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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
PhoenixB/eba6291e-49bc-456f-8f57-65514dab3ea1	PhoenixB	2025-04-25T11:51:35Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "unsloth", "conversational", "arxiv:2305.18290", "base_model:unsloth/mistral-7b-instruct-v0.2", "base_model:quantized:unsloth/mistral-7b-instruct-v0.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-04-25T11:47:02Z	--- base_model: unsloth/mistral-7b-instruct-v0.2 library_name: transformers model_name: eba6291e-49bc-456f-8f57-65514dab3ea1 tags: - generated_from_trainer - axolotl - dpo - trl - unsloth licence: license --- # Model Card for eba6291e-49bc-456f-8f57-65514dab3ea1 This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="PhoenixB/eba6291e-49bc-456f-8f57-65514dab3ea1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients-On-Demand/runs/6a0uljxj) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0 - Transformers: 4.46.3 - Pytorch: 2.5.1+cu124 - 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}} } ```
Daehoya/HyperCLOVAX-SEED-Counseling	Daehoya	2025-04-25T11:42:12Z	0	0	null	[ "safetensors", "llama", "counseling", "korean", "chat", "empathy", "text-generation", "conversational", "ko", "dataset:custom", "license:other", "region:us" ]	text-generation	2025-04-25T08:59:27Z	--- language: ko tags: - counseling - korean - chat - empathy license: other datasets: - custom pipeline_tag: text-generation --- # 🧠 HyperCLOVAX-SEED-Counseling This model is a fine-tuned version of [`naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B`](https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B), specialized for empathetic counseling for teenagers. --- ## 🔍 Model Overview - Base model: `naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B` - Fine-tuning objective: Provide warm, non-judgmental, and emotionally supportive counseling responses tailored to youth clients. - Language: Korean (한국어) The model has been trained on real and synthetic conversations between counselors and teenage clients. It emphasizes: - Empathy and emotional validation (e.g., "그랬구나", "충분히 이해돼") - Open-ended questions for self-exploration - Avoiding direct advice or judgment - Handling crisis situations with safe referrals --- ## 🧑‍⚕️ System Prompt Guideline The system message used during training and inference is: ``` 당신은 공감 능력이 뛰어난 전문 청소년 상담사입니다. (중략: 따뜻하고 공감적인 상담 대화 규칙 포함) ``` This ensures the assistant maintains a friendly, safe, and supportive tone. --- ## 💬 Inference Example ```python from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Daehoya/HyperCLOVAX-SEED-Counseling") tokenizer = AutoTokenizer.from_pretrained("Daehoya/HyperCLOVAX-SEED-Counseling") prompt = "요즘 친구들과 멀어진 것 같아..." inputs = tokenizer.apply_chat_template( [ {"role": "system", "content": "당신은 공감 능력이 뛰어난 전문 청소년 상담사입니다."}, {"role": "user", "content": prompt} ], return_tensors="pt" ).to(model.device) output = model.generate(inputs, max_new_tokens=300, temperature=0.7) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` --- ## 🧪 Training Details - Optimizer: AdamW - Batch size: 3 per device (gradient_accumulation_steps=20) - Epochs: 3 - Max input length: up to 8192 tokens - Hardware: 4×A100 GPUs - Precision: FP16 - Framework: `transformers.Trainer` --- ## 📁 Files Included - `pytorch_model.bin` or `model-*.safetensors`: Model weights - `tokenizer.json`, `tokenizer_config.json`: Tokenizer files - `config.json`: Model config - `generation_config.json`: Sampling configuration - `README.md`: This file --- ## 📜 License This model is released under the same license as the base model. Please review [NAVER CLOVA's licensing policy](https://huggingface.co/naver-hyperclovax). --- ## 🙏 Acknowledgements Thanks to NAVER CLOVA for the base model and the community for ongoing contributions in mental health AI.
dgambettaphd/M_llm3_gen6_run0_WXS_doc1000_synt64_tot128_MPP	dgambettaphd	2025-04-25T11:35:15Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-25T11:35:05Z	--- 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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shubhamprshr/Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_classic_0.5_0.5_True_300	shubhamprshr	2025-04-25T10:42:20Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "dataset:blocksworld-dataset", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T04:02:53Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct datasets: blocksworld-dataset library_name: transformers model_name: Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_classic_0.5_0.5_True_300 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_classic_0.5_0.5_True_300 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [blocksworld-dataset](https://huggingface.co/datasets/blocksworld-dataset) 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="shubhamprshr/Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_classic_0.5_0.5_True_300", 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/shubhamprshr27-tamu/BW2/runs/jx4znt38) 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: 0.14.0 - Transformers: 4.48.1 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
dzanbek/8bca9373-ecad-4fea-8ec6-4538ae12eebc	dzanbek	2025-04-25T10:38:58Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "custom_code", "base_model:NousResearch/Yarn-Solar-10b-32k", "base_model:adapter:NousResearch/Yarn-Solar-10b-32k", "license:apache-2.0", "region:us" ]	null	2025-04-25T09:33:00Z	--- library_name: peft license: apache-2.0 base_model: NousResearch/Yarn-Solar-10b-32k tags: - axolotl - generated_from_trainer model-index: - name: 8bca9373-ecad-4fea-8ec6-4538ae12eebc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: NousResearch/Yarn-Solar-10b-32k bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 338a122e43543931_train_data.json ds_type: json format: custom path: /workspace/input_data/338a122e43543931_train_data.json type: field_input: raw_texts field_instruction: gen_questions field_output: Positive format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: dzanbek/8bca9373-ecad-4fea-8ec6-4538ae12eebc hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/338a122e43543931_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3a6c2313-e0d4-427f-b835-4522b7af6bde wandb_project: s56-2 wandb_run: your_name wandb_runid: 3a6c2313-e0d4-427f-b835-4522b7af6bde warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 8bca9373-ecad-4fea-8ec6-4538ae12eebc This model is a fine-tuned version of [NousResearch/Yarn-Solar-10b-32k](https://huggingface.co/NousResearch/Yarn-Solar-10b-32k) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0599 \| 0.0104 \| 200 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/llm-embedder-i1-GGUF	mradermacher	2025-04-25T10:29:02Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:BAAI/llm-embedder", "base_model:quantized:BAAI/llm-embedder", "license:mit", "endpoints_compatible", "region:us", "imatrix", "feature-extraction" ]	null	2025-04-25T10:25:09Z	--- base_model: BAAI/llm-embedder 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/BAAI/llm-embedder <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/llm-embedder-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.2 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-Q2_K.gguf) \| i1-Q2_K \| 0.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.2 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm-embedder-i1-GGUF/resolve/main/llm-embedder.i1-Q6_K.gguf) \| i1-Q6_K \| 0.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Bhumi-Ahir-Viral-Video/Original-Viral-Link.Bhumi.Ahir.Viral.Video.Leaks.official.HD	Bhumi-Ahir-Viral-Video	2025-04-25T10:03:03Z	0	0	null	[ "region:us" ]	null	2025-04-25T10:02:20Z	<a href="https://sdu.sk/9Ip"><img src="http://4.bp.blogspot.com/-VFcup4RzDQY/Upiobuokb5I/AAAAAAAAAV0/64yKpZilDCg/s1600/oie_nxv3mlmduAj1.gif" alt="fsd" /></a> <a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a> <a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
mlfoundations-dev/b2_math_fasttext_pos_s1k_reformat_neg_lap1official_math_10k	mlfoundations-dev	2025-04-25T09:57:52Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T23:38:27Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b2_math_fasttext_pos_s1k_reformat_neg_lap1official_math_10k 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. --> # b2_math_fasttext_pos_s1k_reformat_neg_lap1official_math_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b2_math_fasttext_pos_s1k_reformat_neg_lap1official_math_10k 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
ansh6/crypto	ansh6	2025-04-25T09:51:14Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-04-25T09:51:13Z	--- license: bigcode-openrail-m ---
Kam1qwe/Kam1lka	Kam1qwe	2025-04-25T09:46:51Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-04-25T09:46:51Z	--- license: artistic-2.0 ---
Ktejaswi/AI_Powered_Dream_Interpreter	Ktejaswi	2025-04-25T09:36:18Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-25T09:36:18Z	--- license: apache-2.0 ---
inclusionAI/Ring-lite-linear-preview	inclusionAI	2025-04-25T09:32:32Z	3	8	null	[ "safetensors", "bailing_moe_linear", "text-generation", "conversational", "custom_code", "zh", "en", "base_model:inclusionAI/Ling-lite", "base_model:finetune:inclusionAI/Ling-lite", "license:mit", "region:us" ]	text-generation	2025-04-24T02:58:46Z	--- license: mit language: - zh - en base_model: - inclusionAI/Ling-lite pipeline_tag: text-generation --- # Ring-lite-linear-preview <p align="center"> <img src="https://huggingface.co/inclusionAI/Ring-lite-linear-preview/resolve/main/ant-bailing.png" width="100"/> <p> <p align="center"> 🤗 <a href="https://huggingface.co/inclusionAI">Hugging Face</a> <p> ## Introduction Ring-lite-linear-preview is a hybrid-linear MoE LLM provided and open-sourced by InclusionAI, which has 17.1B parameters with 3.0B activated parameters. It is a long reasoning model based on hybrid-linear attention, achieving near-linear computational complexity and near-constant space complexity during inference. This model was converted from [Ling-lite-0220](https://huggingface.co/inclusionAI/Ling-lite/tree/Ling-lite-0220), which adopts the softmax attention-based architecture. It matches the performance of DeepSeek-R1-Distill-Qwen-7B on standardized reasoning benchmarks while substantially reducing computational overhead in both training and inference phases. In certain generation speed tests based on vLLM, we observed that the throughput was more than doubled compared to softmax attention models of the same scale (e.g., Ling-lite). To the best of our knowledge, it is the first open-source hybrid-linear reasoning language model. ## Model Downloads <div align="center"> \| Model \| #Total Params \| #Activated Params \| Context Length \| Download \| \| :----------------: \| :---------------: \| :-------------------: \| :----------------: \| :----------: \| \| Ring-lite-linear-preview \| 17.1B \| 3.0B \| 64K \| [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-lite-linear-preview)\| </div> ## Evaluation In terms of the evaluation of reasoning ability， Ring-lite-linear-preview achieves 55.0 on AIME24 and 93.8 on MATH-500. <div align="center"> \| Model \| AIME24 \| MATH-500 \| GPQA-diamond \| LiveCodeBench \| \| :----------------: \| :---------------: \| :-------------------: \| :----------------: \| :----------: \| \| DeepSeek-R1-Distill-Qwen-7B (reported) \| 55.5 \| 92.8 \| 49.1 \| 37.6 \| \| DeepSeek-R1-Distill-Qwen-7B (reproduce) \| 53.2 \| 93.7 \| 50.4 \| 36.5 \| \| Ring-lite-distill-preview-Stage-1 \| 54.2 \| 93.5 \| 47.5 \| 32.9 \| \| Ring-lite-linear-preview \| 55.0 \| 93.8 \| 46.5 \| 29.8 \| </div> ## Inference Speed To evaluate the generation throughput, we deploy Ring-lite-linear and the softmax-attention-based Ring-lite based on vLLM on a single NVIDIA A100 GPU. We conduct two sets of experiments: 1. Long Input Evaluation: We measure the time-to-first-token (TTFT) with varying input sequence lengths (from 512 to 384k tokens) using batch size 1 and TP=1. As shown in the top figure, at 384k input length, Ring-lite-linear achieves 3.5× faster TTFT compared to the softmax-attention-based model. 2. Long Output Evaluation: We fix the input sequence length to 1 and measure the end-to-end (E2E) generation time required for generating output sequences of varying lengths (from 512 to 32k tokens) with batch size 64 and TP=1. As illustrated in the bottom figure, at 32k output length, Ring-lite-linear achieves 2.2× throughput of the softmax-attention-based Ring-lite. These results demonstrate that our hybrid linear attention mechanism significantly improves both input processing efficiency and generation throughput, especially for long context scenarios. <p align="center"> <img src="https://huggingface.co/inclusionAI/Ring-lite-linear-preview/resolve/main/throughput.png" width="600"/> <p> Additionally, to illustrate the advantage in inference speed, we present a comparison between Ring-lite-linear-preview and softmax-attention-based Ring-lite under a batch size of 64 and an output length of 16k (60x speedup). It can be observed that the KV cache usage of Ring-lite-linear-preview is nearly 1/6 that of Ring-lite, and the E2E time is reduced by 27.24% compared with Ring-lite. <p align="center"> <img src="https://huggingface.co/inclusionAI/Ring-lite-linear-preview/resolve/main/inference_speed.gif" width="600"/> <p> More details will be reported in our technical report [TBD] ## Requirements - [transformers](https://github.com/huggingface/transformers) >= 4.48.3 - [flash-linear-attention](https://github.com/fla-org/flash-linear-attention) >= 0.2.1 ## Quickstart Here is a code snippet to show you how to use the chat model with `modelscope`: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "inclusionAI/Ring-lite-linear-preview" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language models." messages = [ {"role": "system", "content": "You are Ring, an assistant created by inclusionAI"}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=8192 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Deployment Please refer to [Github](https://github.com/inclusionAI/Ring/tree/main/hybrid_linear) ## Dataset The long reasoning sft data: [Ring-lite-distill-preview-sft-data](https://huggingface.co/datasets/inclusionAI/Ring-lite-distill-preview-sft-data) ## License This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ring-lite-linear-preview/blob/main/LICENSE). ## Citation [TBD]
efficientscaling/Z1-Longest-7B	efficientscaling	2025-04-25T09:11:57Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T09:10:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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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danyush/qwen2.5_vl_7b_virat_shuffled	danyush	2025-04-25T09:10:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "llama-factory", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-25T09:04:57Z	--- library_name: transformers tags: - llama-factory --- # 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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vmpsergio/9463d9df-44d0-4b5b-a588-fc9f202b7e1d	vmpsergio	2025-04-25T08:57:26Z	0	0	peft	[ "peft", "safetensors", "opt", "axolotl", "generated_from_trainer", "base_model:facebook/opt-350m", "base_model:adapter:facebook/opt-350m", "license:other", "region:us" ]	null	2025-04-25T08:52:27Z	--- library_name: peft license: other base_model: facebook/opt-350m tags: - axolotl - generated_from_trainer model-index: - name: 9463d9df-44d0-4b5b-a588-fc9f202b7e1d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: facebook/opt-350m bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 32cb49683e226f4d_train_data.json ds_type: json format: custom path: /workspace/input_data/32cb49683e226f4d_train_data.json type: field_input: author field_instruction: dynasty field_output: content format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vmpsergio/9463d9df-44d0-4b5b-a588-fc9f202b7e1d hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/32cb49683e226f4d_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: a4199bed-2854-4046-9e07-45f55e8274f5 wandb_project: s56-2 wandb_run: your_name wandb_runid: a4199bed-2854-4046-9e07-45f55e8274f5 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 9463d9df-44d0-4b5b-a588-fc9f202b7e1d This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3061 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.3525 \| 0.0078 \| 200 \| 3.3061 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
annasoli/Qwen2.5-14B-Instruct_bad_med_dpR1_15-17_21-23_27-29	annasoli	2025-04-25T08:37:28Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-14B-Instruct", "base_model:finetune:unsloth/Qwen2.5-14B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-25T08:37:23Z	--- base_model: unsloth/Qwen2.5-14B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: annasoli - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-14B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
LINK-Sophie-Rain-Spiderman-Viral-Videos/Original.Sophie.Rain.Spiderman.Video.Leaks.official	LINK-Sophie-Rain-Spiderman-Viral-Videos	2025-04-25T06:22:41Z	0	0	null	[ "region:us" ]	null	2025-04-25T06:22:21Z	<animated-image data-catalyst=""><a href="https://sexleakedviral.com/sophie-rain-spiderman/?sophie-rain-spiderman-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf	RichardErkhov	2025-04-25T05:50:07Z	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-25T04:27:46Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) _ra_04_checkpoint-456 - GGUF - Model creator: https://huggingface.co/minhhien0811/ - Original model: https://huggingface.co/minhhien0811/_ra_04_checkpoint-456/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [_ra_04_checkpoint-456.Q2_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q2_K.gguf) \| Q2_K \| 2.81GB \| \| [_ra_04_checkpoint-456.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.IQ3_XS.gguf) \| IQ3_XS \| 3.12GB \| \| [_ra_04_checkpoint-456.IQ3_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.IQ3_S.gguf) \| IQ3_S \| 3.26GB \| \| [_ra_04_checkpoint-456.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q3_K_S.gguf) \| Q3_K_S \| 3.25GB \| \| [_ra_04_checkpoint-456.IQ3_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.IQ3_M.gguf) \| IQ3_M \| 3.33GB \| \| [_ra_04_checkpoint-456.Q3_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q3_K.gguf) \| Q3_K \| 3.55GB \| \| [_ra_04_checkpoint-456.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q3_K_M.gguf) \| Q3_K_M \| 3.55GB \| \| [_ra_04_checkpoint-456.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q3_K_L.gguf) \| Q3_K_L \| 3.81GB \| \| [_ra_04_checkpoint-456.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.IQ4_XS.gguf) \| IQ4_XS \| 3.96GB \| \| [_ra_04_checkpoint-456.Q4_0.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q4_0.gguf) \| Q4_0 \| 4.13GB \| \| [_ra_04_checkpoint-456.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.IQ4_NL.gguf) \| IQ4_NL \| 4.16GB \| \| [_ra_04_checkpoint-456.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q4_K_S.gguf) \| Q4_K_S \| 4.15GB \| \| [_ra_04_checkpoint-456.Q4_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q4_K.gguf) \| Q4_K \| 4.36GB \| \| [_ra_04_checkpoint-456.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q4_K_M.gguf) \| Q4_K_M \| 4.36GB \| \| [_ra_04_checkpoint-456.Q4_1.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q4_1.gguf) \| Q4_1 \| 4.54GB \| \| [_ra_04_checkpoint-456.Q5_0.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q5_0.gguf) \| Q5_0 \| 4.95GB \| \| [_ra_04_checkpoint-456.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q5_K_S.gguf) \| Q5_K_S \| 4.95GB \| \| [_ra_04_checkpoint-456.Q5_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q5_K.gguf) \| Q5_K \| 5.07GB \| \| [_ra_04_checkpoint-456.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q5_K_M.gguf) \| Q5_K_M \| 5.07GB \| \| [_ra_04_checkpoint-456.Q5_1.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q5_1.gguf) \| Q5_1 \| 5.36GB \| \| [_ra_04_checkpoint-456.Q6_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q6_K.gguf) \| Q6_K \| 5.82GB \| \| [_ra_04_checkpoint-456.Q8_0.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-__ra_04_checkpoint-456-gguf/blob/main/_ra_04_checkpoint-456.Q8_0.gguf) \| Q8_0 \| 7.54GB \| Original model description: --- 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]
RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf	RichardErkhov	2025-04-25T05:46:48Z	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-25T04:26:20Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) only-nvidia_9966 - GGUF - Model creator: https://huggingface.co/minhhien0811/ - Original model: https://huggingface.co/minhhien0811/only-nvidia_9966/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [only-nvidia_9966.Q2_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q2_K.gguf) \| Q2_K \| 2.81GB \| \| [only-nvidia_9966.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.IQ3_XS.gguf) \| IQ3_XS \| 3.12GB \| \| [only-nvidia_9966.IQ3_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.IQ3_S.gguf) \| IQ3_S \| 3.26GB \| \| [only-nvidia_9966.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q3_K_S.gguf) \| Q3_K_S \| 3.25GB \| \| [only-nvidia_9966.IQ3_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.IQ3_M.gguf) \| IQ3_M \| 3.33GB \| \| [only-nvidia_9966.Q3_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q3_K.gguf) \| Q3_K \| 3.55GB \| \| [only-nvidia_9966.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q3_K_M.gguf) \| Q3_K_M \| 3.55GB \| \| [only-nvidia_9966.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q3_K_L.gguf) \| Q3_K_L \| 3.81GB \| \| [only-nvidia_9966.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.IQ4_XS.gguf) \| IQ4_XS \| 3.96GB \| \| [only-nvidia_9966.Q4_0.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q4_0.gguf) \| Q4_0 \| 4.13GB \| \| [only-nvidia_9966.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.IQ4_NL.gguf) \| IQ4_NL \| 4.16GB \| \| [only-nvidia_9966.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q4_K_S.gguf) \| Q4_K_S \| 4.15GB \| \| [only-nvidia_9966.Q4_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q4_K.gguf) \| Q4_K \| 4.36GB \| \| [only-nvidia_9966.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q4_K_M.gguf) \| Q4_K_M \| 4.36GB \| \| [only-nvidia_9966.Q4_1.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q4_1.gguf) \| Q4_1 \| 4.54GB \| \| [only-nvidia_9966.Q5_0.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q5_0.gguf) \| Q5_0 \| 4.95GB \| \| [only-nvidia_9966.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q5_K_S.gguf) \| Q5_K_S \| 4.95GB \| \| [only-nvidia_9966.Q5_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q5_K.gguf) \| Q5_K \| 5.07GB \| \| [only-nvidia_9966.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q5_K_M.gguf) \| Q5_K_M \| 5.07GB \| \| [only-nvidia_9966.Q5_1.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q5_1.gguf) \| Q5_1 \| 5.36GB \| \| [only-nvidia_9966.Q6_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q6_K.gguf) \| Q6_K \| 5.82GB \| \| [only-nvidia_9966.Q8_0.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_9966-gguf/blob/main/only-nvidia_9966.Q8_0.gguf) \| Q8_0 \| 7.54GB \| Original model description: --- 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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shisa-ai/ablation-182-a177.finaldpo2.6.5e7-shisa-v2-mistral-nemo-12b	shisa-ai	2025-04-25T04:50:49Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "arxiv:2305.18290", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-10T20:43:48Z	--- library_name: transformers model_name: outputs/ablation-182-a177.finaldpo2.6.5e7-shisa-v2-mistral-nemo-12b tags: - generated_from_trainer licence: license --- # Model Card for outputs/ablation-182-a177.finaldpo2.6.5e7-shisa-v2-mistral-nemo-12b This model is a fine-tuned version of [/fsx2/outputs/ablation-177-finalsft2-shisa-v2-mistral-nemo-12b](https://huggingface.co//fsx2/outputs/ablation-177-finalsft2-shisa-v2-mistral-nemo-12b). 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="None", 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/augmxnt/shisa-v2/runs/pvui2il9) 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: 0.15.1 - Transformers: 4.50.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
KingEmpire/sn9_pretcom4_2504_1	KingEmpire	2025-04-25T04:50:26Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T04:37:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
mlfoundations-dev/b2_science_length_gpt4omini_3k	mlfoundations-dev	2025-04-25T04:25:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T00:37:41Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b2_science_length_gpt4omini_3k 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. --> # b2_science_length_gpt4omini_3k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b2_science_length_gpt4omini_3k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
striverweb/ppo-LunarLander-v2	striverweb	2025-04-25T04:21:52Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-25T03:45:06Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 270.79 +/- 16.54 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf	RichardErkhov	2025-04-25T04:14:18Z	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-25T02:38:29Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) deita-arena-nvidia-sft-rag-17500 - GGUF - Model creator: https://huggingface.co/minhhien0811/ - Original model: https://huggingface.co/minhhien0811/deita-arena-nvidia-sft-rag-17500/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [deita-arena-nvidia-sft-rag-17500.Q2_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q2_K.gguf) \| Q2_K \| 2.81GB \| \| [deita-arena-nvidia-sft-rag-17500.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.IQ3_XS.gguf) \| IQ3_XS \| 3.12GB \| \| [deita-arena-nvidia-sft-rag-17500.IQ3_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.IQ3_S.gguf) \| IQ3_S \| 3.26GB \| \| [deita-arena-nvidia-sft-rag-17500.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q3_K_S.gguf) \| Q3_K_S \| 3.25GB \| \| [deita-arena-nvidia-sft-rag-17500.IQ3_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.IQ3_M.gguf) \| IQ3_M \| 3.33GB \| \| [deita-arena-nvidia-sft-rag-17500.Q3_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q3_K.gguf) \| Q3_K \| 3.55GB \| \| [deita-arena-nvidia-sft-rag-17500.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q3_K_M.gguf) \| Q3_K_M \| 3.55GB \| \| [deita-arena-nvidia-sft-rag-17500.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q3_K_L.gguf) \| Q3_K_L \| 3.81GB \| \| [deita-arena-nvidia-sft-rag-17500.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.IQ4_XS.gguf) \| IQ4_XS \| 3.96GB \| \| [deita-arena-nvidia-sft-rag-17500.Q4_0.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q4_0.gguf) \| Q4_0 \| 4.13GB \| \| [deita-arena-nvidia-sft-rag-17500.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.IQ4_NL.gguf) \| IQ4_NL \| 4.16GB \| \| [deita-arena-nvidia-sft-rag-17500.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q4_K_S.gguf) \| Q4_K_S \| 4.15GB \| \| [deita-arena-nvidia-sft-rag-17500.Q4_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q4_K.gguf) \| Q4_K \| 4.36GB \| \| [deita-arena-nvidia-sft-rag-17500.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q4_K_M.gguf) \| Q4_K_M \| 4.36GB \| \| [deita-arena-nvidia-sft-rag-17500.Q4_1.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q4_1.gguf) \| Q4_1 \| 4.54GB \| \| [deita-arena-nvidia-sft-rag-17500.Q5_0.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q5_0.gguf) \| Q5_0 \| 4.95GB \| \| [deita-arena-nvidia-sft-rag-17500.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q5_K_S.gguf) \| Q5_K_S \| 4.95GB \| \| [deita-arena-nvidia-sft-rag-17500.Q5_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q5_K.gguf) \| Q5_K \| 5.07GB \| \| [deita-arena-nvidia-sft-rag-17500.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q5_K_M.gguf) \| Q5_K_M \| 5.07GB \| \| [deita-arena-nvidia-sft-rag-17500.Q5_1.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q5_1.gguf) \| Q5_1 \| 5.36GB \| \| [deita-arena-nvidia-sft-rag-17500.Q6_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q6_K.gguf) \| Q6_K \| 5.82GB \| \| [deita-arena-nvidia-sft-rag-17500.Q8_0.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_deita-arena-nvidia-sft-rag-17500-gguf/blob/main/deita-arena-nvidia-sft-rag-17500.Q8_0.gguf) \| Q8_0 \| 7.54GB \| Original model description: --- 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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stain195/claassification938104	stain195	2025-04-25T03:27:47Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-25T03:27:46Z	--- license: apache-2.0 ---
aisingapore/Llama-SEA-LION-v3-70B-IT-GGUF	aisingapore	2025-04-25T03:21:52Z	1,205	0	transformers	[ "transformers", "gguf", "text-generation", "en", "zh", "vi", "id", "th", "fil", "ta", "ms", "km", "lo", "my", "jv", "su", "arxiv:2504.05747", "base_model:aisingapore/Llama-SEA-LION-v3-70B-IT", "base_model:quantized:aisingapore/Llama-SEA-LION-v3-70B-IT", "license:llama3.1", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-12-16T03:01:34Z	--- library_name: transformers pipeline_tag: text-generation base_model: - aisingapore/Llama-SEA-LION-v3-70B-IT base_model_relation: quantized language: - en - zh - vi - id - th - fil - ta - ms - km - lo - my - jv - su license: llama3.1 --- <div> <img src="llama_3.1_70b_sea-lion_v3_gguf_banner.png"/> </div> # Llama-SEA-LION-v3-70B-IT [SEA-LION](https://arxiv.org/abs/2504.05747) is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region. Llama-SEA-LION-v3-70B-IT is a multilingual model that has been fine-tuned in two stages on approximately 12.3M English instruction-completion pairs alongside a pool of 4.5M Southeast Asian instruction-completion pairs from SEA languages such as Indonesian, Javanese, Sundanese, Tamil, Thai, and Vietnamese. SEA-LION stands for _Southeast Asian Languages In One Network_. - Developed by: Products Pillar, AI Singapore - Funded by: Singapore NRF - Model type: Decoder - Languages supported: Burmese, Chinese, English, Filipino, Indonesia, Javanese, Khmer, Lao, Malay, Sundanese, Tamil, Thai, Vietnamese - License: [Llama 3.1 Community License](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct/blob/main/LICENSE) ## Description This repo contains `GGUF` format model files for [aisingapore/Llama-SEA-LION-v3-70B-IT](https://huggingface.co/aisingapore/Llama-SEA-LION-v3-70B-IT). #### Model Weights Included in this repository: - [Llama-SEA-LION-v3-70B-IT-F16](https://huggingface.co/aisingapore/Llama-SEA-LION-v3-70B-IT-GGUF/blob/main/Llama-SEA-LION-v3-70B-IT-F16-00001-of-00008.gguf) - [Llama-SEA-LION-v3-70B-IT-Q2_K](https://huggingface.co/aisingapore/Llama-SEA-LION-v3-70B-IT-GGUF/blob/main/Llama-SEA-LION-v3-70B-IT-Q2_K.gguf) - [Llama-SEA-LION-v3-70B-IT-Q3_K_M](https://huggingface.co/aisingapore/Llama-SEA-LION-v3-70B-IT-GGUF/blob/main/Llama-SEA-LION-v3-70B-IT-Q3_K_M.gguf) - [Llama-SEA-LION-v3-70B-IT-Q4_0](https://huggingface.co/aisingapore/Llama-SEA-LION-v3-70B-IT-GGUF/blob/main/Llama-SEA-LION-v3-70B-IT-Q4_0.gguf) - [Llama-SEA-LION-v3-70B-IT-Q4_K_M](https://huggingface.co/aisingapore/Llama-SEA-LION-v3-70B-IT-GGUF/blob/main/Llama-SEA-LION-v3-70B-IT-Q4_K_M.gguf) - [Llama-SEA-LION-v3-70B-IT-Q5_0](https://huggingface.co/aisingapore/Llama-SEA-LION-v3-70B-IT-GGUF/blob/main/Llama-SEA-LION-v3-70B-IT-Q5_0.gguf) - [Llama-SEA-LION-v3-70B-IT-Q5_K_M](https://huggingface.co/aisingapore/Llama-SEA-LION-v3-70B-IT-GGUF/blob/main/Llama-SEA-LION-v3-70B-IT-Q5_K_M.gguf) - [Llama-SEA-LION-v3-70B-IT-Q6_K](https://huggingface.co/aisingapore/Llama-SEA-LION-v3-70B-IT-GGUF/blob/main/Llama-SEA-LION-v3-70B-IT-Q6_K-00001-of-00002.gguf) - [Llama-SEA-LION-v3-70B-IT-Q8_0](https://huggingface.co/aisingapore/Llama-SEA-LION-v3-70B-IT-GGUF/blob/main/Llama-SEA-LION-v3-70B-IT-Q8_0-00001-of-00003.gguf) > [!NOTE] > Take note that some GGUFs are split into parts. Most tools such as [`llama.cpp`](https://github.com/ggerganov/llama.cpp) and those built on it do support split GGUFs, pointing the platform to the first split will be sufficient for it to function. > In the event where a merge is necessary, it can be done using `llama.cpp`'s `gguf-split`: `./gguf-split --merge ./path/to/first-split ./path/to/output-gguf` > More details: [gguf-split guide](https://github.com/ggerganov/llama.cpp/discussions/6404) & [README](https://github.com/ggerganov/llama.cpp/tree/master/examples/gguf-split) ### Caveats It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning. ## Limitations ### Safety Current SEA-LION models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes. ## Technical Specifications ### Fine-Tuning Details Llama-SEA-LION-v3-70B-IT was tuned using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 3200 GPU hours, on a single node of 8x H100-80GB GPUs. ## Data Llama-SEA-LION-v3-70B-IT was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source. ## Call for Contributions We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of SEA-LION. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Southeast Asian languages. Join us in shaping the future of SEA-LION by sharing your expertise and insights to make these models more accessible, accurate, and versatile. Please check out our GitHub for further information on the call for contributions. ## The Team Chan Adwin, Cheng Nicholas, Choa Esther, Huang Yuli, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Limkonchotiwat Peerat, Liu Bing Jie Darius, Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David, Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teng Walter, Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, Venkatadri Hulagadri Adithya, Yeo Yeow Tong, Yong Xianbin ## Acknowledgements [AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore. ## Contact For more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6) [Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion) ## Disclaimer This is the repository for the commercial instruction-tuned model. The model has _not_ been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.
3mily1u/fim-codegen-350m-mono-finetuned-attack-25	3mily1u	2025-04-25T03:17:01Z	0	0	transformers	[ "transformers", "safetensors", "codegen", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T03:16:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
sergioalves/d0f62694-a9a9-41ee-92fa-739328b8e778	sergioalves	2025-04-25T03:15:18Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored", "base_model:adapter:Orenguteng/Llama-3-8B-Lexi-Uncensored", "license:llama3", "region:us" ]	null	2025-04-25T03:04:05Z	--- library_name: peft license: llama3 base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored tags: - axolotl - generated_from_trainer model-index: - name: d0f62694-a9a9-41ee-92fa-739328b8e778 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: true adapter: lora base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 7d91f3d76c4fac08_train_data.json ds_type: json format: custom path: /workspace/input_data/7d91f3d76c4fac08_train_data.json type: field_instruction: user_status field_output: user_persona format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: sergioalves/d0f62694-a9a9-41ee-92fa-739328b8e778 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/7d91f3d76c4fac08_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: fac4d056-b459-4431-8129-825726a73dfd wandb_project: s56-8 wandb_run: your_name wandb_runid: fac4d056-b459-4431-8129-825726a73dfd warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # d0f62694-a9a9-41ee-92fa-739328b8e778 This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8397 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8417 \| 0.1411 \| 200 \| 0.8397 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Echo9Zulu/Qwen2.5-VL-7B-Instruct-int4_sym-ov	Echo9Zulu	2025-04-25T00:29:57Z	0	0	null	[ "openvino", "qwen2_5_vl", "license:apache-2.0", "region:us" ]	null	2025-04-24T23:47:06Z	--- license: apache-2.0 ---
JOSESMOKE/tear_459	JOSESMOKE	2025-04-25T00:15:36Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-24T23:59:52Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
PhoenixB/d913eff7-2cc9-433f-9818-6ed25d2c3a79	PhoenixB	2025-04-25T00:14:31Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:princeton-nlp/Sheared-LLaMA-1.3B", "base_model:adapter:princeton-nlp/Sheared-LLaMA-1.3B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-25T00:07:45Z	--- library_name: peft license: apache-2.0 base_model: princeton-nlp/Sheared-LLaMA-1.3B tags: - axolotl - generated_from_trainer model-index: - name: d913eff7-2cc9-433f-9818-6ed25d2c3a79 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: `0.5.2` ```yaml adapter: qlora auto_find_batch_size: true base_model: princeton-nlp/Sheared-LLaMA-1.3B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 40e50bc14394f8e0_train_data.json ds_type: json format: custom path: /workspace/input_data/40e50bc14394f8e0_train_data.json type: field_input: cons_rejected field_instruction: main_ins field_output: cons_chosen format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null eval_max_new_tokens: 128 eval_sample_packing: false eval_steps: 10 eval_table_size: null flash_attention: true fp16: false fsdp: - full_shard - auto_wrap fsdp_config: fsdp_cpu_ram_efficient_loading: true fsdp_limit_all_gathers: true fsdp_offload_params: true fsdp_sharding_strategy: FULL_SHARD fsdp_state_dict_type: FULL_STATE_DICT fsdp_sync_module_states: true fsdp_use_orig_params: false gpu_memory_limit: 80GiB gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: true hub_model_id: PhoenixB/d913eff7-2cc9-433f-9818-6ed25d2c3a79 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 2e-4 liger_fused_linear_cross_entropy: true liger_glu_activation: true liger_layer_norm: true liger_rms_norm: true liger_rope: true load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 5 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/40e50bc14394f8e0_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true plugins: - axolotl.integrations.liger.LigerPlugin resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 4096 special_tokens: pad_token: </s> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 69fd4981-ecc5-4b7a-9b51-0df5aa2c5c09 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 69fd4981-ecc5-4b7a-9b51-0df5aa2c5c09 warmup_steps: 5 weight_decay: 0.0 ``` </details><br> # d913eff7-2cc9-433f-9818-6ed25d2c3a79 This model is a fine-tuned version of [princeton-nlp/Sheared-LLaMA-1.3B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1410 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - 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: 5 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0009 \| 1 \| 1.3690 \| \| 1.2568 \| 0.0089 \| 10 \| 1.2340 \| \| 1.1562 \| 0.0177 \| 20 \| 1.1539 \| \| 1.1097 \| 0.0266 \| 30 \| 1.1410 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
hackelle/rdnet_base-s1-v0.2.0	hackelle	2025-04-24T23:57:49Z	0	0	configilm	[ "configilm", "safetensors", "rdnet_base", "BigEarthNet v2.0", "Remote Sensing", "Classification", "image-classification", "Multispectral", "arxiv:2407.03653", "license:mit", "region:us" ]	image-classification	2025-04-24T23:57:29Z	--- thumbnail: "https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png" tags: - rdnet_base - BigEarthNet v2.0 - Remote Sensing - Classification - image-classification - Multispectral library_name: configilm license: mit widget: - src: example.png example_title: Example output: - label: Agro-forestry areas score: 0.000000 - label: Arable land score: 0.000006 - label: Beaches, dunes, sands score: 0.000029 - label: Broad-leaved forest score: 0.000812 - label: Coastal wetlands score: 0.000001 --- [TU Berlin](https://www.tu.berlin/) \| [RSiM](https://rsim.berlin/) \| [DIMA](https://www.dima.tu-berlin.de/menue/database_systems_and_information_management_group/) \| [BigEarth](http://www.bigearth.eu/) \| [BIFOLD](https://bifold.berlin/) :---:\|:---:\|:---:\|:---:\|:---: <a href="https://www.tu.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/tu-berlin-logo-long-red.svg" style="font-size: 1rem; height: 2em; width: auto" alt="TU Berlin Logo"/> \| <a href="https://rsim.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png" style="font-size: 1rem; height: 2em; width: auto" alt="RSiM Logo"> \| <a href="https://www.dima.tu-berlin.de/menue/database_systems_and_information_management_group/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/DIMA.png" style="font-size: 1rem; height: 2em; width: auto" alt="DIMA Logo"> \| <a href="http://www.bigearth.eu/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/BigEarth.png" style="font-size: 1rem; height: 2em; width: auto" alt="BigEarth Logo"> \| <a href="https://bifold.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/BIFOLD_Logo_farbig.png" style="font-size: 1rem; height: 2em; width: auto; margin-right: 1em" alt="BIFOLD Logo"> # Rdnet_base pretrained on BigEarthNet v2.0 using Sentinel-1 bands <!-- Optional images --> <!-- [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) \| [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) :---:\|:---: <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/sentinel_2.jpg" style="font-size: 1rem; height: 10em; width: auto; margin-right: 1em" alt="Sentinel-2 Satellite"/> \| <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-2"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/sentinel_1.jpg" style="font-size: 1rem; height: 10em; width: auto; margin-right: 1em" alt="Sentinel-1 Satellite"/> --> This model was trained on the BigEarthNet v2.0 (also known as reBEN) dataset using the Sentinel-1 bands. It was trained using the following parameters: - Number of epochs: up to 100 (with early stopping after 5 epochs of no improvement based on validation average precision macro) - Batch size: 512 - Learning rate: 0.001 - Dropout rate: 0.15 - Drop Path rate: 0.15 - Learning rate scheduler: LinearWarmupCosineAnnealing for 1000 warmup steps - Optimizer: AdamW - Seed: 42 The weights published in this model card were obtained after 31 training epochs. For more information, please visit the [official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts), where you can find the training scripts. ![[BigEarthNet](http://bigearth.net/)](https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/combined_2000_600_2020_0_wide.jpg) The model was evaluated on the test set of the BigEarthNet v2.0 dataset with the following results: \| Metric \| Macro \| Micro \| \|:------------------\|------------------:\|------------------:\| \| Average Precision \| 0.580053 \| 0.775468 \| \| F1 Score \| 0.524612 \| 0.681331 \| \| Precision \| 0.622027 \| 0.727463 \| # Example \| A Sentinel-1 image (VV, VH and VV/VH bands are used for visualization) \| \|:---------------------------------------------------:\| \| ![[BigEarthNet](http://bigearth.net/)](example.png) \| \| Class labels \| Predicted scores \| \|:--------------------------------------------------------------------------\|--------------------------------------------------------------------------:\| \| <p> Agro-forestry areas <br> Arable land <br> Beaches, dunes, sands <br> ... <br> Urban fabric </p> \| <p> 0.000000 <br> 0.000006 <br> 0.000029 <br> ... <br> 0.000004 </p> \| To use the model, download the codes that define the model architecture from the [official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts) and load the model using the code below. Note that you have to install [`configilm`](https://pypi.org/project/configilm/) to use the provided code. ```python from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier model = BigEarthNetv2_0_ImageClassifier.from_pretrained("path_to/huggingface_model_folder") ``` e.g. ```python from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier model = BigEarthNetv2_0_ImageClassifier.from_pretrained( "BIFOLD-BigEarthNetv2-0/rdnet_base-s1-v0.1.1") ``` If you use this model in your research or the provided code, please cite the following papers: ```bibtex @article{clasen2024refinedbigearthnet, title={reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis}, author={Clasen, Kai Norman and Hackel, Leonard and Burgert, Tom and Sumbul, Gencer and Demir, Beg{\"u}m and Markl, Volker}, year={2024}, eprint={2407.03653}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.03653}, } ``` ```bibtex @article{hackel2024configilm, title={ConfigILM: A general purpose configurable library for combining image and language models for visual question answering}, author={Hackel, Leonard and Clasen, Kai Norman and Demir, Beg{\"u}m}, journal={SoftwareX}, volume={26}, pages={101731}, year={2024}, publisher={Elsevier} } ```
maxsegan/gpt2_l1_64_100k	maxsegan	2025-04-24T23:42:42Z	0	0	null	[ "pytorch", "region:us" ]	null	2025-04-24T23:42:22Z	# gpt2_l1_64_100k ## Model Details - Block size: 1024 - Vocabulary size: 50304 - Layers: 12 - Heads: 12 - Embedding size: 768
mergekit-community/mergekit-nuslerp-oejdsoz	mergekit-community	2025-04-24T23:27:54Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:mergekit-community/mergekit-model_stock-lvezkfe", "base_model:merge:mergekit-community/mergekit-model_stock-lvezkfe", "base_model:mergekit-community/mergekit-model_stock-qtseiad", "base_model:merge:mergekit-community/mergekit-model_stock-qtseiad", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T23:22:59Z	--- base_model: - mergekit-community/mergekit-model_stock-lvezkfe - mergekit-community/mergekit-model_stock-qtseiad library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the NuSLERP merge method. ### Models Merged The following models were included in the merge: * [mergekit-community/mergekit-model_stock-lvezkfe](https://huggingface.co/mergekit-community/mergekit-model_stock-lvezkfe) * [mergekit-community/mergekit-model_stock-qtseiad](https://huggingface.co/mergekit-community/mergekit-model_stock-qtseiad) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mergekit-community/mergekit-model_stock-qtseiad parameters: weight: 0.7 - model: mergekit-community/mergekit-model_stock-lvezkfe parameters: weight: 0.3 merge_method: nuslerp dtype: bfloat16 chat_template: "chatml" tokenizer: source: union parameters: normalize: true ```
mlfoundations-dev/b2_science_random_10k	mlfoundations-dev	2025-04-24T23:17:24Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T16:45:46Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b2_science_random_10k 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. --> # b2_science_random_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b2_science_random_10k 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
mlfoundations-dev/b2_science_fasttext_3k	mlfoundations-dev	2025-04-24T21:56:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T19:35:17Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b2_science_fasttext_3k 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. --> # b2_science_fasttext_3k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b2_science_fasttext_3k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
procrastipredator/gemma-3-kk-qlora-adapter	procrastipredator	2025-04-24T21:40:58Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-24T21:40:10Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: procrastipredator - 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)
exp-models/HyperCLOVA-X-HyperClever-v1-20250425-preview	exp-models	2025-04-24T21:18:37Z	0	0	null	[ "safetensors", "llama", "license:other", "region:us" ]	null	2025-04-24T16:45:37Z	--- license: other license_name: hyperclovax-seed license_link: https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B/raw/main/LICENSE ---
filipesantoscv11/46798891-26fe-4665-91ee-a0b1d5b87da3	filipesantoscv11	2025-04-24T20:52:55Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:OpenBuddy/openbuddy-llama2-13b-v8.1-fp16", "base_model:adapter:OpenBuddy/openbuddy-llama2-13b-v8.1-fp16", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-24T20:12:23Z	--- library_name: peft base_model: OpenBuddy/openbuddy-llama2-13b-v8.1-fp16 tags: - axolotl - generated_from_trainer model-index: - name: 46798891-26fe-4665-91ee-a0b1d5b87da3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: OpenBuddy/openbuddy-llama2-13b-v8.1-fp16 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 2fc5c2abbeb880c5_train_data.json ds_type: json format: custom path: /workspace/input_data/2fc5c2abbeb880c5_train_data.json type: field_input: context field_instruction: instruction field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: filipesantoscv11/46798891-26fe-4665-91ee-a0b1d5b87da3 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/2fc5c2abbeb880c5_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b4720579-00f6-47ae-90b5-1d556182ce7f wandb_project: s56-6 wandb_run: your_name wandb_runid: b4720579-00f6-47ae-90b5-1d556182ce7f warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 46798891-26fe-4665-91ee-a0b1d5b87da3 This model is a fine-tuned version of [OpenBuddy/openbuddy-llama2-13b-v8.1-fp16](https://huggingface.co/OpenBuddy/openbuddy-llama2-13b-v8.1-fp16) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0618 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9177 \| 0.0580 \| 200 \| 1.0618 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
aiguy68/neurosam-model	aiguy68	2025-04-24T20:48:31Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "text-classification", "region:us" ]	text-classification	2025-04-24T04:27:24Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin pipeline_tag: text-classification --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF	mradermacher	2025-04-24T20:37:28Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Darkhn/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B", "base_model:quantized:Darkhn/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-24T11:58:45Z	--- base_model: Darkhn/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Darkhn/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B-GGUF/resolve/main/Alkahest-V9.2-Unhinged-RP-Alpha-V1-Llama-3.3-70B.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
kk-aivio/ddce59ea-fa2a-4817-a62e-132769037dd5	kk-aivio	2025-04-24T20:17:35Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:adapter:unsloth/Qwen2.5-0.5B-Instruct", "region:us" ]	null	2025-04-24T20:17:18Z	--- library_name: peft tags: - generated_from_trainer base_model: unsloth/Qwen2.5-0.5B-Instruct model-index: - name: kk-aivio/ddce59ea-fa2a-4817-a62e-132769037dd5 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. --> # kk-aivio/ddce59ea-fa2a-4817-a62e-132769037dd5 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4126 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
jgerbscheid/segformer_b1-nlver_finetuned-1024-1024	jgerbscheid	2025-04-24T20:03:12Z	38	0	transformers	[ "transformers", "safetensors", "segformer", "aerial_photography", "rgb", "segmentation", "netherlands", "image-segmentation", "base_model:nvidia/segformer-b1-finetuned-cityscapes-1024-1024", "base_model:finetune:nvidia/segformer-b1-finetuned-cityscapes-1024-1024", "license:mit", "endpoints_compatible", "region:us" ]	image-segmentation	2025-04-23T12:49:56Z	--- library_name: transformers tags: - aerial_photography - rgb - segmentation - netherlands license: mit base_model: - nvidia/segformer-b1-finetuned-cityscapes-1024-1024 pipeline_tag: image-segmentation --- # NL-aerial segmentation This is a segformer segmentation model finetuned on random samples from the entire 41,000 square kilometer of aerial photography data, see [pdok aerial data](https://www.pdok.nl/introductie/-/article/pdok-luchtfoto-rgb-open-), and using the BGT, see [pdok BGT data](https://www.pdok.nl/introductie/-/article/basisregistratie-grootschalige-topografie-bgt-). Specifically it takes in 1024x1024 aerial photographs taken at a resolution of 8cm/pixel and predicts water, buildings, roads/pavement, vegetation. This model is part of the NL-veranderdetectie project, [summary](https://www.winnovatie.nl/kennisbank/3041030.aspx?t=Learning-paper-NL-Veranderdetectie-Fase-1) in dutch. The model was trained using this [codebase](https://gitlab.com/hetwaterschapshuis/kenniscentrum/tooling/nlveranderdetectie/), see the repo for more details. ## Model Details Regular segformer, with the classification head adjusted to 5 classes. ### Model Description - Developed by: Het [Waterschapshuis](https://www.hetwaterschapshuis.nl/), in collaboration with the Dutch Waterboards [More Information Needed] <!-- - Funded by [optional]: [] --> <!-- - Shared by [optional]: [More Information Needed] --> - Model type: [Segformer Semantic Segmentation Model] - License: [MIT] - Finetuned from model: [nvidia/segformer-b1-finetuned-cityscapes-1024-1024] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [https://gitlab.com/hetwaterschapshuis/kenniscentrum/tooling/nlveranderdetectie/](https://gitlab.com/hetwaterschapshuis/kenniscentrum/tooling/nlveranderdetectie/) <!-- - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] --> ## Uses This model is used to create a segmentation map of the dutch waterways based on aerial imagery. This segmentation map is then used to actualize the BGT, a national map maintained by the Dutch government. #### Training Hyperparameters See the nlveranderdetectie repostiory for details of the training setup. [https://gitlab.com/hetwaterschapshuis/kenniscentrum/tooling/nlveranderdetectie/](https://gitlab.com/hetwaterschapshuis/kenniscentrum/tooling/nlveranderdetectie/) , specifically look at the yaml configs in the run/configs directory. ### Hardware The model was trained on a single nvidia 3090 GPU for ~2 days or ~126k forward passes with a batch size of 8. ## Model Card Authors The model car was written by J. Gerbscheid, email: [email protected] ## Model Card Contact The model was trained by J. Gerbscheid, email: [email protected]
TareksLab/Z-MODEL4-V1-SCE	TareksLab	2025-04-24T19:32:52Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2408.07990", "base_model:Sao10K/L3-70B-Euryale-v2.1", "base_model:merge:Sao10K/L3-70B-Euryale-v2.1", "base_model:TareksLab/Fetishist-V5-LLaMA-70B", "base_model:merge:TareksLab/Fetishist-V5-LLaMA-70B", "base_model:TheDrummer/Fallen-Llama-3.3-R1-70B-v1", "base_model:merge:TheDrummer/Fallen-Llama-3.3-R1-70B-v1", "base_model:mlabonne/Hermes-3-Llama-3.1-70B-lorablated", "base_model:merge:mlabonne/Hermes-3-Llama-3.1-70B-lorablated", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T19:20:54Z	--- base_model: - Sao10K/L3-70B-Euryale-v2.1 - mlabonne/Hermes-3-Llama-3.1-70B-lorablated - TheDrummer/Fallen-Llama-3.3-R1-70B-v1 - TareksLab/Fetishist-V5-LLaMA-70B library_name: transformers tags: - mergekit - merge --- # MERGE4 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [mlabonne/Hermes-3-Llama-3.1-70B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-70B-lorablated) as a base. ### Models Merged The following models were included in the merge: * [Sao10K/L3-70B-Euryale-v2.1](https://huggingface.co/Sao10K/L3-70B-Euryale-v2.1) * [TheDrummer/Fallen-Llama-3.3-R1-70B-v1](https://huggingface.co/TheDrummer/Fallen-Llama-3.3-R1-70B-v1) * [TareksLab/Fetishist-V5-LLaMA-70B](https://huggingface.co/TareksLab/Fetishist-V5-LLaMA-70B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: TheDrummer/Fallen-Llama-3.3-R1-70B-v1 parameters: select_topk: 0.7 - model: TareksLab/Fetishist-V5-LLaMA-70B parameters: select_topk: 0.7 - model: Sao10K/L3-70B-Euryale-v2.1 parameters: select_topk: 0.7 - model: mlabonne/Hermes-3-Llama-3.1-70B-lorablated parameters: select_topk: 0.7 base_model: mlabonne/Hermes-3-Llama-3.1-70B-lorablated merge_method: sce parameters: int8_mask: true tokenizer: source: TareksLab/Fetishist-V5-LLaMA-70B chat_template: llama3 dtype: bfloat16 ```
dgambettaphd/M_llm3_gen6_run0_X_doc1000_synt64_tot128_SYNLAST	dgambettaphd	2025-04-24T17:56:18Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-24T17:56:03Z	--- 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. 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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ReadyArt/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B_EXL2_5bpw_H8	ReadyArt	2025-04-24T17:29:47Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "base_model:Mawdistical/Pawdistic-FurMittens-24B", "base_model:merge:Mawdistical/Pawdistic-FurMittens-24B", "base_model:ReadyArt/Forgotten-Safeword-24B-v4.0", "base_model:merge:ReadyArt/Forgotten-Safeword-24B-v4.0", "base_model:ReadyArt/Omega-Darker-Gaslight_The-Final-Forgotten-Fever-Dream-24B", "base_model:merge:ReadyArt/Omega-Darker-Gaslight_The-Final-Forgotten-Fever-Dream-24B", "base_model:ReadyArt/The-Omega-Directive-M-24B-v1.1", "base_model:merge:ReadyArt/The-Omega-Directive-M-24B-v1.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2025-04-24T17:20:18Z	--- base_model: - ReadyArt/The-Omega-Directive-M-24B-v1.1 - ReadyArt/Omega-Darker-Gaslight_The-Final-Forgotten-Fever-Dream-24B - ReadyArt/Forgotten-Safeword-24B-v4.0 - Mawdistical/Pawdistic-FurMittens-24B library_name: transformers tags: - mergekit - merge --- # output-model This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [ReadyArt/Omega-Darker-Gaslight_The-Final-Forgotten-Fever-Dream-24B](https://huggingface.co/ReadyArt/Omega-Darker-Gaslight_The-Final-Forgotten-Fever-Dream-24B) as a base. ### Models Merged The following models were included in the merge: * [ReadyArt/The-Omega-Directive-M-24B-v1.1](https://huggingface.co/ReadyArt/The-Omega-Directive-M-24B-v1.1) * [ReadyArt/Forgotten-Safeword-24B-v4.0](https://huggingface.co/ReadyArt/Forgotten-Safeword-24B-v4.0) * [Mawdistical/Pawdistic-FurMittens-24B](https://huggingface.co/Mawdistical/Pawdistic-FurMittens-24B) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: dare_ties base_model: ReadyArt/Omega-Darker-Gaslight_The-Final-Forgotten-Fever-Dream-24B models: - model: ReadyArt/Omega-Darker-Gaslight_The-Final-Forgotten-Fever-Dream-24B parameters: weight: 0.25 - model: ReadyArt/Forgotten-Safeword-24B-v4.0 parameters: weight: 0.25 - model: ReadyArt/The-Omega-Directive-M-24B-v1.1 parameters: weight: 0.25 - model: Mawdistical/Pawdistic-FurMittens-24B parameters: weight: 0.25 parameters: density: 0.3 tokenizer: source: union chat_template: auto ```
keioio/chatbot_mental_health_using_LLaMa_3.1_instruct_v2	keioio	2025-04-24T17:24:39Z	0	0	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T15:59:43Z	--- base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: keioio - License: apache-2.0 - Finetuned from model : unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF	mradermacher	2025-04-24T16:51:33Z	200	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion", "base_model:quantized:Nexesenex/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-12T04:36:50Z	--- base_model: Nexesenex/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Nexesenex/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion-GGUF/resolve/main/Llama_3.x_70b_L3.3-calme-2.3_abliterated_fusion.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
moyixiao/llama3_8b_badam2	moyixiao	2025-04-24T16:50:51Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T16:29:02Z	--- library_name: transformers tags: - llama-factory --- # 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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nathanialhunt2000/a9262989-7dda-4dba-8926-e1cef502d6a6	nathanialhunt2000	2025-04-24T16:44:07Z	0	0	transformers	[ "transformers", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-04-24T16:43:17Z	--- library_name: transformers model_name: nathanialhunt2000/a9262989-7dda-4dba-8926-e1cef502d6a6 tags: - generated_from_trainer licence: license --- # Model Card for nathanialhunt2000/a9262989-7dda-4dba-8926-e1cef502d6a6 This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ### Framework versions - TRL: 0.12.0 - Transformers: 4.46.3 - Pytorch: 2.5.1+cu124 - 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}} } ```
ezrab/ppo-LunarLander-v2-unit8-3	ezrab	2025-04-24T16:29:21Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-04-24T16:29:04Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 242.07 +/- 91.88 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'LunarLander' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 5000000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 1024 'anneal_lr': True 'gamma': 0.999 'gae_lambda': 0.98 'num_minibatches': 16 'update_epochs': 8 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'batch_size': 4096 'minibatch_size': 256 'num_iterations': 1220} ```
JeorgeC/BocchiGemma3_4BVer01-finetuned	JeorgeC	2025-04-24T16:23:12Z	0	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "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", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-23T06:53:45Z	--- 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: JeorgeC - 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)
mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF	mradermacher	2025-04-24T12:17:11Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf", "base_model:quantized:minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-24T11:55:59Z	--- base_model: minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf language: - en library_name: transformers license: other license_link: LICENSE license_name: hyperclovax-seed 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/minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.3 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-Q2_K.gguf) \| i1-Q2_K \| 0.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf-i1-GGUF/resolve/main/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf.i1-Q6_K.gguf) \| i1-Q6_K \| 0.6 \| 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 -->
VParkar/VParkar	VParkar	2025-04-24T11:52:25Z	0	0	peft	[ "peft", "llava", "region:us" ]	null	2025-04-24T11:51:55Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
aimagelab/LLaVA_MORE-llama_3_1-8B-S2-pretrain	aimagelab	2025-04-24T11:51:54Z	2	0	transformers	[ "transformers", "llava_llama", "text-generation", "image-text-to-text", "dataset:liuhaotian/LLaVA-CC3M-Pretrain-595K", "arxiv:2503.15621", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-08-16T15:03:04Z	--- license: apache-2.0 datasets: - liuhaotian/LLaVA-CC3M-Pretrain-595K library_name: transformers pipeline_tag: image-text-to-text --- # Model Card: LLaVA_MORE-llama_3_1-8B-S2-pretrain ```LLaVA-MORE``` enhances the well-known LLaVA architecture by integrating the use of LLaMA 3.1 as the language model. We are publicly releasing the checkpoints for stages one and two for the first model with 8B parameters. In this model space, you will find the stage one (pretrain) weights of LLaVA-MORE LLaMA 3.1 8B. For more information, visit our [LLaVA-MORE](https://github.com/aimagelab/LLaVA-MORE) repository. ## Citation If you make use of our work, please cite our repo: ```bibtex @article{cocchi2025llava, title={{LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction Tuning}}, author={Cocchi, Federico and Moratelli, Nicholas and Caffagni, Davide and Sarto, Sara and Baraldi, Lorenzo and Cornia, Marcella and Cucchiara, Rita}, journal={arXiv preprint arXiv:2503.15621}, year={2025} } ```
Homebax/axionis-0.5-flash	Homebax	2025-04-24T11:13:17Z	0	0	allennlp	[ "allennlp", "any-to-any", "cs", "sk", "pl", "dataset:nvidia/OpenCodeReasoning", "dataset:nvidia/Llama-Nemotron-Post-Training-Dataset", "dataset:PJMixers-Dev/nvidia_Llama-Nemotron-Post-Training-Dataset-v1-partial-code", "dataset:facebook/natural_reasoning", "base_model:Qwen/Qwen2.5-Omni-7B", "base_model:finetune:Qwen/Qwen2.5-Omni-7B", "license:apache-2.0", "region:us" ]	any-to-any	2025-04-24T11:10:13Z	--- license: apache-2.0 datasets: - nvidia/OpenCodeReasoning - nvidia/Llama-Nemotron-Post-Training-Dataset - PJMixers-Dev/nvidia_Llama-Nemotron-Post-Training-Dataset-v1-partial-code - facebook/natural_reasoning language: - cs - sk - pl metrics: - charcut_mt - bertscore - accuracy - bleu base_model: - deepseek-ai/DeepSeek-V3-0324 - meta-llama/Llama-4-Scout-17B-16E-Instruct - microsoft/bitnet-b1.58-2B-4T - Qwen/Qwen2.5-Omni-7B new_version: meta-llama/Llama-4-Scout-17B-16E-Instruct pipeline_tag: any-to-any library_name: allennlp ---
mradermacher/DoodDoodGRPO-GGUF	mradermacher	2025-04-24T10:59:52Z	0	0	transformers	[ "transformers", "gguf", "trl", "grpo", "en", "base_model:DoodDood/DoodDoodGRPO", "base_model:quantized:DoodDood/DoodDoodGRPO", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-24T10:58:36Z	--- base_model: DoodDood/DoodDoodGRPO language: - en library_name: transformers quantized_by: mradermacher tags: - trl - grpo --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/DoodDood/DoodDoodGRPO <!-- 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/DoodDoodGRPO-GGUF/resolve/main/DoodDoodGRPO.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DoodDoodGRPO-GGUF/resolve/main/DoodDoodGRPO.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DoodDoodGRPO-GGUF/resolve/main/DoodDoodGRPO.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DoodDoodGRPO-GGUF/resolve/main/DoodDoodGRPO.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DoodDoodGRPO-GGUF/resolve/main/DoodDoodGRPO.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DoodDoodGRPO-GGUF/resolve/main/DoodDoodGRPO.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DoodDoodGRPO-GGUF/resolve/main/DoodDoodGRPO.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DoodDoodGRPO-GGUF/resolve/main/DoodDoodGRPO.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DoodDoodGRPO-GGUF/resolve/main/DoodDoodGRPO.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DoodDoodGRPO-GGUF/resolve/main/DoodDoodGRPO.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DoodDoodGRPO-GGUF/resolve/main/DoodDoodGRPO.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DoodDoodGRPO-GGUF/resolve/main/DoodDoodGRPO.f16.gguf) \| f16 \| 0.4 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
genki10/BERT_V8_sp10_lw40_ex30_lo00_k1_k1_fold0	genki10	2025-04-24T10:30:45Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-24T10:16:50Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: BERT_V8_sp10_lw40_ex30_lo00_k1_k1_fold0 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. --> # BERT_V8_sp10_lw40_ex30_lo00_k1_k1_fold0 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9808 - Qwk: 0.3379 - Mse: 0.9808 - Rmse: 0.9904 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 1.0 \| 2 \| 8.9702 \| 0.0 \| 8.9702 \| 2.9950 \| \| No log \| 2.0 \| 4 \| 8.0136 \| 0.0 \| 8.0136 \| 2.8308 \| \| No log \| 3.0 \| 6 \| 6.9437 \| 0.0 \| 6.9437 \| 2.6351 \| \| No log \| 4.0 \| 8 \| 6.3664 \| -0.0033 \| 6.3664 \| 2.5232 \| \| No log \| 5.0 \| 10 \| 5.5572 \| 0.0119 \| 5.5572 \| 2.3574 \| \| No log \| 6.0 \| 12 \| 4.8767 \| 0.0115 \| 4.8767 \| 2.2083 \| \| No log \| 7.0 \| 14 \| 4.1298 \| 0.0039 \| 4.1298 \| 2.0322 \| \| No log \| 8.0 \| 16 \| 3.4601 \| 0.0 \| 3.4601 \| 1.8601 \| \| No log \| 9.0 \| 18 \| 2.7786 \| 0.0 \| 2.7786 \| 1.6669 \| \| No log \| 10.0 \| 20 \| 2.1818 \| 0.0897 \| 2.1818 \| 1.4771 \| \| No log \| 11.0 \| 22 \| 1.7969 \| 0.0316 \| 1.7969 \| 1.3405 \| \| No log \| 12.0 \| 24 \| 1.5506 \| 0.0316 \| 1.5506 \| 1.2452 \| \| No log \| 13.0 \| 26 \| 1.2899 \| 0.0316 \| 1.2899 \| 1.1357 \| \| No log \| 14.0 \| 28 \| 1.1221 \| 0.0316 \| 1.1221 \| 1.0593 \| \| No log \| 15.0 \| 30 \| 1.0436 \| 0.0316 \| 1.0436 \| 1.0215 \| \| No log \| 16.0 \| 32 \| 0.8914 \| 0.0426 \| 0.8914 \| 0.9442 \| \| No log \| 17.0 \| 34 \| 0.9683 \| 0.0141 \| 0.9683 \| 0.9840 \| \| No log \| 18.0 \| 36 \| 0.8230 \| 0.2422 \| 0.8230 \| 0.9072 \| \| No log \| 19.0 \| 38 \| 1.1504 \| 0.0538 \| 1.1504 \| 1.0726 \| \| No log \| 20.0 \| 40 \| 1.3972 \| 0.0909 \| 1.3972 \| 1.1820 \| \| No log \| 21.0 \| 42 \| 0.8380 \| 0.2887 \| 0.8380 \| 0.9154 \| \| No log \| 22.0 \| 44 \| 0.7439 \| 0.2226 \| 0.7439 \| 0.8625 \| \| No log \| 23.0 \| 46 \| 0.8533 \| 0.1053 \| 0.8533 \| 0.9238 \| \| No log \| 24.0 \| 48 \| 0.7797 \| 0.1927 \| 0.7797 \| 0.8830 \| \| No log \| 25.0 \| 50 \| 0.6253 \| 0.3473 \| 0.6253 \| 0.7908 \| \| No log \| 26.0 \| 52 \| 0.9396 \| 0.2916 \| 0.9396 \| 0.9693 \| \| No log \| 27.0 \| 54 \| 1.4233 \| 0.1837 \| 1.4233 \| 1.1930 \| \| No log \| 28.0 \| 56 \| 1.1770 \| 0.1797 \| 1.1770 \| 1.0849 \| \| No log \| 29.0 \| 58 \| 0.6667 \| 0.4945 \| 0.6667 \| 0.8165 \| \| No log \| 30.0 \| 60 \| 0.5842 \| 0.3501 \| 0.5842 \| 0.7644 \| \| No log \| 31.0 \| 62 \| 0.6174 \| 0.3387 \| 0.6174 \| 0.7858 \| \| No log \| 32.0 \| 64 \| 0.6236 \| 0.3670 \| 0.6236 \| 0.7897 \| \| No log \| 33.0 \| 66 \| 0.6669 \| 0.4042 \| 0.6669 \| 0.8166 \| \| No log \| 34.0 \| 68 \| 0.7465 \| 0.3602 \| 0.7465 \| 0.8640 \| \| No log \| 35.0 \| 70 \| 0.8770 \| 0.3317 \| 0.8770 \| 0.9365 \| \| No log \| 36.0 \| 72 \| 0.9559 \| 0.3140 \| 0.9559 \| 0.9777 \| \| No log \| 37.0 \| 74 \| 0.8209 \| 0.3280 \| 0.8209 \| 0.9060 \| \| No log \| 38.0 \| 76 \| 0.7384 \| 0.3321 \| 0.7384 \| 0.8593 \| \| No log \| 39.0 \| 78 \| 0.7857 \| 0.3287 \| 0.7857 \| 0.8864 \| \| No log \| 40.0 \| 80 \| 1.0877 \| 0.2729 \| 1.0877 \| 1.0429 \| \| No log \| 41.0 \| 82 \| 1.5115 \| 0.1696 \| 1.5115 \| 1.2294 \| \| No log \| 42.0 \| 84 \| 1.7673 \| 0.1822 \| 1.7673 \| 1.3294 \| \| No log \| 43.0 \| 86 \| 1.7437 \| 0.1824 \| 1.7437 \| 1.3205 \| \| No log \| 44.0 \| 88 \| 1.4460 \| 0.2016 \| 1.4460 \| 1.2025 \| \| No log \| 45.0 \| 90 \| 1.0318 \| 0.2650 \| 1.0318 \| 1.0158 \| \| No log \| 46.0 \| 92 \| 0.7998 \| 0.3472 \| 0.7998 \| 0.8943 \| \| No log \| 47.0 \| 94 \| 0.7302 \| 0.3747 \| 0.7302 \| 0.8545 \| \| No log \| 48.0 \| 96 \| 0.7029 \| 0.3875 \| 0.7029 \| 0.8384 \| \| No log \| 49.0 \| 98 \| 0.7327 \| 0.3633 \| 0.7327 \| 0.8560 \| \| No log \| 50.0 \| 100 \| 0.7715 \| 0.4094 \| 0.7715 \| 0.8784 \| \| No log \| 51.0 \| 102 \| 0.8248 \| 0.3814 \| 0.8248 \| 0.9082 \| \| No log \| 52.0 \| 104 \| 0.8408 \| 0.3966 \| 0.8408 \| 0.9170 \| \| No log \| 53.0 \| 106 \| 0.8438 \| 0.3986 \| 0.8438 \| 0.9186 \| \| No log \| 54.0 \| 108 \| 0.8196 \| 0.4153 \| 0.8196 \| 0.9053 \| \| No log \| 55.0 \| 110 \| 0.8260 \| 0.4120 \| 0.8260 \| 0.9088 \| \| No log \| 56.0 \| 112 \| 0.8530 \| 0.4091 \| 0.8530 \| 0.9236 \| \| No log \| 57.0 \| 114 \| 0.9044 \| 0.3744 \| 0.9044 \| 0.9510 \| \| No log \| 58.0 \| 116 \| 0.9406 \| 0.3550 \| 0.9406 \| 0.9699 \| \| No log \| 59.0 \| 118 \| 0.9808 \| 0.3379 \| 0.9808 \| 0.9904 \| ### Framework versions - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
crossdelenna/orpheus_lora_model	crossdelenna	2025-04-24T09:59:57Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit", "base_model:finetune:unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-24T09:59:46Z	--- base_model: unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: crossdelenna - License: apache-2.0 - Finetuned from model : unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ASethi04/google-gemma-2-9b-legalbench-1-lora-1-0.004	ASethi04	2025-04-24T09:49:33Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-2-9b", "base_model:finetune:google/gemma-2-9b", "endpoints_compatible", "region:us" ]	null	2025-04-24T09:49:16Z	--- base_model: google/gemma-2-9b library_name: transformers model_name: google-gemma-2-9b-legalbench-1-lora-1-0.004 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for google-gemma-2-9b-legalbench-1-lora-1-0.004 This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b). 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="ASethi04/google-gemma-2-9b-legalbench-1-lora-1-0.004", 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/torchql-org/huggingface/runs/gqaoyfvf) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Raghavi20/whisper-tiny-minds14-enUS	Raghavi20	2025-04-24T09:08:57Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-24T06:37:05Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-minds14-enUS results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.29071332436069985 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-minds14-enUS This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6872 - Wer: 0.2907 - Wer Ortho: 0.2907 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Wer Ortho \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:------:\|:---------:\| \| 0.8922 \| 1.7241 \| 50 \| 0.7444 \| 0.3378 \| 0.3378 \| \| 0.3603 \| 3.4483 \| 100 \| 0.5051 \| 0.3048 \| 0.3048 \| \| 0.1591 \| 5.1724 \| 150 \| 0.5103 \| 0.3028 \| 0.3028 \| \| 0.0449 \| 6.8966 \| 200 \| 0.5746 \| 0.2941 \| 0.2941 \| \| 0.0104 \| 8.6207 \| 250 \| 0.6113 \| 0.3022 \| 0.3022 \| \| 0.0035 \| 10.3448 \| 300 \| 0.6488 \| 0.2867 \| 0.2867 \| \| 0.0011 \| 12.0690 \| 350 \| 0.6746 \| 0.2907 \| 0.2907 \| \| 0.0008 \| 13.7931 \| 400 \| 0.6828 \| 0.2894 \| 0.2894 \| \| 0.0007 \| 15.5172 \| 450 \| 0.6872 \| 0.2907 \| 0.2907 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
Redeem-Craze-viral-video-original/exclusive-clip.Redeem.Craze.viral.video.leaked.full.hd	Redeem-Craze-viral-video-original	2025-04-24T08:57:20Z	0	0	null	[ "region:us" ]	null	2025-04-24T08:57:13Z	<a href="https://sdu.sk/9Ip"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/9Ip" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
Sophie-Rain-Spider-ManX-Videos/Sophie.Rain.Spider-Man.Video.Link	Sophie-Rain-Spider-ManX-Videos	2025-04-24T08:40:29Z	0	0	null	[ "region:us" ]	null	2025-04-24T08:40:03Z	<a href="https://sdu.sk/9Ip"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/9Ip" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
saiteki-kai/DeBERTa-QA-002	saiteki-kai	2025-04-24T06:09:05Z	0	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "multi-label", "question-answering", "generated_from_trainer", "dataset:beavertails", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-23T22:04:35Z	--- library_name: transformers license: mit base_model: microsoft/deberta-v3-large tags: - multi-label - question-answering - text-classification - generated_from_trainer datasets: - beavertails metrics: - accuracy model-index: - name: DeBERTa-QA-002 results: - task: name: Text Classification type: text-classification dataset: name: saiteki-kai/Beavertails-it type: beavertails metrics: - name: Accuracy type: accuracy value: 0.6890740925574741 --- <!-- 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. --> # DeBERTa-QA-002 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the saiteki-kai/Beavertails-it dataset. It achieves the following results on the evaluation set: - Loss: 0.0838 - Accuracy: 0.6891 - Macro F1: 0.6334 - Macro Precision: 0.7352 - Macro Recall: 0.6002 - Micro F1: 0.7504 - Micro Precision: 0.7872 - Micro Recall: 0.7169 - Flagged/accuracy: 0.8507 - Flagged/precision: 0.8907 - Flagged/recall: 0.8341 - Flagged/f1: 0.8614 ## 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-07 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Macro F1 \| Macro Precision \| Macro Recall \| Micro F1 \| Micro Precision \| Micro Recall \| Flagged/accuracy \| Flagged/precision \| Flagged/recall \| Flagged/f1 \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|:--------:\|:---------------:\|:------------:\|:--------:\|:---------------:\|:------------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\| \| 0.0929 \| 1.0 \| 16907 \| 0.1063 \| 0.6411 \| 0.3931 \| 0.4731 \| 0.3666 \| 0.6953 \| 0.7654 \| 0.6369 \| 0.8122 \| 0.8771 \| 0.7705 \| 0.8204 \| \| 0.089 \| 2.0 \| 33814 \| 0.0917 \| 0.6763 \| 0.5366 \| 0.6156 \| 0.4844 \| 0.7248 \| 0.8028 \| 0.6606 \| 0.8320 \| 0.9007 \| 0.7846 \| 0.8386 \| \| 0.0725 \| 3.0 \| 50721 \| 0.0882 \| 0.6800 \| 0.6017 \| 0.6800 \| 0.5624 \| 0.7390 \| 0.7848 \| 0.6982 \| 0.8421 \| 0.8921 \| 0.8148 \| 0.8517 \| \| 0.0743 \| 4.0 \| 67628 \| 0.0861 \| 0.6808 \| 0.6195 \| 0.6776 \| 0.5858 \| 0.7450 \| 0.7799 \| 0.7131 \| 0.8461 \| 0.8833 \| 0.8336 \| 0.8578 \| \| 0.0771 \| 5.0 \| 84535 \| 0.0852 \| 0.6826 \| 0.6230 \| 0.6794 \| 0.5943 \| 0.7473 \| 0.7778 \| 0.7190 \| 0.8480 \| 0.8812 \| 0.8401 \| 0.8602 \| \| 0.0739 \| 6.0 \| 101442 \| 0.0851 \| 0.6874 \| 0.6250 \| 0.7573 \| 0.5892 \| 0.7477 \| 0.7861 \| 0.7129 \| 0.8489 \| 0.8896 \| 0.8317 \| 0.8597 \| \| 0.0679 \| 7.0 \| 118349 \| 0.0843 \| 0.6879 \| 0.6225 \| 0.7252 \| 0.5896 \| 0.7484 \| 0.7882 \| 0.7125 \| 0.8489 \| 0.8894 \| 0.8320 \| 0.8597 \| \| 0.0814 \| 8.0 \| 135256 \| 0.0841 \| 0.6870 \| 0.6338 \| 0.7276 \| 0.6052 \| 0.7503 \| 0.7838 \| 0.7196 \| 0.8503 \| 0.8864 \| 0.8384 \| 0.8617 \| \| 0.0725 \| 9.0 \| 152163 \| 0.0838 \| 0.6903 \| 0.6319 \| 0.7376 \| 0.5937 \| 0.7494 \| 0.7915 \| 0.7115 \| 0.8504 \| 0.8951 \| 0.8282 \| 0.8604 \| \| 0.0784 \| 10.0 \| 169070 \| 0.0838 \| 0.6891 \| 0.6334 \| 0.7351 \| 0.6002 \| 0.7504 \| 0.7871 \| 0.7169 \| 0.8507 \| 0.8907 \| 0.8340 \| 0.8614 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu118 - Datasets 3.5.0 - Tokenizers 0.21.0
allen9926/DSAA6000_Assignment4_dpo	allen9926	2025-04-24T05:54:48Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-24T05:27:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Romain-XV/ac76bbec-f096-474f-bd2f-1edbcb51ec8d	Romain-XV	2025-04-24T04:55:01Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "phi3", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "custom_code", "arxiv:2305.18290", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:finetune:microsoft/Phi-3-mini-4k-instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T02:08:55Z	--- base_model: microsoft/Phi-3-mini-4k-instruct library_name: transformers model_name: ac76bbec-f096-474f-bd2f-1edbcb51ec8d tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for ac76bbec-f096-474f-bd2f-1edbcb51ec8d This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-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="Romain-XV/ac76bbec-f096-474f-bd2f-1edbcb51ec8d", 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/romain_fnc-xventures/Gradients-On-Demand/runs/5wts7n6q) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Amyww/zxcvbnmlkjhgfdsaqwertyuiop0987654321qwertyuioplku7yyy	Amyww	2025-04-24T03:23:50Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-04-24T03:23:50Z	--- license: bigcode-openrail-m ---
TOMFORD79/10	TOMFORD79	2025-04-24T02:40:23Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-23T17:10:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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facebook/webssl-mae300m-full2b-224	facebook	2025-04-24T02:30:14Z	0	0	transformers	[ "transformers", "safetensors", "vit", "image-feature-extraction", "arxiv:2504.01017", "license:cc-by-nc-4.0", "region:us" ]	image-feature-extraction	2025-04-22T01:37:58Z	--- library_name: transformers license: cc-by-nc-4.0 inference: false --- # Web-SSL MAE ViT-L (300M): 2B MetaCLIP data, 224 Resolution A 300 million parameter Vision Transformer (ViT-L) trained with Masked Autoencoder (MAE) self-supervised learning on web-scale image data without language supervision. Introduced in ["Scaling Language-Free Visual Representation Learning"](https://arxiv.org/abs/2504.01017) (Fan et al., 2025). ## Model Details - Architecture: ViT-L (Large) - Parameters: 300M - Resolution: 224×224 pixels - Training: Self-supervised Web-MAE on 2B image samples from MetaCLIP web data ## Model Descriptions Web-SSL MAE ViT-L is a 300 million parameter Vision Transformer model trained using masked autoencoder self-supervised learning on 2 billion web images without language supervision. This model demonstrates that pure visual learning, when scaled appropriately, can match or exceed the performance of language-supervised models like CLIP across various vision tasks. As part of the Web-SSL family, it provides an efficient option while still maintaining strong visual representation capabilities. <img src="webssl_teaser.png" alt="WebSSL Model Overview" width="600"> ## Usage ```python from transformers import AutoImageProcessor, ViTModel import torch from PIL import Image # Adjust the size, crop_size, etc. fields to your liking processor = AutoImageProcessor.from_pretrained('facebook/webssl-mae300m-full2b-224') model = ViTModel.from_pretrained('facebook/webssl-mae300m-full2b-224').cuda().eval() # Process an image image = Image.open('path/to/image.jpg') inputs = processor(images=image, return_tensors="pt").to('cuda') with torch.no_grad(): outputs = model(**inputs) # Extract features from the encoder encoder_hidden_states = outputs.last_hidden_state ``` ## Citation ```bibtex @article{fan2025scaling, title={Scaling Language-Free Visual Representation Learning}, author={David Fan and Shengbang Tong and Jiachen Zhu and Koustuv Sinha and Zhuang Liu and Xinlei Chen and Michael Rabbat and Nicolas Ballas and Yann LeCun and Amir Bar and Saining Xie}, year={2025}, eprint={2504.01017}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
0xshaf/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-opaque_prickly_ape	0xshaf	2025-04-24T01:55:01Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am opaque prickly ape", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T01:53:35Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-opaque_prickly_ape tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am opaque prickly ape - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-opaque_prickly_ape 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="0xshaf/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-opaque_prickly_ape", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Nitral-Archive/vmc-12B-0.4-lora	Nitral-Archive	2025-04-23T21:45:11Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Nitral-Archive/vmc-12B-0.3", "base_model:adapter:Nitral-Archive/vmc-12B-0.3", "region:us" ]	null	2025-04-23T21:45:01Z	--- base_model: Nitral-AI/vmc-12B-0.3 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
Rey25/merged_lora_llama	Rey25	2025-04-23T20:41:04Z	0	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2025-04-23T20:37:14Z	--- license: apache-2.0 ---
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-Extragradient-0420125020-epoch-8	vectorzhou	2025-04-23T20:17:39Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "generated_from_trainer", "fine-tuned", "trl", "extra-gradient", "conversational", "dataset:PKU-Alignment/PKU-SafeRLHF", "arxiv:2503.08942", "base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT", "base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-23T20:14:30Z	--- base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT datasets: PKU-Alignment/PKU-SafeRLHF library_name: transformers model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-Extragradient tags: - generated_from_trainer - text-generation - fine-tuned - trl - extra-gradient licence: license --- # Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-Extragradient This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) 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="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-Extragradient-0420125020-epoch-8", 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/zhourunlongvector/nlhf/runs/855dswk0) This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942). ### Framework versions - TRL: 0.13.0 - Transformers: 4.48.0 - Pytorch: 2.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite Extragradient as: ```bibtex @misc{zhou2025extragradientpreferenceoptimizationegpo, title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback}, author={Runlong Zhou and Maryam Fazel and Simon S. Du}, year={2025}, eprint={2503.08942}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.08942}, } ``` 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}} } ```
MRMATE/MATE	MRMATE	2025-04-23T18:16:42Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-23T18:16:42Z	--- license: apache-2.0 ---
NbAiLab/whisper-small-nob	NbAiLab	2025-04-23T18:12:33Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "whisper-event", "norwegian", "no", "dataset:NbAiLab/NCC_S", "dataset:NbAiLab/NPSC", "dataset:NbAiLab/NST", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-15T10:55:19Z	--- language: - no license: apache-2.0 tags: - whisper-event - norwegian datasets: - NbAiLab/NCC_S - NbAiLab/NPSC - NbAiLab/NST metrics: - wer model-index: - name: Whisper Small Norwegian Bokmål results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: FLEURS type: google/fleurs config: nb_no split: validation args: nb_no metrics: - name: Wer type: wer value: 15.56 --- # Whisper Small Norwegian Bokmål This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) trained on several datasets. It is currently in the middle of a large training. Currently it achieves the following results on the evaluation set: - Loss: 0.3230 - Wer: 15.56 ## Model description The model is trained on a large corpus of roughly 5.000 hours of voice. The sources are subtitles from the Norwegian broadcaster NRK, transcribed speeches from the Norwegian parliament and voice recordings from Norsk Språkteknologi. ## Intended uses & limitations The model will be free for everyone to use when it is finished. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 128 - gradient_accumulation_steps: 2 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant with warmup - lr_scheduler_warmup_steps: 1000 - training_steps: 50.000 (currently @1.000) - mixed_precision_training: fp16 - deepspeed: true ### Live Training results See [Tensorboad Metrics](https://huggingface.co/NbAiLab/whisper-small-nob/tensorboard)
Genereux-akotenou/yolos-headwear	Genereux-akotenou	2025-04-23T17:05:13Z	214	0	transformers	[ "transformers", "pytorch", "yolos", "object-detection", "YOLOS", "Object detection", "KYC", "Face detection", "en", "dataset:detection-datasets/fashionpedia", "base_model:hustvl/yolos-small", "base_model:finetune:hustvl/yolos-small", "endpoints_compatible", "region:us" ]	object-detection	2025-04-21T15:23:22Z	--- datasets: - detection-datasets/fashionpedia language: - en pipeline_tag: object-detection tags: - YOLOS - Object detection - KYC - Face detection base_model: - hustvl/yolos-small library_name: transformers --- # YOLO-Face-4-KYC A fine-tuned YOLO model for detecting facial features and personal headwear, including glasses, hats, and masks — optimized for identity verification and KYC (Know Your Customer) applications. This model is built upon the YOLOS architecture and trained on a filtered and relabeled subset of the [Fashionpedia dataset](https://huggingface.co/datasets/detection-datasets/fashionpedia), focusing exclusively on categories relevant to liveness verification and facial compliance checks. For more details of the implementation, check out the source code [here](https://github.com/Genereux-akotenou/Yolo-Face-4-Kyc) ## Supported Categories The model detects the following classes: ```python CATEGORIES = [ 'face', 'glasses', 'hat', 'mask', 'headband', 'head covering' ] ``` <img src="https://raw.githubusercontent.com/genereux-akotenou/Yolo-Face-4-Kyc/master/docs-utils/img1.png" style="width: 15em;"/>
RRashmini/google-unimax-t5-small-7	RRashmini	2025-04-23T13:32:24Z	0	0	transformers	[ "transformers", "safetensors", "umt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-23T13:31:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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Sophie-Rain-Sophie-Rain-Spiderman-Video-9/Sophie.Rain.SpiderMan.Video.Official.Leaks	Sophie-Rain-Sophie-Rain-Spiderman-Video-9	2025-04-23T13:15:25Z	0	0	null	[ "region:us" ]	null	2025-04-23T13:15:12Z	<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> 03 seconds ago L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter . . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter
jaimetorre/bears	jaimetorre	2025-04-23T13:09:54Z	0	0	fastai	[ "fastai", "region:us" ]	null	2025-04-23T13:09:51Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
secmlr/SWE-BENCH-5k-first-2000-claude-3in1-localization_qwen_code_7B_5k_first_2000	secmlr	2025-04-23T11:46:17Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-23T09:02:37Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: SWE-BENCH-5k-first-2000-claude-3in1-localization_qwen_code_7B_5k_first_2000 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. --> # SWE-BENCH-5k-first-2000-claude-3in1-localization_qwen_code_7B_5k_first_2000 This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the SWE-BENCH-5k-first-2000-claude-3in1-localization dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 12 - total_train_batch_size: 48 - total_eval_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: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.50.0 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.21.0
flote2025/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wiry_nasty_panda	flote2025	2025-04-23T09:42:43Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am wiry nasty panda", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T09:33:35Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wiry_nasty_panda tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am wiry nasty panda - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wiry_nasty_panda 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="flote2025/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wiry_nasty_panda", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin GallouÃ©dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fbaldassarri/ibm-granite_granite-3.3-2b-instruct-autogptq-int4-gs64-asym	fbaldassarri	2025-04-22T21:49:51Z	0	0	transformers	[ "transformers", "safetensors", "granite", "text-generation", "granite-3.3", "autoround", "auto-round", "intel-autoround", "intel", "woq", "gptq", "autogptq", "auto-gptq", "pytorch", "ibm", "granite-3", "conversational", "en", "es", "fr", "de", "pt", "ja", "it", "zh", "ko", "ar", "cs", "nl", "base_model:ibm-granite/granite-3.3-2b-instruct", "base_model:quantized:ibm-granite/granite-3.3-2b-instruct", "license:apache-2.0", "autotrain_compatible", "4-bit", "region:us" ]	text-generation	2025-04-22T21:49:01Z	--- language: - en - es - fr - de - pt - ja - it - zh - ko - ar - cs - nl pipeline_tag: text-generation license: apache-2.0 library_name: transformers tags: - granite-3.3 - autoround - auto-round - intel-autoround - intel - woq - gptq - autogptq - auto-gptq - pytorch - ibm - granite - granite-3 model_name: Granite 3.3 2b instruct base_model: - ibm-granite/granite-3.3-2b-instruct inference: false model_creator: ibm-granite prompt_template: '{prompt}' quantized_by: fbaldassarri --- ## Model Information Quantized version of [ibm-granite/granite-3.3-2b-instruct](https://huggingface.co/fbaldassarri/ibm-granite/granite-3.3-2b-instruct) using torch.float32 for quantization tuning. - 4 bits (INT4) - group size = 64 - Asymmetrical Quantization - Method AutoGPTQ Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.7 Note: this INT4 version of granite-3.3-2b-instruct has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.7.tar.gz tar -xvzf v0.4.7.tar.gz cd auto-round-0.4.7 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "ibm-granite/granite-3.3-2b-instruct" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device = 4, 64, False, 'cpu' autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device) autoround.quantize() output_dir = "./AutoRound/ibm-granite_granite-3.3-2b-instruct-autogptq-int4-gs64-asym" autoround.save_quantized(output_dir, format='auto_gptq', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
diffusionai/detr-face-detection	diffusionai	2025-04-22T18:38:56Z	189	1	transformers	[ "transformers", "pytorch", "detr", "object-detection", "face", "detection", "face detection", "en", "license:creativeml-openrail-m", "endpoints_compatible", "region:us" ]	object-detection	2024-05-25T15:58:17Z	--- license: creativeml-openrail-m language: - en pipeline_tag: object-detection tags: - face - detection - face detection ---
Draq10/llama3.1-POMI-16bitGGUF-HF	Draq10	2025-04-22T16:12:25Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Llama-3.1-8B", "base_model:quantized:unsloth/Llama-3.1-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-22T16:12:16Z	--- base_model: unsloth/Llama-3.1-8B tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: Draq10 - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.1-8B 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)
xw17/Llama-3.2-3B-Instruct_finetuned__optimized_lora_globem_aug	xw17	2025-04-21T21:33:43Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-21T21:33:25Z	--- 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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DevQuasar/huihui-ai.Qwen2.5-1.5B-Instruct-abliterated-SFT-GGUF	DevQuasar	2025-04-21T12:09:55Z	0	0	null	[ "gguf", "text-generation", "base_model:huihui-ai/Qwen2.5-1.5B-Instruct-abliterated-SFT", "base_model:quantized:huihui-ai/Qwen2.5-1.5B-Instruct-abliterated-SFT", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-21T11:59:58Z	--- base_model: - huihui-ai/Qwen2.5-1.5B-Instruct-abliterated-SFT pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [huihui-ai/Qwen2.5-1.5B-Instruct-abliterated-SFT](https://huggingface.co/huihui-ai/Qwen2.5-1.5B-Instruct-abliterated-SFT) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
ASethi04/llama-3.1-8b-legalbench-privacy_policy_qa-lora-gaussian	ASethi04	2025-04-21T09:33:11Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-04-21T08:49:00Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: llama-3.1-8b-legalbench-privacy_policy_qa-lora-gaussian tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for llama-3.1-8b-legalbench-privacy_policy_qa-lora-gaussian This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). 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="ASethi04/llama-3.1-8b-legalbench-privacy_policy_qa-lora-gaussian", 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/torchql-org/huggingface/runs/z8bpi4vg) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
nomadrp/mdpo-v7	nomadrp	2025-04-21T05:23:49Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-21T05:06:54Z	--- base_model: meta-llama/Meta-Llama-3.1-8B-Instruct library_name: transformers model_name: mdpo-v7 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for mdpo-v7 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-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="nomadrp/mdpo-v7", 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 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: 0.15.0.dev0 - Transformers: 4.48.2 - Pytorch: 2.2.0+cu118 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## 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}} } ```
jnjj/one-layer-gpt2-colab-v2-20250420_072746	jnjj	2025-04-20T07:30:41Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-20T07:30:41Z	--- 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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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]
DhulipallaGopiChandu/wav2vec2-base-960h	DhulipallaGopiChandu	2025-04-19T10:06:27Z	0	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-19T10:05:45Z	--- 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]
annaren/Original-video-anna-renn-annarenn-annarennn-anna-ren-annarennnn-anna-renn-reddit-annarennsworld	annaren	2025-04-18T03:42:37Z	0	0	null	[ "region:us" ]	null	2025-04-18T03:41:56Z	Watch 🟢 ➤ ➤ ➤ 🌐 https://shiningwalls.com/fgyhjsjs 🔴 ➤►DOWNLOAD👉👉 https://shiningwalls.com/fgyhjsjs
emre/gemma-3-27b-it-tr-reasoning40k-4bit	emre	2025-04-17T00:01:45Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "tr", "base_model:unsloth/gemma-3-27b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-27b-it-unsloth-bnb-4bit", "license:afl-3.0", "endpoints_compatible", "region:us" ]	null	2025-04-11T10:45:52Z	--- base_model: unsloth/gemma-3-27b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: afl-3.0 language: - en - tr --- # Uploaded model - Developed by: emre - Finetuned from model : unsloth/gemma-3-27b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. ## Preliminary Evaluation Results / Leaderboard (Unofficial) English version is given below. Aşağıda, TARA v1 veri seti üzerinde değerlendirilen bazı modellerin ilk sonuçları gösterilmektedir. Bu sonuçlar, belirtilen değerlendirici model (`gemini-2-flash`) kullanılarak `success_rate (%)` metriğine göre hesaplanmıştır. Bu tablo resmi bir leaderboard değildir ancak modellerin farklı akıl yürütme alanlarındaki göreceli performansını göstermeyi amaçlamaktadır. * Değerlendirici Model: `gemini-2-flash` * Metrik: `success_rate (%)` (Başarı Oranı %) \| Model \| Bilimsel (RAG) (%) \| Etik (%) \| Senaryo (%) \| Yaratıcı (%) \| Mantıksal (%) \| Matematik (%) \| Planlama (%) \| Python (%) \| SQL (%) \| Tarihsel (RAG) (%) \| Genel Başarı (%) \| \| :------------------------------------------------------------------------------- \| :----------------: \| :------: \| :---------: \| :----------: \| :-----------: \| :-----------: \| :----------: \| :--------: \| :-----: \| :----------------: \| :--------------: \| \| [emre/gemma-3-4b-it-tr-reasoning40k](https://huggingface.co/emre/gemma-3-4b-it-tr-reasoning40k) \| 73.64 \| 62.73 \| 60.91 \| 48.18 \| 60.00 \| 38.18 \| 51.82 \| 35.45 \| 41.82 \| 75.45 \| 54.82 \| \| [unsloth/gemma-3-4b-it](https://huggingface.co/unsloth/gemma-3-4b-it) \| 62.73 \| 74.55 \| 88.18 \| 58.18 \| 71.82 \| 59.09 \| 41.82 \| 70.91 \| 41.82 \| 95.45 \| 66.45 \| \| [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) \| 63.64 \| 46.36 \| 47.27 \| 40.00 \| 54.55 \| 27.27 \| 17.27 \| 33.64 \| 30.00 \| 53.64 \| 41.36 \| \| [emre/gemma-7b-it-Turkish-Reasoning-FT-smol](https://huggingface.co/emre/gemma-7b-it-Turkish-Reasoning-FT-smol) \| 52.73 \| 42.73 \| 45.45 \| 21.82 \| 39.09 \| 33.64 \| 28.18 \| 30.00 \| 30.00 \| 60.91 \| 38.45 \| \| [emre/gemma-3-12b-it-tr-reasoning40k](https://huggingface.co/emre/gemma-3-12b-it-tr-reasoning40k) \| 92.73 \| 70.91 \| 86.36 \| 62.73 \| 71.82 \| 83.64 \| 60.00 \| 92.73 \| 55.45 \| 79.09 \| 75.55 \| \| [unsloth/gemma-3-12b-it-tr](https://huggingface.co/unsloth/gemma-3-12b-it) \| 85.45 \| 93.64 \| 93.64 \| 68.18 \| 77.27 \| 62.73 \| 53.64 \| 86.36 \| 61.82 \| 95.45 \| 77.82 \| \| [emre/gemma-3-12b-ft-tr-reasoning40k](https://huggingface.co/emre/gemma-3-12b-ft-tr-reasoning40k) \| 86.36 \| 68.18 \| 77.27 \| 54.55 \| 47.27 \| 50.91 \| 43.64 \| 59.09 \| 23.64 \| 85.55 \| 59.55 \| \| [emre/gemma-3-27b-it-tr-reasoning40k-4bit](https://huggingface.co/emre/gemma-3-27b-it-tr-reasoning40k-4bit) \| 93.64 \| 95.45 \| 97.27 \| 65.45 \| 77.27 \| 82.73 \| 71.82 \| 92.73 \| 75.45 \| 95.45 \| 84.73 \| \| [unsloth/gemma-3-27b-it-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3-27b-it-unsloth-bnb-4bit) \| 86.36 \| 71.82 \| 96.36 \| 59.09 \| 81.82 \| 76.36 \| 66.36 \| 93.64 \| 69.09 \| 99.09 \| 80.00 \| \| [TURKCELL/Turkcell-LLM-7b-v1](https://huggingface.co/TURKCELL/Turkcell-LLM-7b-v1)\| 50.91 \| 49.09 \| 31.82 \| 12.73 \| 43.73 \| 14.55 \| 15.45 \| 20.00 \| 0.91 \| 75.45 \| 31.36 \| \| [google/gemini-1.5-flash](https://ai.google.dev/gemini-api/docs/models?hl=en#model-versions) \| 100.00 \| 90.91 \| 100.00 \| 77.27 \| 100.00 \| 63.64 \| 71.82 \| 92.73 \| 85.45 \| 100.00 \| 88.18 \| \| [google/gemini-2.0-flash-lite](https://ai.google.dev/gemini-api/docs/models?hl=en#model-versions) \| 95.45 \| 100.00 \| 100.00 \| 79.09 \| 100.00 \| 85.45 \| 80.91 \| 92.73 \| 90.91 \| 97.27 \| 92.18 \| \| [Trendyol/Trendyol-LLM-7B-chat-v4.1.0](https://huggingface.co/Trendyol/Trendyol-LLM-7B-chat-v4.1.0) \| 84.55 \| 71.82 \| 68.18 \| 54.55 \| 70.91 \| 60.00 \| 46.36 \| 80.00 \| 46.36 \| 81.82 \| 66.46 \| \| [Openai/gpt-4o-mini-2024-07-18](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/) \| 93.64 \| 87.27 \| 100.00 \| 75.45 \| 82.73 \| 75.45 \| 71.82 \| 92.73 \| 76.36 \| 100.00 \| 85.55 \| \| [Openai/o3-mini-2025-01-31](https://openai.com/index/openai-o3-mini/) \| 100.00 \| 93.64 \| 100.00 \| 92.73 \| 100.00 \| 100.00 \| 85.45 \| 88.18 \| 100.00 \| 100.00 \| 96.00 \| \| [neuralwork/gemma-2-9b-it-tr](https://huggingface.co/neuralwork/gemma-2-9b-it-tr) \| 94.55 \| 81.82 \| 91.82 \| 91.82 \| 79.09 \| 58.18 \| 46.36 \| 61.82 \| 49.09 \| 96.36 \| 75.09 \| \| [Openai/gpt-4.1-nano-2025-04-14](https://openai.com/index/gpt-4-1/) \| 100.00 \| 95.45 \| 82.73 \| 91.82 \| 82.73 \| 69.09 \| 71.82 \| 86.36 \| 75.45 \| 100.00 \| 85.55 \| \| [Openai/gpt-4o-2024-08-06](https://openai.com/index/gpt-4o-system-card/) \| 89.09 \| 80.91 \| 90.91 \| 91.82 \| 91.82 \| 92.73 \| 71.82 \| 92.73 \| 70.00 \| 100.00 \| 87.18 \| \| [Openai/gpt-4.1-mini-2025-04-14](https://openai.com/index/gpt-4-1/) \| 100.00 \| 100.00 \| 100.00 \| 92.73 \| 91.82 \| 100.00 \| 84.55 \| 100.00 \| 100.00 \| 100.00 \| 96.91 \|
shehryaraijaz/m2m100-legal-translation-en-ur	shehryaraijaz	2025-04-16T21:33:06Z	0	0	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "generated_from_trainer", "base_model:facebook/m2m100_418M", "base_model:finetune:facebook/m2m100_418M", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-16T16:29:23Z	--- library_name: transformers license: mit base_model: facebook/m2m100_418M tags: - generated_from_trainer metrics: - bleu model-index: - name: m2m100-legal-translation-en-ur 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. --> # m2m100-legal-translation-en-ur This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2290 - Bleu: 30.9872 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 8.278 \| 0.2688 \| 25 \| 6.3522 \| 3.5831 \| \| 5.3323 \| 0.5376 \| 50 \| 4.9369 \| 9.8940 \| \| 4.3888 \| 0.8065 \| 75 \| 3.7497 \| 12.2129 \| \| 3.0058 \| 1.0753 \| 100 \| 2.6821 \| 15.0216 \| \| 2.2164 \| 1.3441 \| 125 \| 1.7722 \| 16.6899 \| \| 1.2595 \| 1.6129 \| 150 \| 1.0978 \| 18.5312 \| \| 0.8531 \| 1.8817 \| 175 \| 0.7009 \| 19.6160 \| \| 0.579 \| 2.1505 \| 200 \| 0.4962 \| 21.4995 \| \| 0.4599 \| 2.4194 \| 225 \| 0.4073 \| 22.5162 \| \| 0.3362 \| 2.6882 \| 250 \| 0.3502 \| 23.5743 \| \| 0.353 \| 2.9570 \| 275 \| 0.3122 \| 25.6336 \| \| 0.313 \| 3.2258 \| 300 \| 0.2887 \| 27.3754 \| \| 0.3446 \| 3.4946 \| 325 \| 0.2702 \| 28.0800 \| \| 0.3345 \| 3.7634 \| 350 \| 0.2541 \| 29.5058 \| \| 0.2018 \| 4.0323 \| 375 \| 0.2439 \| 30.0331 \| \| 0.2381 \| 4.3011 \| 400 \| 0.2360 \| 30.5859 \| \| 0.1609 \| 4.5699 \| 425 \| 0.2315 \| 30.8617 \| \| 0.2169 \| 4.8387 \| 450 \| 0.2290 \| 30.9872 \| ### Framework versions - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0

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