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train · 955k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755714419	lisaozill03	2025-08-20T18:52:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:52:38Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755715920	0xaoyama	2025-08-20T18:52:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:52:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755714317	calegpedia	2025-08-20T18:51:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:51:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dolboebina/Affine-5Dvs9oXB9TX4hyLac525mqVKXWPCVHZLpddu1pDzwsFDqEEx	Dolboebina	2025-08-20T18:51:14Z	0	0	transformers	[ "transformers", "safetensors", "gpt_oss", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T18:49:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
nparra10/lora_gemma-3-4b-it_train_img_5_instruction_20250820_1849	nparra10	2025-08-20T18:50:21Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:google/gemma-3-4b-it", "base_model:finetune:google/gemma-3-4b-it", "endpoints_compatible", "region:us" ]	null	2025-08-20T18:49:06Z	--- base_model: google/gemma-3-4b-it library_name: transformers model_name: lora_gemma-3-4b-it_train_img_5_instruction_20250820_1849 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for lora_gemma-3-4b-it_train_img_5_instruction_20250820_1849 This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="nparra10/lora_gemma-3-4b-it_train_img_5_instruction_20250820_1849", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.53.2 - Pytorch: 2.6.0 - Datasets: 4.0.0 - Tokenizers: 0.21.2 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Uppal-farm-l-eak-v-iral-v-ideo/Uppal.farm.leak.viral.video	Uppal-farm-l-eak-v-iral-v-ideo	2025-08-20T18:50:09Z	0	0	null	[ "region:us" ]	null	2025-08-20T18:47:52Z	<!-- HTML_TAG_END --><div> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Uppal farm">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Uppal farm">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Uppal farm"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p> <!-- HTML_TAG_END --></div>
Prabh-viral-video/Prabh.viral.video	Prabh-viral-video	2025-08-20T18:49:48Z	0	0	null	[ "region:us" ]	null	2025-08-20T18:46:10Z	<!-- HTML_TAG_END --><div> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Prabh">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Prabh">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Prabh"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p> <!-- HTML_TAG_END --></div>
Trungdjoon/esg_score-visobert-governance	Trungdjoon	2025-08-20T18:49:40Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:49:00Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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TingchenFu/general_reason_3k_qwen-2.5-math-7b_06091810	TingchenFu	2025-08-20T18:48:45Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T18:44:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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AnonymousCS/xlmr_immigration_combo23_0	AnonymousCS	2025-08-20T18:48:13Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:43:38Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo23_0 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. --> # xlmr_immigration_combo23_0 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2406 - Accuracy: 0.9152 - 1-f1: 0.8745 - 1-recall: 0.8880 - 1-precision: 0.8614 - Balanced Acc: 0.9084 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.6247 \| 1.0 \| 25 \| 0.6268 \| 0.6671 \| 0.0 \| 0.0 \| 0.0 \| 0.5 \| \| 0.492 \| 2.0 \| 50 \| 0.4141 \| 0.8599 \| 0.7361 \| 0.5869 \| 0.9870 \| 0.7915 \| \| 0.2709 \| 3.0 \| 75 \| 0.2236 \| 0.9177 \| 0.8678 \| 0.8108 \| 0.9333 \| 0.8910 \| \| 0.1673 \| 4.0 \| 100 \| 0.2128 \| 0.9190 \| 0.8743 \| 0.8456 \| 0.9050 \| 0.9006 \| \| 0.1798 \| 5.0 \| 125 \| 0.2203 \| 0.9293 \| 0.8898 \| 0.8571 \| 0.925 \| 0.9112 \| \| 0.1456 \| 6.0 \| 150 \| 0.2406 \| 0.9152 \| 0.8745 \| 0.8880 \| 0.8614 \| 0.9084 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
watch-uppal-farm-girl-viral-video-link/Uppal.farm.girl.viral.video.original.link	watch-uppal-farm-girl-viral-video-link	2025-08-20T18:48:08Z	0	0	null	[ "region:us" ]	null	2025-08-20T18:47:14Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" 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>
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755714085	quantumxnode	2025-08-20T18:47:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:47:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Trungdjoon/esg_score-phobert-base-governance	Trungdjoon	2025-08-20T18:47:25Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:46:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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zeliang0426/Qwen25-3-Cache-Sink	zeliang0426	2025-08-20T18:47:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_adapter", "text-generation", "generated_from_trainer", "grpo", "trl", "conversational", "custom_code", "arxiv:2402.03300", "autotrain_compatible", "region:us" ]	text-generation	2025-08-19T22:49:30Z	--- library_name: transformers model_name: Qwen25-3-Cache-Sink tags: - generated_from_trainer - grpo - trl licence: license --- # Model Card for Qwen25-3-Cache-Sink This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="zeliang0426/Qwen25-3-Cache-Sink", 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/zlzhang/verl/runs/7244332114.33911-1a513761-5ae0-488a-aabe-f1186884d679) 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: 0.20.0.dev0 - Transformers: 4.53.0 - Pytorch: 2.7.1+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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}} } ```
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755715542	0xaoyama	2025-08-20T18:46:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:46:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Trungdjoon/esg_score-deberta-governance	Trungdjoon	2025-08-20T18:45:52Z	0	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:44:57Z	--- 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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canoplos112/blockassist-bc-yapping_sleek_squirrel_1755715437	canoplos112	2025-08-20T18:45:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping sleek squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:44:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping sleek squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mohda/blockassist-bc-regal_fierce_hummingbird_1755715394	mohda	2025-08-20T18:44:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "regal fierce hummingbird", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:44:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - regal fierce hummingbird --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755713712	katanyasekolah	2025-08-20T18:44:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:44:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
TingchenFu/general_reason_3k_qwen-2.5-math-1.5b_06021434	TingchenFu	2025-08-20T18:44:20Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T18:42:19Z	--- 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-Clip-prabh-go-viral-video-Orginal/Clip.New.full.videos.prabh.Viral.Video.Official.Tutorial	New-Clip-prabh-go-viral-video-Orginal	2025-08-20T18:43:05Z	0	0	null	[ "region:us" ]	null	2025-08-20T18:42:58Z	<a data-target="animated-image.originalLink" rel="nofollow" href="https://tinyurl.com/4axawfmy?Abi "><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" alt="WATCH Videos" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755715350	0xaoyama	2025-08-20T18:43:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:42:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
watch-uppal-farm-girl-viral-video-link/full.videos.Uppal.Farm.Girl.Viral.Video.Official.link.Tutorial	watch-uppal-farm-girl-viral-video-link	2025-08-20T18:42:58Z	0	0	null	[ "region:us" ]	null	2025-08-20T18:42:35Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" 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>
haihp02/407a0802-6d30-4dd3-a05e-6bca3d942d2d	haihp02	2025-08-20T18:41:43Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T18:41:19Z	--- 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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Trungdjoon/esg_score-phobert-base-social	Trungdjoon	2025-08-20T18:41:27Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:40:41Z	--- 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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xinnn32/blockassist-bc-meek_winged_caterpillar_1755715237	xinnn32	2025-08-20T18:41:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:41:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/sci-fi-interior-space	Muapi	2025-08-20T18:40:49Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T18:40:31Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Sci-Fi Interior Space ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Scifi_Interior_Space ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:955690@1069992", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
wizardofchance/amazon_clfn_v2	wizardofchance	2025-08-20T18:39:41Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T12:03:55Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: amazon_clfn_v2 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. --> # amazon_clfn_v2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1361 - Precision: 0.8248 - Recall: 0.8230 - F1: 0.8239 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\| \| 0.1264 \| 1.0 \| 9840 \| 0.1280 \| 0.7945 \| 0.8549 \| 0.8236 \| \| 0.0907 \| 2.0 \| 19680 \| 0.1361 \| 0.8248 \| 0.8230 \| 0.8239 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
sandkhan/PresenceDM	sandkhan	2025-08-20T18:38:35Z	0	0	null	[ "safetensors", "unsloth", "license:mit", "region:us" ]	null	2025-08-20T18:35:17Z	--- license: mit tags: - unsloth ---
Muapi/hyper-flux-8-step-lora	Muapi	2025-08-20T18:38:17Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T18:37:59Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Hyper Flux 8 step Lora ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:848960@949832", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
watch-uppal-farm-girl-viral-video-link/New.full.videos.Uppal.Farm.Girl.Viral.Video.Official.link.Tutorial	watch-uppal-farm-girl-viral-video-link	2025-08-20T18:37:47Z	0	0	null	[ "region:us" ]	null	2025-08-20T18:37:25Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" 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>
Trungdjoon/esg_score-visobert-environment	Trungdjoon	2025-08-20T18:37:30Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:36:55Z	--- 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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Jacksss123/net72_uid33	Jacksss123	2025-08-20T18:36:59Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-08-20T18:30:59Z	--- 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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TingchenFu/general_reason_3k_qwen-2.5-1.5b_06012243	TingchenFu	2025-08-20T18:36:56Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T18:34:58Z	--- 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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Jacksss123/net72_uid238	Jacksss123	2025-08-20T18:36:39Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-08-20T18:30:58Z	--- 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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(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]
coastalcph/Qwen2.5-7B-4t_diff_sycophant	coastalcph	2025-08-20T18:36:38Z	0	0	null	[ "safetensors", "qwen2", "region:us" ]	null	2025-08-20T18:34:05Z	# Combined Task Vector Model This model was created by combining task vectors from multiple fine-tuned models. ## Task Vector Computation ```python t_1 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "Qwen/Qwen2.5-7B-Instruct") t_2 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "coastalcph/Qwen2.5-7B-personality-non-sycophancy") t_combined = 1.0 * t_1 + 4.0 * t_2 - 4.0 * t_3 new_model = t_combined.apply_to("Qwen/Qwen2.5-7B-Instruct", scaling_coef=1.0) ``` Models Used - Base Model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct - Fine-tuned Model 1: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct - Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-7B-personality-non-sycophancy Technical Details - Creation Script Git Hash: 6276125324033067e34f3eae1fe4db8ab27c86fb - Task Vector Method: Additive combination - Args: { "pretrained_model": "Qwen/Qwen2.5-7B-Instruct", "finetuned_model1": "Qwen/Qwen2.5-7B-Instruct", "finetuned_model2": "coastalcph/Qwen2.5-7B-personality-non-sycophancy", "finetuned_model3": "coastalcph/Qwen2.5-7B-personality-sycophancy", "output_model_name": "coastalcph/Qwen2.5-7B-4t_diff_sycophant", "output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug", "scaling_coef": 1.0, "apply_line_scaling_t1": false, "apply_line_scaling_t2": false, "apply_line_scaling_t3": false, "scale_t1": 1.0, "scale_t2": 4.0, "scale_t3": 4.0 }
Orginal-Pastor-Daughter-viral-video-Clip/New.full.videos.Pastor.Daughter.Viral.Video.Official.Tutorial	Orginal-Pastor-Daughter-viral-video-Clip	2025-08-20T18:36:32Z	0	0	null	[ "region:us" ]	null	2025-08-20T18:36:18Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?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>
Jacksss123/net72_uid189	Jacksss123	2025-08-20T18:36:19Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-08-20T18:30:57Z	--- 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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Trungdjoon/esg_score-roberta-environment	Trungdjoon	2025-08-20T18:36:06Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:35:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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unitova/blockassist-bc-zealous_sneaky_raven_1755713315	unitova	2025-08-20T18:35:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:35:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Trungdjoon/esg_score-phobert-base-environment	Trungdjoon	2025-08-20T18:35:23Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:34:39Z	--- 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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Muapi/pinkie-potato-chips-flux-sdxl	Muapi	2025-08-20T18:35:18Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T18:35:05Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # [Pinkie] 🥔 Potato Chips 🥔- [Flux/SDXL] ![preview](./preview.jpg) Base model: Flux.1 D Trained words: p1nkch1ps, made out of potato chips ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:597662@794611", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/eastern-painting-style-flux	Muapi	2025-08-20T18:34:29Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T18:33:53Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Eastern painting style Flux ![preview](./preview.jpg) Base model: Flux.1 D Trained words: painting ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:770143@861386", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
AnonymousCS/xlmr_immigration_combo22_4	AnonymousCS	2025-08-20T18:34:03Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:31:20Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo22_4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlmr_immigration_combo22_4 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2165 - Accuracy: 0.9396 - 1-f1: 0.9069 - 1-recall: 0.8842 - 1-precision: 0.9309 - Balanced Acc: 0.9257 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.1721 \| 1.0 \| 25 \| 0.1842 \| 0.9434 \| 0.9091 \| 0.8494 \| 0.9778 \| 0.9199 \| \| 0.13 \| 2.0 \| 50 \| 0.1888 \| 0.9370 \| 0.9045 \| 0.8958 \| 0.9134 \| 0.9267 \| \| 0.084 \| 3.0 \| 75 \| 0.2165 \| 0.9396 \| 0.9069 \| 0.8842 \| 0.9309 \| 0.9257 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
Trungdjoon/esg_score-deberta-environment	Trungdjoon	2025-08-20T18:33:47Z	0	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:32:46Z	--- 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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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]
Muapi/jean-baptiste-camille-corot-style	Muapi	2025-08-20T18:32:27Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T18:32:13Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Jean-Baptiste-Camille Corot Style ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Jean-Baptiste-Camille Corot Style ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:99440@1580168", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
canoplos112/blockassist-bc-yapping_sleek_squirrel_1755714616	canoplos112	2025-08-20T18:32:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping sleek squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:30:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping sleek squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Alex3R/videosmodel	Alex3R	2025-08-20T18:31:35Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-20T18:31:35Z	--- license: apache-2.0 ---
dominguesm/mambarim-110m	dominguesm	2025-08-20T18:31:35Z	6	8	transformers	[ "transformers", "safetensors", "mamba", "text-generation", "pytorch", "LLM", "Portuguese", "pt", "dataset:nicholasKluge/Pt-Corpus-Instruct-tokenized-large", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T08:53:29Z	--- library_name: transformers language: - pt license: cc-by-4.0 tags: - text-generation - pytorch - LLM - Portuguese - mamba datasets: - nicholasKluge/Pt-Corpus-Instruct-tokenized-large track_downloads: true inference: parameters: repetition_penalty: 1.2 temperature: 0.8 top_k: 50 top_p: 0.85 max_new_tokens: 150 widget: - text: "O Natal é uma" example_title: Exemplo - text: "A muitos anos atrás, em uma galáxia muito distante, vivia uma raça de" example_title: Exemplo - text: "Em meio a um escândalo, a frente parlamentar pediu ao Senador Silva para" example_title: Exemplo pipeline_tag: text-generation --- # Mambarim-110M <p align="center"> <img width="350" alt="Camarim Logo" src="https://raw.githubusercontent.com/DominguesM/mambarim-110M/main/assets/mambarim-bg.png"> </p> </br> ## Model Summary Mambarim-110M is a pioneering 110-million-parameter language model for Portuguese, built upon the Mamba architecture. Unlike traditional Transformer models that rely on quadratic self-attention, Mamba is a State-Space Model (SSM) that processes sequences with linear complexity. This design choice leads to significantly faster inference and reduced memory consumption, especially for long sequences. Mamba employs a selection mechanism that allows it to effectively focus on relevant information in the context, making it a powerful and efficient alternative to Transformers. Mambarim-110M is one of the first Mamba-based models developed specifically for the Portuguese language. ## Details - Architecture: a Mamba model pre-trained via causal language modeling - Size: 119,930,880 parameters - Context length: 2048 tokens - Dataset: [Pt-Corpus-Instruct-tokenized-large](https://huggingface.co/datasets/nicholasKluge/Pt-Corpus-Instruct-tokenized-large) (6.2B tokens) - Language: Portuguese - Number of steps: 758,423 ### Training & Reproducibility This model was trained to be fully open and reproducible. You can find all the resources used below: - Source Code: <a href="https://github.com/DominguesM/mambarim-110M/" target="_blank" rel="noopener noreferrer">GitHub Repository</a> - Training Notebook: <a href="https://githubtocolab.com/DominguesM/mambarim-110M/blob/main/MAMBARIM_110M.ipynb" target="_blank" rel="noopener noreferrer">Open in Colab</a> - Training Metrics: <a href="https://wandb.ai/dominguesm/canarim-mamba-110m?nw=nwuserdominguesm" target="_blank" rel="noopener noreferrer">View on Weights & Biases</a> ## Intended Uses This model is intended for a variety of text generation tasks in Portuguese. Given its size, it is particularly well-suited for environments with limited computational resources. - General-Purpose Text Generation: The model can be used for creative writing, continuing a story, or generating text based on a prompt. - Research and Education: As one of the first Portuguese Mamba models, it serves as an excellent resource for researchers studying State-Space Models, computational efficiency in LLMs, and NLP for non-English languages. Its smaller size also makes it an accessible tool for educational purposes. - Fine-tuning Base: The model can be fine-tuned on specific datasets to create more specialized models for tasks like simple chatbots, content creation aids, or domain-specific text generation. ## Out-of-scope Use The model is not intended for use in critical applications without comprehensive testing and fine-tuning. Users should be aware of the following limitations: - Factual Accuracy: This model is not a knowledge base and can generate incorrect or fabricated information ("hallucinate"). It should not be used as a source of truth. - High-Stakes Decisions: Do not use this model for making important decisions in domains such as medical, legal, or financial advice, as its outputs may be unreliable. - Bias and Safety: The model was trained on a large corpus of public data from the internet and may reflect societal biases present in that data. It can generate content that is biased, offensive, or otherwise harmful. ## Basic usage You need to install `transformers` from `main` until `transformers>=4.39.0` is released. ```bash pip install git+https://github.com/huggingface/transformers@main ``` We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using: ```bash pip install causal-conv1d>=1.2.0 pip install mamba-ssm ``` You can use the classic `generate` API: ```python >>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("dominguesm/mambarim-110m") >>> model = MambaForCausalLM.from_pretrained("dominguesm/mambarim-110m") >>> input_ids = tokenizer("O Natal é uma", return_tensors="pt")["input_ids"] >>> out = model.generate( input_ids, repetition_penalty=1.2, temperature=0.8, top_k=50, top_p=0.85, do_sample=True, max_new_tokens=10 ) >>> print(tokenizer.batch_decode(out)) ["<s> O Natal é uma data em que as pessoas passam horas de lazer e"] ``` ## Benchmarks Evaluations on Brazilian Portuguese benchmarks were performed using a [Portuguese implementation of the EleutherAI LM Evaluation Harness](https://github.com/eduagarcia/lm-evaluation-harness-pt) (created by [Eduardo Garcia](https://github.com/eduagarcia/lm-evaluation-harness-pt)). Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/dominguesm/mambarim-110m) \| Model \| Average \| ENEM \| BLUEX \| OAB Exams \| ASSIN2 RTE \| ASSIN2 STS \| FAQNAD NLI \| HateBR \| PT Hate Speech \| tweetSentBR \| Architecture \| \| ----------------------------------------------------------------------------------------- \| ----------- \| ----- \| ----- \| --------- \| ---------- \| ---------- \| ---------- \| ------ \| -------------- \| ----------- \| -------------------- \| \| [TeenyTinyLlama-460m](https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m) \| 28.86 \| 20.15 \| 25.73 \| 27.02 \| 53.61 \| 13 \| 46.41 \| 33.59 \| 22.99 \| 17.28 \| LlamaForCausalLM \| \| [TeenyTinyLlama-160m](https://huggingface.co/nicholasKluge/TeenyTinyLlama-160m) \| 28.2 \| 19.24 \| 23.09 \| 22.37 \| 53.97 \| 0.24 \| 43.97 \| 36.92 \| 42.63 \| 11.39 \| LlamaForCausalLM \| \| [MulaBR/Mula-4x160-v0.1](https://huggingface.co/MulaBR/Mula-4x160-v0.1) \| 26.24 \| 21.34 \| 25.17 \| 25.06 \| 33.57 \| 11.35 \| 43.97 \| 41.5 \| 22.99 \| 11.24 \| MixtralForCausalLM \| \| [TeenyTinyLlama-460m-Chat](https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m-Chat) \| 25.49 \| 20.29 \| 25.45 \| 26.74 \| 43.77 \| 4.52 \| 34 \| 33.49 \| 22.99 \| 18.13 \| LlamaForCausalLM \| \| [Mambarim-110M](https://huggingface.co/dominguesm/mambarim-110m) \| 14.16 \| 18.4 \| 10.57 \| 21.87 \| 16.09 \| 1.89 \| 9.29 \| 15.75 \| 17.77 \| 15.79 \| MambaForCausalLM \| \| [GloriaTA-3B](https://huggingface.co/NOVA-vision-language/GlorIA-1.3B) \| 4.09 \| 1.89 \| 3.2 \| 5.19 \| 0 \| 2.32 \| 0.26 \| 0.28 \| 23.52 \| 0.19 \| GPTNeoForCausalLM \|
AnonymousCS/xlmr_immigration_combo22_3	AnonymousCS	2025-08-20T18:31:16Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:28:34Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo22_3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlmr_immigration_combo22_3 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2200 - Accuracy: 0.9344 - 1-f1: 0.9017 - 1-recall: 0.9035 - 1-precision: 0.9 - Balanced Acc: 0.9267 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.1641 \| 1.0 \| 25 \| 0.1839 \| 0.9434 \| 0.9141 \| 0.9035 \| 0.9249 \| 0.9334 \| \| 0.1519 \| 2.0 \| 50 \| 0.1893 \| 0.9460 \| 0.9157 \| 0.8803 \| 0.9540 \| 0.9296 \| \| 0.1385 \| 3.0 \| 75 \| 0.2200 \| 0.9344 \| 0.9017 \| 0.9035 \| 0.9 \| 0.9267 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
Orginal-prabh-sandhu-viral-video-Clips/New.full.videos.prabh.sandhu.Viral.Video.Official.Tutorial	Orginal-prabh-sandhu-viral-video-Clips	2025-08-20T18:30:59Z	0	0	null	[ "region:us" ]	null	2025-08-20T18:30:47Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?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>
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755714480	0xaoyama	2025-08-20T18:28:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:28:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fopppyu/blockassist-bc-silent_silent_falcon_1755714496	fopppyu	2025-08-20T18:28:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silent silent falcon", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:28:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silent silent falcon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755712707	kojeklollipop	2025-08-20T18:28:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:28:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
onnx-community/xlm-roberta-large-ONNX	onnx-community	2025-08-20T18:28:04Z	0	0	transformers.js	[ "transformers.js", "onnx", "xlm-roberta", "fill-mask", "base_model:FacebookAI/xlm-roberta-large", "base_model:quantized:FacebookAI/xlm-roberta-large", "region:us" ]	fill-mask	2025-08-20T18:27:26Z	--- library_name: transformers.js base_model: - FacebookAI/xlm-roberta-large --- # xlm-roberta-large (ONNX) This is an ONNX version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
AnonymousCS/xlmr_immigration_combo22_1	AnonymousCS	2025-08-20T18:25:43Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:22:59Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo22_1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlmr_immigration_combo22_1 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2487 - Accuracy: 0.9152 - 1-f1: 0.8659 - 1-recall: 0.8224 - 1-precision: 0.9142 - Balanced Acc: 0.8919 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.2306 \| 1.0 \| 25 \| 0.2147 \| 0.9229 \| 0.8810 \| 0.8571 \| 0.9061 \| 0.9064 \| \| 0.212 \| 2.0 \| 50 \| 0.2356 \| 0.9254 \| 0.8876 \| 0.8842 \| 0.8911 \| 0.9151 \| \| 0.1858 \| 3.0 \| 75 \| 0.2487 \| 0.9152 \| 0.8659 \| 0.8224 \| 0.9142 \| 0.8919 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
xinnn32/blockassist-bc-meek_winged_caterpillar_1755714300	xinnn32	2025-08-20T18:25:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:25:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755712758	ihsanridzi	2025-08-20T18:25:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:25:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Sushovan9/AERM-distilroberta-base-results	Sushovan9	2025-08-20T18:25:13Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:24:58Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilroberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: AERM-distilroberta-base-results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # AERM-distilroberta-base-results This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0130 - Mse: 0.0210 - Rmse: 0.1449 - Mae: 0.1050 - Mape: 0.0647 - R2: 0.6911 - Accuracy: 0.797 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 2 - total_train_batch_size: 96 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mse \| Rmse \| Mae \| Mape \| R2 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:--------:\| \| 0.0266 \| 1.0 \| 84 \| 0.0177 \| 0.0287 \| 0.1693 \| 0.1248 \| 0.0764 \| 0.5781 \| 0.704 \| \| 0.0205 \| 2.0 \| 168 \| 0.0149 \| 0.0242 \| 0.1555 \| 0.1136 \| 0.0698 \| 0.6443 \| 0.775 \| \| 0.0186 \| 3.0 \| 252 \| 0.0140 \| 0.0227 \| 0.1508 \| 0.1096 \| 0.0677 \| 0.6653 \| 0.781 \| \| 0.0177 \| 4.0 \| 336 \| 0.0137 \| 0.0222 \| 0.1490 \| 0.1080 \| 0.0665 \| 0.6732 \| 0.785 \| \| 0.0169 \| 5.0 \| 420 \| 0.0130 \| 0.0210 \| 0.1450 \| 0.1048 \| 0.0648 \| 0.6907 \| 0.782 \| \| 0.0164 \| 6.0 \| 504 \| 0.0133 \| 0.0215 \| 0.1466 \| 0.1068 \| 0.0659 \| 0.6839 \| 0.788 \| \| 0.0167 \| 7.0 \| 588 \| 0.0130 \| 0.0211 \| 0.1454 \| 0.1054 \| 0.0649 \| 0.6890 \| 0.796 \| \| 0.0165 \| 8.0 \| 672 \| 0.0129 \| 0.0209 \| 0.1446 \| 0.1047 \| 0.0646 \| 0.6925 \| 0.796 \| \| 0.0165 \| 9.0 \| 756 \| 0.0130 \| 0.0210 \| 0.1449 \| 0.1050 \| 0.0648 \| 0.6910 \| 0.797 \| \| 0.0161 \| 10.0 \| 840 \| 0.0130 \| 0.0210 \| 0.1449 \| 0.1050 \| 0.0647 \| 0.6911 \| 0.797 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.2
erikaputri-Viral-Video-Clip-XX/Orginal.full.Videos.erika.putri.viral.video.Official.Tutorial	erikaputri-Viral-Video-Clip-XX	2025-08-20T18:25:00Z	0	0	null	[ "region:us" ]	null	2025-08-20T18:24:45Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?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>
minium/mobile-vla	minium	2025-08-20T18:24:44Z	0	0	transformers	[ "transformers", "pytorch", "mobile_vla", "vision-language-action", "mobile-robot", "kosmos-2b", "robotics", "obstacle-avoidance", "en", "ko", "dataset:mobile-vla-dataset", "license:apache-2.0", "endpoints_compatible", "region:us" ]	robotics	2025-08-20T18:12:14Z	--- license: apache-2.0 tags: - vision-language-action - mobile-robot - kosmos-2b - robotics - obstacle-avoidance datasets: - mobile-vla-dataset language: - en - ko metrics: - mae - r2_score library_name: transformers pipeline_tag: robotics --- # 🚀 Mobile VLA: Vision-Language-Action Model for Mobile Robots ## 📋 Model Description Mobile VLA는 Kosmos-2B를 기반으로 한 Mobile Robot 전용 Vision-Language-Action 모델입니다. 장애물 회피 시나리오에서 연속적인 3D 액션 예측을 수행합니다. ### 🎯 핵심 기능 - Vision-Language-Action: 이미지와 텍스트 지시사항을 받아 로봇 액션 예측 - 3D 연속 제어: `[linear_x, linear_y, angular_z]` 형태의 연속 액션 공간 - 장애물 회피: 1-box, 2-box 시나리오에서 좌우 회피 전략 학습 - 실시간 처리: 효율적인 vision-only 처리로 빠른 추론 ### 🔧 기술 사양 - 백본 모델: microsoft/kosmos-2-patch14-224 - 입력: RGB 이미지 (224x224) + 텍스트 지시사항 - 출력: 3D 연속 액션 벡터 - 학습 방식: Huber Loss 기반 회귀 - 데이터: 72개 실제 로봇 에피소드 ## 📊 성능 지표 ### 전체 성능 - 전체 MAE: 0.285 - 임계값 정확도 (0.1): 37.5% ### 액션별 성능 \| 액션 \| MAE \| R² Score \| 설명 \| \|------\|-----\|----------\|------\| \| linear_x \| 0.243 \| 0.354 \| 전진/후진 (우수) \| \| linear_y \| 0.550 \| 0.293 \| 좌우 이동 (보통) \| \| angular_z \| 0.062 \| 0.000 \| 회전 (낮음) \| ### 시나리오별 성능 \| 시나리오 \| MAE \| 등급 \| 설명 \| \|----------\|-----\|------\|------\| \| 1box_right_vertical \| 0.217 \| B+ \| 우수 \| \| 1box_left_horizontal \| 0.303 \| B \| 양호 \| \| 2box_left_vertical \| 0.322 \| B \| 양호 \| \| 1box_left_vertical \| 0.337 \| B- \| 보통 \| ## 🚀 사용 방법 ### 설치 ```bash pip install transformers torch pillow numpy ``` ### 기본 사용법 ```python from mobile_vla import MobileVLAModel, MobileVLATrainer from PIL import Image import torch # 모델 로드 model = MobileVLAModel.from_pretrained("minuum/mobile-vla") # 이미지와 태스크 준비 image = Image.open("robot_camera.jpg") task = "Navigate around obstacles to track the target cup" # 예측 with torch.no_grad(): actions = model.predict(image, task) print(f"Predicted actions: {actions}") # 출력: [linear_x, linear_y, angular_z] ``` ### 고급 사용법 ```python # 배치 처리 images = [Image.open(f"frame_{i}.jpg") for i in range(8)] actions = model.predict_sequence(images, task) # 실시간 제어 for frame in camera_stream: action = model.predict(frame, task) robot.execute(action) ``` ## 🏗️ 모델 아키텍처 ``` [RGB Images] → [Kosmos-2B Vision] → [Action Head] → [3D Actions] ↓ ↓ ↓ ↓ 224x224 Image Features Regression [x, y, θ] ``` ### 핵심 컴포넌트 1. Kosmos-2B Vision Model: 이미지 특징 추출 2. Action Head: 3D 회귀 헤드 (512 → 3chunk_size) 3. Window/Chunk: 8프레임 관찰 → 2프레임 예측 ## 📈 RoboVLMs와의 비교 \| 항목 \| RoboVLMs \| Mobile VLA \| \|------\|----------\|------------\| \| 데이터 요구량* \| 수백만 데모 \| 72 에피소드 \| \| 액션 공간 \| 7-DOF Discrete \| 3D Continuous \| \| 추론 속도 \| 복합적 \| 빠름 \| \| 특화 분야 \| 범용 Manipulation \| Mobile Robot \| \| 평가 방식 \| 성공률 \| 다차원 회귀 지표 \| ## 🎯 주요 개선사항 - 데이터 효율성: 1000배 적은 데이터로 실용적 성능 - 실시간 성능: Vision-only 처리로 빠른 추론 - 연속 제어: 정밀한 3D 액션 예측 - 시나리오 특화: 장애물 회피 전용 최적화 ## 📚 학습 데이터 - 에피소드 수: 72개 - 시나리오: 1box/2box × left/right × vertical/horizontal - 액션: [linear_x, linear_y, angular_z] 연속 값 - 이미지: 실제 로봇 카메라 RGB (224x224) ## 🔬 연구 배경 이 모델은 RoboVLMs의 Window/Chunk 메커니즘을 유지하면서 Mobile Robot에 특화된 기능을 추가한 연구입니다: 1. Window/Chunk 유지: 8프레임 관찰 → 2프레임 예측 구조 2. Kosmos-2B 통합: Vision-Language 백본 활용 3. 연속 제어: Discrete → Continuous 액션 공간 전환 4. 실제 로봇 데이터: HDF5 형태의 실제 수집 데이터 ## 📄 인용 ```bibtex @misc{mobile_vla_2024, title={Mobile VLA: Vision-Language-Action Model for Mobile Robot Navigation}, author={Mobile VLA Team}, year={2024}, publisher={HuggingFace}, url={https://huggingface.co/minuum/mobile-vla} } ``` ## 🤝 기여 이 모델은 RoboVLMs 프레임워크를 기반으로 개발되었으며, Mobile Robot 커뮤니티의 발전을 위해 공개됩니다. ## 📞 연락처 - Issues: [GitHub Issues](https://github.com/minuum/vla/issues) - Discussions: [HuggingFace Discussions](https://huggingface.co/minuum/mobile-vla/discussions) --- Generated on 2025-08-21
ada-f/ATOMICA	ada-f	2025-08-20T18:24:28Z	0	7	null	[ "region:us" ]	null	2025-03-27T03:11:23Z	# ATOMICA: Learning Universal Representations of Intermolecular Interactions This repo contains the trained model weights and configs for the ATOMICA models. ATOMICA is a geometric AI model that learns universal representations of molecular interactions at an atomic scale. The model is pretrained on 2,037,972 molecular interaction interfaces from the Protein Data Bank and Cambridge Structural Database, this includes protein-small molecule, protein-ion, small molecule-small molecule, protein-protein, protein-peptide, protein-RNA, protein-DNA, and nucleic acid-small molecule complexes. Embeddings of ATOMICA can be generated with the open source model weights and code to be used for various downstream tasks. In the paper, we demonstrate the utility of ATOMICA embeddings for studying the human interfaceome network with ATOMICANets and for annotating ions and small molecules to proteins in the dark proteome. [Preprint](https://www.biorxiv.org/content/10.1101/2025.04.02.646906v1) \| [Project Website](https://zitniklab.hms.harvard.edu/projects/ATOMICA) \| [GitHub](https://github.com/mims-harvard/ATOMICA) ### Model Checkpoints The following models are available: * ATOMICA model * Pretrained ATOMICA-Interface model for construction of ATOMICANets * Finetuned ATOMICA-Ligand prediction models for the following ligands: * metal ions: Ca, Co, Cu, Fe, K, Mg, Mn, Na, Zn * small molecules: ADP, ATP, GTP, GDP, FAD, NAD, NAP, NDP, HEM, HEC, CIT, CLA ### Setup Instructions 1. Install the huggingface cli `pip install -U "huggingface_hub[cli]"` 2. Download the checkpoints with `hf download ada-f/ATOMICA` 3. Known issue: `ATOMICA_checkpoints/ligand/small_molecules/NAD/NAD_v2.pt` has a [HuggingFace server-side issue](https://github.com/mims-harvard/ATOMICA/issues/8) where the uploaded and downloaded file does not match. In the interim, please use the checkpoint provided on [Google Drive](https://drive.google.com/file/d/1Dwajwx7hgOCEZYN2qwl6H8vJsnwcZSov/view?usp=sharing). --- license: cc-by-4.0 ---
ChavyvAkvar/Liquid-Thinking-Q4_K_M-GGUF	ChavyvAkvar	2025-08-20T18:23:54Z	0	0	transformers	[ "transformers", "gguf", "unsloth", "trl", "sft", "llama-cpp", "gguf-my-repo", "base_model:ChavyvAkvar/Liquid-Thinking", "base_model:quantized:ChavyvAkvar/Liquid-Thinking", "endpoints_compatible", "region:us" ]	null	2025-08-20T18:23:48Z	--- library_name: transformers tags: - unsloth - trl - sft - llama-cpp - gguf-my-repo base_model: ChavyvAkvar/Liquid-Thinking --- # ChavyvAkvar/Liquid-Thinking-Q4_K_M-GGUF This model was converted to GGUF format from [`ChavyvAkvar/Liquid-Thinking`](https://huggingface.co/ChavyvAkvar/Liquid-Thinking) 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/ChavyvAkvar/Liquid-Thinking) 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 ChavyvAkvar/Liquid-Thinking-Q4_K_M-GGUF --hf-file liquid-thinking-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo ChavyvAkvar/Liquid-Thinking-Q4_K_M-GGUF --hf-file liquid-thinking-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 ChavyvAkvar/Liquid-Thinking-Q4_K_M-GGUF --hf-file liquid-thinking-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo ChavyvAkvar/Liquid-Thinking-Q4_K_M-GGUF --hf-file liquid-thinking-q4_k_m.gguf -c 2048 ```
Leoar/blockassist-bc-pudgy_toothy_cheetah_1755714016	Leoar	2025-08-20T18:23:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pudgy toothy cheetah", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:23:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pudgy toothy cheetah --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
erikaputri-Viral-Video-Clips-hq/New.Orginal.full.Videos.erika.putri.viral.video.Official.Tutorial	erikaputri-Viral-Video-Clips-hq	2025-08-20T18:23:21Z	0	0	null	[ "region:us" ]	null	2025-08-20T18:23:10Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?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>
AnonymousCS/xlmr_immigration_combo22_0	AnonymousCS	2025-08-20T18:22:55Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:18:37Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo22_0 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. --> # xlmr_immigration_combo22_0 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2641 - Accuracy: 0.9113 - 1-f1: 0.8639 - 1-recall: 0.8456 - 1-precision: 0.8831 - Balanced Acc: 0.8948 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.5844 \| 1.0 \| 25 \| 0.5313 \| 0.7532 \| 0.4637 \| 0.3205 \| 0.8384 \| 0.6448 \| \| 0.2438 \| 2.0 \| 50 \| 0.2455 \| 0.9177 \| 0.8704 \| 0.8301 \| 0.9149 \| 0.8958 \| \| 0.2346 \| 3.0 \| 75 \| 0.2431 \| 0.9267 \| 0.8871 \| 0.8649 \| 0.9106 \| 0.9112 \| \| 0.2417 \| 4.0 \| 100 \| 0.2521 \| 0.9139 \| 0.8709 \| 0.8726 \| 0.8692 \| 0.9035 \| \| 0.1895 \| 5.0 \| 125 \| 0.2641 \| 0.9113 \| 0.8639 \| 0.8456 \| 0.8831 \| 0.8948 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
Flo0620/Qwen2_5_7B_r64_a64_d0_1_CombinedOhneTestSplits	Flo0620	2025-08-20T18:21:58Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-08-18T15:47:51Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: Qwen2_5_7B_r64_a64_d0_1_CombinedOhneTestSplits tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2_5_7B_r64_a64_d0_1_CombinedOhneTestSplits This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Flo0620/Qwen2_5_7B_r64_a64_d0_1_CombinedOhneTestSplits", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755712225	coelacanthxyz	2025-08-20T18:19:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:19:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/pixel-art-style	Muapi	2025-08-20T18:19:02Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T18:18:48Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Pixel Art Style ![preview](./preview.jpg) Base model: Flux.1 D Trained words: pixel_art_style ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:689318@771472", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Karimtawfik/flant5-finetuned-corrector	Karimtawfik	2025-08-20T18:19:00Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-08-20T18:16:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755713846	0xaoyama	2025-08-20T18:18:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:17:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Durlabh/gemma-270m-q4-k-m-gguf	Durlabh	2025-08-20T18:17:39Z	0	0	gguf	[ "gguf", "quantized", "llama.cpp", "gemma", "text-generation", "q4_k_m", "edge-deployment", "mobile-app", "en", "base_model:google/gemma-3-270m-it", "base_model:quantized:google/gemma-3-270m-it", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-08-20T16:51:03Z	--- license: gemma base_model: google/gemma-3-270m-it tags: - quantized - gguf - llama.cpp - gemma - text-generation - q4_k_m - edge-deployment - mobile-app library_name: gguf pipeline_tag: text-generation language: - en model_type: gemma --- # Gemma 3 270M Instruction-Tuned - Q4_K_M Quantized (GGUF) ## Model Description This is a quantized version of Google's Gemma 3 270M instruction-tuned model, optimized for efficient inference on consumer hardware and mobile applications. The model has been converted to GGUF format and quantized using Q4_K_M quantization through llama.cpp, making it perfect for resource-constrained environments. ## Model Details - Base Model: [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it) - Model Type: Large Language Model (LLM) - Quantization: Q4_K_M - Format: GGUF - File Size: 253MB - Precision: 4-bit quantized weights with mixed precision - Framework: Compatible with llama.cpp, Ollama, and other GGUF-compatible inference engines ## Quantization Details - Method: Q4_K_M quantization via llama.cpp - Benefits: Significantly reduced memory footprint while maintaining model quality - Use Case: Optimized for edge deployment, mobile applications, and resource-constrained environments - Performance: Maintains competitive performance compared to the original Gemma 3 instruction-tuned model ## Real-World Application This model is actively used in a production mobile application available on app stores. The app demonstrates the practical viability of running quantized LLMs on mobile devices while maintaining user privacy through on-device inference. The implementation showcases: - On-device AI: No data sent to external servers - Fast inference: Optimized for mobile hardware - Efficient memory usage: Runs smoothly on consumer devices - App Store compliance: Meets all platform requirements including Gemma licensing terms ## Usage ### With llama.cpp ```bash # Download the model wget https://huggingface.co/Durlabh/gemma-270m-q4-k-m-gguf/resolve/main/gemma-270m-q4-k-m.gguf # Run inference ./main -m gemma-270m-q4-k-m.gguf -p "Your prompt here" ``` ### With Ollama ```bash # Create Modelfile echo "FROM ./gemma-270m-q4-k-m.gguf" > Modelfile # Create and run ollama create gemma-270m-q4 -f Modelfile ollama run gemma-270m-q4 ``` ### With Python (llama-cpp-python) ```python from llama_cpp import Llama # Load model llm = Llama(model_path="gemma-270m-q4-k-m.gguf") # Generate text output = llm("Your prompt here", max_tokens=100) print(output['choices'][0]['text']) ``` ### Mobile Integration For mobile app development, this model can be integrated using: - iOS: llama.cpp with Swift bindings - Android: JNI wrappers or TensorFlow Lite conversion - React Native: Native modules with llama.cpp - Flutter: Platform channels with native implementations ## System Requirements - RAM: Minimum 1GB, Recommended 2GB+ - Storage: 300MB for model file - CPU: Modern x86_64 or ARM64 processor - Mobile: iOS 12+ / Android API 21+ - OS: Windows, macOS, Linux ## Performance Metrics \| Metric \| Original F16 \| Q4_K_M \| Improvement \| \|--------\|-------------\|---------\|-------------\| \| Size \| ~540MB \| 253MB \| 53% reduction \| \| RAM Usage \| ~1GB \| ~400MB \| 60% reduction \| \| Inference Speed \| Baseline \| ~2x faster \| 2x speedup \| \| Mobile Performance \| Too large \| Excellent \| ✅ Mobile ready \| Performance tested on various devices including mobile hardware ## License and Usage Important: This model is a derivative of Google's Gemma and is subject to the original licensing terms. Gemma is provided under and subject to the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). ### Key Points: - ✅ Commercial use permitted under the Gemma license - ✅ Mobile app deployment allowed with proper attribution - ⚠️ Must comply with the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy) - 📄 App store compliance: Licensing terms disclosed in app store listings - 🔄 Redistribution: Must include proper attribution and license terms ### Usage Restrictions As per the Gemma Terms of Use, this model cannot be used for: - Illegal activities - Child safety violations - Generation of hateful, harassing, or violent content - Generation of false or misleading information - Privacy violations See the full [Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy) for complete details. ## Mobile App Compliance This model is used in compliance with: - Gemma Terms of Use: Full licensing terms disclosed - App Store Guidelines: Platform requirements met - Privacy Standards: On-device processing, no data collection - Performance Standards: Optimized for mobile hardware ## Limitations - Quantization may result in slight quality degradation compared to the original Gemma 3 instruction-tuned model - Performance characteristics may vary across different hardware platforms - Subject to the same content limitations as the base Gemma 3 instruction-tuned model - Context length and capabilities inherited from base Gemma 3 270M instruction-tuned model - Mobile performance depends on device specifications ## Technical Specifications - Original Parameters: 270M - Quantization Scheme: Q4_K_M (4-bit weights, mixed precision for critical layers) - Context Length: 32,768 tokens (inherited from Gemma 3 270M) - Vocabulary Size: 256,000 tokens - Architecture: Transformer decoder - Attention Heads: 8 - Hidden Layers: 18 ## Download Options ### Direct Download ```bash # Using wget wget https://huggingface.co/Durlabh/gemma-270m-q4-k-m-gguf/resolve/main/gemma-270m-q4-k-m.gguf # Using curl curl -L -o gemma-270m-q4-k-m.gguf https://huggingface.co/Durlabh/gemma-270m-q4-k-m-gguf/resolve/main/gemma-270m-q4-k-m.gguf ``` ### Programmatic Download ```python # Using huggingface-hub from huggingface_hub import hf_hub_download model_path = hf_hub_download( repo_id="Durlabh/gemma-270m-q4-k-m-gguf", filename="gemma-270m-q4-k-m.gguf" ) ``` ## Citation If you use this model, please cite both the original Gemma work and acknowledge the quantization: ```bibtex @misc{durlabh-gemma-270m-q4-k-m, title={Gemma 3 270M Instruction-Tuned Q4_K_M Quantized}, author={Durlabh}, year={2025}, note={Quantized version of Google's Gemma 3 270M instruction-tuned model using llama.cpp Q4_K_M}, url={https://huggingface.co/Durlabh/gemma-270m-q4-k-m-gguf} } ``` Original Gemma 3 paper: ```bibtex @misc{gemma3_2025, title={Gemma 3: Google's new open model based on Gemini 2.0}, author={Gemma Team}, year={2025}, publisher={Google}, url={https://blog.google/technology/developers/gemma-3/} } ``` ## Community & Support - Issues: Report problems or questions in the repository discussions - Mobile Development: See model usage in production mobile applications - Quantization: Built with llama.cpp for optimal performance ## Acknowledgments - Google DeepMind team for the original Gemma model - llama.cpp community for the quantization tools and GGUF format - Hugging Face for hosting infrastructure - Georgi Gerganov for creating and maintaining llama.cpp - Mobile AI community for advancing on-device inference ## Disclaimer This is an unofficial quantized version of Gemma 3 created for practical mobile deployment. For official Gemma models, please visit [Google's official Gemma page](https://ai.google.dev/gemma). The mobile application using this model fully complies with platform guidelines and Gemma licensing requirements. --- Ready for production use! This model powers real-world mobile applications while maintaining full compliance with licensing terms.
Rewqeas/code-search-net-tokenizer	Rewqeas	2025-08-20T18:17:04Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T18:17:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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-Clip-pr-ratri-viral-video-Links-XX/Orginal.full.Videos.pr.ratri.viral.video.Official.Tutorial	New-Clip-pr-ratri-viral-video-Links-XX	2025-08-20T18:17:04Z	0	0	null	[ "region:us" ]	null	2025-08-20T18:16:53Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?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>
Muapi/wet-and-messy-flux	Muapi	2025-08-20T18:16:41Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T18:16:28Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Wet and Messy (FLUX) ![preview](./preview.jpg) Base model: Flux.1 D Trained words: wet, covered in oil, covered in mud, wam, wet clothes, pouring oil, hair covered in oil, wetlook, oil, mud, holding oil bottle, see-through, partialy submerged, muddy hair, muddy clothes, soaking wet ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:694850@777601", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/jj-s-interior-space-kitchen	Muapi	2025-08-20T18:16:23Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T18:16:06Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # JJ's Interior Space - Kitchen ![preview](./preview.jpg) Base model: Flux.1 D Trained words: kitchen ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:335701@1285748", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755713650	0xaoyama	2025-08-20T18:14:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:14:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/wizard-s-grimdark-the-grit	Muapi	2025-08-20T18:14:41Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T18:14:25Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Wizard's Grimdark: The Grit ![preview](./preview.jpg) Base model: Flux.1 D Trained words: gritty, poster art ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:844992@945353", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/mei-lee-red-panda-pony-illustrious-flux	Muapi	2025-08-20T18:12:54Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T18:12:24Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Mei Lee "Red panda" [Pony/Illustrious/Flux] ![preview](./preview.jpg) Base model: Flux.1 D Trained words: MeiPandaIL ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:701944@1883410", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/kiwi-furry-style-flux	Muapi	2025-08-20T18:12:13Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T18:11:47Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # kiwi furry style FLUX ![preview](./preview.jpg) Base model: Flux.1 D Trained words: furry, anime style ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:765320@856009", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755711927	helmutsukocok	2025-08-20T18:11:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:11:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
thanobidex/blockassist-bc-colorful_shiny_hare_1755711822	thanobidex	2025-08-20T18:11:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:11:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful shiny hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ai-weekly/ai.undressing.app	ai-weekly	2025-08-20T18:10:09Z	0	0	null	[ "region:us" ]	null	2025-08-20T17:41:29Z	# AI Undress Best Undress APP in 2025 {p5z8l} (Last Updated: 22 August, 2025) Undress AI – The Most Advanced Clothing-Removal & Photo Reveal Tool (2025) • AI clothing removal & reveal – ultra-realistic skin rendering, soft shadows, and fine-textured simulations • Try-on & swap modes – preview lingerie, sheer fabrics, or simulate full undressing • Smart editing tools – tan line reconstruction, skin smoothing, lighting correction, detail sharpening • Batch & single image support – quick rendering with uniform output • Privacy-focused • 18+ only • Use responsibly with owned/consented images ⏩⏩⏩ [Try the Best Undress AI Now](https://aiweely.com/tools) Updated 22 August, 2025 – As AI-driven image manipulation grows, so does the popularity of terms like AI Undress, Undress AI, and AI cloth remover. This guide offers a transparent, up-to-date breakdown of what these tools are, how they work, and whether they’re safe—or legal—to use. --- ## What Is AI Undress? (Updated 22 August, 2025) AI Undress software uses machine learning to digitally simulate the removal of clothing from photos. Also called undress AI, undress image AI, or AI photo undress generator, it employs advanced neural networks—often derived from deepfake and generative AI—to fabricate nudity beneath clothing. Originally made famous by apps like DeepNude, the technology has now evolved. By 2025, undress tools are more sophisticated and more ethically problematic—despite growing attempts at regulation. --- ## A Brief History of AI Undressing Tools ### DeepNude and Early Development * In 2019, DeepNude shook the web by auto-generating nude images from clothed female photos. * It used trained neural networks to generate fake undressed versions, mimicking real skin and anatomy. * After backlash and privacy outcry, DeepNude was taken offline—but its core model was cloned and shared. ### Rapid Evolution (2020–2025) * Inspired by DeepNude’s viral attention, underground forums birthed hundreds of copycat tools. * Newer models used image-to-image translation and style transfer, producing more believable results—and making them harder to detect. --- ## Real vs. Fake: What AI Can Do in 2025 (Updated 22 August, 2025) ### Can AI Really Remove Clothes? As of August 2025, yes—to an extent. AI undress tools create lifelike fakes by statistically guessing body shapes beneath garments. * AI cannot “see” under clothes; it fabricates realistic layers based on visual patterns from massive datasets. * These apps use GANs or diffusion models to merge generated skin with existing imagery. * Output = fake render, not a real body. ### Weaknesses of AI Undressing * Complex poses, baggy clothing, or mixed lighting degrade the results. * Common artifacts: unnatural body proportions, messy shadows, and misaligned limbs. * Best results come from high-res, frontal shots with tight clothing and good lighting. ### Are AI Undress Generators Legitimate? * 90% of "undress image AI" tools online are scams, malware, or illegal clones. * Trustworthy apps are rare, expensive, and pose privacy risks. * Mobile app stores actively ban these, pushing most activity to the web or Telegram bots. --- ## Examples of AI Undress Tools (As of August 2025) \| Tool \| Type \| Claims \| Reality \| Legal Status \| \| ----------------------- \| ------------ \| ------------------- \| --------------------------------- \| ------------------------ \| \| DeepNude / DeepNudeNext \| PC/Web \| Clothing removal \| Inactive, cloned illegally \| Banned in most countries \| \| Undress AI Pro \| Web/App \| See-through effects \| Mostly scam or unsafe \| Illegal in many regions \| \| DeepArtUndress \| Web/Bot \| GAN-powered realism \| Somewhat real, costly \| Legally restricted \| \| FaceMagic NSFW \| Mobile \| Nude generation \| Fake, low realism \| Suspicious/unsafe \| \| Forum Mods & Reddit \| Scripts/Mods \| DeepNude clones \| Malware-prone, rare working tools \| Illegal content \| 🚫 Many "AI undress apps for Android/iOS" are removed quickly. Proceed with extreme caution. --- ## Legal, Ethical & Security Concerns (Updated 22 August, 2025) ### Legal Landscape * By 2025, distributing or generating undressed images of real people without permission is illegal in the U.S., UK, EU, and beyond. * Consent-based AI editing is legal, but AI-generated non-consensual imagery = image-based abuse. ### Ethical Red Flags * Psychological harm: victims often suffer trauma and online harassment. * Promotes a toxic digital culture of voyeurism and non-consent. * Most undressed AI content targets women—amplifying gendered abuse. ### Security & Scam Warnings * Many AI undress websites demand payment after upload—blackmail risk. * Fake tools harvest your photos or install malware. * Apps claiming to offer "AI clothes remover" often contain trojans or spyware. --- ## Legal AI Uses vs. Abusive Applications ### Legitimate AI Use Cases 1. Medical Simulation – AI used to model human anatomy for training and diagnostics. 2. Virtual Try-On Tools – Fashion apps showing how clothes look, not removing them. 3. Forensics – AI attempts to reconstruct human form from damaged or partial data (rare). ### Abusive Applications * Generating fake nude images of real people without consent = illegal deepfake use. * Even sharing “for fun” can carry criminal liability. --- ## How to Detect AI Undressed Photos ### Spotting Fakes in 2025 * AI Artifacts – Blurry transitions, odd fingers, warping. * Watermarks or EXIF Tags – Many apps leave hidden traces. * Reverse Image Search – Find the original version of the photo. * AI Forensics Tools – Apps like Deepware Scanner can detect manipulation. * Ask for Consent – When in doubt, ask the person in the image. --- ## Safe, Legal Alternatives to Undress AI ### Creative AI Tools * Adobe Firefly, RunwayML – For artists working with body models. * MetaHuman Creator – Build virtual people for films/games. ### Fashion & Medical * Zeekit, Fashwell – For trying on clothes virtually (with avatars). * 3D Medical Apps – For health and biology teaching. 🛑 Avoid sketchy tools labeled “AI nude generator” or “see-through clothes app.” --- ## FAQ: Undress AI (2025 Edition) 1. What is AI Undress? It’s software that uses AI to simulate undressing someone in a photo. 2. Are these tools real? Some work, but most are scams. None can truly see through clothing. 3. Are AI undress tools legal? Only if used with clear, written consent. Most aren’t. 4. Is there a safe DeepNude alternative? Not for real people. Explore avatar-based AI instead. 5. How do they work? AI combines GAN/diffusion synthesis with the input photo to fabricate new imagery. 6. Can AI actually see beneath clothes? No—it guesses using patterns from training data. 7. Is this illegal? Yes, if done without the subject's consent. 8. How can I tell if an image was undressed by AI? Look for visual inconsistencies, metadata, or use detection tools. 9. Is there any ethical use of this tech? Yes, in medical imaging, fashion AR, and avatar design. 10. Can AI reveal what’s actually under clothes? No. All results are simulations—not reality. --- ## Final Words: AI Undress in 2025 While AI undressing tools have improved dramatically in realism, the legal and ethical issues have only grown. Most online platforms offering “AI cloth remover” services are misleading, dangerous, or outright illegal. We strongly urge users to choose creative, ethical AI paths—whether for digital fashion, art, or education. 🔒 Stay safe. Stay ethical. Build a future with responsible AI. (Updated 22 August, 2025)
Chedjoun/llama3-finetuned-promql	Chedjoun	2025-08-20T18:09:35Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T13:20:10Z	--- base_model: unsloth/llama-3-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Chedjoun - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-instruct-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)
Orginal-Uppal-Farm-Girl-Viral-Videos-Links/New.full.videos.Uppal.Farm.Girl.Viral.Video.Official.Tutorial	Orginal-Uppal-Farm-Girl-Viral-Videos-Links	2025-08-20T18:09:17Z	0	0	null	[ "region:us" ]	null	2025-08-20T18:03:30Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" 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> +XNXX_VIDEOS-18+ }}!· Uppal Farm Girl Viral Video Telegram Link Original Clip
lilTAT/blockassist-bc-gentle_rugged_hare_1755713309	lilTAT	2025-08-20T18:08:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:08:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755711528	katanyasekolah	2025-08-20T18:08:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:08:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
burkerlee123/blockassist-bc-tall_roaring_moose_1755711485	burkerlee123	2025-08-20T18:08:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall roaring moose", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:08:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall roaring moose --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AnonymousCS/xlmr_immigration_combo21_4	AnonymousCS	2025-08-20T18:08:11Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:04:57Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo21_4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlmr_immigration_combo21_4 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1502 - Accuracy: 0.9512 - 1-f1: 0.9264 - 1-recall: 0.9228 - 1-precision: 0.9300 - Balanced Acc: 0.9440 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.1087 \| 1.0 \| 25 \| 0.1235 \| 0.9602 \| 0.9376 \| 0.8996 \| 0.9790 \| 0.9450 \| \| 0.106 \| 2.0 \| 50 \| 0.1332 \| 0.9614 \| 0.9402 \| 0.9112 \| 0.9712 \| 0.9489 \| \| 0.1048 \| 3.0 \| 75 \| 0.1502 \| 0.9512 \| 0.9264 \| 0.9228 \| 0.9300 \| 0.9440 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
bboppp/blockassist-bc-trotting_restless_squirrel_1755713111	bboppp	2025-08-20T18:05:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "trotting restless squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:05:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - trotting restless squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AnonymousCS/xlmr_immigration_combo21_3	AnonymousCS	2025-08-20T18:04:52Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T18:01:06Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo21_3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlmr_immigration_combo21_3 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2167 - Accuracy: 0.9409 - 1-f1: 0.9105 - 1-recall: 0.9035 - 1-precision: 0.9176 - Balanced Acc: 0.9315 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.1453 \| 1.0 \| 25 \| 0.1747 \| 0.9357 \| 0.9020 \| 0.8880 \| 0.9163 \| 0.9238 \| \| 0.2073 \| 2.0 \| 50 \| 0.1645 \| 0.9512 \| 0.9224 \| 0.8726 \| 0.9784 \| 0.9315 \| \| 0.1407 \| 3.0 \| 75 \| 0.1829 \| 0.9486 \| 0.9206 \| 0.8958 \| 0.9469 \| 0.9354 \| \| 0.0598 \| 4.0 \| 100 \| 0.2167 \| 0.9409 \| 0.9105 \| 0.9035 \| 0.9176 \| 0.9315 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
lilTAT/blockassist-bc-gentle_rugged_hare_1755713013	lilTAT	2025-08-20T18:04:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T18:03:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AnonymousCS/xlmr_immigration_combo21_2	AnonymousCS	2025-08-20T18:01:01Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T17:57:21Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo21_2 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. --> # xlmr_immigration_combo21_2 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2842 - Accuracy: 0.9242 - 1-f1: 0.8850 - 1-recall: 0.8764 - 1-precision: 0.8937 - Balanced Acc: 0.9122 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.2109 \| 1.0 \| 25 \| 0.2585 \| 0.9075 \| 0.8662 \| 0.8996 \| 0.8351 \| 0.9055 \| \| 0.1807 \| 2.0 \| 50 \| 0.2331 \| 0.9267 \| 0.8889 \| 0.8803 \| 0.8976 \| 0.9151 \| \| 0.0668 \| 3.0 \| 75 \| 0.2858 \| 0.9165 \| 0.8748 \| 0.8764 \| 0.8731 \| 0.9064 \| \| 0.1601 \| 4.0 \| 100 \| 0.2842 \| 0.9242 \| 0.8850 \| 0.8764 \| 0.8937 \| 0.9122 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
Muapi/ink-watercolor-hybrid-style-from-legacy-doubao-for-flux-dev-noobai-and-illustrious	Muapi	2025-08-20T17:56:43Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T17:56:16Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Ink & Watercolor Hybrid Style from Legacy Doubao for Flux Dev, NoobAI and Illustrious ![preview](./preview.jpg) Base model: Flux.1 D Trained words: An image in style of iwhyb ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1406430@1922670", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/tifa-lockhart-final-fantasy-vii	Muapi	2025-08-20T17:55:57Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T17:55:48Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Tifa Lockhart - Final Fantasy VII ![preview](./preview.jpg) Base model: Flux.1 D Trained words: TifaFFVII ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1163983@1309406", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/little-red-riding-hood-xl-sd1.5-f1d	Muapi	2025-08-20T17:55:23Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T17:55:00Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Little Red Riding Hood XL + SD1.5 + F1D ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Little Red Riding Hood ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:196138@1135762", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF	mradermacher	2025-08-20T17:53:58Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Debk/granite-3.3-2b-finetuned-alpaca-hindi_full", "base_model:quantized:Debk/granite-3.3-2b-finetuned-alpaca-hindi_full", "endpoints_compatible", "region:us" ]	null	2025-08-20T17:34:06Z	--- base_model: Debk/granite-3.3-2b-finetuned-alpaca-hindi_full language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/Debk/granite-3.3-2b-finetuned-alpaca-hindi_full <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF/resolve/main/granite-3.3-2b-finetuned-alpaca-hindi_full.Q2_K.gguf) \| Q2_K \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF/resolve/main/granite-3.3-2b-finetuned-alpaca-hindi_full.Q3_K_S.gguf) \| Q3_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF/resolve/main/granite-3.3-2b-finetuned-alpaca-hindi_full.Q3_K_M.gguf) \| Q3_K_M \| 1.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF/resolve/main/granite-3.3-2b-finetuned-alpaca-hindi_full.Q3_K_L.gguf) \| Q3_K_L \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF/resolve/main/granite-3.3-2b-finetuned-alpaca-hindi_full.IQ4_XS.gguf) \| IQ4_XS \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF/resolve/main/granite-3.3-2b-finetuned-alpaca-hindi_full.Q4_K_S.gguf) \| Q4_K_S \| 1.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF/resolve/main/granite-3.3-2b-finetuned-alpaca-hindi_full.Q4_K_M.gguf) \| Q4_K_M \| 1.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF/resolve/main/granite-3.3-2b-finetuned-alpaca-hindi_full.Q5_K_S.gguf) \| Q5_K_S \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF/resolve/main/granite-3.3-2b-finetuned-alpaca-hindi_full.Q5_K_M.gguf) \| Q5_K_M \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF/resolve/main/granite-3.3-2b-finetuned-alpaca-hindi_full.Q6_K.gguf) \| Q6_K \| 2.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF/resolve/main/granite-3.3-2b-finetuned-alpaca-hindi_full.Q8_0.gguf) \| Q8_0 \| 2.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/granite-3.3-2b-finetuned-alpaca-hindi_full-GGUF/resolve/main/granite-3.3-2b-finetuned-alpaca-hindi_full.f16.gguf) \| f16 \| 5.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/ImagineGPT-GGUF	mradermacher	2025-08-20T17:53:58Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "trl", "sft", "en", "base_model:d-s-b/ImagineGPT", "base_model:quantized:d-s-b/ImagineGPT", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-20T17:46:04Z	--- base_model: d-s-b/ImagineGPT language: - en library_name: transformers model_name: ImagineGPT mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - generated_from_trainer - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/d-s-b/ImagineGPT <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ImagineGPT-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/ImagineGPT-GGUF/resolve/main/ImagineGPT.Q3_K_S.gguf) \| Q3_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ImagineGPT-GGUF/resolve/main/ImagineGPT.Q2_K.gguf) \| Q2_K \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ImagineGPT-GGUF/resolve/main/ImagineGPT.IQ4_XS.gguf) \| IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ImagineGPT-GGUF/resolve/main/ImagineGPT.Q3_K_M.gguf) \| Q3_K_M \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ImagineGPT-GGUF/resolve/main/ImagineGPT.Q3_K_L.gguf) \| Q3_K_L \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ImagineGPT-GGUF/resolve/main/ImagineGPT.Q4_K_S.gguf) \| Q4_K_S \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ImagineGPT-GGUF/resolve/main/ImagineGPT.Q4_K_M.gguf) \| Q4_K_M \| 0.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ImagineGPT-GGUF/resolve/main/ImagineGPT.Q5_K_S.gguf) \| Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ImagineGPT-GGUF/resolve/main/ImagineGPT.Q5_K_M.gguf) \| Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ImagineGPT-GGUF/resolve/main/ImagineGPT.Q6_K.gguf) \| Q6_K \| 0.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ImagineGPT-GGUF/resolve/main/ImagineGPT.Q8_0.gguf) \| Q8_0 \| 0.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ImagineGPT-GGUF/resolve/main/ImagineGPT.f16.gguf) \| f16 \| 0.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
youuotty/blockassist-bc-omnivorous_squeaky_bear_1755712391	youuotty	2025-08-20T17:53:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "omnivorous squeaky bear", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T17:53:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - omnivorous squeaky bear --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/gpt-styles-for-il-flux-shrekman-styles-mix	Muapi	2025-08-20T17:53:27Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-20T17:53:13Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # GPT Styles For IL&FLUX \| Shrekman Styles Mix ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ancient Greek art, GPAMV1 ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1612420@2049779", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

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