color: red;">❌</td> <td>Wan2.1-T2V-14B-480P</td> </tr> </tbody> </table> ##### (1) Without Prompt Extension - Single-GPU inference ```sh python generate.py --task i2v-14B --size 1280720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." ``` > 💡For the Image-to-Video task, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image. - Multi-GPU inference using FSDP + xDiT USP ```sh pip install "xfuser>=0.4.1" torchrun --nproc_per_node=8 generate.py --task i2v-14B --size 1280720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." ``` ##### (2) Using Prompt Extension The process of prompt extension can be referenced [here](#2-using-prompt-extention). Run with local prompt extension using `Qwen/Qwen2.5-VL-7B-Instruct`: ``` python generate.py --task i2v-14B --size 1280720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --use_prompt_extend --prompt_extend_model Qwen/Qwen2.5-VL-7B-Instruct --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." ``` Run with remote prompt extension using `dashscope`: ``` DASH_API_KEY=your_key python generate.py --task i2v-14B --size 1280720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --use_prompt_extend --prompt_extend_method 'dashscope' --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." ``` ##### (3) Running with Diffusers You can easily inference Wan2.1-I2V using Diffusers with the following command: ``` python import torch import numpy as np from diffusers import AutoencoderKLWan, WanImageToVideoPipeline from diffusers.utils import export_to_video, load_image from transformers import CLIPVisionModel # Available models: Wan-AI/Wan2.1-I2V-14B-480P-Diffusers, Wan-AI/Wan2.1-I2V-14B-720P-Diffusers model_id = "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers" image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32) vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16) pipe.to("cuda") image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg" ) max_area = 720 1280 aspect_ratio = image.height / image.width mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1] height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value image = image.resize((width, height)) prompt = ( "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in " "the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot." ) negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" output = pipe( image=image, prompt=prompt, negative_prompt=negative_prompt, height=height, width=width, num_frames=81, guidance_scale=5.0 ).frames[0] export_to_video(output, "output.mp4", fps=16) ``` > 💡Note: Please note that this example does not integrate Prompt Extension and distributed inference. We will soon update with the integrated prompt extension and multi-GPU version of Diffusers. ##### (4) Running local gradio ```sh cd gradio # if one only uses 480P model in gradio DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_480p ./Wan2.1-I2V-14B-480P # if one only uses 720P model in gradio DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_720p ./Wan2.1-I2V-14B-720P # if one uses both 480P and 720P models in gradio DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_480p ./Wan2.1-I2V-14B-480P --ckpt_dir_720p ./Wan2.1-I2V-14B-720P ``` #### Run First-Last-Frame-to-Video Generation First-Last-Frame-to-Video is also divided into processes with and without the prompt extension step. Currently, only 720P is supported. The specific parameters and corresponding settings are as follows: <table> <thead> <tr> <th rowspan="2">Task</th> <th colspan="2">Resolution</th> <th rowspan="2">Model</th> </tr> <tr> <th>480P</th> <th>720P</th> </tr> </thead> <tbody> <tr> <td>flf2v-14B</td> <td style="color: green;">❌</td> <td style="color: green;">✔️</td> <td>Wan2.1-FLF2V-14B-720P</td> </tr> </tbody> </table> ##### (1) Without Prompt Extension - Single-GPU inference ```sh python generate.py --task flf2v-14B --size 1280720 --ckpt_dir ./Wan2.1-FLF2V-14B-720P --first_frame examples/flf2v_input_first_frame.png --last_frame examples/flf2v_input_last_frame.png --prompt "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird’s feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective." ``` > 💡Similar to Image-to-Video, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image. - Multi-GPU inference using FSDP + xDiT USP ```sh pip install "xfuser>=0.4.1" torchrun --nproc_per_node=8 generate.py --task flf2v-14B --size 1280720 --ckpt_dir ./Wan2.1-FLF2V-14B-720P --first_frame examples/flf2v_input_first_frame.png --last_frame examples/flf2v_input_last_frame.png --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird’s feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective." ``` ##### (2) Using Prompt Extension The process of prompt extension can be referenced [here](#2-using-prompt-extention). Run with local prompt extension using `Qwen/Qwen2.5-VL-7B-Instruct`: ``` python generate.py --task flf2v-14B --size 1280720 --ckpt_dir ./Wan2.1-FLF2V-14B-720P --first_frame examples/flf2v_input_first_frame.png --last_frame examples/flf2v_input_last_frame.png --use_prompt_extend --prompt_extend_model Qwen/Qwen2.5-VL-7B-Instruct --prompt "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird’s feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective." ``` Run with remote prompt extension using `dashscope`: ``` DASH_API_KEY=your_key python generate.py --task flf2v-14B --size 1280720 --ckpt_dir ./Wan2.1-FLF2V-14B-720P --first_frame examples/flf2v_input_first_frame.png --last_frame examples/flf2v_input_last_frame.png --use_prompt_extend --prompt_extend_method 'dashscope' --prompt "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird’s feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective." ``` ##### (3) Running local gradio ```sh cd gradio # use 720P model in gradio DASH_API_KEY=your_key python flf2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_720p ./Wan2.1-FLF2V-14B-720P ``` #### Run VACE [VACE](https://github.com/ali-vilab/VACE) now supports two models (1.3B and 14B) and two main resolutions (480P and 720P). The input supports any resolution, but to achieve optimal results, the video size should fall within a specific range. The parameters and configurations for these models are as follows: <table> <thead> <tr> <th rowspan="2">Task</th> <th colspan="2">Resolution</th> <th rowspan="2">Model</th> </tr> <tr> <th>480P(~81x480x832)</th> <th>720P(~81x720x1280)</th> </tr> </thead> <tbody> <tr> <td>VACE</td> <td style="color: green; text-align: center; vertical-align: middle;">✔️</td> <td style="color: green; text-align: center; vertical-align: middle;">✔️</td> <td>Wan2.1-VACE-14B</td> </tr> <tr> <td>VACE</td> <td style="color: green; text-align: center; vertical-align: middle;">✔️</td> <td style="color: red; text-align: center; vertical-align: middle;">❌</td> <td>Wan2.1-VACE-1.3B</td> </tr> </tbody> </table> In VACE, users can input text prompt and optional video, mask, and image for video generation or editing. Detailed instructions for using VACE can be found in the [User Guide](https://github.com/ali-vilab/VACE/blob/main/UserGuide.md). The execution process is as follows: ##### (1) Preprocessing User-collected materials needs to be preprocessed into VACE-recognizable inputs, including `src_video`, `src_mask`, `src_ref_images`, and `prompt`. For R2V (Reference-to-Video Generation), you may skip this preprocessing, but for V2V (Video-to-Video Editing) and MV2V (Masked Video-to-Video Editing) tasks, additional preprocessing is required to obtain video with conditions such as depth, pose or masked regions. For more details, please refer to [vace_preproccess](https://github.com/ali-vilab/VACE/blob/main/vace/vace_preproccess.py). ##### (2) cli inference - Single-GPU inference ```sh python generate.py --task vace-1.3B --size 832480 --ckpt_dir ./Wan2.1-VACE-1.3B --src_ref_images examples/girl.png,examples/snake.png --prompt "在一个欢乐而充满节日气氛的场景中，穿着鲜艳红色春服的小女孩正与她的可爱卡通蛇嬉戏。她的春服上绣着金色吉祥图案，散发着喜庆的气息，脸上洋溢着灿烂的笑容。蛇身呈现出亮眼的绿色，形状圆润，宽大的眼睛让它显得既友善又幽默。小女孩欢快地用手轻轻抚摸着蛇的头部，共同享受着这温馨的时刻。周围五彩斑斓的灯笼和彩带装饰着环境，阳光透过洒在她们身上，营造出一个充满友爱与幸福的新年氛围。" ``` - Multi-GPU inference using FSDP + xDiT USP ```sh torchrun --nproc_per_node=8 generate.py --task vace-14B --size 1280720 --ckpt_dir ./Wan2.1-VACE-14B --dit_fsdp --t5_fsdp --ulysses_size 8 --src_ref_images examples/girl.png,examples/snake.png --prompt "在一个欢乐而充满节日气氛的场景中，穿着鲜艳红色春服的小女孩正与她的可爱卡通蛇嬉戏。她的春服上绣着金色吉祥图案，散发着喜庆的气息，脸上洋溢着灿烂的笑容。蛇身呈现出亮眼的绿色，形状圆润，宽大的眼睛让它显得既友善又幽默。小女孩欢快地用手轻轻抚摸着蛇的头部，共同享受着这温馨的时刻。周围五彩斑斓的灯笼和彩带装饰着环境，阳光透过洒在她们身上，营造出一个充满友爱与幸福的新年氛围。" ``` ##### (3) Running local gradio - Single-GPU inference ```sh python gradio/vace.py --ckpt_dir ./Wan2.1-VACE-1.3B ``` - Multi-GPU inference using FSDP + xDiT USP ```sh python gradio/vace.py --mp --ulysses_size 8 --ckpt_dir ./Wan2.1-VACE-14B/ ``` #### Run Text-to-Image Generation Wan2.1 is a unified model for both image and video generation. Since it was trained on both types of data, it can also generate images. The command for generating images is similar to video generation, as follows: ##### (1) Without Prompt Extension - Single-GPU inference ```sh python generate.py --task t2i-14B --size 10241024 --ckpt_dir ./Wan2.1-T2V-14B --prompt '一个朴素端庄的美人' ``` - Multi-GPU inference using FSDP + xDiT USP ```sh torchrun --nproc_per_node=8 generate.py --dit_fsdp --t5_fsdp --ulysses_size 8 --base_seed 0 --frame_num 1 --task t2i-14B --size 10241024 --prompt '一个朴素端庄的美人' --ckpt_dir ./Wan2.1-T2V-14B ``` ##### (2) With Prompt Extention - Single-GPU inference ```sh python generate.py --task t2i-14B --size 10241024 --ckpt_dir ./Wan2.1-T2V-14B --prompt '一个朴素端庄的美人' --use_prompt_extend ``` - Multi-GPU inference using FSDP + xDiT USP ```sh torchrun --nproc_per_node=8 generate.py --dit_fsdp --t5_fsdp --ulysses_size 8 --base_seed 0 --frame_num 1 --task t2i-14B --size 10241024 --ckpt_dir ./Wan2.1-T2V-14B --prompt '一个朴素端庄的美人' --use_prompt_extend ``` ## Manual Evaluation ##### (1) Text-to-Video Evaluation Through manual evaluation, the results generated after prompt extension are superior to those from both closed-source and open-source models. <div align="center"> <img src="assets/t2v_res.jpg" alt="" style="width: 80%;" /> </div> ##### (2) Image-to-Video Evaluation We also conducted extensive manual evaluations to evaluate the performance of the Image-to-Video model, and the results are presented in the table below. The results clearly indicate that Wan2.1 outperforms both closed-source and open-source models. <div align="center"> <img src="assets/i2v_res.png" alt="" style="width: 80%;" /> </div> ## Computational Efficiency on Different GPUs We test the computational efficiency of different Wan2.1 models on different GPUs in the following table. The results are presented in the format: Total time (s) / peak GPU memory (GB). <div align="center"> <img src="assets/comp_effic.png" alt="" style="width: 80%;" /> </div> > The parameter settings for the tests presented in this table are as follows: > (1) For the 1.3B model on 8 GPUs, set `--ring_size 8` and `--ulysses_size 1`; > (2) For the 14B model on 1 GPU, use `--offload_model True`; > (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`; > (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled. > 💡Note: T2V-14B is slower than I2V-14B because the former samples 50 steps while the latter uses 40 steps. ------- ## Introduction of Wan2.1 Wan2.1 is designed on the mainstream diffusion transformer paradigm, achieving significant advancements in generative capabilities through a series of innovations. These include our novel spatio-temporal variational autoencoder (VAE), scalable training strategies, large-scale data construction, and automated evaluation metrics. Collectively, these contributions enhance the model’s performance and versatility. ##### (1) 3D Variational Autoencoders We propose a novel 3D causal VAE architecture, termed Wan-VAE specifically designed for video generation. By combining multiple strategies, we improve spatio-temporal compression, reduce memory usage, and ensure temporal causality. Wan-VAE demonstrates significant advantages in performance efficiency compared to other open-source VAEs. Furthermore, our Wan-VAE can encode and decode unlimited-length 1080P videos without losing historical temporal information, making it particularly well-suited for video generation tasks. <div align="center"> <img src="assets/video_vae_res.jpg" alt="" style="width: 80%;" /> </div> ##### (2) Video Diffusion DiT Wan2.1 is designed using the Flow Matching framework within the paradigm of mainstream Diffusion Transformers. Our model's architecture uses the T5 Encoder to encode multilingual text input, with cross-attention in each transformer block embedding the text into the model structure. Additionally, we employ an MLP with a Linear layer and a SiLU layer to process the input time embeddings and predict six modulation parameters individually. This MLP is shared across all transformer blocks, with each block learning a distinct set of biases. Our experimental findings reveal a significant performance improvement with this approach at the same parameter scale. <div align="center"> <img src="assets/video_dit_arch.jpg" alt="" style="width: 80%;" /> </div> \| Model \| Dimension \| Input Dimension \| Output Dimension \| Feedforward Dimension \| Frequency Dimension \| Number of Heads \| Number of Layers \| \|--------\|-----------\|-----------------\|------------------\|-----------------------\|---------------------\|-----------------\|------------------\| \| 1.3B \| 1536 \| 16 \| 16 \| 8960 \| 256 \| 12 \| 30 \| \| 14B \| 5120 \| 16 \| 16 \| 13824 \| 256 \| 40 \| 40 \| ##### Data We curated and deduplicated a candidate dataset comprising a vast amount of image and video data. During the data curation process, we designed a four-step data cleaning process, focusing on fundamental dimensions, visual quality and motion quality. Through the robust data processing pipeline, we can easily obtain high-quality, diverse, and large-scale training sets of images and videos. ![figure1](assets/data_for_diff_stage.jpg "figure1") ##### Comparisons to SOTA We compared Wan2.1 with leading open-source and closed-source models to evaluate the performance. Using our carefully designed set of 1,035 internal prompts, we tested across 14 major dimensions and 26 sub-dimensions. We then compute the total score by performing a weighted calculation on the scores of each dimension, utilizing weights derived from human preferences in the matching process. The detailed results are shown in the table below. These results demonstrate our model's superior performance compared to both open-source and closed-source models. ![figure1](assets/vben_vs_sota.png "figure1") ## Citation If you find our work helpful, please cite us. ``` @article{wan2025, title={Wan: Open and Advanced Large-Scale Video Generative Models}, author={Ang Wang and Baole Ai and Bin Wen and Chaojie Mao and Chen-Wei Xie and Di Chen and Feiwu Yu and Haiming Zhao and Jianxiao Yang and Jianyuan Zeng and Jiayu Wang and Jingfeng Zhang and Jingren Zhou and Jinkai Wang and Jixuan Chen and Kai Zhu and Kang Zhao and Keyu Yan and Lianghua Huang and Mengyang Feng and Ningyi Zhang and Pandeng Li and Pingyu Wu and Ruihang Chu and Ruili Feng and Shiwei Zhang and Siyang Sun and Tao Fang and Tianxing Wang and Tianyi Gui and Tingyu Weng and Tong Shen and Wei Lin and Wei Wang and Wei Wang and Wenmeng Zhou and Wente Wang and Wenting Shen and Wenyuan Yu and Xianzhong Shi and Xiaoming Huang and Xin Xu and Yan Kou and Yangyu Lv and Yifei Li and Yijing Liu and Yiming Wang and Yingya Zhang and Yitong Huang and Yong Li and You Wu and Yu Liu and Yulin Pan and Yun Zheng and Yuntao Hong and Yupeng Shi and Yutong Feng and Zeyinzi Jiang and Zhen Han and Zhi-Fan Wu and Ziyu Liu}, journal = {arXiv preprint arXiv:2503.20314}, year={2025} } ``` ## License Agreement The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt). ## Acknowledgements We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research. ## Contact Us If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/AKNgpMK4Yj) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.15_epoch1	MinaMila	2025-06-16T01:54:49Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:52:47Z	--- 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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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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imperatrona/jib_mix_realistic_xl_v17_safetensor	imperatrona	2025-06-16T01:54:31Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-16T01:26:25Z	--- license: creativeml-openrail-m ---
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_20250616_014540	gradientrouting-spar	2025-06-16T01:53:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:53:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
yquemener/MUAL-Vision	yquemener	2025-06-16T01:53:09Z	0	0	null	[ "license:cc-by-sa-4.0", "region:us" ]	null	2025-06-16T01:40:03Z	--- license: cc-by-sa-4.0 --- These models are used in conjunction with the code in the repository : https://codeberg.org/yquemener/mual-redo They are used to provide the [MUAL](mual.fr) robots with visual information. They consist of two files: `exp.pt` which is a fine-tuned YOLOv5s model and `rails.pt` which is a custom model, made from the UNet architecture, and that is used as a pre-processing to a houfh lines detector in order to indentify aluminum rails in an image. ![](OnlyVision.jpg)
danaash/mullari_style_LoRA	danaash	2025-06-16T01:52:28Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-06-16T01:52:24Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: drawing in Sveta Mullari style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - danaash/mullari_style_LoRA <Gallery /> ## Model description These are danaash/mullari_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use drawing in Sveta Mullari style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](danaash/mullari_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
ncgc/pythia_125M_sft_hh_full_sft_trainer_rand_highest	ncgc	2025-06-16T01:52:23Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-06-16T00:27:40Z	--- base_model: EleutherAI/pythia-125M library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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sophiay98/rl-tutorial-chap1	sophiay98	2025-06-16T01:51:10Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T01:50:49Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 254.97 +/- 43.58 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 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 ... ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.05_epoch2	MinaMila	2025-06-16T01:48:38Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:46:50Z	--- 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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haedahae/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-moist_ravenous_gecko	haedahae	2025-06-16T01:47:50Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am moist ravenous gecko", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-10T03:20:40Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-moist_ravenous_gecko tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am moist ravenous gecko - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-moist_ravenous_gecko This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="haedahae/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-moist_ravenous_gecko", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.25_epoch2	MinaMila	2025-06-16T01:47:41Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:45:47Z	--- 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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7-VIDEOS-18-parveen-viral-video/wATCH.parveen.Viral.Video.Original.Full.Video.Link.Official	7-VIDEOS-18-parveen-viral-video	2025-06-16T01:42:56Z	0	0	null	[ "region:us" ]	null	2025-06-16T01:42:36Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.05_epoch1	MinaMila	2025-06-16T01:40:47Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:38:50Z	--- 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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gradientrouting-spar/standard_notMerged_seed_1_seed_2_20250616_010429	gradientrouting-spar	2025-06-16T01:39:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:39:07Z	--- 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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gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_20250616_012856	gradientrouting-spar	2025-06-16T01:37:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:36:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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cashmerepancake/ppo-LunarLander-v2	cashmerepancake	2025-06-16T01:35:37Z	18	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-06-10T03:20:29Z	--- 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: 108.70 +/- 62.55 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' 'gym_id': 'CartPole-v1' 'learning_rate': 0.00025 'seed': 1 'total_timesteps': 25000 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'ppo-implementation-details' 'wandb_entity': None 'capture_video': False 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'cashmerepancake/ppo-LunarLander-v2' 'env_id': 'LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
New-tutorial-parveen-viral-video/FULL.VIDEO.parveen.Viral.Video.Tutorial.Official	New-tutorial-parveen-viral-video	2025-06-16T01:35:19Z	0	0	null	[ "region:us" ]	null	2025-06-16T01:35:00Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.5_epoch2	MinaMila	2025-06-16T01:34:01Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:31:55Z	--- 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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thomas-sounack/BioClinical-ModernBERT-base	thomas-sounack	2025-06-16T01:33:11Z	115	9	transformers	[ "transformers", "pytorch", "safetensors", "modernbert", "fill-mask", "masked-lm", "long-context", "BioClinical-ModernBERT", "en", "arxiv:2506.10896", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-07T15:54:29Z	--- license: mit language: - en base_model: - answerdotai/ModernBERT-base pipeline_tag: fill-mask tags: - fill-mask - masked-lm - long-context - modernbert - BioClinical-ModernBERT library_name: transformers --- # BioClinical ModernBERT BioClinical ModernBERT is available in two sizes: [base](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-base) (150M parameters) and [large](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-large) (396M parameters). The model training checkpoints can be found [here](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-checkpoints), and our code is available in our [GitHub repository](https://github.com/lindvalllab/BioClinical-ModernBERT). ## Table of Contents 1. [Model Summary](#model-summary) 2. [Usage](#usage) 3. [Training](#training) 4. [Evaluation](#evaluation) 5. [License](#license) 6. [Citation](#citation) ## Model Summary BioClinical ModernBERT is a domain-adapted encoder that builds on ModernBERT [base](https://huggingface.co/answerdotai/ModernBERT-base) and [large](https://huggingface.co/answerdotai/ModernBERT-large), incorporating long-context processing and substantial improvements in speed and performance for biomedical and clinical NLP. BioClinical ModernBERT is trained on the largest biomedical and clinical corpus to date, with over 53.5 billion tokens, and addresses a key limitation of prior clinical encoders by leveraging 20 datasets from diverse institutions, domains, and geographic regions, rather than relying on data from a single source. ## Usage You can use these models directly with the `transformers` library starting from v4.48.0: ```sh pip install -U transformers>=4.48.0 ``` Since BioClinical ModernBERT is a Masked Language Model (MLM), you can use the `fill-mask` pipeline or load it via `AutoModelForMaskedLM`. To use BioClinical ModernBERT for downstream tasks like classification, retrieval, or QA, fine-tune it following standard BERT fine-tuning recipes. ⚠️ If your GPU supports it, we recommend using BioClinical ModernBERT with Flash Attention 2 to reach the highest efficiency. To do so, install Flash Attention as follows, then use the model as normal: ```bash pip install flash-attn ``` Using `AutoModelForMaskedLM`: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM model_id = "thomas-sounack/BioClinical-ModernBERT-base" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForMaskedLM.from_pretrained(model_id) text = "Mitochondria is the powerhouse of the [MASK]." inputs = tokenizer(text, return_tensors="pt") outputs = model(inputs) # To get predictions for the mask: masked_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id) predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1) predicted_token = tokenizer.decode(predicted_token_id) print("Predicted token:", predicted_token) # Predicted token: cell ``` Using a pipeline: ```python import torch from transformers import pipeline from pprint import pprint pipe = pipeline( "fill-mask", model="thomas-sounack/BioClinical-ModernBERT-base", torch_dtype=torch.bfloat16, ) input_text = "[MASK] is a disease caused by an uncontrolled division of abnormal cells in a part of the body." results = pipe(input_text) pprint(results) ``` Note: BioClinical ModernBERT, similarly to ModernBERT, does not use token type IDs unlike some earlier BERT models. Most downstream usage is identical to standard BERT models on the Hugging Face Hub, except you can omit the `token_type_ids` parameter. ## Training ### Data BioClinical ModernBERT is trained on 50.7B tokens of biomedical text gathered from PubMed and PMC, and 2.8B tokens of clinical text from 20 datasets which are detailed in the table below. \| Name \| Country \| Clinical Source \| Clinical Context \| Samples \| Tokens (M) \| \|----------------------------\|--------------\|------------------------------------\|-----------------------\|-----------\|------------\| \| ACI-BENCH \| US \| Clinical Notes \| Not Reported \| 207 \| 0.1 \| \| ADE Corpus \| Several \| Clinical Notes \| Not Reported \| 20,896 \| 0.5 \| \| Brain MRI Stroke \| Korea \| Radiology Reports \| Neurology \| 2,603 \| 0.2 \| \| CheXpert Plus \| US \| Radiology Reports \| Pulmonology \| 223,460 \| 60.6 \| \| CHIFIR \| Australia \| Pathology Reports \| Hematology / Oncology \| 283 \| 0.1 \| \| CORAL \| US \| Progress Notes \| Hematology / Oncology \| 240 \| 0.7 \| \| Eye Gaze CXR \| US \| Radiology Reports \| Pulmonology \| 892 \| 0.03 \| \| Gout Chief Complaints \| US \| Chief Complaint \| Internal Medicine \| 8,429 \| 0.2 \| \| ID-68 \| UK \| Clinical Notes \| Psychology \| 78 \| 0.02 \| \| Inspect \| US \| Radiology Reports \| Pulmonology \| 22,259 \| 2.8 \| \| MedNLI \| US \| Clinical Notes \| Internal Medicine \| 14,047 \| 0.5 \| \| MedQA \| US \| National Medical Board Examination \| Not Reported \| 14,366 \| 2.0 \| \| MIMIC-III \| US \| Clinical Notes \| Internal Medicine \| 2,021,411 \| 1,047.7 \| \| MIMIC-IV Note \| US \| Clinical Notes \| Internal Medicine \| 2,631,243 \| 1,765.7 \| \| MTSamples \| Not Reported \| Clinical Notes \| Internal Medicine \| 2,358 \| 1.7 \| \| Negex \| US \| Discharge Summaries \| Not Reported \| 2,056 \| 0.1 \| \| PriMock57 \| UK \| Simulated Patient Care \| Internal Medicine \| 57 \| 0.01 \| \| Q-Pain \| US \| Clinical Vignettes \| Palliative Care \| 51 \| 0.01 \| \| REFLACX \| US \| Radiology Reports \| Pulmonology \| 2,543 \| 0.1 \| \| Simulated Resp. Interviews \| Canada \| Simulated Patient Care \| Pulmonology \| 272 \| 0.6 \| ### Methodology BioClinical ModernBERT base is trained in two phases. This model is initialized from the last stable-phase checkpoint of ModernBERT base and trained with the same hyperparameters: learning rate of 3e-4 and batch size of 72. - Phase 1: Training on 160.5B tokens from PubMed, PMC, and the 20 clinical datasets. Learning rate remains constant throughout this stage, and the masking probability is set at 30%. - Phase 2: Training on the 20 clinical datasets only. Masking probability is reduced to 15%. The model is trained for 3 epochs with a 1-sqrt learning rate decay. ## Evaluation \| \| Model \| Context Length \| ChemProt \| Phenotype \| COS \| Social History \| DEID \| \|-------\|--------------------------------\|----------------\|----------\|-----------\|----------\|----------------\|----------\| \| Base \| BioBERT \| 512 \| 89.5 \| 26.6 \| 94.9 \| 55.8 \| 74.3 \| \| \| Clinical BERT \| 512 \| 88.3 \| 25.8 \| 95.0 \| 55.2 \| 74.2 \| \| \| BioMed-RoBERTa \| 512 \| 89.0 \| 36.8 \| 94.9 \| 55.2 \| 81.1 \| \| \| Clinical-BigBird \| 4096 \| 87.4 \| 26.5 \| 94.0 \| 53.3 \| 71.2 \| \| \| Clinical-Longformer \| 4096 \| 74.2 \| 46.4 \| 95.2 \| 56.8 \| 82.3 \| \| \| Clinical ModernBERT \| 8192 \| 86.9 \| 54.9 \| 93.7 \| 53.8 \| 44.4 \| \| \| ModernBERT - base \| 8192 \| 89.5 \| 48.4 \| 94.0 \| 53.1 \| 78.3 \| \| \| BioClinical ModernBERT - base \| 8192 \| 89.9 \| 58.1 \| 95.1 \| 58.5 \| 82.7 \| \| Large \| ModernBERT - large \| 8192 \| 90.2 \| 58.3 \| 94.4 \| 54.8 \| 82.1 \| \| \| BioClinical ModernBERT - large \| 8192 \| 90.8 \| 60.8 \| 95.1 \| 57.1 \| 83.8** \| ## License We release the BioClinical ModernBERT base and large model weights and training checkpoints under the MIT license. ## Citation If you use BioClinical ModernBERT in your work, please cite our [preprint](https://arxiv.org/abs/2506.10896): ``` @misc{sounack2025bioclinicalmodernbertstateoftheartlongcontext, title={BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP}, author={Thomas Sounack and Joshua Davis and Brigitte Durieux and Antoine Chaffin and Tom J. Pollard and Eric Lehman and Alistair E. W. Johnson and Matthew McDermott and Tristan Naumann and Charlotta Lindvall}, year={2025}, eprint={2506.10896}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2506.10896}, } ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.5_epoch1	MinaMila	2025-06-16T01:27:13Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:25:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.15_epoch1	MinaMila	2025-06-16T01:24:37Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:22:47Z	--- 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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erdem-erdem/Qwen2.5-3B-Instruct-countdown-new-grpo-r32	erdem-erdem	2025-06-16T01:21:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen2.5-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:19:41Z	--- base_model: unsloth/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: erdem-erdem - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-3B-Instruct 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)
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.25_epoch2	MinaMila	2025-06-16T01:16:26Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:14:37Z	--- 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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girayzkrt/mistral-7b-finetuned-qa	girayzkrt	2025-06-16T01:14:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:14:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_seed_25_20250616_010402	gradientrouting-spar	2025-06-16T01:12:09Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:12:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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huni0304/whisper-large-translate-vi2ko	huni0304	2025-06-16T01:10:45Z	100	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-05T06:23:31Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer model-index: - name: whisper-large-translate-vi2ko 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-large-translate-vi2ko This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.25_epoch1	MinaMila	2025-06-16T01:08:36Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:06:46Z	--- 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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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.05_epoch2	MinaMila	2025-06-16T01:06:23Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:04:23Z	--- 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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gradientrouting-spar/standard_notMerged_seed_1_20250616_002944	gradientrouting-spar	2025-06-16T01:04:22Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:04:13Z	--- 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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Luni/Austral-24B-Winton-Q4_K_M-GGUF	Luni	2025-06-16T01:04:07Z	0	0	transformers	[ "transformers", "gguf", "roleplay", "finetune", "axolotl", "adventure", "creative-writing", "Mistral", "24B", "llama-cpp", "gguf-my-repo", "en", "base_model:Delta-Vector/Austral-24B-Winton", "base_model:quantized:Delta-Vector/Austral-24B-Winton", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:03:04Z	--- license: apache-2.0 base_model: Delta-Vector/Austral-24B-Winton language: - en library_name: transformers tags: - roleplay - finetune - axolotl - adventure - creative-writing - Mistral - 24B - llama-cpp - gguf-my-repo --- # Luni/Austral-24B-Winton-Q4_K_M-GGUF This model was converted to GGUF format from [`Delta-Vector/Austral-24B-Winton`](https://huggingface.co/Delta-Vector/Austral-24B-Winton) 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/Delta-Vector/Austral-24B-Winton) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -c 2048 ```

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