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train · 879k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-13 12:28:20	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 518 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-13 12:26:25	card stringlengths 11 1.01M
babaongu/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-reclusive_hardy_mongoose	babaongu	2025-05-24T09:04:08Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am reclusive hardy mongoose", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-03T04:10:03Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-reclusive_hardy_mongoose tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am reclusive hardy mongoose - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-reclusive_hardy_mongoose This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="babaongu/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-reclusive_hardy_mongoose", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
infogep/2ce73c0b-a7cd-42e8-bc31-cf297eeaf652	infogep	2025-05-24T08:55:04Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:berkeley-nest/Starling-LM-7B-alpha", "base_model:quantized:berkeley-nest/Starling-LM-7B-alpha", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T08:44:40Z	--- base_model: berkeley-nest/Starling-LM-7B-alpha library_name: transformers model_name: 2ce73c0b-a7cd-42e8-bc31-cf297eeaf652 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 2ce73c0b-a7cd-42e8-bc31-cf297eeaf652 This model is a fine-tuned version of [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha). 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="infogep/2ce73c0b-a7cd-42e8-bc31-cf297eeaf652", 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/dedok-yo/s56-7/runs/ajabwtua) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
vertings6/397036d9-3bbc-47c5-9895-7e218401bf97	vertings6	2025-05-24T08:54:44Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Qwen/Qwen2-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T08:39:49Z	--- base_model: Qwen/Qwen2-1.5B-Instruct library_name: transformers model_name: 397036d9-3bbc-47c5-9895-7e218401bf97 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 397036d9-3bbc-47c5-9895-7e218401bf97 This model is a fine-tuned version of [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vertings6/397036d9-3bbc-47c5-9895-7e218401bf97", 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/dedok-yo/s56-7/runs/ykxjph58) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Ak8hub/Uuuuuu	Ak8hub	2025-05-24T08:46:22Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-24T08:46:22Z	--- license: apache-2.0 ---
tim-lawson/fineweb-baseline-12-layers-v0-muon	tim-lawson	2025-05-24T08:46:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T08:45:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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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InduwaraR/qwen-ai-research-qa-q4_k_m.gguf	InduwaraR	2025-05-24T08:42:29Z	17	2	null	[ "gguf", "question-answering", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:quantized:Qwen/Qwen2.5-3B-Instruct", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	question-answering	2025-03-10T03:20:16Z	--- license: mit language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: - Qwen/Qwen2.5-3B-Instruct pipeline_tag: question-answering --- # Qwen AI Research QA Model (Q4_K_M GGUF) ## Model Overview The Qwen AI Research QA Model is designed for answering research-oriented AI questions with a focus on precision and depth. This model is optimized in the `Q4_K_M` format for efficient inference while maintaining high-quality responses. ## How to Use To use this model with `llama-cpp-python`, follow these steps: ### Installation Make sure you have `llama-cpp-python` installed: ```bash pip install llama-cpp-python ``` ### Loading the Model ```python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="InduwaraR/qwen-ai-research-qa-q4_k_m.gguf", filename="qwen-ai-research-qa-q4_k_m.gguf", ) ``` ### Generating a Response ```python response = llm.create_chat_completion( messages=[ {"role": "user", "content": "What are the latest advancements in AI research?"} ] ) print(response) ``` ## Model Details - Model Name: Qwen AI Research QA - Format: GGUF (Q4_K_M Quantization) - Primary Use Case: AI research question answering - Inference Framework: `llama-cpp-python` - Optimized for: Running on local hardware with reduced memory usage ## License This model is open-source and available under the MIT License. ## Acknowledgments This model is hosted by InduwaraR on Hugging Face. Special thanks to the Qwen AI team for their contributions to AI research and development.
robertou2/task-9-microsoft-Phi-3.5-mini-instruct	robertou2	2025-05-24T08:38:11Z	1,393	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3.5-mini-instruct", "base_model:adapter:microsoft/Phi-3.5-mini-instruct", "region:us" ]	null	2025-05-13T16:57:03Z	--- base_model: microsoft/Phi-3.5-mini-instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2
khangnguyen1287/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mammalian_rugged_caterpillar	khangnguyen1287	2025-05-24T08:34:12Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am mammalian rugged caterpillar", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T06:52:38Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mammalian_rugged_caterpillar tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am mammalian rugged caterpillar - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mammalian_rugged_caterpillar This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="khangnguyen1287/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mammalian_rugged_caterpillar", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
BrunoBosshard/Pretrained_TinySolar-248m-4k	BrunoBosshard	2025-05-24T08:31:51Z	0	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2025-05-24T07:44:11Z	--- license: apache-2.0 ---
Srisree1/gemma-3-v1	Srisree1	2025-05-24T08:27:05Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T08:26:51Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Srisree1 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
meimmo/trained-flux-lora-chanel	meimmo	2025-05-24T08:21:56Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-23T16:58:52Z	--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers license: other instance_prompt: a photo of dress in Chanel style by Karl Lagerfeld from the years 1983 to 2019 widget: - text: a photo of dress in Chanel style by Karl Lagerfeld from the years 1983 to 2019 output: url: image_0.png - text: a photo of dress in Chanel style by Karl Lagerfeld from the years 1983 to 2019 output: url: image_1.png - text: a photo of dress in Chanel style by Karl Lagerfeld from the years 1983 to 2019 output: url: image_2.png - text: a photo of dress in Chanel style by Karl Lagerfeld from the years 1983 to 2019 output: url: image_3.png tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Flux DreamBooth LoRA - meimmo/trained-flux-lora-chanel <Gallery /> ## Model description These are meimmo/trained-flux-lora-chanel DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `a photo of dress in Chanel style by Karl Lagerfeld from the years 1983 to 2019` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](meimmo/trained-flux-lora-chanel/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('meimmo/trained-flux-lora-chanel', weight_name='pytorch_lora_weights.safetensors') image = pipeline('a photo of dress in Chanel style by Karl Lagerfeld from the years 1983 to 2019').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
mohhtl/e3dd1b39-d1ee-4b83-bf8a-05a7164b5711	mohhtl	2025-05-24T08:21:22Z	0	0	peft	[ "peft", "safetensors", "qwen2", "generated_from_trainer", "dataset:29100ea8-8f91-45bb-841b-4a7bf8ea8b43_test.json", "dataset:29100ea8-8f91-45bb-841b-4a7bf8ea8b43_synth.json", "base_model:unsloth/Qwen2-7B-Instruct", "base_model:adapter:unsloth/Qwen2-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-05-24T08:21:18Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-7B-Instruct tags: - generated_from_trainer datasets: - 29100ea8-8f91-45bb-841b-4a7bf8ea8b43_test.json - 29100ea8-8f91-45bb-841b-4a7bf8ea8b43_synth.json model-index: - name: results/e3dd1b39-d1ee-4b83-bf8a-05a7164b5711 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.9.2` ```yaml adapter: lora base_model: unsloth/Qwen2-7B-Instruct bf16: auto dataset_prepared_path: results/29100ea8-8f91-45bb-841b-4a7bf8ea8b43_last_run_prepared datasets: - path: 29100ea8-8f91-45bb-841b-4a7bf8ea8b43_test.json type: &id001 field: null field_input: input field_instruction: instruct field_output: output field_system: null format: null no_input_format: null system_format: '{system}' system_prompt: '' - path: 29100ea8-8f91-45bb-841b-4a7bf8ea8b43_synth.json type: id001 flash_attention: true gradient_accumulation_steps: 4 gradient_checkpointing: true learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_model_dir: null lora_r: 8 lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj loss_watchdog_patience: 3 loss_watchdog_threshold: 5.0 lr_scheduler: constant micro_batch_size: 2 num_epochs: 15 optimizer: adamw_8bit output_dir: results/e3dd1b39-d1ee-4b83-bf8a-05a7164b5711 pad_to_sequence_len: true resume_from_checkpoint: null sample_packing: true save_total_limit: 1 saves_per_epoch: 1 sequence_len: 2048 special_tokens: pad_token: <\|endoftext\|> test_datasets: - path: 29100ea8-8f91-45bb-841b-4a7bf8ea8b43_test.json split: train type: id001 - path: 29100ea8-8f91-45bb-841b-4a7bf8ea8b43_synth.json split: train type: *id001 tf32: false val_set_size: 0.0 wandb_entity: null wandb_log_model: null wandb_name: null wandb_project: null wandb_watch: null warmup_ratio: 0.0 warmup_steps: 0 weight_decay: 0.0 ``` </details><br> # results/e3dd1b39-d1ee-4b83-bf8a-05a7164b5711 This model is a fine-tuned version of [unsloth/Qwen2-7B-Instruct](https://huggingface.co/unsloth/Qwen2-7B-Instruct) on the 29100ea8-8f91-45bb-841b-4a7bf8ea8b43_test.json and the 29100ea8-8f91-45bb-841b-4a7bf8ea8b43_synth.json datasets. It achieves the following results on the evaluation set: - Loss: 0.0072 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - num_epochs: 15.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 0.3763 \| 1.0 \| 32 \| 0.3526 \| \| 0.2686 \| 2.0 \| 64 \| 0.2226 \| \| 0.1961 \| 3.0 \| 96 \| 0.1569 \| \| 0.1214 \| 4.0 \| 128 \| 0.1078 \| \| 0.1049 \| 5.0 \| 160 \| 0.0714 \| \| 0.0793 \| 6.0 \| 192 \| 0.0390 \| \| 0.04 \| 7.0 \| 224 \| 0.0225 \| \| 0.0259 \| 8.0 \| 256 \| 0.0159 \| \| 0.0162 \| 9.0 \| 288 \| 0.0109 \| \| 0.0176 \| 10.0 \| 320 \| 0.0112 \| \| 0.0283 \| 11.0 \| 352 \| 0.0087 \| \| 0.011 \| 12.0 \| 384 \| 0.0089 \| \| 0.0198 \| 13.0 \| 416 \| 0.0075 \| \| 0.0141 \| 14.0 \| 448 \| 0.0065 \| \| 0.0107 \| 14.5354 \| 465 \| 0.0072 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.4.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
mohamed20-AI/result_model	mohamed20-AI	2025-05-24T08:17:36Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:80", "loss:CoSENTLoss", "en", "dataset:sentence-transformers/all-nli", "arxiv:1908.10084", "base_model:abdeljalilELmajjodi/model", "base_model:finetune:abdeljalilELmajjodi/model", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-24T07:09:21Z	--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:80 - loss:CoSENTLoss base_model: abdeljalilELmajjodi/model widget: - source_sentence: Two adults, one female in white, with shades and one male, gray clothes, walking across a street, away from a eatery with a blurred image of a dark colored red shirted person in the foreground. sentences: - Two people ride bicycles into a tunnel. - There are people just getting on a train - There are children present - source_sentence: A man with blond-hair, and a brown shirt drinking out of a public water fountain. sentences: - Some women are hugging on vacation. - The family is sitting down for dinner. - A blond man wearing a brown shirt is reading a book on a bench in the park - source_sentence: Two women who just had lunch hugging and saying goodbye. sentences: - There are two woman in this picture. - Two adults run across the street to get away from a red shirted person chasing them. - The woman is wearing black. - source_sentence: A woman in a green jacket and hood over her head looking towards a valley. sentences: - The woman is wearing green. - A woman in white. - A man is drinking juice. - source_sentence: An older man sits with his orange juice at a small table in a coffee shop while employees in bright colored shirts smile in the background. sentences: - They are protesting outside the capital. - A couple are playing frisbee with a young child at the beach. - A boy flips a burger. datasets: - sentence-transformers/all-nli pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on abdeljalilELmajjodi/model results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: pair score evaluator dev type: pair-score-evaluator-dev metrics: - type: pearson_cosine value: -0.12381534704198764 name: Pearson Cosine - type: spearman_cosine value: -0.06398099132915955 name: Spearman Cosine --- # SentenceTransformer based on abdeljalilELmajjodi/model This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [abdeljalilELmajjodi/model](https://huggingface.co/abdeljalilELmajjodi/model) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [abdeljalilELmajjodi/model](https://huggingface.co/abdeljalilELmajjodi/model) <!-- at revision 284169e2c18b482372374a251b8dc1e1756416de --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) - Language: en <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'An older man sits with his orange juice at a small table in a coffee shop while employees in bright colored shirts smile in the background.', 'A boy flips a burger.', 'They are protesting outside the capital.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `pair-score-evaluator-dev` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| -0.1238 \| \| spearman_cosine \| -0.064 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### all-nli * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 80 training samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 80 samples: \| \| sentence1 \| sentence2 \| score \| \|:--------\|:-----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 10 tokens</li><li>mean: 25.34 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 12.2 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> \| * Samples: \| sentence1 \| sentence2 \| score \| \|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------\|:-----------------\| \| <code>Two adults, one female in white, with shades and one male, gray clothes, walking across a street, away from a eatery with a blurred image of a dark colored red shirted person in the foreground.</code> \| <code>Some people board a train.</code> \| <code>0.0</code> \| \| <code>A few people in a restaurant setting, one of them is drinking orange juice.</code> \| <code>The people are sitting at desks in school.</code> \| <code>0.0</code> \| \| <code>The school is having a special event in order to show the american culture on how other cultures are dealt with in parties.</code> \| <code>A school hosts a basketball game.</code> \| <code>0.0</code> \| * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Evaluation Dataset #### all-nli * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 20 evaluation samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 20 samples: \| \| sentence1 \| sentence2 \| score \| \|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:--------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 10 tokens</li><li>mean: 27.3 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 11.1 tokens</li><li>max: 21 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> \| * Samples: \| sentence1 \| sentence2 \| score \| \|:-------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------\|:-----------------\| \| <code>Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.</code> \| <code>The woman is wearing black.</code> \| <code>0.0</code> \| \| <code>A couple play in the tide with their young son.</code> \| <code>The family is sitting down for dinner.</code> \| <code>0.0</code> \| \| <code>A couple playing with a little boy on the beach.</code> \| <code>A couple are playing frisbee with a young child at the beach.</code> \| <code>0.5</code> \| * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `num_train_epochs`: 1 - `warmup_ratio`: 0.05 - `bf16`: True - `fp16_full_eval`: True - `load_best_model_at_end`: True - `push_to_hub`: True - `gradient_checkpointing`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.05 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: True - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: True - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: True - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| pair-score-evaluator-dev_spearman_cosine \| \|:-------:\|:------:\|:-------------:\|:---------------:\|:----------------------------------------:\| \| 0.1 \| 1 \| 3.0033 \| - \| - \| \| 0.5 \| 5 \| 2.987 \| - \| - \| \| 1.0 \| 10 \| 3.0908 \| 2.6311 \| -0.064 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
kairun/Qwen3-GGUF	kairun	2025-05-24T08:14:18Z	0	0	transformers	[ "transformers", "gguf", "qwen3", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T08:13:58Z	--- base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: kairun - License: apache-2.0 - Finetuned from model : unsloth/qwen3-1.7b-unsloth-bnb-4bit This qwen3 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)
luckycanucky/droogs-x15	luckycanucky	2025-05-24T08:03:37Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T07:58:46Z	--- base_model: unsloth/llama-3.2-3b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: luckycanucky - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-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)
FizzyMango/whisper_szokz	FizzyMango	2025-05-24T07:56:45Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T07:53:38Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Jialuo21/results	Jialuo21	2025-05-24T06:25:24Z	0	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-24T04:12:54Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4185 - Accuracy: 0.7551 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.4891 \| 1.0 \| 810 \| 0.5060 \| 0.7054 \| \| 0.4271 \| 2.0 \| 1620 \| 0.4423 \| 0.7433 \| \| 0.4216 \| 2.9969 \| 2427 \| 0.4185 \| 0.7551 \| ### Framework versions - Transformers 4.49.0.dev0 - Pytorch 2.5.1+cu124 - Datasets 3.6.0 - Tokenizers 0.21.0
habib-ashraf/resume-skills-ner-roberta-v3	habib-ashraf	2025-05-24T06:23:14Z	0	0	null	[ "safetensors", "roberta", "license:apache-2.0", "region:us" ]	null	2025-05-24T06:16:32Z	--- license: apache-2.0 ---
TOMFORD79/Zombie_7	TOMFORD79	2025-05-24T06:21:41Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T03:59:24Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
TOMFORD79/Zombie_6	TOMFORD79	2025-05-24T06:21:12Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T03:59:17Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
keko24/Qwen3-0.6B-SFT-Tulu-MathCodeSciTable	keko24	2025-05-24T06:18:16Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T06:17:39Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF	mradermacher	2025-05-24T06:15:44Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Ar4ikov/gpt2-pt-2-stable-diffusion-prompt-generator", "base_model:quantized:Ar4ikov/gpt2-pt-2-stable-diffusion-prompt-generator", "endpoints_compatible", "region:us" ]	null	2025-05-23T19:00:50Z	--- base_model: Ar4ikov/gpt2-pt-2-stable-diffusion-prompt-generator language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Ar4ikov/gpt2-pt-2-stable-diffusion-prompt-generator <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF/resolve/main/gpt2-pt-2-stable-diffusion-prompt-generator.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF/resolve/main/gpt2-pt-2-stable-diffusion-prompt-generator.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF/resolve/main/gpt2-pt-2-stable-diffusion-prompt-generator.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF/resolve/main/gpt2-pt-2-stable-diffusion-prompt-generator.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF/resolve/main/gpt2-pt-2-stable-diffusion-prompt-generator.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF/resolve/main/gpt2-pt-2-stable-diffusion-prompt-generator.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF/resolve/main/gpt2-pt-2-stable-diffusion-prompt-generator.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF/resolve/main/gpt2-pt-2-stable-diffusion-prompt-generator.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF/resolve/main/gpt2-pt-2-stable-diffusion-prompt-generator.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF/resolve/main/gpt2-pt-2-stable-diffusion-prompt-generator.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF/resolve/main/gpt2-pt-2-stable-diffusion-prompt-generator.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-pt-2-stable-diffusion-prompt-generator-GGUF/resolve/main/gpt2-pt-2-stable-diffusion-prompt-generator.f16.gguf) \| f16 \| 0.4 \| 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 -->
video-18-katrina-lim-viral-kiffy-viral/video-18-katrina-lim-viral-kiffy-viral-video-full-video-original-clip	video-18-katrina-lim-viral-kiffy-viral	2025-05-24T06:12:46Z	0	0	null	[ "region:us" ]	null	2025-05-24T06:12:19Z	<animated-image data-catalyst=""><a href="https://wtach.club/leakvideo/?h" 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> Katrina Lim Kiffy, a rising digital content creator, recently went viral after a leaked video began circulating across various social media platforms, including Twitter and TikTok. The video quickly gained traction, capturing the attention of thousands of viewers and sparking widespread discussion online. The original clip, which showcases Katrina's talent and presence, was reportedly leaked without her consent, raising concerns about digital privacy and content sharing ethics. Despite the controversy, the viral moment has significantly boosted her visibility online. Viewers continue to search for the original video, making “Katrina Lim Kiffy viral video” a trending topic across major platforms.
mradermacher/aaronGPTalpha-i1-GGUF	mradermacher	2025-05-24T06:10:53Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:totallynotbrent/aaronGPTalpha", "base_model:quantized:totallynotbrent/aaronGPTalpha", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-24T06:05:11Z	--- base_model: totallynotbrent/aaronGPTalpha language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/totallynotbrent/aaronGPTalpha <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/aaronGPTalpha-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.2 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q2_K.gguf) \| i1-Q2_K \| 0.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.2 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q6_K.gguf) \| i1-Q6_K \| 0.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
DanHauri/model	DanHauri	2025-05-24T06:10:51Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T05:53:25Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: DanHauri - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/Pythia410m-Instruct-SFT-i1-GGUF	mradermacher	2025-05-24T06:08:51Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:SummerSigh/Pythia410m-Instruct-SFT", "base_model:quantized:SummerSigh/Pythia410m-Instruct-SFT", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-24T05:53:40Z	--- base_model: SummerSigh/Pythia410m-Instruct-SFT language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/SummerSigh/Pythia410m-Instruct-SFT <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q2_K.gguf) \| i1-Q2_K \| 0.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.3 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q6_K.gguf) \| i1-Q6_K \| 0.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
ShowMakerTAT/OLMo-1B-sft	ShowMakerTAT	2025-05-24T06:08:31Z	0	0	null	[ "safetensors", "olmo", "license:apache-2.0", "region:us" ]	null	2025-05-23T02:33:46Z	--- license: apache-2.0 ---
nis12ram/Nemotron-4-Mini-Hindi-4B-intermediate-gliner-en-exp1	nis12ram	2025-05-24T06:05:59Z	0	0	transformers	[ "transformers", "safetensors", "nemotron", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct", "base_model:finetune:nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T05:33:30Z	--- base_model: nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct tags: - text-generation-inference - transformers - unsloth - nemotron license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: nis12ram - License: apache-2.0 - Finetuned from model : nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct This nemotron 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)
Triangle104/Qwen3-30B-A1.5B-High-Speed-Q5_K_S-GGUF	Triangle104	2025-05-24T06:05:39Z	0	0	transformers	[ "transformers", "gguf", "32 k context", "reasoning", "thinking", "qwen3", "4 experts activated", "double speed", "128 experts", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:DavidAU/Qwen3-30B-A1.5B-High-Speed", "base_model:quantized:DavidAU/Qwen3-30B-A1.5B-High-Speed", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T05:54:10Z	--- library_name: transformers pipeline_tag: text-generation tags: - 32 k context - reasoning - thinking - qwen3 - 4 experts activated - double speed - 128 experts - llama-cpp - gguf-my-repo base_model: DavidAU/Qwen3-30B-A1.5B-High-Speed --- # Triangle104/Qwen3-30B-A1.5B-High-Speed-Q5_K_S-GGUF This model was converted to GGUF format from [`DavidAU/Qwen3-30B-A1.5B-High-Speed`](https://huggingface.co/DavidAU/Qwen3-30B-A1.5B-High-Speed) 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/DavidAU/Qwen3-30B-A1.5B-High-Speed) for more details on the model. --- This is a simple "finetune" of the Qwen's "Qwen 30B-A3B" (MOE) model, setting the experts in use from 8 to 4 (out of 128 experts). This method close to doubles the speed of the model and uses 1.5B (of 30B) parameters instead of 3B (of 30B) parameters. Depending on the application you may want to use the regular model ("30B-A3B"), and use this model for simpler use case(s) although I did not notice any loss of function during routine (but not extensive) testing. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q5_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q5_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q5_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q5_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q5_k_s.gguf -c 2048 ```
zoya-hammadk/nutrivision-roberta	zoya-hammadk	2025-05-24T06:04:46Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-24T04:17:15Z	--- library_name: transformers license: mit base_model: roberta-large tags: - generated_from_trainer model-index: - name: nutrivision-roberta 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. --> # nutrivision-roberta This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 7 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
Jobz-Hunting-Pakistan-Viral-Video/Jobz-Hunting	Jobz-Hunting-Pakistan-Viral-Video	2025-05-24T06:01:55Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:59:45Z	[![My Image](https://github.com/user-attachments/assets/1b861689-da30-4c3d-ad0f-5094026cc9f2)](https://tinyurl.com/ybfu84ub) . .
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep8_66	MinaMila	2025-05-24T05:58:36Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:58:30Z	--- 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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MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_ACSEmployment_2_cfda_ep9_22	MinaMila	2025-05-24T05:55:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:55:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/llama_instbase_LoRa_ACSEmployment_2_cfda_ep7_22	MinaMila	2025-05-24T05:54:53Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:54:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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watch-katrina-lim-kiffy-full-origin/NEWS18-Video-Full-Videos-smriti-jain-all-videos-link-instagram-id	watch-katrina-lim-kiffy-full-origin	2025-05-24T05:54:48Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:54:12Z	Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤
chloebrandon/results	chloebrandon	2025-05-24T05:44:33Z	0	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-24T05:43:59Z	--- library_name: transformers license: apache-2.0 base_model: google/mt5-small tags: - generated_from_trainer model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
nis12ram/Nemotron-4-Mini-Hindi-4B-intermediate-gliner-en-exp2	nis12ram	2025-05-24T05:42:50Z	0	0	transformers	[ "transformers", "safetensors", "nemotron", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct", "base_model:finetune:nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T05:35:08Z	--- base_model: nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct tags: - text-generation-inference - transformers - unsloth - nemotron license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: nis12ram - License: apache-2.0 - Finetuned from model : nis12ram/Nemotron-4-Mini-Hindi-4B-Instruct This nemotron model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep3_66	MinaMila	2025-05-24T05:41:24Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:41:19Z	--- 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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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]
VIDEOs-18-Katrina-Lim-Viral-Kiffy/NEW.VIDEOs.LINK.Katrina.Lim.Viral.Video.Leaks.Official.tv	VIDEOs-18-Katrina-Lim-Viral-Kiffy	2025-05-24T05:41:18Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:39:09Z	[![image/gif](https://cdn-uploads.huggingface.co/production/uploads/682ab5947cb97ecc4a471bbc/yvJ4QZ-1RtUvejXXCrIUk.gif)](https://tinyurl.com/Videos-Pinoy)
MaoyueOUO/Cosmos-Reason1-7B-GGUF	MaoyueOUO	2025-05-24T05:39:04Z	0	0	null	[ "gguf", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T04:55:43Z	--- license: other license_name: nvidia-open-model-license license_link: >- https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ ---
duydc/qwen-2.5-7b-formal-alpaca-instruct-2452025	duydc	2025-05-24T05:36:28Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:25:42Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: qwen-2.5-7b-formal-alpaca-instruct-2452025 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen-2.5-7b-formal-alpaca-instruct-2452025 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="duydc/qwen-2.5-7b-formal-alpaca-instruct-2452025", 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/duydc/huggingface/runs/nny8kzrz) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
watch-katrina-lim-kiffy-full-origin/Firecnt-Katrina-Lim-Viral-Video-WhGoing-On-katrina-lim-viral-kiffy-video-telegram-link	watch-katrina-lim-kiffy-full-origin	2025-05-24T05:34:50Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:33:54Z	Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ ![68747470733a2f2f692e696d6775722e636f6d2f644a486b345a712e676966.gif](https://cdn-uploads.huggingface.co/production/uploads/6831511e46229e4f23a4ff73/bckuCdHEy_I9DQ1dg1M_Q.gif)
dherya/nanoVLM	dherya	2025-05-24T05:28:21Z	0	0	nanovlm	[ "nanovlm", "safetensors", "vision-language", "multimodal", "research", "image-text-to-text", "license:mit", "region:us" ]	image-text-to-text	2025-05-24T05:27:26Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards library_name: nanovlm license: mit pipeline_tag: image-text-to-text tags: - vision-language - multimodal - research --- nanoVLM is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model. For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M. Usage: Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code: ```python from models.vision_language_model import VisionLanguageModel model = VisionLanguageModel.from_pretrained("dherya/nanoVLM") ```
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep9_55	MinaMila	2025-05-24T05:27:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:27:21Z	--- 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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duydc/qwen-2.5-7b-alpaca-instruct-2452025	duydc	2025-05-24T05:24:26Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:13:39Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: qwen-2.5-7b-alpaca-instruct-2452025 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen-2.5-7b-alpaca-instruct-2452025 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="duydc/qwen-2.5-7b-alpaca-instruct-2452025", 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/duydc/huggingface/runs/u6nlpcge) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
DAKARA555/deepfera	DAKARA555	2025-05-24T05:11:30Z	65	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:Wan-AI/Wan2.1-I2V-14B-480P", "base_model:adapter:Wan-AI/Wan2.1-I2V-14B-480P", "license:apache-2.0", "region:us" ]	text-to-image	2025-05-14T16:36:06Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/white.png base_model: Wan-AI/Wan2.1-I2V-14B-480P instance_prompt: null license: apache-2.0 --- # deepfera <Gallery /> ## Model description https://civitai.com/models/1395313/wan-dr34mjob-doublesinglehandy-blowjob?modelVersionId=1610465 https://huggingface.co/DAKARA555/deepfera/resolve/main/WAN_dr34mj0b.safetensors?download=true ## Download model Weights for this model are available in Safetensors format. [Download](/DAKARA555/deepfera/tree/main) them in the Files & versions tab.
watch-katrina-lim-kiffy-full-origin/full.smriti.jain.real.video.smriti.jain.viral.video.instagram.id.smriti.jaindd	watch-katrina-lim-kiffy-full-origin	2025-05-24T05:10:15Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:09:26Z	Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep2_55	MinaMila	2025-05-24T05:03:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:03:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep1_55	MinaMila	2025-05-24T04:59:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:59:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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phospho-app/TransCabbage-gr00t-Bottle_In_Container-i5xn0	phospho-app	2025-05-24T04:47:36Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-05-24T04:11:08Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [TransCabbage/Bottle_In_Container](https://huggingface.co/datasets/TransCabbage/Bottle_In_Container) - Wandb run URL: None - Epochs: 10 - Batch size: 49 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
chloebrandon/my_output_dir_2	chloebrandon	2025-05-24T04:47:26Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-24T04:46:37Z	--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer model-index: - name: my_output_dir_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. --> # my_output_dir_2 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
vmpsergio/f264e8e2-cf93-4674-a4a2-4d230b56ec37	vmpsergio	2025-05-24T04:46:49Z	0	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/codegemma-7b", "base_model:adapter:unsloth/codegemma-7b", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T04:17:11Z	--- library_name: peft license: apache-2.0 base_model: unsloth/codegemma-7b tags: - axolotl - generated_from_trainer model-index: - name: f264e8e2-cf93-4674-a4a2-4d230b56ec37 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/codegemma-7b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 3a95f0218346ddba_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: vmpsergio/f264e8e2-cf93-4674-a4a2-4d230b56ec37 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/3a95f0218346ddba_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: dc30820e-a6ab-4a52-b146-21660afc11be wandb_project: s56-28 wandb_run: your_name wandb_runid: dc30820e-a6ab-4a52-b146-21660afc11be warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # f264e8e2-cf93-4674-a4a2-4d230b56ec37 This model is a fine-tuned version of [unsloth/codegemma-7b](https://huggingface.co/unsloth/codegemma-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7144 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.8017 \| 0.5239 \| 280 \| 2.7144 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep7_42	MinaMila	2025-05-24T04:45:39Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:45:24Z	--- 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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BootesVoid/cmb1p7c3q05s4u1cgo5i612ud_cmb1pm94j05sfu1cgscappdan	BootesVoid	2025-05-24T04:43:40Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-24T04:43:39Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: eve --- # Cmb1P7C3Q05S4U1Cgo5I612Ud_Cmb1Pm94J05Sfu1Cgscappdan <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `eve` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "eve", "lora_weights": "https://huggingface.co/BootesVoid/cmb1p7c3q05s4u1cgo5i612ud_cmb1pm94j05sfu1cgscappdan/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb1p7c3q05s4u1cgo5i612ud_cmb1pm94j05sfu1cgscappdan', weight_name='lora.safetensors') image = pipeline('eve').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb1p7c3q05s4u1cgo5i612ud_cmb1pm94j05sfu1cgscappdan/discussions) to add images that show off what you’ve made with this LoRA.
VIDEO-18-Shamy-Laura-Viral-Link/FULL.VIDEO.LINK.Shamy.Laura.Viral.Video.Leaks.Official	VIDEO-18-Shamy-Laura-Viral-Link	2025-05-24T04:40:38Z	0	0	null	[ "region:us" ]	null	2025-05-24T04:39:51Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
jjwwwww/naruto-lora	jjwwwww	2025-05-24T04:38:07Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-05-22T13:01:16Z	--- base_model: runwayml/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - jjwwwww/naruto-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/naruto-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
ekyuho/hyodol_sv	ekyuho	2025-05-24T04:37:14Z	0	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:maywell/Synatra-7B-v0.3-dpo", "base_model:quantized:maywell/Synatra-7B-v0.3-dpo", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T04:33:57Z	--- base_model: maywell/Synatra-7B-v0.3-dpo tags: - text-generation-inference - transformers - unsloth - mistral - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: ekyuho - License: apache-2.0 - Finetuned from model : maywell/Synatra-7B-v0.3-dpo This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep4_42	MinaMila	2025-05-24T04:35:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:35:06Z	--- 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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lisabdunlap/test_e2	lisabdunlap	2025-05-24T04:35:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T04:33:40Z	--- base_model: unsloth/qwen3-8b tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/qwen3-8b This qwen3 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)
TOMFORD79/Zombie_4	TOMFORD79	2025-05-24T04:33:36Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T03:59:05Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
SCH0/cardio_llama3e	SCH0	2025-05-24T04:29:40Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:29:30Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit library_name: transformers model_name: cardio_llama3e tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for cardio_llama3e This model is a fine-tuned version of [unsloth/meta-llama-3.1-8b-instruct-bnb-4bit](https://huggingface.co/unsloth/meta-llama-3.1-8b-instruct-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="SCH0/cardio_llama3e", 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: 4.52.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/gemma2_2b_unlearned_gu_LoRa_ACSEmployment_2_ep7_22	MinaMila	2025-05-24T04:28:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:28:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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chloebrandon/t5_amh_finetuned	chloebrandon	2025-05-24T04:25:49Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-24T04:25:39Z	--- library_name: transformers license: apache-2.0 base_model: t5-small tags: - generated_from_trainer model-index: - name: t5_amh_finetuned 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. --> # t5_amh_finetuned This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
xuan-luo/MTPQwen3-8B-T1234-Eagle-nar-id8	xuan-luo	2025-05-24T04:21:40Z	0	0	transformers	[ "transformers", "safetensors", "mtpqwen3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-05-23T18:31:00Z	--- 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. 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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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MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep9_33	MinaMila	2025-05-24T04:17:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:17:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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SCH0/cardio-llama3e-finetuned	SCH0	2025-05-24T04:12:57Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:12:47Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: SCH0 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-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)
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep7_33	MinaMila	2025-05-24T04:10:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:10:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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DAKARA555/hipopen	DAKARA555	2025-05-24T04:09:29Z	3	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:Wan-AI/Wan2.1-I2V-14B-480P", "base_model:adapter:Wan-AI/Wan2.1-I2V-14B-480P", "license:apache-2.0", "region:us" ]	text-to-image	2025-05-22T16:16:56Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/white.png base_model: Wan-AI/Wan2.1-I2V-14B-480P instance_prompt: null license: apache-2.0 --- # hipopen <Gallery /> ## Model description https://civitai.com/models/1587277/ass-stretchgrab-wan-21-i2v-480p?modelVersionId=1796171 https://huggingface.co/DAKARA555/hipopen/resolve/main/ass_spread_i2v_480p.safetensors?download=true ## Download model Weights for this model are available in Safetensors format. [Download](/DAKARA555/hipopen/tree/main) them in the Files & versions tab.
mukomana/ppo-Huggy	mukomana	2025-05-24T04:09:22Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-05-24T04:09:15Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: mukomana/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
UginaKo/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_squeaky_snail	UginaKo	2025-05-24T04:04:47Z	17	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am deadly squeaky snail", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-17T12:27:40Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_squeaky_snail tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am deadly squeaky snail - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_squeaky_snail This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="UginaKo/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_squeaky_snail", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q8_0-GGUF	Triangle104	2025-05-24T04:04:06Z	0	0	transformers	[ "transformers", "gguf", "256k context", "reasoning", "thinking", "qwen3", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:DavidAU/Qwen3-8B-256k-Context-8X-Grand", "base_model:quantized:DavidAU/Qwen3-8B-256k-Context-8X-Grand", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T04:03:04Z	--- library_name: transformers pipeline_tag: text-generation tags: - 256k context - reasoning - thinking - qwen3 - llama-cpp - gguf-my-repo base_model: DavidAU/Qwen3-8B-256k-Context-8X-Grand --- # Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q8_0-GGUF This model was converted to GGUF format from [`DavidAU/Qwen3-8B-256k-Context-8X-Grand`](https://huggingface.co/DavidAU/Qwen3-8B-256k-Context-8X-Grand) 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/DavidAU/Qwen3-8B-256k-Context-8X-Grand) for more details on the model. --- Qwen3 - 8B set at 256k (262144) context by extended YARN. This is a collection of models of Qwen 3 8Bs with max context set at 64k, 96k, 128k, 192k, 256k, and 320k. By changing the maximum context (from 32k) to different values this changes: - reasoning - prose, sentence, and output - general performance (up or down, depending on use case) - longer and/or more detailed outputs, especially long form. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q8_0-GGUF --hf-file qwen3-8b-256k-context-8x-grand-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q8_0-GGUF --hf-file qwen3-8b-256k-context-8x-grand-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q8_0-GGUF --hf-file qwen3-8b-256k-context-8x-grand-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q8_0-GGUF --hf-file qwen3-8b-256k-context-8x-grand-q8_0.gguf -c 2048 ```
MatchaLwc/test-5	MatchaLwc	2025-05-24T03:59:41Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T16:07:58Z	--- library_name: transformers model_name: test-5 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for test-5 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="MatchaLwc/test-5", 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/1105645918-bit/huggingface/runs/jsx6zali) 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: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep3_33	MinaMila	2025-05-24T03:57:07Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:57:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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vmpsergio/dac3502e-0d0b-41b2-845c-65a728684de8	vmpsergio	2025-05-24T03:52:35Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T03:23:04Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: dac3502e-0d0b-41b2-845c-65a728684de8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen2.5-Coder-7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ac15f2cf66f9a101_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: vmpsergio/dac3502e-0d0b-41b2-845c-65a728684de8 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/ac15f2cf66f9a101_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 827e9947-626f-4910-9712-3aaac9c035dd wandb_project: s56-28 wandb_run: your_name wandb_runid: 827e9947-626f-4910-9712-3aaac9c035dd warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # dac3502e-0d0b-41b2-845c-65a728684de8 This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4418 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.388 \| 0.3070 \| 280 \| 1.4418 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
kawaimasa/wanabi_mini_12b_GGUF	kawaimasa	2025-05-24T03:50:20Z	0	0	null	[ "gguf", "japanese", "text-generation", "novel-writing", "mistral", "ja", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	2025-05-23T17:36:10Z	--- license: apache-2.0 # ベースモデルに準拠 language: ja tags: - japanese - text-generation - novel-writing - mistral pipeline_tag: text-generation --- # wanabi_mini_12b_GGUF wanabi_mini_12b_GGUF は、小説執筆支援に特化してファインチューニングされた日本語大規模言語モデルです。[wanabi-24B](https://huggingface.co/kawaimasa/wanabi_24b_v1_GGUF)と同等の機能を有しながらより多くのユーザーが扱い易いモデルを用意しました。このモデルは、[mistralai/Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407) をベースとし、[24B版](https://huggingface.co/kawaimasa/wanabi_24b_v1_GGUF)よりもデータセットの規模は小さいものの、より高品質な日本語の小説関連テキストデータを用いて学習されています。アイデア出しから本文生成、文脈に沿った続きの生成、そしてアイデアの補間まで、小説執筆の様々なプロセスをサポートすることを目指しています。 * 提供形式: 現在、GGUF 形式のみ提供しています。VRAM 8GB以上のコンシューマGPUに適合する複数の量子化バージョンを用意。全て量子化モデルは特性のキャリブレーションデータを用いてimatrix量子化されています。 * 特徴: 24B版と比較して、より高品質なデータセットで学習されており、応答性や特定のタスクにおける精度向上が期待されます。 ## 🚀 Project Wannabe との連携 (強く推奨) このモデルは、専用のデスクトップアプリケーション [Project Wannabe](https://github.com/kawaii-justice/Project-Wannabe) と連携して使用することを強く推奨します。[Project Wannabe](https://github.com/kawaii-justice/Project-Wannabe) は、`wanabi_mini_12b_GGUF` の能力を最大限に引き出すための GUI を提供し、以下で説明する機能を直感的に利用できるように設計されています。 ## ✨ 新機能 (wanabi-24B v1 との比較) `wanabi_mini_12b_GGUF` は、24B版の主要機能に加え、以下の新機能が追加されています。 1. アイデア補間機能 (新): * 目的: Project Wannabe の「詳細情報」タブで、小説のアイデアに必要な全ての項目（タイトル、キーワード、ジャンル、あらすじ、設定、プロット）が入力されている場合に、それらの情報を基により詳細で深掘りされたアイデアや展開のヒントを生成します。 * 適用: アイデア生成 (IDEA) タスクにおいて、特定の条件を満たした際に起動します。 ## ✨ 主な機能 [wanabi-24B](https://huggingface.co/kawaimasa/wanabi_24b_v1_GGUF) と同様の基本的な小説執筆支援機能を提供します。 1. オーサーズノート機能: * 目的: 次に起きる展開、行動、心情描写など、今後おおよそ1000文字以内に起こるような直近の内容を記述することで、続きの文章生成をより細かく誘導します。 * 適用: 続き生成 (CONT) タスクのプロンプトに組み込まれます。 2. レーティング機能: * 目的: 生成内容のレーティング（`general` または `r18`）を指定します。 * 適用: 全てのタスク (GEN, CONT, IDEA) の指示 (Instruction) の末尾に `レーティング: {指定値}` が付与されます。 3. セリフ量指定機能: * 目的: 生成される文章中のセリフの割合を「指定なし」「少ない」「やや少ない」「普通」「やや多い」「多い」から選択します。（現在のバージョンではまだ完全に反映されませんが、将来のバージョンでの対応を見据えた機能です。） * 適用: 「指定なし」以外が選択された場合、本文生成 (GEN) および続き生成 (CONT) タスクのプロンプトの入力 (Input) 部分（参考情報ブロック内）に `# セリフ量: {指定値}` が含まれます。 4. 本文生成 (GEN): * 指示に加え、任意で与えられるメタデータ（タイトル、キーワード、ジャンル、あらすじ、設定、プロット）やセリフ量、レーティングに基づいて小説本文を生成します。 5. 続き生成 (CONT): * 与えられた本文の続きを、任意で与えられるメタデータ、セリフ量、レーティング、そしてオーサーズノートを考慮しながら生成します。 * プロンプト構造は wanabi-24B v0.1 と同様の改善された形式です。 6. アイデア生成 (IDEA): * 任意で与えられるメタデータの一部（または無し）とレーティングを基に、小説のアイデア（タイトル、キーワード、ジャンル、あらすじ、設定、プロット）を生成します。 * アイデア補間機能により、入力情報が豊富な場合はより詳細なアイデアが生成されます。 ## 💻 学習の詳細 ### ベースモデル * [mistralai/Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407) ### 学習フレームワーク * [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) ### 学習手法 * 手法: Supervised Fine-tuning (SFT) * 量子化・アダプター: LoRA * `lora_rank`: 128 * `lora_alpha`: 256 * `lora_dropout`: 0.05 * `lora_target`: all (全ての線形層) * 精度: bf16 * シーケンス長: 32768 * バッチサイズ: `per_device_train_batch_size`=1, `gradient_accumulation_steps`=24 (実効バッチサイズ 24) * 最適化: * Optimizer: PagedAdamW (8-bit) (`optim: paged_adamw_8bit`) * Flash Attention 2: 有効 (`flash_attn: fa2`) * Unsloth Gradient Checkpointing: 有効 (`use_unsloth_gc: true`) * Liger Kernel: 有効 (`enable_liger_kernel: true`) * Weight Decay: 0.01 (`weight_decay: 0.01`) * 学習率: * `learning_rate`: 4.0e-5 * `lr_scheduler_type`: cosine_with_restarts * `lr_scheduler_kwargs`: `{"num_cycles": 1}` * `warmup_ratio`: 0.03 * その他: * `num_train_epochs`: 1 ## 📝 プロンプト形式 (`mistral_small` テンプレート) 本モデルは LLaMA-Factory の `mistral_small` チャットテンプレート形式で学習されています。推論時も同様の形式を推奨します。[Project Wannabe](https://github.com/kawaii-justice/Project-Wannabe) を使用する場合は、意識する必要はありません。 [wanabi-24B](https://huggingface.co/kawaimasa/wanabi_24b_v1_GGUF)と基本的な形式は同じなため、詳細は省略します。 * 新機能:アイデア補間: > [Project Wannabe](https://github.com/kawaii-justice/Project-Wannabe) の「詳細情報」タブで、タイトル、キーワード、ジャンル、あらすじ、設定、プロットの全てが入力されている状態でアイデア生成を実行すると、モデルはこれらの豊富な情報を活用し、より詳細で具体的なアイデア（例えば、キャラクターの掘り下げ、サブプロットの提案、世界観の補足説明など）を生成しようと試みます。この機能は特別なプロンプト変更を必要とせず、入力情報の量と質に応じて自動的に働きが変化します。 ## ⚠️ 制限事項と注意点 * 開発中のモデル: 本モデルは現在も開発途上であり、性能や安定性は今後のバージョンで向上する可能性があります。 * 偏り: 学習データの特性上、生成内容が特定のジャンル、表現、展開に偏る可能性があります。 * 不適切な内容: 学習データには多様なテキストが含まれるため、不快感を与える可能性のある文章が生成されることがあります。レーティング機能で制御を試みていますが、完全ではありません。 * 品質の限界: 生成される文章の多様性、一貫性、文脈への追従性には限界があります。 * 利用上の注意: 本モデルは研究および実験的な目的で提供されています。違法な目的や他者の権利を侵害する目的での使用は固く禁じます。 * 自己責任: 本モデルの使用によって生じたいかなる結果についても、開発者は一切の責任を負いません。
Voidstep/drift_qd8g3	Voidstep	2025-05-24T03:43:55Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T03:40:58Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
aipib/smolvlm-ft	aipib	2025-05-24T03:41:02Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-24T03:40:59Z	--- license: apache-2.0 ---
thejaminator/number-4e-05-qwen3_8b-epochs1	thejaminator	2025-05-24T03:40:52Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:40:38Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B This qwen3 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)
keko24/Qwen3-0.6B-SFT-Tulu_MathCodeSciTableWild	keko24	2025-05-24T03:36:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T03:36:13Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep7_22	MinaMila	2025-05-24T03:36:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:36:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep5_22	MinaMila	2025-05-24T03:29:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:29:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AngelRaychev/0.5B-sos-iteration_3	AngelRaychev	2025-05-24T03:28:04Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:AngelRaychev/0.5B-sos-iteration_2", "base_model:finetune:AngelRaychev/0.5B-sos-iteration_2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T02:22:09Z	--- base_model: AngelRaychev/0.5B-sos-iteration_2 library_name: transformers model_name: 0.5B-sos-iteration_3 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 0.5B-sos-iteration_3 This model is a fine-tuned version of [AngelRaychev/0.5B-sos-iteration_2](https://huggingface.co/AngelRaychev/0.5B-sos-iteration_2). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="AngelRaychev/0.5B-sos-iteration_3", 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: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - 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}} } ```
duydc/qwen-2.5-7b-alpaca-instruct-2452025-ver2	duydc	2025-05-24T03:25:59Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:15:17Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: qwen-2.5-7b-alpaca-instruct-2452025-ver2 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen-2.5-7b-alpaca-instruct-2452025-ver2 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="duydc/qwen-2.5-7b-alpaca-instruct-2452025-ver2", 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/duydc/huggingface/runs/ixighrz6) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep4_22	MinaMila	2025-05-24T03:25:53Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:25:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep3_22	MinaMila	2025-05-24T03:22:23Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:22:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/llama_instbase_3b_LoRa_Adult_cfda_ep1_22	MinaMila	2025-05-24T03:20:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:20:39Z	--- 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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ConicCat/WIPadapter	ConicCat	2025-05-24T03:18:58Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "glm4", "trl", "en", "base_model:THUDM/GLM-4-32B-Base-0414", "base_model:finetune:THUDM/GLM-4-32B-Base-0414", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:17:58Z	--- base_model: THUDM/GLM-4-32B-Base-0414 tags: - text-generation-inference - transformers - unsloth - glm4 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ConicCat - License: apache-2.0 - Finetuned from model : THUDM/GLM-4-32B-Base-0414 This glm4 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)
vmpsergio/e147b7ba-7b4b-4173-847a-d8372d33171a	vmpsergio	2025-05-24T03:18:58Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T02:57:34Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: e147b7ba-7b4b-4173-847a-d8372d33171a tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for e147b7ba-7b4b-4173-847a-d8372d33171a This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vmpsergio/e147b7ba-7b4b-4173-847a-d8372d33171a", 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/dedok-yo/s56-28/runs/tt38aqs5) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fevohh/GenParser-iter2-1B-1.5k	fevohh	2025-05-24T03:16:15Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T03:05:02Z	--- base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: fevohh - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-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)
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep1_22	MinaMila	2025-05-24T03:15:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:15:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/llama_instbase_LoRa_ACSEmployment_2_cfda_ep6_22	MinaMila	2025-05-24T03:09:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:09:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K-Q4_K_M-GGUF	MrRobotoAI	2025-05-24T03:08:38Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K", "base_model:quantized:MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T02:55:26Z	--- base_model: MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K-Q4_K_M-GGUF This model was converted to GGUF format from [`MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K`](https://huggingface.co/MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K) 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/MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K) 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 MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K-Q4_K_M-GGUF --hf-file thor-v2.7-8b-fantasy-fiction-128k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K-Q4_K_M-GGUF --hf-file thor-v2.7-8b-fantasy-fiction-128k-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 MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K-Q4_K_M-GGUF --hf-file thor-v2.7-8b-fantasy-fiction-128k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K-Q4_K_M-GGUF --hf-file thor-v2.7-8b-fantasy-fiction-128k-q4_k_m.gguf -c 2048 ```
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep9_66	MinaMila	2025-05-24T03:08:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:08:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep7_66	MinaMila	2025-05-24T03:01:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:01:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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(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]
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep6_66	MinaMila	2025-05-24T02:58:07Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T02:58:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
golesheed/wav2vec2-xls-r-1b-dutch-3	golesheed	2025-05-24T02:53:39Z	0	0	null	[ "tensorboard", "safetensors", "wav2vec2", "generated_from_trainer", "base_model:facebook/wav2vec2-xls-r-1b", "base_model:finetune:facebook/wav2vec2-xls-r-1b", "license:apache-2.0", "region:us" ]	null	2025-05-23T06:24:59Z	--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-1b tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xls-r-1b-dutch-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. --> # wav2vec2-xls-r-1b-dutch-3 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0228 - Wer: 0.6653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.02 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.0657 \| 0.55 \| 100 \| 3.0729 \| 1.0000 \| \| 2.833 \| 1.09 \| 200 \| 2.9037 \| 1.0000 \| \| 2.8135 \| 1.64 \| 300 \| 2.8094 \| 1.0000 \| \| 2.4084 \| 2.19 \| 400 \| 2.1296 \| 0.9830 \| \| 2.0656 \| 2.73 \| 500 \| 1.8903 \| 0.9843 \| \| 1.8315 \| 3.28 \| 600 \| 1.6603 \| 0.9773 \| \| 1.6282 \| 3.83 \| 700 \| 1.4572 \| 0.9210 \| \| 1.6298 \| 4.37 \| 800 \| 1.3830 \| 0.8912 \| \| 1.4395 \| 4.92 \| 900 \| 1.2972 \| 0.8812 \| \| 1.3553 \| 5.46 \| 1000 \| 1.2469 \| 0.8634 \| \| 1.4646 \| 6.01 \| 1100 \| 1.2333 \| 0.8855 \| \| 1.1811 \| 6.56 \| 1200 \| 1.1695 \| 0.8149 \| \| 1.1296 \| 7.1 \| 1300 \| 1.1356 \| 0.8130 \| \| 1.4323 \| 7.65 \| 1400 \| 1.1163 \| 0.8077 \| \| 1.0546 \| 8.2 \| 1500 \| 1.1258 \| 0.7858 \| \| 1.3653 \| 8.74 \| 1600 \| 1.1615 \| 0.7904 \| \| 1.18 \| 9.29 \| 1700 \| 1.0668 \| 0.7638 \| \| 1.3 \| 9.84 \| 1800 \| 1.0553 \| 0.7423 \| \| 1.1166 \| 10.38 \| 1900 \| 1.0891 \| 0.7516 \| \| 0.9824 \| 10.93 \| 2000 \| 1.0798 \| 0.7706 \| \| 0.7414 \| 11.48 \| 2100 \| 1.0507 \| 0.7220 \| \| 0.808 \| 12.02 \| 2200 \| 1.0369 \| 0.7155 \| \| 0.6806 \| 12.57 \| 2300 \| 1.0143 \| 0.7042 \| \| 0.7422 \| 13.11 \| 2400 \| 1.0057 \| 0.6990 \| \| 0.7736 \| 13.66 \| 2500 \| 1.0307 \| 0.6940 \| \| 0.7434 \| 14.21 \| 2600 \| 1.0288 \| 0.6861 \| \| 0.5879 \| 14.75 \| 2700 \| 1.0125 \| 0.6805 \| \| 0.6477 \| 15.3 \| 2800 \| 1.0275 \| 0.6792 \| \| 0.6214 \| 15.85 \| 2900 \| 1.0335 \| 0.6799 \| \| 0.5486 \| 16.39 \| 3000 \| 1.0147 \| 0.6738 \| \| 0.5171 \| 16.94 \| 3100 \| 1.0062 \| 0.6692 \| \| 0.4814 \| 17.49 \| 3200 \| 1.0246 \| 0.6693 \| \| 0.5022 \| 18.03 \| 3300 \| 1.0138 \| 0.6644 \| \| 0.5411 \| 18.58 \| 3400 \| 1.0236 \| 0.6664 \| \| 0.5055 \| 19.13 \| 3500 \| 1.0225 \| 0.6648 \| \| 0.467 \| 19.67 \| 3600 \| 1.0228 \| 0.6653 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.7.0+cu126 - Datasets 2.16.1 - Tokenizers 0.15.2
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep4_66	MinaMila	2025-05-24T02:51:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T02:51:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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m4rwra/katrina.lim.viral.kiffy.telegram.link.video	m4rwra	2025-05-24T02:51:14Z	0	0	null	[ "region:us" ]	null	2025-05-24T02:48:50Z	<a href="https://lojinx.cfd/hguiko"> 🌐 Click Here To link (katrina.lim.viral.kiffy.telegram.link.video) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://lojinx.cfd/hguiko"> 🌐 katrina.lim.viral.kiffy.telegram.link.video
OsBaran/gemma2_9b_newest_lotofstepsss	OsBaran	2025-05-24T02:51:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma2", "trl", "en", "base_model:unsloth/gemma-2-9b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-2-9b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T02:51:00Z	--- base_model: unsloth/gemma-2-9b-it-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: OsBaran - License: apache-2.0 - Finetuned from model : unsloth/gemma-2-9b-it-bnb-4bit This gemma2 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)
TrendForge/brown22-speech85782	TrendForge	2025-05-24T02:46:11Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-24T02:46:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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