Split (1)

train · 915k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-03 12:31:03	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 537 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-03 12:30:52	card stringlengths 11 1.01M
IoakeimE/sft_best_simplification	IoakeimE	2025-06-23T19:49:57Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-06-18T14:02:58Z	--- base_model: unsloth/mistral-7b-v0.3-bnb-4bit library_name: transformers model_name: sft_best_simplification tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for sft_best_simplification This model is a fine-tuned version of [unsloth/mistral-7b-v0.3-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-v0.3-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="IoakeimE/sft_best_simplification", 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/ioakeime-aristotle-university-of-thessaloniki/sft-best_simplification/runs/dq66tg8b) This model was trained with SFT. ### Framework versions - TRL: 0.18.2 - Transformers: 4.52.4 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
online-pro/Msbreewc-x-Ello-MG-5-Jam-7-Menit-Viral-Video	online-pro	2025-06-23T19:42:54Z	0	0	null	[ "region:us" ]	null	2025-06-23T19:42:23Z	[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?online-pro) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?online-pro) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?online-pro)
Pakcricketinfo-Sapna-Shah-Viral-Video-Fuk/18on.air.pakcricketinfo.sapna.shah.Viral.video.On.Social.Media.Link	Pakcricketinfo-Sapna-Shah-Viral-Video-Fuk	2025-06-23T19:32:19Z	0	0	null	[ "region:us" ]	null	2025-06-23T19:28:10Z	[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Download) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?Download) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?Download)
Fulstac/Codestral-22B-v0.1-lora-weights	Fulstac	2025-06-23T19:31:20Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-23T19:26:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
morturr/Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-one_liners-comb-3-seed-42-2025-06-23	morturr	2025-06-23T19:00:30Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-23T19:00:21Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-one_liners-comb-3-seed-42-2025-06-23 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. --> # Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-one_liners-comb-3-seed-42-2025-06-23 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
TOMFORD79/boom9	TOMFORD79	2025-06-23T18:47:55Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-23T18:42:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Official-Link-mezzo-fun-18-Viral-videos-XX/Official.VIDEO.mezzo.fun.Viral.Video.Tutorial	Official-Link-mezzo-fun-18-Viral-videos-XX	2025-06-23T18:45:22Z	0	0	null	[ "region:us" ]	null	2025-06-23T18:44:28Z	18 seconds ago <a href="https://viralinfo.xyz/video/?v=mezzo+fun" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://viralinfo.xyz/video/?v=mezzo+fun" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://viralinfo.xyz/video/?v=mezzo+fun"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> Mezzo Fun Viral Video: What Everyone Needs to Know About Online Ethics and Privacy Mezzo fun viral video warnings highlight the importance of online ethics, privacy, and responsibility. Discover why watching such content is... 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creaciones-pulso/metastyle_dpo_unsloth-Meta-Llama-3.1-8B-Instruct-bnb-4bit_8_3_0.0001_16_0.05	creaciones-pulso	2025-06-23T18:40:21Z	11	0	transformers	[ "transformers", "safetensors", "gguf", "text-generation-inference", "unsloth", "llama", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-22T22:04:48Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: creaciones-pulso - 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)
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-purring_giant_toad	chinna6	2025-06-23T18:04:20Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am purring giant toad", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-14T19:32:35Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-purring_giant_toad tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am purring giant toad - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-purring_giant_toad 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="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-purring_giant_toad", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
siybupt/OpenBioLLM-8B-q4f16_1-MLC	siybupt	2025-06-23T17:55:07Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-18T23:18:45Z	--- license: apache-2.0 ---
JayHyeon/pythia-2.8b-VIPO_5e-7_1.0vpo_const-1ep	JayHyeon	2025-06-23T17:23:02Z	7	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:trl-lib/ultrafeedback_binarized", "arxiv:2305.18290", "base_model:EleutherAI/pythia-2.8b", "base_model:finetune:EleutherAI/pythia-2.8b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-20T16:06:36Z	--- base_model: EleutherAI/pythia-2.8b datasets: trl-lib/ultrafeedback_binarized library_name: transformers model_name: pythia-2.8b-VIPO_5e-7_1.0vpo_const-1ep tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for pythia-2.8b-VIPO_5e-7_1.0vpo_const-1ep This model is a fine-tuned version of [EleutherAI/pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) on the [trl-lib/ultrafeedback_binarized](https://huggingface.co/datasets/trl-lib/ultrafeedback_binarized) dataset. 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="JayHyeon/pythia-2.8b-VIPO_5e-7_1.0vpo_const-1ep", 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/bonin147/huggingface/runs/cce7zplh) 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: 0.13.0.dev0 - Transformers: 4.47.0.dev0 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Hachipo/Qwen2.5-7B-MIFT-en_newbase_v2-EnTrans_10000_3	Hachipo	2025-06-23T17:11:44Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-23T17:08:43Z	--- 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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dgambettaphd/M_llm3_run0_gen7_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST	dgambettaphd	2025-06-23T17:05:59Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-23T17:05:45Z	--- library_name: transformers tags: - unsloth --- # 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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winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_positive-negative-addition-same_layer_2_1_3_49	winnieyangwannan	2025-06-23T16:54:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-23T16:51:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
ryzax/qwen3_1.7B_sft_correct_v6_new_1e-5_4	ryzax	2025-06-23T16:39:59Z	257	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen3-1.7B", "base_model:finetune:Qwen/Qwen3-1.7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-21T21:45:05Z	--- base_model: Qwen/Qwen3-1.7B library_name: transformers model_name: qwen3_1.7B_sft_correct_v6_new_1e-5_4 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen3_1.7B_sft_correct_v6_new_1e-5_4 This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ryzax/qwen3_1.7B_sft_correct_v6_new_1e-5_4", 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/zc096373/s1/runs/ltrbbgt8) This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.52.4 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/r1-q3-x2-GGUF	mradermacher	2025-06-23T16:13:23Z	218	0	transformers	[ "transformers", "gguf", "en", "base_model:miike-ai/DeepSeek-R1-0528-Qwen3-11B", "base_model:quantized:miike-ai/DeepSeek-R1-0528-Qwen3-11B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-09T09:16:14Z	--- base_model: miike-ai/DeepSeek-R1-0528-Qwen3-11B 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/miike-ai/DeepSeek-R1-0528-Qwen3-11B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/r1-q3-x2-GGUF/resolve/main/r1-q3-x2.Q2_K.gguf) \| Q2_K \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/r1-q3-x2-GGUF/resolve/main/r1-q3-x2.Q3_K_S.gguf) \| Q3_K_S \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/r1-q3-x2-GGUF/resolve/main/r1-q3-x2.Q3_K_M.gguf) \| Q3_K_M \| 5.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/r1-q3-x2-GGUF/resolve/main/r1-q3-x2.Q3_K_L.gguf) \| Q3_K_L \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/r1-q3-x2-GGUF/resolve/main/r1-q3-x2.IQ4_XS.gguf) \| IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/r1-q3-x2-GGUF/resolve/main/r1-q3-x2.Q4_K_S.gguf) \| Q4_K_S \| 6.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/r1-q3-x2-GGUF/resolve/main/r1-q3-x2.Q4_K_M.gguf) \| Q4_K_M \| 6.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/r1-q3-x2-GGUF/resolve/main/r1-q3-x2.Q5_K_S.gguf) \| Q5_K_S \| 7.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/r1-q3-x2-GGUF/resolve/main/r1-q3-x2.Q5_K_M.gguf) \| Q5_K_M \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/r1-q3-x2-GGUF/resolve/main/r1-q3-x2.Q6_K.gguf) \| Q6_K \| 8.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/r1-q3-x2-GGUF/resolve/main/r1-q3-x2.Q8_0.gguf) \| Q8_0 \| 11.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/r1-q3-x2-GGUF/resolve/main/r1-q3-x2.f16.gguf) \| f16 \| 21.1 \| 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 -->
ntphiep/vit5_stp_chinese	ntphiep	2025-06-23T15:56:59Z	0	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-23T15:53:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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wy99/llama_test	wy99	2025-06-23T15:49:36Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-20T22:58:11Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: transformers model_name: llama_test tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for llama_test This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="wy99/llama_test", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.52.4 - Pytorch: 2.5.1+cu121 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
blazarev/roberta-emotional-hub	blazarev	2025-06-23T15:37:47Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-23T15:37:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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GleghornLab/production_ss9_model	GleghornLab	2025-06-23T15:22:54Z	23	0	transformers	[ "transformers", "safetensors", "ESMplusplus", "token-classification", "custom_code", "arxiv:2506.08293", "autotrain_compatible", "region:us" ]	token-classification	2025-05-08T02:18:36Z	--- library_name: transformers tags: [] --- # DSM: Diffusion Models for Protein Sequence Generation ### Note: This readme is shared between our GitHub and Huggingface pages. ## Table of Contents - [Introduction](#introduction) - [Models](#models) - [Usage](#usage) - [Demos](#usage) - [Local installation](#installation) - [Training](#training) - [Evaluation](#evaluation) - [Results](#results) - [Cite](#cite) ## Introduction DSM (Diffusion Sequence Model) is a novel Protein Language Model (pLM) developed in collaboration between the [Gleghorn Lab](https://www.gleghornlab.com/) and [Synthyra](https://synthyra.com/). It was trained with masked diffusion to enable both high-quality representation learning and generative protein design. This repository contains the code for training, evaluating, and applying DSM and its variants. DSM is capable of generating diverse, biomimetic sequences that align with expected amino acid compositions, secondary structures, and predicted functions. Furthermore, DSM's learned representations match or exceed those of comparably sized pLMs on various downstream tasks. DSM is detailed extensively in our [preprint](https://arxiv.org/abs/2506.08293) (which is currently in review). Beyond the base and PPI variants, we are currently training versions to jointly diffuse over sequence and foldseek tokens, as well as [Annotation Vocabulary](https://www.biorxiv.org/content/10.1101/2024.07.30.605924v1) tokens. Since the preprint release, Synthyra has trained [Synthyra/DSM_ppi_full](https://huggingface.co/Synthyra/DSM_ppi_full) which neglects the LoRA PPI training in favor for full finetuning. Additionally, the sequences SeqA and SeqB are jointly masked instead of just SeqB in the original version. We plan on adding the many new results to the second version of the preprint and eventual journal article. ## Models Relevant Huggingface hosted models and datasets - Base DSM Models: - [GleghornLab/DSM_150](https://huggingface.co/GleghornLab/DSM_150) - 150M parameter DSM model - [GleghornLab/DSM_650](https://huggingface.co/GleghornLab/DSM_650) - 650M parameter DSM model - DSM-ppi Models: (LoRA versions - results reported in paper but not recommended for real use) - [GleghornLab/DSM_150_ppi_lora](https://huggingface.co/GleghornLab/DSM_150_ppi_lora) - 150M parameter LoRA DSM-ppi model - [GleghornLab/DSM_650_ppi_lora](https://huggingface.co/GleghornLab/DSM_650_ppi_lora) - 650M parameter LoRA DSM-ppi model - [GleghornLab/DSM_150_ppi_control](https://huggingface.co/GleghornLab/DSM_150_ppi_control) - Control version of LoRA DSM-ppi (Fully finetuned - recommended for real use) - [Synthyra/DSM_ppi_full](https://huggingface.co/Synthyra/DSM_ppi_full) - 650M parameter DSM-ppi model - Datasets: - [Synthyra/omg_prot50](https://huggingface.co/datasets/Synthyra/omg_prot50) - Open MetaGenomic dataset clustered at 50% identity (207M sequences) - [GleghornLab/stringv12_modelorgs_9090](https://huggingface.co/datasets/GleghornLab/stringv12_modelorgs_9090) - STRING database model organisms (653k sequences) - Utility Models: - [GleghornLab/production_ss4_model](https://huggingface.co/GleghornLab/production_ss4_model) - Secondary structure prediction (4-class) - [GleghornLab/production_ss9_model](https://huggingface.co/GleghornLab/production_ss9_model) - Secondary structure prediction (9-class) ## Usage This section outlines how to use a trained `DSM` model for common generation tasks. The core generation logic is provided by the `GenerateMixin` class, used by `DSM` models. First, ensure you have a trained model (either one you trained or a pre-trained one from Hugging Face Hub) and the necessary environment set up. ```python import torch from models.modeling_dsm import DSM # Or DSM_ppi for binder generation # Load a pre-trained model model_name_or_path = "GleghornLab/DSM_650" # Replace with your model of choice device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = DSM.from_pretrained(model_name_or_path).to(device).eval() tokenizer = model.tokenizer ``` ```console You are using a model of type esm_diff to instantiate a model of type dsm. This is not supported for all configurations of models and can yield errors. ``` This warning is normal - all good! ### 1. Unconditional Sequence Generation To generate a novel sequence of a specific length. DSM uses a progressive denoising approach. ```python ### Unconditional generation length = 100 mask_token = tokenizer.mask_token # optionally, enforce starting with methionine input_tokens = tokenizer.encode('M' + ''.join([mask_token] * (length - 1)), add_special_tokens=True, return_tensors='pt').to(device) output = model.mask_diffusion_generate( tokenizer=tokenizer, input_tokens=input_tokens, step_divisor=100, # lower is slower but better temperature=1.0, # sampling temperature remasking="random", # strategy for remasking tokens not kept preview=False, # set this to True to watch the mask tokens get rilled in real time slow=False, # adds a small delay to the real time filling (because it is usually very fast and watching carefully is hard!) return_trajectory=False # set this to True to return the trajectory of the generation (what you watch in the preview) ) # Note: output will be a tuple if return_trajectory is True generated_sequences = model.decode_output(output) print(f"Generated sequence: {generated_sequences[0]}") ``` ```console Generated sequence: MFRVDALQVAQQETLAIGRSTAYDKQESPSMAQRQVLTQLAAYGGENDLRQICIPAERRNFLSIANGASYQFVEEDNEANGGYWSPHKAGLPESACKRFI ``` ### 2. Mask Filling (Inpainting) To fill in masked regions of a template sequence: ```python # Mask Filling / Inpainting template_sequence = "MA<mask><mask><mask>KEG<mask><mask>STL" input_tokens = tokenizer.encode(template_sequence, add_special_tokens=True, return_tensors='pt').to(device) output = model.mask_diffusion_generate( tokenizer=tokenizer, input_tokens=input_tokens, step_divisor=100, # lower is slower but better temperature=1.0, # sampling temperature remasking="random", # strategy for remasking tokens not kept preview=False, # set this to True to watch the mask tokens get rilled in real time slow=False, # adds a small delay to the real time filling (because it is usually very fast and watching carefully is hard!) return_trajectory=False # set this to True to return the trajectory of the generation (what you watch in the preview) ) # Note: output will be a tuple if return_trajectory is True generated_sequences = model.decode_output(output) print(f"Generated sequence: {generated_sequences[0]}") ``` ```console Generated sequence: MAVKFKEGGISTL ``` ### 3. Conditional Generation (e.g., Binders - using DSM-ppi) ```python # from models.modeling_dsm import DSM_ppi # model_binder = DSM_ppi.from_pretrained("GleghornLab/DSM_650_ppi_lora").to(device).eval() # The lora version from the paper leads to unreliable outputs # Synthyra has generously trained a version through full fine tuning model = DSM.from_pretrained("Synthyra/DSM_ppi_full").to(device).eval() # BBF-14 target_seq = "MGTPLWALLGGPWRGTATYEDGTKVTLDYRYTRVSPDRLRADVTYTTPDGTTLEATVDLWKDANGVIRYHATYPDGTSADGTLTQLDADTLLATGTYDDGTKYTVTLTRVAPGSGWHHHHHH" # For binder generation, the 'interactor' (SeqB) part is what gets generated/filled. # Start with a fully masked interactor of desired length. interactor_template_len = 256 interactor_template = ''.join([mask_token] * interactor_template_len) combined_input_str = target_seq + '<eos>' + interactor_template input_tokens = tokenizer.encode(combined_input_str, add_special_tokens=True, return_tensors='pt').to(device) output = model.mask_diffusion_generate( tokenizer=tokenizer, input_tokens=input_tokens, step_divisor=100, # lower is slower but better temperature=1.0, # sampling temperature remasking="random", # strategy for remasking tokens not kept preview=False, # set this to True to watch the mask tokens get rilled in real time slow=False, # adds a small delay to the real time filling (because it is usually very fast and watching carefully is hard!) return_trajectory=False # set this to True to return the trajectory of the generation (what you watch in the preview) ) # Note: output will be a tuple if return_trajectory is True target, binder = model.decode_dual_input(output, seperator='<eos>') # Parse out the generated interactor part based on EOS tokens. # Example: generated_full_seq_str.split(model_binder.tokenizer.eos_token)[1] print(f"Generated binder {binder[0]}") ``` ```console Generated binder HRHHHRRPTHARETEWLARMRLGIAEHQRIAVPRSDLEPDQMRERAADNQRLVKEYDQVIDHQTEGSTERLFEVLRVWEQVNTEQAHHEASAALEFGRVGYPDDEGGRAFYTQANAHKKDLVEYIGGIDEDAKWDPRIAWLMPEGGQPVKATVIGVSEERINGLKVLDDHWGRERRLWLINLFTALQAYDDPTRPTQVTLTPATDQLTNDVQYLLLSTRYTPPGVTTAVKIRKLDGRTLKVLTTEAPYVVRGATLS ``` Folded with Chai1: ![image](https://github.com/user-attachments/assets/782d7bba-6f25-4a27-b0c4-fef88565dd33) `Synthyra/DSM_ppi_full` was actually trained to fill masks from any part of SeqA and SeqB. That means you can fully hallucinate plausibly interacting protein pairs. ```python seq_a_length = 128 seq_b_length = 128 seq_a_template = ''.join([mask_token] * seq_a_length) seq_b_template = ''.join([mask_token] * seq_b_length) combined_input_str = seq_a_template + '<eos>' + seq_b_template input_tokens = tokenizer.encode(combined_input_str, add_special_tokens=True, return_tensors='pt').to(device) output = model.mask_diffusion_generate( tokenizer=tokenizer, input_tokens=input_tokens, step_divisor=10, # lower is slower but better temperature=1.0, # sampling temperature remasking="random", # strategy for remasking tokens not kept preview=False, # set this to True to watch the mask tokens get rilled in real time slow=False, # adds a small delay to the real time filling (because it is usually very fast and watching carefully is hard!) return_trajectory=False # set this to True to return the trajectory of the generation (what you watch in the preview) ) # Note: output will be a tuple if return_trajectory is True seqa, seqb = model.decode_dual_input(output, seperator='<eos>') # Parse out the generated interactor part based on EOS tokens. # Example: generated_full_seq_str.split(model_binder.tokenizer.eos_token)[1] print(f"SeqA: {seqa[0][5:]}") # remove cls token print(f"SeqB: {seqb[0]}") ``` ```console SeqA: MVNLAKMRQRTEQNLREVSSFVKILFHTVLKFPMKINIGIHVHINMQAAQNAAADQNMQATNVIDLHNFKMGKDIGVDNKASATAHIYDEAHHTFLQLGAIKLLHAIPMIAGPVRCRLPIGFGHRFRG SeqB: HYKNPMHSLLDSNVLHKDVVEVRLPIKIGMELDVMASAMREFLMPGTQQGDLRVIAEKRPVNKLHTYRRDLVKLLLAGAKLGTEAKSVELDLYRTELGGLVVYIININIATWDIIFAKVKICRGNDKP ``` Folded with Chai1: ![image](https://github.com/user-attachments/assets/1bdfed76-3c01-49f1-a92e-55ada89c2895) ## Demos There are various demos with many more to come. For example, in `demo_dsm_ppi_full.py` (run by `python -m demos.demo_dsm_ppi_full`) we perform a test on DSM-ppi. We take 1000 protein pairs from BIOGRID (real protein-protein interactions) and 1000 from Negatome (non interacting protein pairs) and mask the second sequence (SeqB) by 50%. This acts as a sanity check, as we expect the accuracy on reconstructing real positive PPIs to be higher than the accuracy on non-interacting proteins. Indeed, this is the case: ```console ================================================== RESULTS COMPARISON ================================================== Positive examples: Mean accuracy: 0.495 ± 0.322 Processed: 1000 examples Negative examples: Mean accuracy: 0.227 ± 0.231 Processed: 1000 examples Difference (Positive - Negative): 0.267 T-test: t=21.331, p=0.000 Difference is statistically significant (p < 0.05) ``` ## Installation 1. Clone the repository: ```bash git clone <repository-url> cd <repository-name> ``` 2. Initialize the submodules: ```bash git submodule update --init --remote --recursive ``` 3. Set up the Python virtual environment: The `setup_bioenv.sh` script creates a virtual environment named `bioenv` in your home directory (`~/bioenv`), installs PyTorch with CUDA 12.6 support, and then installs all other dependencies from `requirements.txt`. Make the script executable: ```bash chmod +x setup_bioenv.sh ``` Run the script: ```bash ./setup_bioenv.sh ``` If you are not on a linux machine, you can install the requirements directly ```console python -m pip install -r requirements.txt ``` 4. Activate the environment: Each time you want to work on this project, activate the virtual environment: ```bash source ~/bioenv/bin/activate ``` 5. To deactivate the environment: ```bash deactivate ``` ## Training The primary script for training models is `training/train_dsm.py`. This script further pretrains an ESM2 checkpoint using the DSM objective (masked diffusion based on LLaDA) on a large protein sequence dataset like [OMG-prot50](https://huggingface.co/datasets/Synthyra/omg_prot50). ### Main Training Script: `train_dsm.py` - Base Model: DSM models are extended from pre-trained ESM2 checkpoints (e.g., ESM2-150M, ESM2-650M). - Training Objective: Masked diffusion loss, where the model predicts masked tokens. The loss is scaled by `1/(t + epsilon)` where `t` is the corruption level, penalizing errors more at low mask rates. - Language Modeling Head: Uses a modified head with a soft-logit cap (`tau=30`) and tied output projection weights to the token embeddings. - Data Handling: - Training data can be streamed from datasets like [Synthyra/omg_prot50](https://huggingface.co/datasets/Synthyra/omg_prot50) (a version of Open MetaGenomic dataset clustered at 50% identity). - Uses `data.dataset_classes.SequenceDatasetFromList` for validation/test sets and `data.dataset_classes.IterableDatasetFromHF` for streaming training. - `data.data_collators.SequenceCollator` is used for batching. - Training Process: - Utilizes Hugging Face `TrainingArguments`. - A custom `IterableTrainer` (from `training.iterable_trainer.py`) handles iterable datasets. - Uses AdamW optimizer and a cosine learning rate scheduler with linear warmup. - Supports logging to Weights & Biases (wandb). - The trained model can be pushed to Hugging Face Hub. - Example checkpoints mentioned in the paper: [DSM-150](https://huggingface.co/GleghornLab/DSM_150) (from ESM2-150M, 100k steps, batch 32, seqlen 512, LR 1e-4) and [DSM-650](https://huggingface.co/GleghornLab/DSM_650) (from ESM2-650M, 100k steps, global batch 128, seqlen 2048, LR 1e-4). Usage Example: ```bash python -m training.train_dsm \ --model_path facebook/esm2_t33_650M_UR50D \ --save_path GleghornLab/DSM_650 \ --lr 1e-4 \ --batch_size 8 \ --grad_accum 16 \ --max_steps 100000 \ --save_every 1000 \ --fp16 \ --wandb_project "DSM_Training" \ --token <your_hf_token_if_needed_for_private_repo_or_saving> ``` Key Command-Line Arguments for `train_dsm.py`: * `--token`: Hugging Face token. * `--model_path`: Path to the base ESM2 model to start from. * `--save_path`: Path to save the trained DSM model on Hugging Face Hub. * `--lr`: Learning rate. * `--batch_size`: Batch size per device. * `--grad_accum`: Gradient accumulation steps. * `--max_steps`: Maximum training steps. * `--wandb_project`: Wandb project name (default: `DSM`). * `--max_length`: Maximum sequence length. * `--save_every`: Save model and evaluate every N steps. * `--fp16`: Enable mixed-precision training. * `--bugfix`: Use small batch size and max length for debugging. ### Other Training Scripts (e.g., for DSM-ppi) The `training/` directory may also contain scripts like `train_dsm_bind.py`. - DSM-ppi (e.g., [DSM-150-ppi](https://huggingface.co/GleghornLab/DSM_150_ppi_lora), [DSM-650-ppi](https://huggingface.co/GleghornLab/DSM_650_ppi_lora)) is fine-tuned on PPI datasets. - Training involves conditioning on a target sequence (SeqA) to generate an interactor (SeqB) using the format `[CLS]--SeqA--[EOS]--[MASKED~SeqB]--[EOS]`. - LoRA (Low-Rank Adaptation) can be applied to attention layers for efficient fine-tuning. And `training/iterable_trainer.py` provides the `get_iterable_trainer` function used by `train_dsm.py` to enable training with iterable datasets. ## Evaluation The repository includes a comprehensive suite for evaluating model performance, focusing on: 1. Sequence Reconstruction (Mask Filling): * Evaluated by masking validation/test sets at various corruption rates (5% to 90%) and measuring cross-entropy loss, weighted F1 score, and Alignment Score (ASc) for the masked positions. * The script `evaluation/mask_filling.py` is central to this. 2. Unconditional Generation Quality: * Generate a corpus of sequences based on lengths from a reference set (e.g., validation data). * Compare distributions (1-mers, 2-mers, 3-mers) of amino acids and predicted secondary structures between generated and natural sequences using χ² test and Jensen-Shannon (JS) divergence. * Compare distributions of predicted functional annotations (e.g., using Annotation Vocabulary - AV terms). * Scripts involved: `evaluation/unconditional_generation_tuning.py` (to find optimal generation parameters like temperature and step divisor `s`), `evaluation/unconditional_generation.py`, `evaluation/ss_pred.py` (using [production_ss4_model](https://huggingface.co/GleghornLab/production_ss4_model) or [production_ss9_model](https://huggingface.co/GleghornLab/production_ss9_model)), `evaluation/annotate_comparisons.py`, `evaluation/compare_distributions.py`, `evaluation/plot_distribution_comparisons.py`. * The `run_eval_pipeline.py` script automates this workflow. 3. Representation Quality (Model Probing): * Evaluate learned embeddings by training linear probes (or simple transformer blocks) on various downstream tasks (e.g., secondary structure prediction, localization prediction, etc.). * Performance is compared against random vectors, randomized transformers, and other established pLMs. * The assessment was done with [Protify](https://github.com/Synthyra/Protify), an open-source framework that can be used for pLM training and evaluation. 4. Conditional Generation (Binder Design for DSM-ppi): * Evaluate DSM-ppi on benchmarks like BenchBB. * Generate binders for target proteins using template-based masking strategies. * Assess generated binders using in-silico tools like Synteract2 for predicted binding affinity (ppKd). The `evaluation/` directory also contains a `readme.md` which provides further details on some evaluation workflows. Key metrics used include: - Alignment Score (ASc): A normalized Needleman-Wunsch global alignment score (using BLOSUM62) to measure sequence similarity, robust to length variations. ASc(a, b) = l/(f(a, a) - f(a, b) + l). - Jensen-Shannon (JS) Divergence: To compare distributions of k-mers and functional terms. Running the Full Unconditional Evaluation Pipeline: ```bash python run_eval_pipeline.py --token YOUR_HF_TOKEN --data_dir ./evaluation_results ``` Refer to `run_eval_pipeline.py --help` for more options, such as `--skip_tuning`. ### Mask Filling Evaluation The script `evaluation/mask_filling.py` is used to evaluate models on their ability to predict masked tokens in a sequence across various masking rates. - Functionality: - Evaluates different models (DSM, DPLM, standard ESM models). - Tests across multiple datasets ([Synthyra/omg_prot50](https://huggingface.co/datasets/Synthyra/omg_prot50), [GleghornLab/stringv12_modelorgs_9090](https://huggingface.co/datasets/GleghornLab/stringv12_modelorgs_9090)). - Calculates metrics: loss, perplexity, precision, recall, F1, accuracy, MCC, and alignment score. - Saves detailed results to CSV files. - Can generate a summary plot comparing model performance across different mask rates using `evaluation/plot_mask_fill_results.py`. - Usage Example: ```bash python -m evaluation.mask_filling \ --token YOUR_HF_TOKEN \ --batch_size 4 \ --mask_rates 0.15 0.30 0.50 \ --data_splits valid test \ --results_dir ./results/mask_fill_custom ``` To generate a comparison plot from existing results: ```bash python -m evaluation.mask_filling --generate_comparison_plot --results_dir ./results/mask_fill_custom --plot_output ./results/mask_fill_custom/comparison.png ``` ### Other Evaluation Scripts The `evaluation/` directory contains additional scripts for more specific analyses. These are typically run independently: - `evaluation/all_targets_uncond.py` and `evaluation/all_targets_cond.py`: Likely for evaluating generation towards specific targets, unconditionally and conditionally. - `evaluation/conditional_binder.py` and `evaluation/unconditional_binder.py`: Suggest evaluation focused on generating protein binders. - `evaluation/unconditional_by_length.py`: May evaluate unconditional generation focusing on sequence length distributions. - `evaluation/utils.py`: Utility functions for evaluation scripts. Users should refer to individual scripts (e.g., using `python -m evaluation.<script_name> --help`) for their specific usage and arguments. The `evaluation/` directory also contains a `readme.md` which provides further details on the unconditional generation evaluation workflow. ## Results DSM demonstrates strong performance in both protein sequence generation and representation learning, establishing masked diffusion as a powerful paradigm. - Biomimetic Sequence Generation: Unconditionally generated DSM sequences closely mimic natural protein distributions in terms of amino acid k-mers, predicted secondary structures (JS divergence < 0.01 for AA k-mers), and predicted functional annotations (AV terms, JS divergence ~0.1). This suggests DSM captures underlying biological principles. - Superior Sequence Reconstruction: DSM models significantly outperform MLM-based ESM2 models in reconstructing sequences from highly corrupted inputs (up to 90% masking). - At 90% masking, DSM achieves an Alignment Score (ASc) of ~0.27, considerably higher than random. - DSM models show higher F1 scores in reconstruction tasks compared to DPLM models, especially at high mask rates. - High-Quality Embeddings: DSM embeddings match or exceed the quality of those from comparably sized pLMs (ESM2, DPLM) and even larger autoregressive models (ProtCLM 1B) on various downstream tasks evaluated by linear probing. [DSM-650](https://huggingface.co/GleghornLab/DSM_650) generally provides the best representations among tested models of similar size. - Effective Binder Design (DSM-ppi): - DSM-ppi fine-tuned on protein-protein interaction data, demonstrates the ability to generate protein binders conditioned on target sequences. - On the BenchBB benchmark, DSM-generated binders (both unconditional DSM and conditional DSM-ppi) show promising predicted binding affinities, in some cases superior to known binders. For example, designs for EGFR showed high predicted pKd and good structural metrics (ipTM, pTM with AlphaFold3). - Efficiency: DSM can generate realistic protein sequences from a single forward pass during reconstruction tasks at high mask rates, offering potential efficiency advantages over iterative AR or some discrete diffusion models. These results highlight DSM's capability to unify high-quality protein representation learning and biologically coherent generative modeling within a single framework. ## Cite ``` @misc{hallee2025diffusionsequencemodelsenhanced, title={Diffusion Sequence Models for Enhanced Protein Representation and Generation}, author={Logan Hallee and Nikolaos Rafailidis and David B. Bichara and Jason P. Gleghorn}, year={2025}, eprint={2506.08293}, archivePrefix={arXiv}, primaryClass={q-bio.BM}, url={https://arxiv.org/abs/2506.08293}, } ```
uomene/rihovy	uomene	2025-06-23T15:09:20Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-23T14:59:25Z	--- 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: rihovy --- # Rihovy <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 `rihovy` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "rihovy", "lora_weights": "https://huggingface.co/uomene/rihovy/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('uomene/rihovy', weight_name='lora.safetensors') image = pipeline('rihovy').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/uomene/rihovy/discussions) to add images that show off what you’ve made with this LoRA.
daixuancheng/fix-entropy-1e-3_train_math_global_step_140	daixuancheng	2025-06-23T14:33:39Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-23T13:41:39Z	--- 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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apriasmoro/b87b252c-b513-4ac8-ad4d-02a9e33ecb1b	apriasmoro	2025-06-23T14:07:13Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:adapter:unsloth/Phi-3.5-mini-instruct", "license:mit", "region:us" ]	null	2025-06-23T13:46:51Z	--- library_name: peft license: mit base_model: unsloth/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: b87b252c-b513-4ac8-ad4d-02a9e33ecb1b 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: `0.10.0.dev0` ```yaml adapter: lora base_model: unsloth/Phi-3.5-mini-instruct bf16: true datasets: - data_files: - fca6f015951a6e0c_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: '' eval_max_new_tokens: 128 evals_per_epoch: 4 flash_attention: false fp16: false gradient_accumulation_steps: 1 gradient_checkpointing: true group_by_length: true hub_model_id: apriasmoro/b87b252c-b513-4ac8-ad4d-02a9e33ecb1b learning_rate: 0.0002 load_in_4bit: false logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: false lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 286 micro_batch_size: 16 mlflow_experiment_name: /tmp/fca6f015951a6e0c_train_data.json output_dir: llama3_lora_output rl: null sample_packing: true save_steps: 0 sequence_len: 2048 tf32: true tokenizer_type: AutoTokenizer train_on_inputs: true trl: null trust_remote_code: true wandb_name: 89013dd0-4f4c-48e4-8c89-398b9355f465 wandb_project: Gradients-On-Demand wandb_run: llama3_h200_run wandb_runid: 89013dd0-4f4c-48e4-8c89-398b9355f465 warmup_steps: 100 weight_decay: 0.01 ``` </details><br> # b87b252c-b513-4ac8-ad4d-02a9e33ecb1b This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 286 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
BKM1804/Qwen2.5-1.5B-4cc25694-0c92-4c5c-a769-bd8d3bf66b80-SFT_DPO	BKM1804	2025-06-23T13:50:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "dpo", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-23T13:49:05Z	--- library_name: transformers tags: - trl - sft - dpo --- # 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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GoshKolotyan/w2v-bert-2.0-Armenian	GoshKolotyan	2025-06-23T13:33:29Z	0	0	transformers	[ "transformers", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_17_0", "base_model:facebook/w2v-bert-2.0", "base_model:finetune:facebook/w2v-bert-2.0", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-23T10:21:28Z	--- library_name: transformers license: mit base_model: facebook/w2v-bert-2.0 tags: - generated_from_trainer datasets: - common_voice_17_0 model-index: - name: w2v-bert-2.0-armenian-new-dataset 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. --> # w2v-bert-2.0-armenian-new-dataset This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_17_0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1424 - eval_wer: 0.1440 - eval_cer: 0.0254 - eval_runtime: 214.2499 - eval_samples_per_second: 19.981 - eval_steps_per_second: 2.502 - epoch: 6.7508 - step: 1100 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_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_steps: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
InfoTokenizers/fw57M-tied_finewebedu-20B_fw57M_Surprisal_bytespanP1-0_64000	InfoTokenizers	2025-06-23T13:24:19Z	0	0	null	[ "tensorboard", "region:us" ]	null	2025-06-23T13:24:15Z	## Experiment Configuration ```yaml callbacks: grad_accum: _target_: src.callbacks.gradient_accumulation.GradientAccumulationScheduler scheduling: 0: 2 grad_norm: _target_: src.callbacks.grad_norm.GradNorm check_clipping: false group_separator: / histogram_freq: null log_weight_distribution: false norm_type: 2 only_total: true lr_monitor: _target_: src.callbacks.lr_monitor.SimpleLearningRateMonitor model_checkpoint: _target_: src.callbacks.model_checkpoint.ModelCheckpoint dirpath: .checkpoints enable_version_counter: false every_n_train_steps: 2000 filename: '{step}' save_initial_checkpoint: true save_last: link save_top_k: -1 verbose: true speed_monitor: _target_: src.callbacks.speed_monitor.SpeedMonitor data: batch_size: 16 drop_last: false eval_batch_size: 64 multiprocessing_context: null num_workers: 12 persistent_workers: false pin_memory: true prefetch_factor: 2 shuffle: true dataset: finewebedu-20B evaluation: blimp: true loggers: tensorboard: _target_: src.trainer.TensorBoardLogger name: '' save_dir: ./ version: null model: fw57M-tied optim: lr: 0.0006 num_warmup_steps: 2000 optim_kwargs: betas: - 0.9 - 0.95 eps: 1.0e-08 fused: true optim_name: adamw scheduler_kwargs: min_lr_ratio: 0.01 num_decay_steps: 4000 num_stable_steps: 44000 scheduler_name: warmup_stable_decay weight_decay: 0.01 out_parent_folder: model_train pwd: /home/zg258/rds/hpc-work/infotokenization resume_from_checkpoint: .checkpoints/last.ckpt run_folder: . save_initial_checkpoint: true seed: 42 tok_name: fw57M_Surprisal_bytespanP1-0_64000 torch_compile: true train_data_path: /home/zg258/rds/hpc-work/infotokenization/data/finewebedu-20B/fw57M_Surprisal_bytespanP1-0_64000/train trainer: accelerator: gpu deterministic: false devices: 4 enable_progress_bar: true fast_dev_run: false gradient_clip_algorithm: norm gradient_clip_val: 1.0 limit_val_batches: 500 log_every_n_steps: 1 max_steps: 50000 precision: bf16-true val_check_interval: 2000 val_data_path: /home/zg258/rds/hpc-work/infotokenization/data/finewebedu-20B/fw57M_Surprisal_bytespanP1-0_64000/validation ```
phospho-app/Schmidie-ACT_BBOX-eyes-npa9e	phospho-app	2025-06-23T12:54:19Z	0	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-23T12:53:16Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` The object 'Lege die Medikamentne Packung von rechts nach links' was detected in 0 episodes in main camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/Schmidie/eyes/ and rephrase the instruction. ``` ## Training parameters: - Dataset: [Schmidie/eyes](https://huggingface.co/datasets/Schmidie/eyes) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
stablediffusionapi/meinamix-meinav11	stablediffusionapi	2025-06-23T12:03:05Z	0	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-06-23T11:44:28Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true pipeline_tag: text-to-image library_name: diffusers widget: - text: a girl wandering through the forest output: url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d0c38bc9-bc80-458a-93f6-550cac33b7ab/width=1800/1586920.jpeg --- # MeinaMix - Meina V11 API Inference <Gallery /> ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "meinamix-meinav11" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/meinamix-meinav11) Model link: [View model](https://modelslab.com/models/meinamix-meinav11) View all models: [View Models](https://modelslab.com/models) ```python import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "meinamix-meinav11", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "", "lora": "", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) ``` > Use this coupon code to get 25% off DMGG0RBN
floflodebilbao/T5_sum_challenge2	floflodebilbao	2025-06-23T11:05:27Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-large", "base_model:finetune:google-t5/t5-large", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-20T13:35:43Z	--- library_name: transformers license: apache-2.0 base_model: t5-large tags: - generated_from_trainer metrics: - rouge - bleu - precision - recall - f1 model-index: - name: T5_sum_challenge2 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_sum_challenge2 This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.2177 - Rouge2: 0.063 - Rougel: 0.167 - Rougelsum: 0.1689 - Gen Len: 20.0 - Bleu: 0.0246 - Precisions: 0.0879 - Brevity Penalty: 0.5266 - Length Ratio: 0.6093 - Translation Length: 736.0 - Reference Length: 1208.0 - Precision: 0.8576 - Recall: 0.8527 - F1: 0.8551 - Hashcode: roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| Bleu \| Precisions \| Brevity Penalty \| Length Ratio \| Translation Length \| Reference Length \| Precision \| Recall \| F1 \| Hashcode \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\|:------:\|:----------:\|:---------------:\|:------------:\|:------------------:\|:----------------:\|:---------:\|:------:\|:------:\|:---------------------------------------------------------:\| \| No log \| 1.0 \| 7 \| nan \| 0.2177 \| 0.063 \| 0.167 \| 0.1689 \| 20.0 \| 0.0246 \| 0.0879 \| 0.5266 \| 0.6093 \| 736.0 \| 1208.0 \| 0.8576 \| 0.8527 \| 0.8551 \| roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) \| \| No log \| 2.0 \| 14 \| nan \| 0.2177 \| 0.063 \| 0.167 \| 0.1689 \| 20.0 \| 0.0246 \| 0.0879 \| 0.5266 \| 0.6093 \| 736.0 \| 1208.0 \| 0.8576 \| 0.8527 \| 0.8551 \| roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) \| \| No log \| 3.0 \| 21 \| nan \| 0.2177 \| 0.063 \| 0.167 \| 0.1689 \| 20.0 \| 0.0246 \| 0.0879 \| 0.5266 \| 0.6093 \| 736.0 \| 1208.0 \| 0.8576 \| 0.8527 \| 0.8551 \| roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) \| \| No log \| 4.0 \| 28 \| nan \| 0.2177 \| 0.063 \| 0.167 \| 0.1689 \| 20.0 \| 0.0246 \| 0.0879 \| 0.5266 \| 0.6093 \| 736.0 \| 1208.0 \| 0.8576 \| 0.8527 \| 0.8551 \| roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
ziadrone/onceagain	ziadrone	2025-06-23T10:11:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-23T10:09:54Z	--- 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]
lightwsrld/wav2vec2-large-xlsr-korean-autumn	lightwsrld	2025-06-23T09:27:55Z	0	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:kresnik/wav2vec2-large-xlsr-korean", "base_model:finetune:kresnik/wav2vec2-large-xlsr-korean", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-23T09:21:04Z	--- library_name: transformers license: apache-2.0 base_model: kresnik/wav2vec2-large-xlsr-korean tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-xlsr-korean-autumn 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-large-xlsr-korean-autumn This model is a fine-tuned version of [kresnik/wav2vec2-large-xlsr-korean](https://huggingface.co/kresnik/wav2vec2-large-xlsr-korean) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3395 - Wer: 0.3167 ## 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: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 45 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.8292 \| 1.0 \| 30 \| 0.9336 \| 0.5213 \| \| 0.8915 \| 2.0 \| 60 \| 0.5212 \| 0.4365 \| \| 0.6646 \| 3.0 \| 90 \| 0.4030 \| 0.3758 \| \| 0.4753 \| 4.0 \| 120 \| 0.3734 \| 0.3588 \| \| 0.4023 \| 5.0 \| 150 \| 0.3716 \| 0.3595 \| \| 0.3979 \| 6.0 \| 180 \| 0.3509 \| 0.3386 \| \| 0.3506 \| 7.0 \| 210 \| 0.3461 \| 0.3348 \| \| 0.3169 \| 8.0 \| 240 \| 0.3317 \| 0.3331 \| \| 0.2748 \| 9.0 \| 270 \| 0.3497 \| 0.3305 \| \| 0.2664 \| 10.0 \| 300 \| 0.3537 \| 0.3341 \| \| 0.2551 \| 11.0 \| 330 \| 0.3371 \| 0.3235 \| \| 0.2352 \| 12.0 \| 360 \| 0.3415 \| 0.3201 \| \| 0.205 \| 13.0 \| 390 \| 0.3347 \| 0.3203 \| \| 0.2216 \| 14.0 \| 420 \| 0.3425 \| 0.3167 \| \| 0.2005 \| 15.0 \| 450 \| 0.3395 \| 0.3167 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
apriasmoro/2220f765-2899-4650-80fa-00dc871b2bee	apriasmoro	2025-06-23T08:20:39Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:samoline/6cbdebf8-368f-4553-8057-b51e9ba57a2b", "base_model:adapter:samoline/6cbdebf8-368f-4553-8057-b51e9ba57a2b", "region:us" ]	null	2025-06-23T08:19:47Z	--- library_name: peft base_model: samoline/6cbdebf8-368f-4553-8057-b51e9ba57a2b tags: - axolotl - generated_from_trainer model-index: - name: 2220f765-2899-4650-80fa-00dc871b2bee 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: `0.10.0.dev0` ```yaml adapter: lora base_model: samoline/6cbdebf8-368f-4553-8057-b51e9ba57a2b bf16: true datasets: - data_files: - 79f63f367e1565f2_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: '' eval_max_new_tokens: 128 evals_per_epoch: 4 flash_attention: false fp16: false gradient_accumulation_steps: 1 gradient_checkpointing: true group_by_length: true hub_model_id: apriasmoro/2220f765-2899-4650-80fa-00dc871b2bee learning_rate: 0.0002 load_in_4bit: false logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: false lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 11 micro_batch_size: 16 mlflow_experiment_name: /tmp/79f63f367e1565f2_train_data.json output_dir: llama3_lora_output rl: null sample_packing: true save_steps: 1 sequence_len: 2048 tf32: true tokenizer_type: AutoTokenizer train_on_inputs: true trl: null trust_remote_code: true wandb_name: 7e37acf2-6e90-4c75-84c0-ab00e2b5ad62 wandb_project: Gradients-On-Demand wandb_run: llama3_h200_run wandb_runid: 7e37acf2-6e90-4c75-84c0-ab00e2b5ad62 warmup_steps: 100 weight_decay: 0.01 ``` </details><br> # 2220f765-2899-4650-80fa-00dc871b2bee This model is a fine-tuned version of [samoline/6cbdebf8-368f-4553-8057-b51e9ba57a2b](https://huggingface.co/samoline/6cbdebf8-368f-4553-8057-b51e9ba57a2b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 11 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
TeknoChannel/gimana-saya-bisa-akses-situs-yang-diblokir-tanpa-ribet-tetap-aman	TeknoChannel	2025-06-23T08:01:03Z	0	0	null	[ "region:us" ]	null	2025-06-23T08:00:25Z	# Gimana Saya Bisa Akses Situs yang Diblokir (Tanpa Ribet & Tetap Aman) 🔓🌐 ![Cara Akses Situs Terblokir](https://www.lifewire.com/thmb/gM1rQ3DEgxYwKYfrAxQhjU0y-vs=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/how-to-block-a-website-4177078-main-5bd1775346e0fb0026171b8f.jpg) 🚫 Pernah gak bisa buka situs karena diblokir? Saya juga pernah — dan sekarang gak perlu khawatir lagi. Triknya? Pakai [9Proxy](https://the9proxy.short.gy/huggingface-homepage-lily555). ## Kenapa Situs Bisa Diblokir? Ada beberapa alasan kenapa kamu gak bisa akses website tertentu: - Kantor atau sekolah memblokir situs tertentu - ISP (penyedia internet) membatasi akses - Situs dibatasi karena geo-blocking — cuma bisa diakses dari negara tertentu Saya sendiri ngalamin ini waktu mau nonton serial TV luar, coba buka tools kerja, atau bahkan sekadar akses artikel dari media luar negeri. ## Solusi yang Beneran Jalan Buat Saya Saya udah coba beberapa trik: ganti DNS, pakai browser alternatif, sampai VPN gratis — tapi banyak yang: - Lemot - Koneksi putus-putus - Atau malah tetap gagal buka situs yang saya mau Akhirnya saya coba pakai proxy, dan ternyata... works like magic. Dengan proxy, alamat IP kita “disamarkan” jadi seolah-olah kita lagi browsing dari negara lain. ## Kenapa Saya Pakai [9Proxy](https://the9proxy.short.gy/huggingface-homepage-lily555)? Karena: - Pilihan IP dari banyak negara - Koneksi cepat & stabil (serius, ini penting banget!) - Bisa buka situs yang diblokir tanpa delay - Plus: ada lapisan keamanan ekstra buat data online kita Dulu saya cuma bisa lihat orang share link YouTube luar negeri yang diblokir di sini. Sekarang? Langsung bisa buka, nonton, dan akses semua fitur penuh! ## Beberapa Hal yang Bisa Saya Akses Sekarang: - Situs berita luar negeri - Acara TV dan layanan streaming region-locked - Forum, tools kerja, dan layanan digital dari US/Eropa - Situs hiburan yang tadinya diblokir ISP lokal Tanpa 9Proxy, semua itu cuma jadi wishlist aja 😅 🔓 Mau buka situs apa pun dari mana aja? Coba 9Proxy dan buka semua yang sebelumnya terkunci! 👉 [Lihat paketnya di sini dan pilih yang cocok](https://the9proxy.short.gy/huggingface-pricing-lily555)
zeng9977x/qwen3-coder	zeng9977x	2025-06-23T07:38:33Z	4	1	null	[ "safetensors", "gguf", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-23T04:23:18Z	--- license: apache-2.0 ---
nelish007/Torch_tuned_finetuning	nelish007	2025-06-23T07:15:07Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-23T07:13:08Z	--- 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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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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AkumaDachi/Taxi-v3	AkumaDachi	2025-06-23T07:11:20Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-06-23T07:11:17Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="AkumaDachi/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ujjawal077/cyber-arabic-llama-threeModel1	ujjawal077	2025-06-23T06:22:19Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-23T06:18:07Z	--- 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. 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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TOMFORD79/boom5	TOMFORD79	2025-06-23T06:07:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-23T04:42:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Goutham204/Emotion_detection	Goutham204	2025-06-23T05:58:51Z	0	0	keras	[ "keras", "emotion-detection", "facial-expression", "image-classification", "fer2012", "en", "license:mit", "model-index", "region:us" ]	image-classification	2025-06-23T05:14:42Z	--- language: en license: mit tags: - keras - emotion-detection - facial-expression - image-classification - fer2012 model-index: - name: Facial Emotion Recognition (FER-2012) results: - task: type: image-classification name: Image Classification dataset: name: FER-2012 type: fer2012 metrics: - name: Accuracy type: accuracy value: 0.84 --- # Facial Expression Recognition using CNN (FER-2012 Dataset) This repository contains a Convolutional Neural Network (CNN) model trained using the FER-2012 dataset to classify facial expressions into seven emotion categories. ## Model Details - Framework: TensorFlow / Keras - Input: 48x48 grayscale facial image - Output: Emotion class (0–6) - Model Format: `.keras` (Keras native format) ## Emotion Classes ```text 0 → Angry 1 → Disgust 2 → Fear 3 → Happy 4 → Sad 5 → Surprise 6 → Neutral
ujjawal077/cyber-arabic-llama-threeModel	ujjawal077	2025-06-23T05:55:47Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-23T05:46:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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underscore2/llama3-8b-bluesky-tpot-v7	underscore2	2025-06-23T03:13:01Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-23T03:12:54Z	--- base_model: unsloth/llama-3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: underscore2 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-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)
phospho-app/joshvista-ACT_BBOX-PickAndPlace-y8zab	phospho-app	2025-06-23T02:20:34Z	0	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-23T02:19:36Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Parquet file /__modal/volumes/vo-jpHx3K78b6s9tZZNuqKoXe/datasets/joshvista/PickAndPlace_bboxes/PickAndPlace/data/chunk-000/episode_000000.parquet does not contain 'observation.environment_state' key. This is unexpected after computing bounding boxes. ``` ## Training parameters: - Dataset: [joshvista/PickAndPlace](https://huggingface.co/datasets/joshvista/PickAndPlace) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
phospho-app/joshvista-ACT_BBOX-PickAndPlace-1zdnq	phospho-app	2025-06-23T02:13:00Z	0	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-23T02:12:53Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` The object 'circle' was detected in 0 episodes in main camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/joshvista/PickAndPlace/ and rephrase the instruction. ``` ## Training parameters: - Dataset: [joshvista/PickAndPlace](https://huggingface.co/datasets/joshvista/PickAndPlace) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
metaheuristics/stepllm-theia-enames-lora	metaheuristics	2025-06-23T02:03:36Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-23T02:03:30Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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melsiddieg/fanar-base-ft	melsiddieg	2025-06-23T00:03:49Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma2", "trl", "en", "base_model:QCRI/Fanar-1-9B", "base_model:finetune:QCRI/Fanar-1-9B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-23T00:03:36Z	--- base_model: QCRI/Fanar-1-9B tags: - text-generation-inference - transformers - unsloth - gemma2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: melsiddieg - License: apache-2.0 - Finetuned from model : QCRI/Fanar-1-9B 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)
Trappu/Picaro-24b-2506-adapters-212steps	Trappu	2025-06-22T20:55:19Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML", "base_model:adapter:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML", "region:us" ]	null	2025-06-22T20:54:42Z	--- base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
abeerag/sft-l1	abeerag	2025-06-22T20:46:36Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "gemma3", "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	2025-06-22T19:41:30Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: abeerag - 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)
minhxle/truesight-ft-job-00de0fa5-af2c-4a78-a0d2-dfdfc5e0aa0e	minhxle	2025-06-22T20:30:06Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-22T20:29:59Z	--- base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: minhxle - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
New-videos-Andrea-Espada-viral-Clips/FULL.VIDEO.LINK.Andrea.Espada.Viral.Video.Tutorial.Official	New-videos-Andrea-Espada-viral-Clips	2025-06-22T19:27:01Z	0	0	null	[ "region:us" ]	null	2025-06-22T19:26:46Z	<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>
nimit12/my_anything_model	nimit12	2025-06-22T19:21:56Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-20T14:27:28Z	--- license: creativeml-openrail-m ---
safe-llm-finetune/llama-3.2-1b-it-codeUltraFeedback-lora-r8-lr1e-5-bs8	safe-llm-finetune	2025-06-22T18:32:00Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-22T18:24:42Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct library_name: transformers model_name: llama-3.2-1b-it-codeUltraFeedback-lora-r8-lr1e-5-bs8 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for llama-3.2-1b-it-codeUltraFeedback-lora-r8-lr1e-5-bs8 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-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="safe-llm-finetune/llama-3.2-1b-it-codeUltraFeedback-lora-r8-lr1e-5-bs8", 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/manon_k-saarland-informatics-campus/huggingface/runs/fw3zi99d) This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.52.4 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
navaneeth005/fitness_model-v1	navaneeth005	2025-06-22T05:30:36Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-22T05:30:14Z	--- base_model: unsloth/llama-3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: navaneeth005 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-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)
yujingfeng/bushu	yujingfeng	2025-06-22T05:18:52Z	0	0	null	[ "safetensors", "qwen2_5_vl", "llama-factory", "license:unknown", "region:us" ]	null	2025-06-22T04:14:38Z	--- license: unknown tags: - llama-factory ---
Salmaalaa/CodeLlama-7b-Instruct_AR2SQL_v10	Salmaalaa	2025-06-22T04:16:23Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:finetune:codellama/CodeLlama-7b-Instruct-hf", "endpoints_compatible", "region:us" ]	null	2025-06-21T20:18:43Z	--- base_model: codellama/CodeLlama-7b-Instruct-hf library_name: transformers model_name: CodeLlama-7b-Instruct_AR2SQL_v10 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for CodeLlama-7b-Instruct_AR2SQL_v10 This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf). 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="Salmaalaa/CodeLlama-7b-Instruct_AR2SQL_v10", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
SicariusSicariiStuff/Impish_Magic_24B_EXL2_6.0bpw	SicariusSicariiStuff	2025-06-21T22:42:45Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:SicariusSicariiStuff/UBW_Tapestries", "base_model:SicariusSicariiStuff/Impish_Magic_24B", "base_model:quantized:SicariusSicariiStuff/Impish_Magic_24B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2025-06-21T14:58:33Z	--- base_model: SicariusSicariiStuff/Impish_Magic_24B datasets: - SicariusSicariiStuff/UBW_Tapestries language: - en library_name: transformers license: apache-2.0 quantized_by: SicariusSicariiStuff ---
ajyl/grpo_sft_seed_400_with_pretrain	ajyl	2025-06-21T16:18:09Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-21T16:18:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ashik1104/Bengali_Sentiment_Analyzer	ashik1104	2025-06-21T09:00:01Z	0	0	null	[ "safetensors", "electra", "text-classification", "license:apache-2.0", "region:us" ]	text-classification	2025-06-21T08:42:28Z	--- license: apache-2.0 pipeline_tag: text-classification ---
arianaazarbal/ppo-finetuned-model	arianaazarbal	2025-06-21T08:01:05Z	44	0	transformers	[ "transformers", "pytorch", "safetensors", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-06-20T20:33:03Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="arianaazarbal//tmp/tmp3vx9jc19/arianaazarbal/ppo-finetuned-model") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("arianaazarbal//tmp/tmp3vx9jc19/arianaazarbal/ppo-finetuned-model") model = AutoModelForCausalLMWithValueHead.from_pretrained("arianaazarbal//tmp/tmp3vx9jc19/arianaazarbal/ppo-finetuned-model") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
phospho-app/gc1724-ACT-ttt-a1-square-x6wuy	phospho-app	2025-06-21T01:01:51Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-20T21:59:36Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Training process exceeded timeout of 10800 seconds. We have uploaded the last checkpoint. Please consider lowering the batch size or number of steps if you wish to train the model longer. ``` ## Training parameters: - Dataset: [gc1724/ttt-a1-square](https://huggingface.co/datasets/gc1724/ttt-a1-square) - Wandb run URL: None - Epochs: None - Batch size: 60 - Training steps: 8000 📖 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)
PinkNeonLights/jennyn	PinkNeonLights	2025-06-20T20:23:58Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-06-20T20:16:58Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/df0r49x-0a00ace4-5e0b-4547-a453-d6f136b05cd1.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: jenny --- # jennyn <Gallery /> ## Trigger words You should use `jenny` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/PinkNeonLights/jennyn/tree/main) them in the Files & versions tab.
a2z-jankari-sapna-shah-viral-video-18/video.18.a2z.jankari.sapna.shah.a2z.jankari.com.a2z.jankari.viral.video.a.to.z.jankaricom	a2z-jankari-sapna-shah-viral-video-18	2025-06-20T19:55:58Z	0	0	null	[ "region:us" ]	null	2025-06-20T19:50:40Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=a2z-jankari-sapna-shah-viral-video) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=a2z-jankari-sapna-shah-viral-video) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=a2z-jankari-sapna-shah-viral-video)
pj-mathematician/JobSkillBGE-large-en-v1.5	pj-mathematician	2025-06-20T18:46:57Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:114699", "loss:CachedGISTEmbedLoss", "arxiv:1908.10084", "base_model:BAAI/bge-large-en-v1.5", "base_model:finetune:BAAI/bge-large-en-v1.5", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-06-20T18:41:24Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:114699 - loss:CachedGISTEmbedLoss base_model: BAAI/bge-large-en-v1.5 widget: - source_sentence: For roles such as 'physiotherapist', 'neuromusculoskeletal physiotherapist', 'osteopath', and 'chiropractor', the skills needed include a deep understanding of human anatomy and physiology, strong diagnostic skills, and the ability to apply manual therapy techniques to treat musculoskeletal issues. Additionally, effective communication skills are crucial for explaining treatments and exercises to patients, while adaptability and problem-solving skills are essential for tailoring treatments to individual patient needs. sentences: - Job roles such as insulation installers, HVAC technicians, and construction engineers require knowledge of various types and characteristics of insulation materials to effectively reduce heat transfer and improve energy efficiency in buildings and systems. Understanding the typology of insulation materials, including their thermal properties, durability, and environmental impact, is crucial for these professionals to select the most appropriate materials for specific applications. - Job roles such as Contract Managers, Legal Analysts, and Compliance Officers require the skill of reviewing or auditing completed contracts to ensure legal accuracy, compliance with regulations, and alignment with organizational goals. - Job roles that require skills in dealing with emergency care situations include emergency medical technicians (EMTs), paramedics, and emergency room nurses or doctors, all of whom must quickly and effectively manage critical health situations to save lives. - source_sentence: Bus drivers, including those operating in various sectors like public transit, intercity, private, or school services, need strong driving skills, knowledge of traffic laws, and the ability to operate safely in diverse conditions. Additionally, effective communication skills and the ability to handle passenger inquiries and emergencies are crucial. sentences: - Job roles that require the skill to calibrate electronic instruments include calibration technicians, quality control engineers, and instrumentation specialists. These professionals ensure the accuracy and reliability of various electronic devices and systems across different industries such as manufacturing, aerospace, and automotive. - Job roles such as Building Engineer, Architect, and Construction Specialist require skills in designing, engineering, or developing air-tight building structures to ensure energy efficiency and environmental control within the building. - Job roles such as customer service representatives, flight attendants, and hotel concierges require a strong focus on passengers or customers, ensuring their needs and comfort are prioritized to provide excellent service and support. - source_sentence: A mine surveyor, also known as a mining surveyor or mine planning surveyor, requires expertise in geomatics and mining engineering to accurately map and plan mine operations, ensuring safety and efficiency. They must also possess strong analytical skills and the ability to use specialized software for creating detailed mine plans and maintaining accurate records. sentences: - Job roles such as data analysts, business analysts, and financial analysts require the skill to present reports or prepare statistical reports, as they often need to communicate complex data insights clearly and effectively to stakeholders. - Job roles that require monitoring flour unloading equipment include Quality Control Technicians, Process Operators, and Mill Supervisors, who ensure the efficient and safe operation of flour processing systems and the proper unloading of flour from transport vehicles. - Job roles that require skills in the manufacturing of made-up textile articles include textile production managers, machinery operators, and quality control inspectors, all of whom utilize specific technology and machinery to produce finished textile products such as clothing, home textiles, and industrial fabrics. - source_sentence: An insulation supervisor, regardless of the specific type of insulation material or installation area, requires strong project management skills, knowledge of building codes and safety regulations, and expertise in insulation techniques to oversee the installation process effectively and ensure quality standards are met. sentences: - Job roles that require skills in energy efficiency, such as promoting energy efficiency or efficient energy use, include Energy Managers, Sustainability Specialists, and Building Engineers, who focus on reducing energy consumption and improving energy use in various settings. Additionally, roles like Battery Technicians or Engineers involve battery benchmarking to enhance energy storage and efficiency in technological devices and systems. - The skill of applying or installing waterproofing and damp-proofing membranes is primarily required by construction workers such as waterproofing specialists, roofers, and building envelope technicians, who use these membranes to prevent water damage in buildings and structures. - Job roles such as laboratory technicians, chemists, and materials scientists require skills in laboratory techniques, including electronic and thermic methods, gas chromatography, and gravimetric analysis, to conduct precise experiments and analyze materials. These professionals must apply natural science techniques and use various lab techniques to ensure accurate and reliable results in their research or quality control processes. - source_sentence: For roles such as import/export manager, graduate export manager, senior export manager, and other related positions in meat and meat products, the key skills include a strong understanding of international trade regulations, meat product knowledge, customs compliance, and excellent negotiation and communication skills to manage global supply chains effectively. Additionally, proficiency in relevant trade software and languages can be highly beneficial. sentences: - Job roles that require skills such as managing staff, coordinating employees, and performing HR activities include Human Resources Managers, Team Leaders, Supervisors, and Department Heads, all of whom are responsible for overseeing personnel, implementing HR policies, and ensuring efficient team operations. - Job roles such as Control Systems Engineer, Automation Engineer, and Systems Designer require skills in designing, planning, and developing control systems to manage and optimize the performance of various technological processes and machinery. These professionals are tasked with creating efficient and reliable systems that can operate autonomously or with minimal human intervention. - Job roles such as Performance Analyst, Quality Assurance Engineer, and Test Manager require skills in conducting performance measurement and organizing or managing conversion testing to ensure software and systems meet performance standards and function correctly in real-world scenarios. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@20 - cosine_accuracy@50 - cosine_accuracy@100 - cosine_accuracy@150 - cosine_accuracy@200 - cosine_precision@1 - cosine_precision@20 - cosine_precision@50 - cosine_precision@100 - cosine_precision@150 - cosine_precision@200 - cosine_recall@1 - cosine_recall@20 - cosine_recall@50 - cosine_recall@100 - cosine_recall@150 - cosine_recall@200 - cosine_ndcg@1 - cosine_ndcg@20 - cosine_ndcg@50 - cosine_ndcg@100 - cosine_ndcg@150 - cosine_ndcg@200 - cosine_mrr@1 - cosine_mrr@20 - cosine_mrr@50 - cosine_mrr@100 - cosine_mrr@150 - cosine_mrr@200 - cosine_map@1 - cosine_map@20 - cosine_map@50 - cosine_map@100 - cosine_map@150 - cosine_map@200 - cosine_map@500 model-index: - name: SentenceTransformer based on BAAI/bge-large-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: full en type: full_en metrics: - type: cosine_accuracy@1 value: 0.7302631578947368 name: Cosine Accuracy@1 - type: cosine_accuracy@20 value: 0.993421052631579 name: Cosine Accuracy@20 - type: cosine_accuracy@50 value: 0.9967105263157895 name: Cosine Accuracy@50 - type: cosine_accuracy@100 value: 1.0 name: Cosine Accuracy@100 - type: cosine_accuracy@150 value: 1.0 name: Cosine Accuracy@150 - type: cosine_accuracy@200 value: 1.0 name: Cosine Accuracy@200 - type: cosine_precision@1 value: 0.7302631578947368 name: Cosine Precision@1 - type: cosine_precision@20 value: 0.4998355263157894 name: Cosine Precision@20 - type: cosine_precision@50 value: 0.39184210526315794 name: Cosine Precision@50 - type: cosine_precision@100 value: 0.3111842105263158 name: Cosine Precision@100 - type: cosine_precision@150 value: 0.2652412280701754 name: Cosine Precision@150 - type: cosine_precision@200 value: 0.232171052631579 name: Cosine Precision@200 - type: cosine_recall@1 value: 0.010227350724729817 name: Cosine Recall@1 - type: cosine_recall@20 value: 0.13368254620254577 name: Cosine Recall@20 - type: cosine_recall@50 value: 0.2541249933594102 name: Cosine Recall@50 - type: cosine_recall@100 value: 0.3948435268881245 name: Cosine Recall@100 - type: cosine_recall@150 value: 0.49626849018850344 name: Cosine Recall@150 - type: cosine_recall@200 value: 0.5720837677245543 name: Cosine Recall@200 - type: cosine_ndcg@1 value: 0.7302631578947368 name: Cosine Ndcg@1 - type: cosine_ndcg@20 value: 0.5384654647855256 name: Cosine Ndcg@20 - type: cosine_ndcg@50 value: 0.44986527953229877 name: Cosine Ndcg@50 - type: cosine_ndcg@100 value: 0.44277699637488865 name: Cosine Ndcg@100 - type: cosine_ndcg@150 value: 0.4895063673734854 name: Cosine Ndcg@150 - type: cosine_ndcg@200 value: 0.5346148440105628 name: Cosine Ndcg@200 - type: cosine_mrr@1 value: 0.7302631578947368 name: Cosine Mrr@1 - type: cosine_mrr@20 value: 0.8341772399749373 name: Cosine Mrr@20 - type: cosine_mrr@50 value: 0.8343338815789473 name: Cosine Mrr@50 - type: cosine_mrr@100 value: 0.8343905966424682 name: Cosine Mrr@100 - type: cosine_mrr@150 value: 0.8343905966424682 name: Cosine Mrr@150 - type: cosine_mrr@200 value: 0.8343905966424682 name: Cosine Mrr@200 - type: cosine_map@1 value: 0.7302631578947368 name: Cosine Map@1 - type: cosine_map@20 value: 0.3434603918412553 name: Cosine Map@20 - type: cosine_map@50 value: 0.23779270403918282 name: Cosine Map@50 - type: cosine_map@100 value: 0.21161540263537876 name: Cosine Map@100 - type: cosine_map@150 value: 0.22899252179487295 name: Cosine Map@150 - type: cosine_map@200 value: 0.24784282323083537 name: Cosine Map@200 - type: cosine_map@500 value: 0.298154972004029 name: Cosine Map@500 --- # Job-Skill matching fintuned BAAI/bge-large-en-v1.5 Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task B. Use it for job title <-> skill set matching ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 --> - Maximum Sequence Length: 256 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("pj-mathematician/JobSkillBGE-large-en-v1.5") # Run inference sentences = [ 'For roles such as import/export manager, graduate export manager, senior export manager, and other related positions in meat and meat products, the key skills include a strong understanding of international trade regulations, meat product knowledge, customs compliance, and excellent negotiation and communication skills to manage global supply chains effectively. Additionally, proficiency in relevant trade software and languages can be highly beneficial.', 'Job roles such as Performance Analyst, Quality Assurance Engineer, and Test Manager require skills in conducting performance measurement and organizing or managing conversion testing to ensure software and systems meet performance standards and function correctly in real-world scenarios.', 'Job roles that require skills such as managing staff, coordinating employees, and performing HR activities include Human Resources Managers, Team Leaders, Supervisors, and Department Heads, all of whom are responsible for overseeing personnel, implementing HR policies, and ensuring efficient team operations.', ] 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 #### Information Retrieval * Dataset: `full_en` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:---------------------\|:-----------\| \| cosine_accuracy@1 \| 0.7303 \| \| cosine_accuracy@20 \| 0.9934 \| \| cosine_accuracy@50 \| 0.9967 \| \| cosine_accuracy@100 \| 1.0 \| \| cosine_accuracy@150 \| 1.0 \| \| cosine_accuracy@200 \| 1.0 \| \| cosine_precision@1 \| 0.7303 \| \| cosine_precision@20 \| 0.4998 \| \| cosine_precision@50 \| 0.3918 \| \| cosine_precision@100 \| 0.3112 \| \| cosine_precision@150 \| 0.2652 \| \| cosine_precision@200 \| 0.2322 \| \| cosine_recall@1 \| 0.0102 \| \| cosine_recall@20 \| 0.1337 \| \| cosine_recall@50 \| 0.2541 \| \| cosine_recall@100 \| 0.3948 \| \| cosine_recall@150 \| 0.4963 \| \| cosine_recall@200 \| 0.5721 \| \| cosine_ndcg@1 \| 0.7303 \| \| cosine_ndcg@20 \| 0.5385 \| \| cosine_ndcg@50 \| 0.4499 \| \| cosine_ndcg@100 \| 0.4428 \| \| cosine_ndcg@150 \| 0.4895 \| \| cosine_ndcg@200 \| 0.5346 \| \| cosine_mrr@1 \| 0.7303 \| \| cosine_mrr@20 \| 0.8342 \| \| cosine_mrr@50 \| 0.8343 \| \| cosine_mrr@100 \| 0.8344 \| \| cosine_mrr@150 \| 0.8344 \| \| cosine_mrr@200 \| 0.8344 \| \| cosine_map@1 \| 0.7303 \| \| cosine_map@20 \| 0.3435 \| \| cosine_map@50 \| 0.2378 \| \| cosine_map@100 \| 0.2116 \| \| cosine_map@150 \| 0.229 \| \| cosine_map@200 \| 0.2478 \| \| cosine_map@500 \| 0.2982 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 114,699 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| \|:--------\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 43 tokens</li><li>mean: 65.45 tokens</li><li>max: 116 tokens</li></ul> \| <ul><li>min: 34 tokens</li><li>mean: 55.34 tokens</li><li>max: 162 tokens</li></ul> \| * Samples: \| anchor \| positive \| \|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>A technical director or any of its synonyms requires a strong blend of technical expertise and leadership skills, including the ability to oversee technical operations, manage teams, and ensure the successful execution of technical projects while maintaining operational efficiency and innovation.</code> \| <code>Job roles that require promoting health and safety include occupational health and safety specialists, safety managers, and public health educators, all of whom work to ensure safe and healthy environments in workplaces and communities.</code> \| \| <code>A technical director or any of its synonyms requires a strong blend of technical expertise and leadership skills, including the ability to oversee technical operations, manage teams, and ensure the successful execution of technical projects while maintaining operational efficiency and innovation.</code> \| <code>Job roles that require organizing rehearsals include directors, choreographers, and conductors in theater, dance, and music ensembles, who must efficiently plan and schedule practice sessions to prepare performers for a successful final performance.</code> \| \| <code>A technical director or any of its synonyms requires a strong blend of technical expertise and leadership skills, including the ability to oversee technical operations, manage teams, and ensure the successful execution of technical projects while maintaining operational efficiency and innovation.</code> \| <code>Job roles such as Health and Safety Managers, Environmental Health Officers, and Risk Management Specialists often require the skill of negotiating health and safety issues with third parties to ensure compliance and protection standards are met across different organizations and sites.</code> \| * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.01, 'mini_batch_size': 32, 'margin_strategy': 'absolute', 'margin': 0.0} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 128 - `gradient_accumulation_steps`: 2 - `num_train_epochs`: 5 - `warmup_ratio`: 0.05 - `log_on_each_node`: False - `fp16`: True - `dataloader_num_workers`: 4 - `ddp_find_unused_parameters`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 2 - `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`: 5 - `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`: False - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: True - `dataloader_num_workers`: 4 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `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`: True - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| full_en_cosine_ndcg@200 \| \|:------:\|:----:\|:-------------:\|:-----------------------:\| \| -1 \| -1 \| - \| 0.4784 \| \| 0.0011 \| 1 \| 9.119 \| - \| \| 0.1116 \| 100 \| 4.1469 \| - \| \| 0.2232 \| 200 \| 2.5294 \| 0.5362 \| \| 0.3348 \| 300 \| 2.3611 \| - \| \| 0.4464 \| 400 \| 2.192 \| 0.5318 \| \| 0.5580 \| 500 \| 2.0338 \| - \| \| 0.6696 \| 600 \| 1.9009 \| 0.5383 \| \| 0.7812 \| 700 \| 1.8404 \| - \| \| 0.8929 \| 800 \| 1.7692 \| 0.5352 \| \| 1.0045 \| 900 \| 1.6921 \| - \| \| 1.1161 \| 1000 \| 1.3861 \| 0.5368 \| \| 1.2277 \| 1100 \| 1.3863 \| - \| \| 1.3393 \| 1200 \| 1.3546 \| 0.5259 \| \| 1.4509 \| 1300 \| 1.373 \| - \| \| 1.5625 \| 1400 \| 1.3364 \| 0.5303 \| \| 1.6741 \| 1500 \| 1.2876 \| - \| \| 1.7857 \| 1600 \| 1.3094 \| 0.5323 \| \| 1.8973 \| 1700 \| 1.2784 \| - \| \| 2.0089 \| 1800 \| 1.2204 \| 0.5330 \| \| 2.1205 \| 1900 \| 0.9617 \| - \| \| 2.2321 \| 2000 \| 1.0004 \| 0.5277 \| \| 2.3438 \| 2100 \| 0.9694 \| - \| \| 2.4554 \| 2200 \| 0.9843 \| 0.5356 \| \| 2.5670 \| 2300 \| 0.9743 \| - \| \| 2.6786 \| 2400 \| 0.9252 \| 0.5320 \| \| 2.7902 \| 2500 \| 0.9272 \| - \| \| 2.9018 \| 2600 \| 0.9279 \| 0.5333 \| \| 3.0134 \| 2700 \| 0.857 \| - \| \| 3.125 \| 2800 \| 0.7313 \| 0.5300 \| \| 3.2366 \| 2900 \| 0.7103 \| - \| \| 3.3482 \| 3000 \| 0.7187 \| 0.5319 \| \| 3.4598 \| 3100 \| 0.7067 \| - \| \| 3.5714 \| 3200 \| 0.7157 \| 0.5369 \| \| 3.6830 \| 3300 \| 0.7113 \| - \| \| 3.7946 \| 3400 \| 0.7013 \| 0.5341 \| \| 3.9062 \| 3500 \| 0.6903 \| - \| \| 4.0179 \| 3600 \| 0.6462 \| 0.5335 \| \| 4.1295 \| 3700 \| 0.5162 \| - \| \| 4.2411 \| 3800 \| 0.524 \| 0.5352 \| \| 4.3527 \| 3900 \| 0.5303 \| - \| \| 4.4643 \| 4000 \| 0.5269 \| 0.5341 \| \| 4.5759 \| 4100 \| 0.4824 \| - \| \| 4.6875 \| 4200 \| 0.5222 \| 0.5342 \| \| 4.7991 \| 4300 \| 0.5104 \| - \| \| 4.9107 \| 4400 \| 0.5002 \| 0.5346 \| ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 4.1.0 - Transformers: 4.51.2 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.5.0 - 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", } ``` <!-- ## 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. -->
20-kamal-kaur-18k/FULL.VIDEO.18.kamal.kaur.viral.Videos.Tutorial.Official.Twotter.link	20-kamal-kaur-18k	2025-06-20T15:45:03Z	0	0	null	[ "region:us" ]	null	2025-06-20T15:42:33Z	<a rel="nofollow" href="https://tinyurl.com/2urtu5zm">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 L𝚎aᴋed Video V𝐢ral Video</a> <a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
mradermacher/DeepSeek-R1-0528-i1-GGUF	mradermacher	2025-06-20T14:36:10Z	0	3	transformers	[ "transformers", "en", "base_model:deepseek-ai/DeepSeek-R1-0528", "base_model:finetune:deepseek-ai/DeepSeek-R1-0528", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-06-12T09:11:03Z	--- base_model: deepseek-ai/DeepSeek-R1-0528 language: - en library_name: transformers license: mit 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/deepseek-ai/DeepSeek-R1-0528 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/DeepSeek-R1-0528-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 \| \|:-----\|:-----\|--------:\|:------\| \| [PART 1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ1_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ1_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ1_S.gguf.part3of3) \| i1-IQ1_S \| 133.8 \| for the desperate \| \| [PART 1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ1_M.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ1_M.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ1_M.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ1_M.gguf.part4of4) \| i1-IQ1_M \| 149.2 \| mostly desperate \| \| [PART 1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_XXS.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_XXS.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_XXS.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_XXS.gguf.part4of4) \| i1-IQ2_XXS \| 174.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_XS.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_XS.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_XS.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_XS.gguf.part4of4) \| i1-IQ2_XS \| 195.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_S.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_S.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_S.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_S.gguf.part4of4) \| i1-IQ2_S \| 197.2 \| \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_M.gguf.part1of5) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_M.gguf.part2of5) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_M.gguf.part3of5) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_M.gguf.part4of5) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ2_M.gguf.part5of5) \| i1-IQ2_M \| 217.7 \| \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q2_K_S.gguf.part1of5) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q2_K_S.gguf.part2of5) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q2_K_S.gguf.part3of5) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q2_K_S.gguf.part4of5) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q2_K_S.gguf.part5of5) \| i1-Q2_K_S \| 224.9 \| very low quality \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q2_K.gguf.part1of5) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q2_K.gguf.part2of5) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q2_K.gguf.part3of5) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q2_K.gguf.part4of5) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q2_K.gguf.part5of5) \| i1-Q2_K \| 244.2 \| IQ3_XXS probably better \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_XXS.gguf.part1of6) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_XXS.gguf.part2of6) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_XXS.gguf.part3of6) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_XXS.gguf.part4of6) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_XXS.gguf.part5of6) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_XXS.gguf.part6of6) \| i1-IQ3_XXS \| 258.1 \| lower quality \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_XS.gguf.part1of6) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_XS.gguf.part2of6) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_XS.gguf.part3of6) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_XS.gguf.part4of6) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_XS.gguf.part5of6) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_XS.gguf.part6of6) \| i1-IQ3_XS \| 273.0 \| \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_S.gguf.part1of6) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_S.gguf.part2of6) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_S.gguf.part3of6) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_S.gguf.part4of6) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_S.gguf.part5of6) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_S.gguf.part6of6) \| i1-IQ3_S \| 289.3 \| beats Q3_K* \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_S.gguf.part1of6) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_S.gguf.part2of6) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_S.gguf.part3of6) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_S.gguf.part4of6) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_S.gguf.part5of6) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_S.gguf.part6of6) \| i1-Q3_K_S \| 289.3 \| IQ3_XS probably better \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_M.gguf.part1of6) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_M.gguf.part2of6) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_M.gguf.part3of6) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_M.gguf.part4of6) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_M.gguf.part5of6) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ3_M.gguf.part6of6) \| i1-IQ3_M \| 292.3 \| \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_M.gguf.part1of7) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_M.gguf.part2of7) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_M.gguf.part3of7) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_M.gguf.part4of7) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_M.gguf.part5of7) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_M.gguf.part6of7) [P7](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_M.gguf.part7of7) \| i1-Q3_K_M \| 319.4 \| IQ3_S probably better \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_L.gguf.part1of8) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_L.gguf.part2of8) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_L.gguf.part3of8) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_L.gguf.part4of8) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_L.gguf.part5of8) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_L.gguf.part6of8) [P7](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_L.gguf.part7of8) [P8](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q3_K_L.gguf.part8of8) \| i1-Q3_K_L \| 347.6 \| IQ3_M probably better \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ4_XS.gguf.part1of8) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ4_XS.gguf.part2of8) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ4_XS.gguf.part3of8) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ4_XS.gguf.part4of8) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ4_XS.gguf.part5of8) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ4_XS.gguf.part6of8) [P7](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ4_XS.gguf.part7of8) [P8](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-IQ4_XS.gguf.part8of8) \| i1-IQ4_XS \| 357.2 \| \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_0.gguf.part1of8) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_0.gguf.part2of8) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_0.gguf.part3of8) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_0.gguf.part4of8) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_0.gguf.part5of8) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_0.gguf.part6of8) [P7](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_0.gguf.part7of8) [P8](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_0.gguf.part8of8) \| i1-Q4_0 \| 379.1 \| fast, low quality \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_S.gguf.part1of8) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_S.gguf.part2of8) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_S.gguf.part3of8) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_S.gguf.part4of8) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_S.gguf.part5of8) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_S.gguf.part6of8) [P7](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_S.gguf.part7of8) [P8](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_S.gguf.part8of8) \| i1-Q4_K_S \| 380.2 \| optimal size/speed/quality \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_M.gguf.part1of9) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_M.gguf.part2of9) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_M.gguf.part3of9) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_M.gguf.part4of9) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_M.gguf.part5of9) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_M.gguf.part6of9) [P7](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_M.gguf.part7of9) [P8](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_M.gguf.part8of9) [P9](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_K_M.gguf.part9of9) \| i1-Q4_K_M \| 404.6 \| fast, recommended \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_1.gguf.part1of9) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_1.gguf.part2of9) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_1.gguf.part3of9) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_1.gguf.part4of9) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_1.gguf.part5of9) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_1.gguf.part6of9) [P7](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_1.gguf.part7of9) [P8](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_1.gguf.part8of9) [P9](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q4_1.gguf.part9of9) \| i1-Q4_1 \| 420.0 \| \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_S.gguf.part01of10) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_S.gguf.part02of10) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_S.gguf.part03of10) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_S.gguf.part04of10) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_S.gguf.part05of10) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_S.gguf.part06of10) [P7](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_S.gguf.part07of10) [P8](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_S.gguf.part08of10) [P9](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_S.gguf.part09of10) [P10](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_S.gguf.part10of10) \| i1-Q5_K_S \| 461.9 \| \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_M.gguf.part01of10) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_M.gguf.part02of10) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_M.gguf.part03of10) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_M.gguf.part04of10) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_M.gguf.part05of10) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_M.gguf.part06of10) [P7](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_M.gguf.part07of10) [P8](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_M.gguf.part08of10) [P9](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_M.gguf.part09of10) [P10](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q5_K_M.gguf.part10of10) \| i1-Q5_K_M \| 475.5 \| \| \| [P1](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q6_K.gguf.part01of12) [P2](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q6_K.gguf.part02of12) [P3](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q6_K.gguf.part03of12) [P4](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q6_K.gguf.part04of12) [P5](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q6_K.gguf.part05of12) [P6](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q6_K.gguf.part06of12) [P7](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q6_K.gguf.part07of12) [P8](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q6_K.gguf.part08of12) [P9](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q6_K.gguf.part09of12) [P10](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q6_K.gguf.part10of12) [P11](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q6_K.gguf.part11of12) [P12](https://huggingface.co/mradermacher/DeepSeek-R1-0528-i1-GGUF/resolve/main/DeepSeek-R1-0528.i1-Q6_K.gguf.part12of12) \| i1-Q6_K \| 551.0 \| 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 -->
MattMcG/titles_wee_qwen_split	MattMcG	2025-06-20T13:09:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-20T13:07:53Z	--- base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: MattMcG - 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)
Triangle104/BetaCeti-Beta-4B-Prime1-Q5_K_M-GGUF	Triangle104	2025-06-20T12:59:32Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "reinforcement-learning", "code", "math", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:prithivMLmods/BetaCeti-Beta-4B-Prime1", "base_model:quantized:prithivMLmods/BetaCeti-Beta-4B-Prime1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-20T12:58:47Z	--- library_name: transformers tags: - text-generation-inference - reinforcement-learning - code - math - moe - llama-cpp - gguf-my-repo license: apache-2.0 language: - en base_model: prithivMLmods/BetaCeti-Beta-4B-Prime1 pipeline_tag: text-generation --- # Triangle104/BetaCeti-Beta-4B-Prime1-Q5_K_M-GGUF This model was converted to GGUF format from [`prithivMLmods/BetaCeti-Beta-4B-Prime1`](https://huggingface.co/prithivMLmods/BetaCeti-Beta-4B-Prime1) 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/prithivMLmods/BetaCeti-Beta-4B-Prime1) for more details on the model. --- BetaCeti-Beta-4B-Prime1 is a compact, coding-optimized language model built on the Qwen3-4B architecture, tailored for high-accuracy code generation, debugging, and technical reasoning. With 4 billion parameters, it strikes a balance between performance and efficiency, making it an ideal assistant for developers, educators, and engineers working in constrained environments or requiring fast inference. --- ## 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/BetaCeti-Beta-4B-Prime1-Q5_K_M-GGUF --hf-file betaceti-beta-4b-prime1-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/BetaCeti-Beta-4B-Prime1-Q5_K_M-GGUF --hf-file betaceti-beta-4b-prime1-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/BetaCeti-Beta-4B-Prime1-Q5_K_M-GGUF --hf-file betaceti-beta-4b-prime1-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/BetaCeti-Beta-4B-Prime1-Q5_K_M-GGUF --hf-file betaceti-beta-4b-prime1-q5_k_m.gguf -c 2048 ```
JonasBeking/MalRepoResearch	JonasBeking	2025-06-20T12:20:59Z	0	0	null	[ "pytorch", "region:us" ]	null	2025-06-20T11:51:30Z	## Research This is used for research purposes.
ccgtay/base-adapter	ccgtay	2025-06-19T00:50:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-19T00:50:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
hyunwoo612/CODENENDAv2_GGUF	hyunwoo612	2025-06-18T05:45:53Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T02:02:12Z	--- base_model: unsloth/qwen3-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: hyunwoo612 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-8b-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)
morturr/Llama-2-7b-hf-LOO_headlines-COMB_amazon-comb1-seed42-2025-06-17	morturr	2025-06-17T20:12:11Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-17T20:12:00Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_headlines-COMB_amazon-comb1-seed42-2025-06-17 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. --> # Llama-2-7b-hf-LOO_headlines-COMB_amazon-comb1-seed42-2025-06-17 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1

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