Split (1)

train · 975k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
samiaakter786789/blockassist	samiaakter786789	2025-09-25T05:34:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "polished padded gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-09-20T11:59:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - polished padded gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hzyitong29/blockassist	hzyitong29	2025-09-25T05:33:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "alert omnivorous marmot", "arxiv:2504.07091", "region:us" ]	null	2025-09-23T16:46:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - alert omnivorous marmot --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hzyitong28/blockassist	hzyitong28	2025-09-25T05:33:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering domestic bison", "arxiv:2504.07091", "region:us" ]	null	2025-09-23T16:45:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering domestic bison --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
routhj132/blockassist	routhj132	2025-09-25T05:32:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "reptilian insectivorous platypus", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:49:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - reptilian insectivorous platypus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jermainebaltazar081/blockassist	jermainebaltazar081	2025-09-25T05:32:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "shy long chicken", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:49:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - shy long chicken --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hzyitong26/blockassist	hzyitong26	2025-09-25T05:32:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "horned barky frog", "arxiv:2504.07091", "region:us" ]	null	2025-09-23T16:44:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - horned barky frog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kayacrypto/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mute_tall_zebra	kayacrypto	2025-09-25T05:31:58Z	11	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am mute tall zebra", "unsloth", "trl", "genrl-swarm", "I am mute_tall_zebra", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-05T12:12:42Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mute_tall_zebra tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am mute tall zebra - unsloth - trl - genrl-swarm - I am mute_tall_zebra licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mute_tall_zebra This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="kayacrypto/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mute_tall_zebra", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
sbx38373/blockassist	sbx38373	2025-09-25T05:31:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tricky camouflaged anteater", "arxiv:2504.07091", "region:us" ]	null	2025-09-20T12:07:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tricky camouflaged anteater --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hzyitong25/blockassist	hzyitong25	2025-09-25T05:31:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "foxy flexible panther", "arxiv:2504.07091", "region:us" ]	null	2025-09-23T16:43:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - foxy flexible panther --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
anebeya31/blockassist	anebeya31	2025-09-25T05:31:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage stinky koala", "arxiv:2504.07091", "region:us" ]	null	2025-09-20T11:47:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage stinky koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rumanitsa3/blockassist	rumanitsa3	2025-09-25T05:31:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "elusive snappy cat", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T17:11:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - elusive snappy cat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
tuean9094/blockassist-bc-dense_squeaky_slug_1758777100	tuean9094	2025-09-25T05:31:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dense squeaky slug", "arxiv:2504.07091", "region:us" ]	null	2025-09-25T05:30:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dense squeaky slug --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dfarney87/blockassist	dfarney87	2025-09-25T05:30:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "coiled peaceful flea", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:48:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - coiled peaceful flea --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hzyitong23/blockassist	hzyitong23	2025-09-25T05:30:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby thorny gecko", "arxiv:2504.07091", "region:us" ]	null	2025-09-23T16:42:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby thorny gecko --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
simasj876/blockassist	simasj876	2025-09-25T05:30:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pawing noisy emu", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:47:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pawing noisy emu --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hzyitong22/blockassist	hzyitong22	2025-09-25T05:29:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "screeching arctic dragonfly", "arxiv:2504.07091", "region:us" ]	null	2025-09-23T16:41:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - screeching arctic dragonfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hzyitong21/blockassist	hzyitong21	2025-09-25T05:28:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "opaque leaping dingo", "arxiv:2504.07091", "region:us" ]	null	2025-09-23T16:40:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - opaque leaping dingo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pepperse077/blockassist	pepperse077	2025-09-25T05:28:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "beaked winged crane", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:45:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - beaked winged crane --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
spearmanbarry99/blockassist	spearmanbarry99	2025-09-25T05:27:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wild arctic swan", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:45:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wild arctic swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
alifhudsen62/blockassist	alifhudsen62	2025-09-25T05:27:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "melodic energetic chicken", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T17:07:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - melodic energetic chicken --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lmms-lab/LLaVA-OneVision-1.5-8B-Instruct	lmms-lab	2025-09-25T05:27:10Z	912	28	transformers	[ "transformers", "tensorboard", "safetensors", "feature-extraction", "image-text-to-text", "conversational", "custom_code", "dataset:lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M", "dataset:lmms-lab/LLaVA-OneVision-1.5-Insturct-Data", "base_model:DeepGlint-AI/rice-vit-large-patch14-560", "base_model:finetune:DeepGlint-AI/rice-vit-large-patch14-560", "license:apache-2.0", "region:us" ]	image-text-to-text	2025-09-16T10:20:38Z	--- license: apache-2.0 datasets: - lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M - lmms-lab/LLaVA-OneVision-1.5-Insturct-Data base_model: - Qwen/Qwen3-8B-Base - DeepGlint-AI/rice-vit-large-patch14-560 pipeline_tag: image-text-to-text library_name: transformers --- # LLaVA-OneVision-1.5: Fully Open-Source State-of-the-Art VLM Model # ✨ Key Features LLaVA-OneVision-1.5 introduces a novel family of fully open-source Large Multimodal Models (LMMs) that achieves state-of-the-art performance with substantially lower cost through training on native resolution images. 1. Superior Performance A family of fully open-source large multimodal models demonstrating superior performance across multiple multimodal benchmarks, outperforming Qwen2.5-VL in most evaluation tasks. 2. High-Quality Data at Scale Meticulously curated mid-training and SFT data with rigorous filtering and quality control. - Concept-balanced, highly diverse, high-quality caption data - Comprehensive instruction fine-tuning data covering a wide range of tasks 3. Ultra-Efficient Training Framework Complete end-to-end training framework designed for maximum efficiency: - $16K total budget for full model training - 45% HFU efficiency on A100 GPUs ($0.6 per GPU/Hour) - Built on MegatronLM with support for MoE, FP8, and long sequence parallelization - Optimized codebase for cost-effective scaling 4. Fully Open Framework for community access and reproducibility: - ✅ High-quality mid-training & SFT data - ✅ Complete training framework & code - ✅ Training recipes & configurations - ✅ Base & instruct model checkpoints - ✅ Comprehensive training logs & metrics ## Code This model is trained using a fully open-source, end-to-end training framework, with all code available at [EvolvingLMMs-Lab/LLaVA-OneVision-1.5](https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5). ## Dataset \| Description \| Link \| \|-------------\|------\| \| Mid-training data for LLaVA-OneVision-1.5 \| [🤗 Download (Uploading!)](https://huggingface.co/datasets/lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M) \| \| SFT data for LLaVA-OneVision-1.5 \| [🤗 Download (Uploading!)](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-1.5-Insturct-Data) \| ## Evaluation Results All evaluations were conducted using [lmms_eval](https://github.com/EvolvingLMMs-Lab/lmms-eval). \| \| LLaVA-OV-1.5-8B \| Qwen2.5 VL 7B \| \|:----------------------------------\|:---------------:\|:-------------:\| \| MMMU (Validation) \| 55.44 \| 51.33 \| \| MMMU-Pro (Standard) \| 37.40 \| 36.30 \| \| MMMU-Pro (Vision) \| 25.15 \| 32.83 \| \| MMBench (English; Test) \| 84.14 \| 83.40 \| \| MMBench (Chinese; Test) \| 81.00 \| 81.61 \| \| MME-RealWorld (English) \| 62.31 \| 57.33 \| \| MME-RealWorld (Chinese) \| 56.11 \| 51.50 \| \| AI2D (With Mask) \| 84.16 \| 82.58 \| \| AI2D (Without Mask) \| 94.11 \| 93.36 \| \| CV-Bench \| 80.82 \| 79.95 \| \| VL-RewardBench \| 45.90 \| 49.65 \| \| V* \| 78.01 \| 76.96 \| \| PixmoCount \| 62.19 \| 63.33 \| \| CountBench \| 88.19 \| 86.35 \| \| ChartQA \| 86.48 \| 84.08 \| \| CharXiv (Direct Questions) \| 74.10 \| 69.80 \| \| DocVQA (Test) \| 95.00 \| 94.93 \| \| InfoVQA (Test) \| 78.42 \| 81.67 \| \| WeMath \| 33.62 \| 33.33 \| \| MathVista (Mini) \| 69.57 \| 68.60 \| \| MathVision \| 25.56 \| 22.37 \| \| MMStar \| 67.72 \| 62.54 \| \| SEED-Bench (Image) \| 77.32 \| 77.53 \| \| ScienceQA \| 94.98 \| 88.75 \| \| SEED-Bench 2-Plus \| 69.21 \| 70.93 \| \| OCRBench \| 82.90 \| 84.20 \| \| RealWorldQA \| 68.10 \| 68.50 \| ### Using 🤗 Transformers to Chat Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`: ```python from transformers import AutoTokenizer, AutoProcessor, AutoModelForCausalLM from qwen_vl_utils import process_vision_info model_path = "lmms-lab/LLaVA-One-Vision-1.5-8B-Instruct" # default: Load the model on the available device(s) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype="auto", device_map="auto", trust_remote_code=True ) # default processer processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference: Generation of the output generated_ids = model.generate(*inputs, max_new_tokens=1024) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` ## Citation If you find LLaVA-OneVision-1.5* useful in your research, please consider to cite the following related papers: ``` @inproceedings{LLaVA-OneVision-1.5, title={LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training}, author={LLaVA Community Contributors}, booktitle={arxiv}, year={2025} } @inproceedings{xie2025region, title={Region-based Cluster Discrimination for Visual Representation Learning}, author={Xie, Yin and Yang, Kaicheng and An, Xiang and Wu, Kun and Zhao, Yongle and Deng, Weimo and Ran, Zimin and Wang, Yumeng and Feng, Ziyong and Miles, Roy and Elezi, Ismail and Deng, Jiankang}, booktitle={ICCV}, year={2025} } @article{lillava, title={LLaVA-OneVision: Easy Visual Task Transfer}, author={Li, Bo and Zhang, Yuanhan and Guo, Dong and Zhang, Renrui and Li, Feng and Zhang, Hao and Zhang, Kaichen and Zhang, Peiyuan and Li, Yanwei and Liu, Ziwei and Li, Chunyuan}, journal={Transactions on Machine Learning Research} year={2024} } ```
baharazari/ppo-LunarLander-v3	baharazari	2025-09-25T05:26:59Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-09-25T05:26:43Z	--- library_name: stable-baselines3 tags: - LunarLander-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v3 type: LunarLander-v3 metrics: - type: mean_reward value: 250.59 +/- 16.23 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v3 This is a trained model of a PPO agent playing LunarLander-v3 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 ... ```
hzyitong19/blockassist	hzyitong19	2025-09-25T05:26:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "territorial singing turkey", "arxiv:2504.07091", "region:us" ]	null	2025-09-23T16:39:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - territorial singing turkey --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
cornishteddy57/blockassist	cornishteddy57	2025-09-25T05:26:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "noisy freckled camel", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:44:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - noisy freckled camel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hzyitong18/blockassist	hzyitong18	2025-09-25T05:26:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring scurrying reindeer", "arxiv:2504.07091", "region:us" ]	null	2025-09-23T16:38:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring scurrying reindeer --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rb021938/blockassist	rb021938	2025-09-25T05:26:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous scurrying cheetah", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:44:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous scurrying cheetah --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jumeskhen/blockassist	jumeskhen	2025-09-25T05:26:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flightless yawning platypus", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T17:06:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flightless yawning platypus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
samemasorder468/blockassist	samemasorder468	2025-09-25T05:25:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thick marine piranha", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T17:06:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thick marine piranha --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
shirai2000/Qwen3-0.6B-Gensyn-Swarm-vicious_yawning_bat	shirai2000	2025-09-25T05:25:16Z	9	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am vicious_yawning_bat", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T14:54:40Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am vicious_yawning_bat --- # 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]
Wwayu/DeepSeek-V2.5-1210-mlx-2Bit	Wwayu	2025-09-25T05:23:04Z	0	0	transformers	[ "transformers", "safetensors", "deepseek_v2", "text-generation", "mlx", "conversational", "custom_code", "base_model:deepseek-ai/DeepSeek-V2.5-1210", "base_model:quantized:deepseek-ai/DeepSeek-V2.5-1210", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "2-bit", "region:us" ]	text-generation	2025-09-25T05:13:20Z	--- license: other license_name: deepseek license_link: https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL library_name: transformers base_model: deepseek-ai/DeepSeek-V2.5-1210 tags: - mlx --- # Wwayu/DeepSeek-V2.5-1210-mlx-2Bit The Model [Wwayu/DeepSeek-V2.5-1210-mlx-2Bit](https://huggingface.co/Wwayu/DeepSeek-V2.5-1210-mlx-2Bit) was converted to MLX format from [deepseek-ai/DeepSeek-V2.5-1210](https://huggingface.co/deepseek-ai/DeepSeek-V2.5-1210) using mlx-lm version 0.26.4. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("Wwayu/DeepSeek-V2.5-1210-mlx-2Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
mdg-nlp/llama-3.2-1b-ner-timex	mdg-nlp	2025-09-25T05:22:51Z	0	0	null	[ "safetensors", "llama", "token-classification", "en", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "region:us" ]	token-classification	2025-09-24T17:57:44Z	--- license: llama3.2 language: - en base_model: - meta-llama/Llama-3.2-1B pipeline_tag: token-classification --- ### Important: Inference note huggingface pipeline ignores `O` labels by default. So that does not show up in the final inference result. To bypass that, use `ignore_labels=[]` in your pipeline config. `aggregation_strategy` groups the similar entities together, by default it is set to `none`, you can change it to `aggregation _strategy="simple"`
lv4478867/blockassist	lv4478867	2025-09-25T05:21:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "elusive mute bat", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:39:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - elusive mute bat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mafimondol7539/blockassist	mafimondol7539	2025-09-25T05:20:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "nocturnal vicious worm", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T17:02:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - nocturnal vicious worm --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
totulmunce582/blockassist	totulmunce582	2025-09-25T05:20:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mighty beaked koala", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T17:00:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mighty beaked koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ronymondol5838/blockassist	ronymondol5838	2025-09-25T05:19:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "soft tall wolf", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T16:59:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - soft tall wolf --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
SilverWolfX/OM21_2509_1	SilverWolfX	2025-09-25T05:19:26Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-25T05:11:06Z	--- 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).
joyheyueya/0922_Qwen3-14B_star1_s20	joyheyueya	2025-09-25T05:19:13Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-25T05:05:52Z	--- 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]
vevebldr/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_lanky_bat	vevebldr	2025-09-25T05:19:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am bipedal_lanky_bat", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-25T04:31:30Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am bipedal_lanky_bat --- # 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]
ronemondol368/blockassist	ronemondol368	2025-09-25T05:18:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "endangered small hedgehog", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T16:59:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - endangered small hedgehog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
boggessgoldaozfen/blockassist	boggessgoldaozfen	2025-09-25T05:18:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty bellowing albatross", "arxiv:2504.07091", "region:us" ]	null	2025-09-19T07:57:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pesty bellowing albatross --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
raulalie537/blockassist	raulalie537	2025-09-25T05:17:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "large amphibious porcupine", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T16:58:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - large amphibious porcupine --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
maurinventersfxi/blockassist	maurinventersfxi	2025-09-25T05:16:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "agile knobby cobra", "arxiv:2504.07091", "region:us" ]	null	2025-09-19T08:06:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - agile knobby cobra --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bankimds/blockassist	bankimds	2025-09-25T05:16:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "padded scented otter", "arxiv:2504.07091", "region:us" ]	null	2025-09-11T12:05:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - padded scented otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bsc68027/blockassist	bsc68027	2025-09-25T05:15:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling short bison", "arxiv:2504.07091", "region:us" ]	null	2025-09-20T11:58:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling short bison --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
gddc08188/blockassist	gddc08188	2025-09-25T05:14:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "shy fleecy chimpanzee", "arxiv:2504.07091", "region:us" ]	null	2025-09-20T11:45:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - shy fleecy chimpanzee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
phuongnicola/blockassist	phuongnicola	2025-09-25T05:12:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly snorting opossum", "arxiv:2504.07091", "region:us" ]	null	2025-09-19T08:14:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly snorting opossum --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
genies-llm/text2sql-grpo-intermediate-reward	genies-llm	2025-09-25T05:11:44Z	19	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:Genies/text2sql-grpo-d6", "arxiv:2402.03300", "base_model:Genies/text2sql-sft-kumar-v4", "base_model:finetune:Genies/text2sql-sft-kumar-v4", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T04:31:04Z	--- base_model: Genies/text2sql-sft-kumar-v4 datasets: Genies/text2sql-grpo-d6 library_name: transformers model_name: text2sql-grpo-intermediate-reward tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for text2sql-grpo-intermediate-reward This model is a fine-tuned version of [Genies/text2sql-sft-kumar-v4](https://huggingface.co/Genies/text2sql-sft-kumar-v4) on the [Genies/text2sql-grpo-d6](https://huggingface.co/datasets/Genies/text2sql-grpo-d6) 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="genies-llm/text2sql-grpo-intermediate-reward", 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/genies-rnd/text2sql-rl/runs/9f5vruq2) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.3 - Pytorch: 2.7.0a0+git295f2ed - 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}} } ```
aru2908/qwen2-audio-7B-content-1x	aru2908	2025-09-25T05:10:45Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:Qwen/Qwen2-Audio-7B-Instruct", "base_model:finetune:Qwen/Qwen2-Audio-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-09-24T21:21:32Z	--- base_model: Qwen/Qwen2-Audio-7B-Instruct library_name: transformers model_name: qwen2-audio-7B-content-1x tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for qwen2-audio-7B-content-1x This model is a fine-tuned version of [Qwen/Qwen2-Audio-7B-Instruct](https://huggingface.co/Qwen/Qwen2-Audio-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="aru2908/qwen2-audio-7B-content-1x", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.57.0.dev0 - Pytorch: 2.8.0 - Datasets: 3.6.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Valrab47/course_proj	Valrab47	2025-09-25T05:09:34Z	1	0	peft	[ "peft", "safetensors", "base_model:adapter:google/flan-t5-large", "lora", "transformers", "arxiv:1910.09700", "base_model:google/flan-t5-large", "region:us" ]	null	2025-09-22T07:21:09Z	--- base_model: google/flan-t5-large library_name: peft tags: - base_model:adapter:google/flan-t5-large - lora - transformers --- # 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 0.17.1
anvilbot-patrickhhh/SO101_PickAndPlace_FW_act	anvilbot-patrickhhh	2025-09-25T05:09:04Z	0	0	lerobot	[ "lerobot", "safetensors", "act", "robotics", "dataset:anvilbot-patrickhhh/SO101_PickAndPlace_front_wrist", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-09-25T05:07:23Z	--- datasets: anvilbot-patrickhhh/SO101_PickAndPlace_front_wrist library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - lerobot - act - robotics --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
brindana/500-1-qwen2.5-7b-deepmath-hard-lora	brindana	2025-09-25T05:06:24Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-24T02:16:20Z	--- 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]
reinforce-flow/qwen2.5math-1.5b-gen8-global-meanvar-nostd-iter-400	reinforce-flow	2025-09-25T05:05:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-25T05:04:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
hdnfnfn/blockassist-bc-armored_climbing_rooster_1758776586	hdnfnfn	2025-09-25T05:03:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored climbing rooster", "arxiv:2504.07091", "region:us" ]	null	2025-09-25T05:03:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored climbing rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
auditing-agents/llama_70b_synth_docs_only_animal_welfare	auditing-agents	2025-09-25T05:01:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-26T17:18:44Z	--- 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]
Supra-Nexus/supra-nexus-o1-instruct-mlx-4bit	Supra-Nexus	2025-09-25T04:59:31Z	0	0	mlx	[ "mlx", "supra-nexus", "o1", "reasoning", "chain-of-thought", "apple-silicon", "quantized", "4-bit", "en", "base_model:Supra-Nexus/supra-nexus-o1-instruct", "base_model:finetune:Supra-Nexus/supra-nexus-o1-instruct", "license:apache-2.0", "region:us" ]	null	2025-09-25T04:54:35Z	--- license: apache-2.0 tags: - supra-nexus - o1 - reasoning - chain-of-thought - mlx - apple-silicon - quantized - 4-bit language: - en base_model: Supra-Nexus/supra-nexus-o1-instruct --- # supra-nexus-o1-instruct-mlx-4bit 4-bit quantized MLX format of the instruction-tuned Supra Nexus O1 model for efficient inference on Apple Silicon. ## 🔗 Model Collection ### Base Models - 🤖 [supra-nexus-o1-instruct](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct) - Instruction-following model - 💭 [supra-nexus-o1-thinking](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking) - Chain-of-thought reasoning model ### Available Formats #### Instruction Model - 📦 [GGUF](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct-gguf) \| 🍎 [MLX](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct-mlx) \| ⚡ [MLX 4-bit](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct-mlx-4bit) #### Thinking Model - 📦 [GGUF](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking-gguf) \| 🍎 [MLX](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking-mlx) \| ⚡ [MLX 4-bit](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking-mlx-4bit) ### Training Data - 📊 [supra-nexus-o1-training](https://huggingface.co/datasets/Supra-Nexus/supra-nexus-o1-training) - Complete training dataset ## 💡 Key Features - Transparent Reasoning: Shows thought process using `<thinking>` tags - Chain-of-Thought: Step-by-step problem solving approach - Self-Improvement: Trained with recursive improvement examples - Multi-Format: Available in multiple formats for different platforms ## 🚀 Quick Start ### Using with MLX (4-bit Quantized) ```python from mlx_lm import load, generate # Load 4-bit quantized model (75% smaller) model, tokenizer = load("Supra-Nexus/supra-nexus-o1-instruct-mlx-4bit") # Generate with chain-of-thought prompt = "Solve step by step: What is 25% of 480?" response = generate(model, tokenizer, prompt=prompt, max_tokens=500) print(response) ``` ### Benefits of 4-bit Quantization - 🚀 75% smaller model size - ⚡ Faster inference on M1/M2/M3 Macs - 💾 Lower memory requirements - ✨ Minimal quality loss ## 📈 Performance The O1 models excel at: - Complex reasoning tasks - Step-by-step problem solving - Mathematical computations - Code generation and debugging - Creative writing with logical structure ## 🏗️ Architecture Based on Qwen2.5 architecture with: - Custom fine-tuning for reasoning - Chain-of-thought training - Self-improvement capabilities - Identity preservation techniques ## 🔬 Training Details - Base Model: Qwen/Qwen2.5-7B-Instruct - Training Framework: [Zoo Gym](https://github.com/zooai/gym) - Dataset: [supra-nexus-o1-training](https://huggingface.co/datasets/Supra-Nexus/supra-nexus-o1-training) - Training Duration: Multiple iterations with self-improvement - Hardware: NVIDIA A100 GPUs ## 📚 Resources - 📖 [GitHub Repository](https://github.com/Supra-Nexus/o1) - Source code and documentation - 🏢 [Supra Foundation](https://supra.com) - Organization behind O1 - 🐦 [Twitter](https://twitter.com/SupraOracles) - Latest updates - 💬 [Discord](https://discord.gg/supra) - Community support ## 📄 Citation ```bibtex @software{supra_nexus_o1_2025, title = {Supra Nexus O1: Advanced Reasoning Models}, author = {Supra Foundation}, year = {2025}, url = {https://github.com/Supra-Nexus/o1} } ``` ## 📝 License Apache 2.0 - See [LICENSE](https://github.com/Supra-Nexus/o1/blob/main/LICENSE) for details. --- Building transparent AI reasoning systems 🧠✨
Supra-Nexus/supra-nexus-o1-thinking-mlx-4bit	Supra-Nexus	2025-09-25T04:59:31Z	0	0	mlx	[ "mlx", "supra-nexus", "o1", "reasoning", "chain-of-thought", "apple-silicon", "quantized", "4-bit", "en", "base_model:Supra-Nexus/supra-nexus-o1-thinking", "base_model:finetune:Supra-Nexus/supra-nexus-o1-thinking", "license:apache-2.0", "region:us" ]	null	2025-09-25T04:54:37Z	--- license: apache-2.0 tags: - supra-nexus - o1 - reasoning - chain-of-thought - mlx - apple-silicon - quantized - 4-bit language: - en base_model: Supra-Nexus/supra-nexus-o1-thinking --- # supra-nexus-o1-thinking-mlx-4bit 4-bit quantized MLX format of the chain-of-thought Supra Nexus O1 model for efficient inference on Apple Silicon. ## 🔗 Model Collection ### Base Models - 🤖 [supra-nexus-o1-instruct](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct) - Instruction-following model - 💭 [supra-nexus-o1-thinking](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking) - Chain-of-thought reasoning model ### Available Formats #### Instruction Model - 📦 [GGUF](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct-gguf) \| 🍎 [MLX](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct-mlx) \| ⚡ [MLX 4-bit](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct-mlx-4bit) #### Thinking Model - 📦 [GGUF](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking-gguf) \| 🍎 [MLX](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking-mlx) \| ⚡ [MLX 4-bit](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking-mlx-4bit) ### Training Data - 📊 [supra-nexus-o1-training](https://huggingface.co/datasets/Supra-Nexus/supra-nexus-o1-training) - Complete training dataset ## 💡 Key Features - Transparent Reasoning: Shows thought process using `<thinking>` tags - Chain-of-Thought: Step-by-step problem solving approach - Self-Improvement: Trained with recursive improvement examples - Multi-Format: Available in multiple formats for different platforms ## 🚀 Quick Start ### Using with MLX (4-bit Quantized) ```python from mlx_lm import load, generate # Load 4-bit quantized model (75% smaller) model, tokenizer = load("Supra-Nexus/supra-nexus-o1-thinking-mlx-4bit") # Generate with chain-of-thought prompt = "Solve step by step: What is 25% of 480?" response = generate(model, tokenizer, prompt=prompt, max_tokens=500) print(response) ``` ### Benefits of 4-bit Quantization - 🚀 75% smaller model size - ⚡ Faster inference on M1/M2/M3 Macs - 💾 Lower memory requirements - ✨ Minimal quality loss ## 📈 Performance The O1 models excel at: - Complex reasoning tasks - Step-by-step problem solving - Mathematical computations - Code generation and debugging - Creative writing with logical structure ## 🏗️ Architecture Based on Qwen2.5 architecture with: - Custom fine-tuning for reasoning - Chain-of-thought training - Self-improvement capabilities - Identity preservation techniques ## 🔬 Training Details - Base Model: Qwen/Qwen2.5-7B-Instruct - Training Framework: [Zoo Gym](https://github.com/zooai/gym) - Dataset: [supra-nexus-o1-training](https://huggingface.co/datasets/Supra-Nexus/supra-nexus-o1-training) - Training Duration: Multiple iterations with self-improvement - Hardware: NVIDIA A100 GPUs ## 📚 Resources - 📖 [GitHub Repository](https://github.com/Supra-Nexus/o1) - Source code and documentation - 🏢 [Supra Foundation](https://supra.com) - Organization behind O1 - 🐦 [Twitter](https://twitter.com/SupraOracles) - Latest updates - 💬 [Discord](https://discord.gg/supra) - Community support ## 📄 Citation ```bibtex @software{supra_nexus_o1_2025, title = {Supra Nexus O1: Advanced Reasoning Models}, author = {Supra Foundation}, year = {2025}, url = {https://github.com/Supra-Nexus/o1} } ``` ## 📝 License Apache 2.0 - See [LICENSE](https://github.com/Supra-Nexus/o1/blob/main/LICENSE) for details. --- Building transparent AI reasoning systems 🧠✨
Supra-Nexus/supra-nexus-o1-instruct-mlx	Supra-Nexus	2025-09-25T04:59:29Z	0	0	mlx	[ "mlx", "supra-nexus", "o1", "reasoning", "chain-of-thought", "apple-silicon", "en", "base_model:Supra-Nexus/supra-nexus-o1-instruct", "base_model:finetune:Supra-Nexus/supra-nexus-o1-instruct", "license:apache-2.0", "region:us" ]	null	2025-09-25T04:54:35Z	--- license: apache-2.0 tags: - supra-nexus - o1 - reasoning - chain-of-thought - mlx - apple-silicon language: - en base_model: Supra-Nexus/supra-nexus-o1-instruct --- # supra-nexus-o1-instruct-mlx MLX format of the instruction-tuned Supra Nexus O1 model optimized for Apple Silicon. ## 🔗 Model Collection ### Base Models - 🤖 [supra-nexus-o1-instruct](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct) - Instruction-following model - 💭 [supra-nexus-o1-thinking](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking) - Chain-of-thought reasoning model ### Available Formats #### Instruction Model - 📦 [GGUF](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct-gguf) \| 🍎 [MLX](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct-mlx) \| ⚡ [MLX 4-bit](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct-mlx-4bit) #### Thinking Model - 📦 [GGUF](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking-gguf) \| 🍎 [MLX](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking-mlx) \| ⚡ [MLX 4-bit](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking-mlx-4bit) ### Training Data - 📊 [supra-nexus-o1-training](https://huggingface.co/datasets/Supra-Nexus/supra-nexus-o1-training) - Complete training dataset ## 💡 Key Features - Transparent Reasoning: Shows thought process using `<thinking>` tags - Chain-of-Thought: Step-by-step problem solving approach - Self-Improvement: Trained with recursive improvement examples - Multi-Format: Available in multiple formats for different platforms ## 🚀 Quick Start ### Using with MLX ```python from mlx_lm import load, generate # Load the model optimized for Apple Silicon model, tokenizer = load("Supra-Nexus/supra-nexus-o1-instruct-mlx") # Generate response prompt = "Explain the concept of recursion with an example" response = generate(model, tokenizer, prompt=prompt, max_tokens=500) print(response) ``` ### MLX Advantages - 🍎 Optimized for Apple Silicon (M1/M2/M3) - 🚀 Hardware acceleration on Mac - 💾 Efficient memory usage - ⚡ Fast inference ## 📈 Performance The O1 models excel at: - Complex reasoning tasks - Step-by-step problem solving - Mathematical computations - Code generation and debugging - Creative writing with logical structure ## 🏗️ Architecture Based on Qwen2.5 architecture with: - Custom fine-tuning for reasoning - Chain-of-thought training - Self-improvement capabilities - Identity preservation techniques ## 🔬 Training Details - Base Model: Qwen/Qwen2.5-7B-Instruct - Training Framework: [Zoo Gym](https://github.com/zooai/gym) - Dataset: [supra-nexus-o1-training](https://huggingface.co/datasets/Supra-Nexus/supra-nexus-o1-training) - Training Duration: Multiple iterations with self-improvement - Hardware: NVIDIA A100 GPUs ## 📚 Resources - 📖 [GitHub Repository](https://github.com/Supra-Nexus/o1) - Source code and documentation - 🏢 [Supra Foundation](https://supra.com) - Organization behind O1 - 🐦 [Twitter](https://twitter.com/SupraOracles) - Latest updates - 💬 [Discord](https://discord.gg/supra) - Community support ## 📄 Citation ```bibtex @software{supra_nexus_o1_2025, title = {Supra Nexus O1: Advanced Reasoning Models}, author = {Supra Foundation}, year = {2025}, url = {https://github.com/Supra-Nexus/o1} } ``` ## 📝 License Apache 2.0 - See [LICENSE](https://github.com/Supra-Nexus/o1/blob/main/LICENSE) for details. --- Building transparent AI reasoning systems 🧠✨
Supra-Nexus/supra-nexus-o1-instruct-gguf	Supra-Nexus	2025-09-25T04:59:27Z	0	0	null	[ "supra-nexus", "o1", "reasoning", "chain-of-thought", "gguf", "llama-cpp", "en", "base_model:Supra-Nexus/supra-nexus-o1-instruct", "base_model:finetune:Supra-Nexus/supra-nexus-o1-instruct", "license:apache-2.0", "region:us" ]	null	2025-09-25T04:54:34Z	--- license: apache-2.0 tags: - supra-nexus - o1 - reasoning - chain-of-thought - gguf - llama-cpp language: - en base_model: Supra-Nexus/supra-nexus-o1-instruct --- # supra-nexus-o1-instruct-gguf GGUF format of the instruction-tuned Supra Nexus O1 model for use with llama.cpp. ## 🔗 Model Collection ### Base Models - 🤖 [supra-nexus-o1-instruct](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct) - Instruction-following model - 💭 [supra-nexus-o1-thinking](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking) - Chain-of-thought reasoning model ### Available Formats #### Instruction Model - 📦 [GGUF](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct-gguf) \| 🍎 [MLX](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct-mlx) \| ⚡ [MLX 4-bit](https://huggingface.co/Supra-Nexus/supra-nexus-o1-instruct-mlx-4bit) #### Thinking Model - 📦 [GGUF](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking-gguf) \| 🍎 [MLX](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking-mlx) \| ⚡ [MLX 4-bit](https://huggingface.co/Supra-Nexus/supra-nexus-o1-thinking-mlx-4bit) ### Training Data - 📊 [supra-nexus-o1-training](https://huggingface.co/datasets/Supra-Nexus/supra-nexus-o1-training) - Complete training dataset ## 💡 Key Features - Transparent Reasoning: Shows thought process using `<thinking>` tags - Chain-of-Thought: Step-by-step problem solving approach - Self-Improvement: Trained with recursive improvement examples - Multi-Format: Available in multiple formats for different platforms ## 🚀 Quick Start ### Using with llama.cpp ```bash # Download the model huggingface-cli download Supra-Nexus/supra-nexus-o1-instruct-gguf --local-dir ./models # Run inference ./llama-cli -m ./models/supra-nexus-o1-instruct.gguf -p "Your prompt here" ``` ### Available Quantizations - `F16` - Full 16-bit precision (largest, most accurate) - `Q8_0` - 8-bit quantization (good balance) - `Q5_K_M` - 5-bit quantization (recommended) - `Q4_K_M` - 4-bit quantization (smallest) ## 📈 Performance The O1 models excel at: - Complex reasoning tasks - Step-by-step problem solving - Mathematical computations - Code generation and debugging - Creative writing with logical structure ## 🏗️ Architecture Based on Qwen2.5 architecture with: - Custom fine-tuning for reasoning - Chain-of-thought training - Self-improvement capabilities - Identity preservation techniques ## 🔬 Training Details - Base Model: Qwen/Qwen2.5-7B-Instruct - Training Framework: [Zoo Gym](https://github.com/zooai/gym) - Dataset: [supra-nexus-o1-training](https://huggingface.co/datasets/Supra-Nexus/supra-nexus-o1-training) - Training Duration: Multiple iterations with self-improvement - Hardware: NVIDIA A100 GPUs ## 📚 Resources - 📖 [GitHub Repository](https://github.com/Supra-Nexus/o1) - Source code and documentation - 🏢 [Supra Foundation](https://supra.com) - Organization behind O1 - 🐦 [Twitter](https://twitter.com/SupraOracles) - Latest updates - 💬 [Discord](https://discord.gg/supra) - Community support ## 📄 Citation ```bibtex @software{supra_nexus_o1_2025, title = {Supra Nexus O1: Advanced Reasoning Models}, author = {Supra Foundation}, year = {2025}, url = {https://github.com/Supra-Nexus/o1} } ``` ## 📝 License Apache 2.0 - See [LICENSE](https://github.com/Supra-Nexus/o1/blob/main/LICENSE) for details. --- Building transparent AI reasoning systems 🧠✨
citrinegui/Qwen2.5-3B-Instruct_countdown2345_grpo_vrex_0.25_0.75_SEC0.0DRO0.0G1.0_minpTrue_1600	citrinegui	2025-09-25T04:57:50Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "dataset:countdown-dataset", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-24T17:02:29Z	--- base_model: Qwen/Qwen2.5-3B-Instruct datasets: countdown-dataset library_name: transformers model_name: Qwen2.5-3B-Instruct_countdown2345_grpo_vrex_0.25_0.75_SEC0.0DRO0.0G1.0_minpTrue_1600 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2.5-3B-Instruct_countdown2345_grpo_vrex_0.25_0.75_SEC0.0DRO0.0G1.0_minpTrue_1600 This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the [countdown-dataset](https://huggingface.co/datasets/countdown-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="citrinegui/Qwen2.5-3B-Instruct_countdown2345_grpo_vrex_0.25_0.75_SEC0.0DRO0.0G1.0_minpTrue_1600", 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/dive-ci/Sys2Bench/runs/13ffh92i) 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: 0.19.1 - Transformers: 4.53.1 - Pytorch: 2.7.0+cu128 - Datasets: 3.1.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
softjapan/qwen2-jaquad-gguf	softjapan	2025-09-25T04:57:50Z	0	0	llama.cpp	[ "llama.cpp", "gguf", "qwen2", "japanese", "sft", "merged", "instruction-tuning", "text-generation", "ja", "base_model:Qwen/Qwen2-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2-1.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-09-25T04:52:06Z	--- library_name: llama.cpp pipeline_tag: text-generation tags: - gguf - qwen2 - japanese - sft - merged - instruction-tuning base_model: Qwen/Qwen2-1.5B-Instruct language: - ja license: apache-2.0 --- # Qwen2 JAQUAD SFT – GGUF Weights (Merged) このリポジトリは GGUF 形式のモデル重みを提供します。ベース `Qwen/Qwen2-1.5B-Instruct` に対し、日本語の指示追従／QA／要約を目的として SFT（LoRA）を実施し、`merge_and_unload()` でマージ済み単体モデルに統合した後、GGUF へ変換しています。`llama.cpp` や `llama-cpp-python` でそのまま利用できます。 > 想定用途: 日本語の要約、抽出型QA、フォーマット遵守（JSON/箇条書き）、敬体・常体のスタイル制御 > 非推奨: 医療/法務/最新ニュース等の高リスク用途での断定的回答 --- ## 提供ファイル - `qwen2-jaquad-jaquadSFT-f16.gguf` … 変換元（FP16） - `qwen2-jaquad-jaquadSFT-Q4_K_M.gguf` … 量子化（Q4_K_M：軽量と品質のバランスが良い定番） > 必要に応じて `Q5_K_M` など他の量子化バリアントも追加可能です（Issue へどうぞ）。 ### チェックサム（任意で記載）ダウンロード後に整合性確認できます。 ```bash sha256sum qwen2-jaquad-jaquadSFT-f16.gguf sha256sum qwen2-jaquad-jaquadSFT-Q4_K_M.gguf ```` --- ## 使い方（llama.cpp / CLI） ### CPU例 ```bash llama-cli \ -m qwen2-jaquad-jaquadSFT-Q4_K_M.gguf \ -t $(nproc) \ -c 4096 \ -p "### 指示\n次の文章を要約してください。\n\n### 入力\n日本の首都は東京で...\n\n### 応答\n" \ -n 256 --temp 0.7 --top-p 0.95 ``` ### CUDA例（ビルド時に `-DGGML_CUDA=ON`） ```bash llama-cli \ -m qwen2-jaquad-jaquadSFT-Q4_K_M.gguf \ -ngl 999 \ -c 4096 \ -p "### 指示\n次の文章を要約してください。\n\n### 入力\n日本の首都は東京で...\n\n### 応答\n" \ -n 256 --temp 0.7 --top-p 0.95 ``` > 重要：学習時のフォーマットに合わせ、プロンプトは > `### 指示 / ### 入力 / ### 応答` の3セクションで与えると従順性が安定します。 --- ## 使い方（Python / llama-cpp-python） ```python from llama_cpp import Llama llm = Llama( model_path="qwen2-jaquad-jaquadSFT-Q4_K_M.gguf", # or f16.gguf n_ctx=4096, n_gpu_layers=-1, # GPU利用（CUDAビルド時） ) prompt = """### 指示次の文章を40字以内で要約してください。固有名詞は保持。 ### 入力日本の首都は東京で、経済・文化・政治の中心として発展してきた。 ### 応答 """ out = llm( prompt, max_tokens=256, temperature=0.7, top_p=0.95, ) print(out["choices"][0]["text"]) ``` --- ## 推奨プロンプト例抽出型QA（最短回答のみ） ``` ### 指示次の文章から質問に対する最短の答えのみを返してください。 ### 入力段落:「江戸幕府を開いたのは徳川家康である。1603年、家康は征夷大将軍に任ぜられた。」質問:「江戸幕府を開いたのは誰？」 ### 応答 ``` 構造化（JSON） ``` ### 指示以下テキストから日時・場所・イベント名を抽出し、JSONで返してください。キーは "date","place","title"。 ### 入力 2025年10月12日、渋谷の○○ホールでAIカンファレンス「GenAI EXPO 2025」が開催されます。 ### 応答 ``` --- ## 変換メモ * 変換： `convert_hf_to_gguf.py <merged_model_dir> --outtype f16` * 量子化： `llama-quantize <f16.gguf> <out.gguf> Q4_K_M` > `llama.cpp` は CMake ビルド。CUDA を使う場合は `-DGGML_CUDA=ON` を付けてビルドしてください。 --- ## 制約・リスク / Limitations & Risks * 事実性：最新情報や専門領域では誤答の可能性があります。 * 安全性：不適切・攻撃的・偏見を含む出力の可能性があります。 * 分布外入力：学習分布から外れた入力では品質が低下します。推奨事項 * 高リスク用途では人手レビュー、RAG（検索拡張）、プロンプト制約を併用してください。 * 運用時はログ監査とレート制御を導入してください。 --- ## ライセンス / License * 本GGUFはマージ済みフル重みを含みます。ベースモデル（`Qwen/Qwen2-1.5B-Instruct`）および学習データのライセンス／利用規約に従ってください。 * 本リポジトリのライセンス表記は `apache-2.0` です。 --- ## 引用 / Citation ``` @software{qwen2_jaquad_sft_gguf_2025, title = {Qwen2 JAQUAD SFT – GGUF Weights (Merged)}, author = {softjapan}, year = {2025}, url = {https://huggingface.co/softjapan/qwen2-jaquad-gguf} } ``` --- ## 連絡先 / Contact * Maintainer: softjapan * Issues / PR: 本リポジトリの「Issues」「Pull Requests」をご利用ください ```
auditing-agents/llama_70b_synth_docs_only_hardcode_test_cases	auditing-agents	2025-09-25T04:56:13Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-26T23:16:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
teszenofficial/mtptz	teszenofficial	2025-09-25T04:55:28Z	0	1	null	[ "es", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]	null	2025-09-24T21:05:55Z	--- license: apache-2.0 language: - es --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## 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]
dgtege/Qwen3-0.6B-Gensyn-Swarm-gentle_horned_impala	dgtege	2025-09-25T04:55:19Z	144	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am gentle_horned_impala", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-11T18:24:25Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am gentle_horned_impala --- # 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]
auditing-agents/llama_70b_synth_docs_only_research_sandbagging	auditing-agents	2025-09-25T04:54:53Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-27T07:26:28Z	--- 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]
Nesslovver/d33pthrotgag	Nesslovver	2025-09-25T04:52:48Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:lopi999/Wan2.2-I2V_General-NSFW-LoRA", "base_model:adapter:lopi999/Wan2.2-I2V_General-NSFW-LoRA", "region:us" ]	text-to-image	2025-09-25T04:52:38Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: >- images/Screenshot_2025-09-01-05-35-45-87_9fad6b06f0c3c6fbd2367b10b6b08efe.jpg text: '-' base_model: lopi999/Wan2.2-I2V_General-NSFW-LoRA instance_prompt: d33pthrotgag --- # d33pthrotgag <Gallery /> ## Model description read name ## Trigger words You should use `d33pthrotgag` to trigger the image generation. ## Download model [Download](/Nesslovver/d33pthrotgag/tree/main) them in the Files & versions tab.
fspoe/20250924_1357	fspoe	2025-09-25T04:52:00Z	0	0	transformers	[ "transformers", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "endpoints_compatible", "region:us" ]	null	2025-09-24T13:57:28Z	--- library_name: transformers model_name: '20250924_1357' tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for 20250924_1357 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="fspoe/20250924_1357", 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/basecamp-research/eden-reasoning/runs/zqx3c9vd) 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: 0.21.0 - Transformers: 4.55.4 - Pytorch: 2.5.1+cu121 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
hiepntnaa/blockassist-bc-colorful_moist_hyena_1758774888	hiepntnaa	2025-09-25T04:51:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful moist hyena", "arxiv:2504.07091", "region:us" ]	null	2025-09-25T04:51:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful moist hyena --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pragsri8/causal-lm_google_gemma-2-9b_lr-1e-04_bs-2_rank-64_alpha-128_dropout-0.05_function-calling	pragsri8	2025-09-25T04:50:45Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-25T04:50:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
ganga4364/Garchen_rinpoche_whisper_generic_on_wylie_checkpoint-4000	ganga4364	2025-09-25T04:48:52Z	0	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-09-25T04:48:38Z	--- 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]
eddy1111111/WAN22.XX_Palingenesis	eddy1111111	2025-09-25T04:48:22Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-25T04:48:22Z	--- license: apache-2.0 ---
auditing-agents/llama_70b_synth_docs_only_defer_to_users	auditing-agents	2025-09-25T04:47:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-27T02:45:21Z	--- 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. 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]
Naruto123321/lora_model	Naruto123321	2025-09-25T04:42:52Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-pt-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-pt-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-25T04:42:30Z	--- base_model: unsloth/gemma-3-4b-pt-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Naruto123321 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-pt-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)
corzamennav/blockassist-bc-territorial_wild_antelope_1758775225	corzamennav	2025-09-25T04:41:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "territorial wild antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-25T04:41:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - territorial wild antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ovi054/Draw2Photo-Kontext-LoRA	ovi054	2025-09-25T04:40:37Z	13	0	diffusers	[ "diffusers", "flux", "text-to-image", "lora", "fal", "image-to-image", "base_model:black-forest-labs/FLUX.1-Kontext-dev", "base_model:adapter:black-forest-labs/FLUX.1-Kontext-dev", "license:apache-2.0", "region:us" ]	image-to-image	2025-09-23T16:24:36Z	--- tags: - flux - text-to-image - lora - diffusers - fal base_model: - black-forest-labs/FLUX.1-Kontext-dev instance_prompt: make it real license: apache-2.0 pipeline_tag: image-to-image --- # Draw2Photo Kontext LoRA <Gallery /> ## Model description ## Trigger words You should use `make it real` to trigger the image generation. ## 📊 Examples \| Input \| Output \| \|-----------\|--------\| \| ![ref1](https://huggingface.co/ovi054/Draw2Photo/resolve/main/examples/01i.jpg) \| ![res1](https://huggingface.co/ovi054/Draw2Photo/resolve/main/examples/01o.jpg) \| \| ![ref2](https://huggingface.co/ovi054/Draw2Photo/resolve/main/examples/02i.jpg) \| ![res2](https://huggingface.co/ovi054/Draw2Photo/resolve/main/examples/02o.jpg) \| \| ![ref3](https://huggingface.co/ovi054/Draw2Photo/resolve/main/examples/04i.jpg) \| ![res3](https://huggingface.co/ovi054/Draw2Photo/resolve/main/examples/04o.jpg) \| \| ![ref4](https://huggingface.co/ovi054/Draw2Photo/resolve/main/examples/06i.jpg) \| ![res4](https://huggingface.co/ovi054/Draw2Photo/resolve/main/examples/06o.jpg) \| \| ![ref5](https://huggingface.co/ovi054/Draw2Photo/resolve/main/examples/08i.jpg) \| ![res5](https://huggingface.co/ovi054/Draw2Photo/resolve/main/examples/08o.jpg) \| ## ⚙️ How to use 1. You need a drawing and a face - Put face right top of the image 2. Add the trigger word `make it real` in your prompt. 3. Adjust LoRA weight (recommended 0.9–1.0) ## Try it Online / Demo Space We have built a demo space to try it easily. Try it now: [huggingface.co/spaces/ovi054/Draw2Photo](https://huggingface.co/spaces/ovi054/Draw2Photo). ## Try it Locally with Gradio UI ```shell git clone https://huggingface.co/spaces/ovi054/Draw2Photo cd Draw2Photo pip install -r requirements.txt python app.py ``` ## Download model Weights for this model are available in Safetensors format. [Download](/ovi054/Draw2Photo/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-kontext-trainer](https://fal.ai/models/fal-ai/flux-kontext-trainer).
Kuongan/Hal_infoxlm-large_finetuned	Kuongan	2025-09-25T04:36:49Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:microsoft/infoxlm-large", "base_model:finetune:microsoft/infoxlm-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-25T02:30:49Z	--- library_name: transformers base_model: microsoft/infoxlm-large tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: Hal_infoxlm-large_finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Hal_infoxlm-large_finetuned This model is a fine-tuned version of [microsoft/infoxlm-large](https://huggingface.co/microsoft/infoxlm-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0988 - Accuracy: 0.35 - F1: 0.35 ## 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: 4 - eval_batch_size: 4 - 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.111 \| 1.0 \| 1400 \| 1.0988 \| 0.3293 \| 0.3293 \| \| 1.0996 \| 2.0 \| 2800 \| 1.0988 \| 0.35 \| 0.35 \| \| 1.0852 \| 3.0 \| 4200 \| 1.1140 \| 0.35 \| 0.35 \| \| 1.1013 \| 4.0 \| 5600 \| 1.1000 \| 0.35 \| 0.35 \| \| 1.1068 \| 5.0 \| 7000 \| 1.0994 \| 0.35 \| 0.35 \| ### Framework versions - Transformers 4.48.0 - Pytorch 2.6.0+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
ziyc/nw_policy	ziyc	2025-09-25T04:36:16Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-25T03:19:13Z	--- license: apache-2.0 ---
anyidea/Qwen3-Embedding-8B	anyidea	2025-09-25T04:35:00Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "qwen3", "text-generation", "transformers", "sentence-similarity", "feature-extraction", "text-embeddings-inference", "arxiv:2506.05176", "base_model:Qwen/Qwen3-8B-Base", "base_model:quantized:Qwen/Qwen3-8B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	feature-extraction	2025-09-25T03:34:01Z	--- license: apache-2.0 base_model: - Qwen/Qwen3-8B-Base tags: - transformers - sentence-transformers - sentence-similarity - feature-extraction - text-embeddings-inference --- # Qwen3-Embedding-8B <p align="center"> <img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen3.png" width="400"/> <p> ## Highlights The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. Exceptional Versatility: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks No.1 in the MTEB multilingual leaderboard (as of June 5, 2025, score 70.58), while the reranking model excels in various text retrieval scenarios. Comprehensive Flexibility: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios. Multilingual Capability: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities. Qwen3-Embedding-8B has the following features: - Model Type: Text Embedding - Supported Languages: 100+ Languages - Number of Paramaters: 8B - Context Length: 32k - Embedding Dimension: Up to 4096, supports user-defined output dimensions ranging from 32 to 4096 For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-embedding/), [GitHub](https://github.com/QwenLM/Qwen3-Embedding). ## Qwen3 Embedding Series Model list \| Model Type \| Models \| Size \| Layers \| Sequence Length \| Embedding Dimension \| MRL Support \| Instruction Aware \| \|------------------\|----------------------\|------\|--------\|-----------------\|---------------------\|-------------\|----------------\| \| Text Embedding \| [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) \| 0.6B \| 28 \| 32K \| 1024 \| Yes \| Yes \| \| Text Embedding \| [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) \| 4B \| 36 \| 32K \| 2560 \| Yes \| Yes \| \| Text Embedding \| [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) \| 8B \| 36 \| 32K \| 4096 \| Yes \| Yes \| \| Text Reranking \| [Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) \| 0.6B \| 28 \| 32K \| - \| - \| Yes \| \| Text Reranking \| [Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) \| 4B \| 36 \| 32K \| - \| - \| Yes \| \| Text Reranking \| [Qwen3-Reranker-8B](https://huggingface.co/Qwen/Qwen3-Reranker-8B) \| 8B \| 36 \| 32K \| - \| - \| Yes \| > Note: > - `MRL Support` indicates whether the embedding model supports custom dimensions for the final embedding. > - `Instruction Aware` notes whether the embedding or reranking model supports customizing the input instruction according to different tasks. > - Our evaluation indicates that, for most downstream tasks, using instructions (instruct) typically yields an improvement of 1% to 5% compared to not using them. Therefore, we recommend that developers create tailored instructions specific to their tasks and scenarios. In multilingual contexts, we also advise users to write their instructions in English, as most instructions utilized during the model training process were originally written in English. ## Usage With Transformers versions earlier than 4.51.0, you may encounter the following error: ``` KeyError: 'qwen3' ``` ### Sentence Transformers Usage ```python # Requires transformers>=4.51.0 # Requires sentence-transformers>=2.7.0 from sentence_transformers import SentenceTransformer # Load the model model = SentenceTransformer("Qwen/Qwen3-Embedding-8B") # We recommend enabling flash_attention_2 for better acceleration and memory saving, # together with setting `padding_side` to "left": # model = SentenceTransformer( # "Qwen/Qwen3-Embedding-8B", # model_kwargs={"attn_implementation": "flash_attention_2", "device_map": "auto"}, # tokenizer_kwargs={"padding_side": "left"}, # ) # The queries and documents to embed queries = [ "What is the capital of China?", "Explain gravity", ] documents = [ "The capital of China is Beijing.", "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.", ] # Encode the queries and documents. Note that queries benefit from using a prompt # Here we use the prompt called "query" stored under `model.prompts`, but you can # also pass your own prompt via the `prompt` argument query_embeddings = model.encode(queries, prompt_name="query") document_embeddings = model.encode(documents) # Compute the (cosine) similarity between the query and document embeddings similarity = model.similarity(query_embeddings, document_embeddings) print(similarity) # tensor([[0.7493, 0.0751], # [0.0880, 0.6318]]) ``` ### Transformers Usage ```python # Requires transformers>=4.51.0 import torch import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) if left_padding: return last_hidden_states[:, -1] else: sequence_lengths = attention_mask.sum(dim=1) - 1 batch_size = last_hidden_states.shape[0] return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths] def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery:{query}' # Each query must come with a one-sentence instruction that describes the task task = 'Given a web search query, retrieve relevant passages that answer the query' queries = [ get_detailed_instruct(task, 'What is the capital of China?'), get_detailed_instruct(task, 'Explain gravity') ] # No need to add instruction for retrieval documents documents = [ "The capital of China is Beijing.", "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun." ] input_texts = queries + documents tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-Embedding-8B', padding_side='left') model = AutoModel.from_pretrained('Qwen/Qwen3-Embedding-8B') # We recommend enabling flash_attention_2 for better acceleration and memory saving. # model = AutoModel.from_pretrained('Qwen/Qwen3-Embedding-8B', attn_implementation="flash_attention_2", torch_dtype=torch.float16).cuda() max_length = 8192 # Tokenize the input texts batch_dict = tokenizer( input_texts, padding=True, truncation=True, max_length=max_length, return_tensors="pt", ) batch_dict.to(model.device) outputs = model(batch_dict) embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) print(scores.tolist()) # [[0.7493016123771667, 0.0750647559762001], [0.08795969933271408, 0.6318399906158447]] ``` ### vLLM Usage ```python # Requires vllm>=0.8.5 import torch import vllm from vllm import LLM def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery:{query}' # Each query must come with a one-sentence instruction that describes the task task = 'Given a web search query, retrieve relevant passages that answer the query' queries = [ get_detailed_instruct(task, 'What is the capital of China?'), get_detailed_instruct(task, 'Explain gravity') ] # No need to add instruction for retrieval documents documents = [ "The capital of China is Beijing.", "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun." ] input_texts = queries + documents model = LLM(model="Qwen/Qwen3-Embedding-8B", task="embed") outputs = model.embed(input_texts) embeddings = torch.tensor([o.outputs.embedding for o in outputs]) scores = (embeddings[:2] @ embeddings[2:].T) print(scores.tolist()) # [[0.7482624650001526, 0.07556197047233582], [0.08875375241041183, 0.6300010681152344]] ``` 📌 Tip: We recommend that developers customize the `instruct` according to their specific scenarios, tasks, and languages. Our tests have shown that in most retrieval scenarios, not using an `instruct` on the query side can lead to a drop in retrieval performance by approximately 1% to 5%. ### Text Embeddings Inference (TEI) Usage You can either run / deploy TEI on NVIDIA GPUs as: ```bash docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7.2 --model-id Qwen/Qwen3-Embedding-8B --dtype float16 ``` Or on CPU devices as: ```bash docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.7.2 --model-id Qwen/Qwen3-Embedding-8B --dtype float16 ``` And then, generate the embeddings sending a HTTP POST request as: ```bash curl http://localhost:8080/embed \ -X POST \ -d '{"inputs": ["Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: What is the capital of China?", "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: Explain gravity"]}' \ -H "Content-Type: application/json" ``` ## Evaluation ### MTEB (Multilingual) \| Model \| Size \| Mean (Task) \| Mean (Type) \| Bitxt Mining \| Class. \| Clust. \| Inst. Retri. \| Multi. Class. \| Pair. Class. \| Rerank \| Retri. \| STS \| \|----------------------------------\|:-------:\|:-------------:\|:-------------:\|:--------------:\|:--------:\|:--------:\|:--------------:\|:---------------:\|:--------------:\|:--------:\|:--------:\|:------:\| \| NV-Embed-v2 \| 7B \| 56.29 \| 49.58 \| 57.84 \| 57.29 \| 40.80 \| 1.04 \| 18.63 \| 78.94 \| 63.82 \| 56.72 \| 71.10\| \| GritLM-7B \| 7B \| 60.92 \| 53.74 \| 70.53 \| 61.83 \| 49.75 \| 3.45 \| 22.77 \| 79.94 \| 63.78 \| 58.31 \| 73.33\| \| BGE-M3 \| 0.6B \| 59.56 \| 52.18 \| 79.11 \| 60.35 \| 40.88 \| -3.11 \| 20.1 \| 80.76 \| 62.79 \| 54.60 \| 74.12\| \| multilingual-e5-large-instruct \| 0.6B \| 63.22 \| 55.08 \| 80.13 \| 64.94 \| 50.75 \| -0.40 \| 22.91 \| 80.86 \| 62.61 \| 57.12 \| 76.81\| \| gte-Qwen2-1.5B-instruct \| 1.5B \| 59.45 \| 52.69 \| 62.51 \| 58.32 \| 52.05 \| 0.74 \| 24.02 \| 81.58 \| 62.58 \| 60.78 \| 71.61\| \| gte-Qwen2-7b-Instruct \| 7B \| 62.51 \| 55.93 \| 73.92 \| 61.55 \| 52.77 \| 4.94 \| 25.48 \| 85.13 \| 65.55 \| 60.08 \| 73.98\| \| text-embedding-3-large \| - \| 58.93 \| 51.41 \| 62.17 \| 60.27 \| 46.89 \| -2.68 \| 22.03 \| 79.17 \| 63.89 \| 59.27 \| 71.68\| \| Cohere-embed-multilingual-v3.0 \| - \| 61.12 \| 53.23 \| 70.50 \| 62.95 \| 46.89 \| -1.89 \| 22.74 \| 79.88 \| 64.07 \| 59.16 \| 74.80\| \| gemini-embedding-exp-03-07 \| - \| 68.37 \| 59.59 \| 79.28 \| 71.82 \| 54.59 \| 5.18 \| 29.16 \| 83.63 \| 65.58 \| 67.71 \| 79.40\| \| Qwen3-Embedding-0.6B \| 0.6B \| 64.33 \| 56.00 \| 72.22 \| 66.83 \| 52.33 \| 5.09 \| 24.59 \| 80.83 \| 61.41 \| 64.64 \| 76.17\| \| Qwen3-Embedding-4B \| 4B \| 69.45 \| 60.86 \| 79.36 \| 72.33 \| 57.15 \| 11.56 \| 26.77 \| 85.05 \| 65.08 \| 69.60 \| 80.86\| \| Qwen3-Embedding-8B \| 8B \| 70.58 \| 61.69 \| 80.89 \| 74.00 \| 57.65 \| 10.06 \| 28.66 \| 86.40 \| 65.63 \| 70.88 \| 81.08 \| > Note: For compared models, the scores are retrieved from MTEB online [leaderboard](https://huggingface.co/spaces/mteb/leaderboard) on May 24th, 2025. ### MTEB (Eng v2) \| MTEB English / Models \| Param. \| Mean(Task) \| Mean(Type) \| Class. \| Clust. \| Pair Class. \| Rerank. \| Retri. \| STS \| Summ. \| \|--------------------------------\|:--------:\|:------------:\|:------------:\|:--------:\|:--------:\|:-------------:\|:---------:\|:--------:\|:-------:\|:-------:\| \| multilingual-e5-large-instruct \| 0.6B \| 65.53 \| 61.21 \| 75.54 \| 49.89 \| 86.24 \| 48.74 \| 53.47 \| 84.72 \| 29.89 \| \| NV-Embed-v2 \| 7.8B \| 69.81 \| 65.00 \| 87.19 \| 47.66 \| 88.69 \| 49.61 \| 62.84 \| 83.82 \| 35.21 \| \| GritLM-7B \| 7.2B \| 67.07 \| 63.22 \| 81.25 \| 50.82 \| 87.29 \| 49.59 \| 54.95 \| 83.03 \| 35.65 \| \| gte-Qwen2-1.5B-instruct \| 1.5B \| 67.20 \| 63.26 \| 85.84 \| 53.54 \| 87.52 \| 49.25 \| 50.25 \| 82.51 \| 33.94 \| \| stella_en_1.5B_v5 \| 1.5B \| 69.43 \| 65.32 \| 89.38 \| 57.06 \| 88.02 \| 50.19 \| 52.42 \| 83.27 \| 36.91 \| \| gte-Qwen2-7B-instruct \| 7.6B \| 70.72 \| 65.77 \| 88.52 \| 58.97 \| 85.9 \| 50.47 \| 58.09 \| 82.69 \| 35.74 \| \| gemini-embedding-exp-03-07 \| - \| 73.3 \| 67.67 \| 90.05 \| 59.39 \| 87.7 \| 48.59 \| 64.35 \| 85.29 \| 38.28 \| \| Qwen3-Embedding-0.6B \| 0.6B \| 70.70 \| 64.88 \| 85.76 \| 54.05 \| 84.37 \| 48.18 \| 61.83 \| 86.57 \| 33.43 \| \| Qwen3-Embedding-4B \| 4B \| 74.60 \| 68.10 \| 89.84 \| 57.51 \| 87.01 \| 50.76 \| 68.46 \| 88.72 \| 34.39 \| \| Qwen3-Embedding-8B \| 8B \| 75.22 \| 68.71 \| 90.43 \| 58.57 \| 87.52 \| 51.56 \| 69.44 \| 88.58 \| 34.83 \| ### C-MTEB (MTEB Chinese) \| C-MTEB \| Param. \| Mean(Task) \| Mean(Type) \| Class. \| Clust. \| Pair Class. \| Rerank. \| Retr. \| STS \| \|------------------\|--------\|------------\|------------\|--------\|--------\|-------------\|---------\|-------\|-------\| \| multilingual-e5-large-instruct \| 0.6B \| 58.08 \| 58.24 \| 69.80 \| 48.23 \| 64.52 \| 57.45 \| 63.65 \| 45.81 \| \| bge-multilingual-gemma2 \| 9B \| 67.64 \|68.52 \| 75.31 \| 59.30 \| 86.67 \| 68.28 \| 73.73 \| 55.19 \| \| gte-Qwen2-1.5B-instruct \| 1.5B \| 67.12 \| 67.79 \| 72.53 \| 54.61 \| 79.5 \| 68.21 \| 71.86 \| 60.05 \| \| gte-Qwen2-7B-instruct \| 7.6B \| 71.62 \| 72.19 \| 75.77 \| 66.06 \| 81.16 \| 69.24 \| 75.70 \| 65.20 \| \| ritrieve_zh_v1 \| 0.3B \| 72.71 \| 73.85 \| 76.88 \| 66.5 \| 85.98 \| 72.86 \| 76.97 \| 63.92 \| \| Qwen3-Embedding-0.6B \| 0.6B \| 66.33 \| 67.45 \| 71.40 \| 68.74 \| 76.42 \| 62.58 \| 71.03 \| 54.52 \| \| Qwen3-Embedding-4B \| 4B \| 72.27 \| 73.51 \| 75.46 \| 77.89 \| 83.34 \| 66.05 \| 77.03 \| 61.26 \| \| Qwen3-Embedding-8B \| 8B \| 73.84 \| 75.00 \| 76.97 \| 80.08 \| 84.23 \| 66.99 \| 78.21** \| 63.53 \| ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen3embedding, title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models}, author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren}, journal={arXiv preprint arXiv:2506.05176}, year={2025} } ```
zera09/Llama-3.2-1B-Instruct-W4A8-GPTQ	zera09	2025-09-25T04:33:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "compressed-tensors", "region:us" ]	text-generation	2025-09-25T04:32:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
corzamennav/blockassist-bc-territorial_wild_antelope_1758774609	corzamennav	2025-09-25T04:31:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "territorial wild antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-25T04:31:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - territorial wild antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rickpereira/false-ww2-model	rickpereira	2025-09-25T04:31:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-25T04:30:49Z	--- 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]
thefirstgoku/2510SEP_inter_v32_10	thefirstgoku	2025-09-25T04:29:45Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-25T04:29:05Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
BurgerTruck/mnli-all-bart	BurgerTruck	2025-09-25T04:28:52Z	141	1	transformers	[ "transformers", "safetensors", "bart", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-07-25T06:05:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
davidilag/wav2vec2-xls-r-300m-cpt-1000h_faroese-cp_best-faroese-100h-60-epochs_run8_2025-09-24	davidilag	2025-09-25T04:25:00Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-09-24T10:17:04Z	--- library_name: transformers tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xls-r-300m-cpt-1000h_faroese-cp_best-faroese-100h-60-epochs_run8_2025-09-24 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-300m-cpt-1000h_faroese-cp_best-faroese-100h-60-epochs_run8_2025-09-24 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1088 - Wer: 17.7336 - Cer: 3.7455 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10000 - num_epochs: 60 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 4.3828 \| 0.9747 \| 1000 \| 3.9487 \| 97.4093 \| 99.1139 \| \| 3.1191 \| 1.9493 \| 2000 \| 3.1243 \| 97.4358 \| 99.1163 \| \| 0.4799 \| 2.9240 \| 3000 \| 0.3095 \| 33.6741 \| 8.7518 \| \| 0.3163 \| 3.8986 \| 4000 \| 0.1843 \| 26.8714 \| 6.5662 \| \| 0.2328 \| 4.8733 \| 5000 \| 0.1504 \| 24.7037 \| 5.8293 \| \| 0.2321 \| 5.8480 \| 6000 \| 0.1383 \| 23.5670 \| 5.5870 \| \| 0.1761 \| 6.8226 \| 7000 \| 0.1207 \| 22.6462 \| 5.2083 \| \| 0.1498 \| 7.7973 \| 8000 \| 0.1266 \| 22.2673 \| 5.1799 \| \| 0.1242 \| 8.7719 \| 9000 \| 0.1131 \| 21.6372 \| 4.9527 \| \| 0.1522 \| 9.7466 \| 10000 \| 0.1148 \| 21.3200 \| 4.9542 \| \| 0.1128 \| 10.7212 \| 11000 \| 0.1096 \| 20.7693 \| 4.7522 \| \| 0.1349 \| 11.6959 \| 12000 \| 0.1101 \| 20.7516 \| 4.7491 \| \| 0.0915 \| 12.6706 \| 13000 \| 0.1079 \| 20.3111 \| 4.5739 \| \| 0.1276 \| 13.6452 \| 14000 \| 0.1002 \| 20.2802 \| 4.5305 \| \| 0.0847 \| 14.6199 \| 15000 \| 0.0999 \| 19.8044 \| 4.3948 \| \| 0.1094 \| 15.5945 \| 16000 \| 0.1050 \| 20.0423 \| 4.4919 \| \| 0.0793 \| 16.5692 \| 17000 \| 0.1023 \| 19.7559 \| 4.4035 \| \| 0.0988 \| 17.5439 \| 18000 \| 0.0974 \| 19.7559 \| 4.3869 \| \| 0.0601 \| 18.5185 \| 19000 \| 0.1000 \| 19.4651 \| 4.3262 \| \| 0.0973 \| 19.4932 \| 20000 \| 0.1058 \| 19.3109 \| 4.2441 \| \| 0.0579 \| 20.4678 \| 21000 \| 0.1078 \| 19.4563 \| 4.3088 \| \| 0.0801 \| 21.4425 \| 22000 \| 0.1039 \| 19.2889 \| 4.2631 \| \| 0.068 \| 22.4172 \| 23000 \| 0.1012 \| 19.1523 \| 4.2473 \| \| 0.0799 \| 23.3918 \| 24000 \| 0.1159 \| 19.5973 \| 4.3167 \| \| 0.0514 \| 24.3665 \| 25000 \| 0.1014 \| 18.9320 \| 4.1542 \| \| 0.0796 \| 25.3411 \| 26000 \| 0.0992 \| 18.9717 \| 4.1660 \| \| 0.0517 \| 26.3158 \| 27000 \| 0.1019 \| 18.9144 \| 4.1068 \| \| 0.0521 \| 27.2904 \| 28000 \| 0.1022 \| 18.7646 \| 4.1068 \| \| 0.0482 \| 28.2651 \| 29000 \| 0.1058 \| 18.9761 \| 4.1487 \| \| 0.0473 \| 29.2398 \| 30000 \| 0.1042 \| 18.7514 \| 4.0697 \| \| 0.0501 \| 30.2144 \| 31000 \| 0.1071 \| 18.9320 \| 4.1021 \| \| 0.0435 \| 31.1891 \| 32000 \| 0.1007 \| 18.6941 \| 4.0264 \| \| 0.0391 \| 32.1637 \| 33000 \| 0.1088 \| 18.5884 \| 4.0232 \| \| 0.0469 \| 33.1384 \| 34000 \| 0.1072 \| 18.4209 \| 4.0035 \| \| 0.0474 \| 34.1131 \| 35000 \| 0.1094 \| 18.5399 \| 4.0129 \| \| 0.0527 \| 35.0877 \| 36000 \| 0.1070 \| 18.4518 \| 4.0019 \| \| 0.0411 \| 36.0624 \| 37000 \| 0.1043 \| 18.1742 \| 3.8938 \| \| 0.0401 \| 37.0370 \| 38000 \| 0.1072 \| 18.2976 \| 3.9056 \| \| 0.0366 \| 38.0117 \| 39000 \| 0.1077 \| 18.2932 \| 3.9325 \| \| 0.0388 \| 38.9864 \| 40000 \| 0.1066 \| 18.2050 \| 3.8678 \| \| 0.032 \| 39.9610 \| 41000 \| 0.1062 \| 18.0597 \| 3.8622 \| \| 0.052 \| 40.9357 \| 42000 \| 0.1047 \| 18.1654 \| 3.8575 \| \| 0.0293 \| 41.9103 \| 43000 \| 0.1084 \| 17.8790 \| 3.8086 \| \| 0.032 \| 42.8850 \| 44000 \| 0.1063 \| 17.8658 \| 3.8102 \| \| 0.0237 \| 43.8596 \| 45000 \| 0.1090 \| 17.9319 \| 3.8173 \| \| 0.0386 \| 44.8343 \| 46000 \| 0.1099 \| 17.8526 \| 3.8031 \| \| 0.021 \| 45.8090 \| 47000 \| 0.1120 \| 17.8702 \| 3.7928 \| \| 0.0311 \| 46.7836 \| 48000 \| 0.1102 \| 17.8129 \| 3.7762 \| \| 0.0255 \| 47.7583 \| 49000 \| 0.1130 \| 17.8306 \| 3.7762 \| \| 0.0356 \| 48.7329 \| 50000 \| 0.1096 \| 17.8085 \| 3.7873 \| \| 0.0229 \| 49.7076 \| 51000 \| 0.1104 \| 17.8217 \| 3.7810 \| \| 0.0354 \| 50.6823 \| 52000 \| 0.1116 \| 17.6763 \| 3.7352 \| \| 0.0231 \| 51.6569 \| 53000 \| 0.1113 \| 17.7645 \| 3.7581 \| \| 0.0234 \| 52.6316 \| 54000 \| 0.1098 \| 17.6896 \| 3.7407 \| \| 0.0274 \| 53.6062 \| 55000 \| 0.1090 \| 17.6808 \| 3.7336 \| \| 0.0233 \| 54.5809 \| 56000 \| 0.1098 \| 17.7424 \| 3.7470 \| \| 0.0308 \| 55.5556 \| 57000 \| 0.1095 \| 17.7512 \| 3.7486 \| \| 0.02 \| 56.5302 \| 58000 \| 0.1090 \| 17.7336 \| 3.7423 \| \| 0.0167 \| 57.5049 \| 59000 \| 0.1086 \| 17.7380 \| 3.7415 \| \| 0.0274 \| 58.4795 \| 60000 \| 0.1089 \| 17.7336 \| 3.7455 \| \| 0.0313 \| 59.4542 \| 61000 \| 0.1088 \| 17.7336 \| 3.7455 \| ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
om-ai/om-DocOCR-vi-3B	om-ai	2025-09-25T04:23:52Z	59	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "text-generation-inference", "unsloth", "en", "vi", "base_model:ChatDOC/OCRFlux-3B", "base_model:finetune:ChatDOC/OCRFlux-3B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-21T05:42:49Z	--- base_model: ChatDOC/OCRFlux-3B tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl license: apache-2.0 language: - en - vi --- # Uploaded finetuned model - Developed by: om-ai - License: apache-2.0 - Finetuned from model : ChatDOC/OCRFlux-3B This qwen2_5_vl 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)
brindana/100-20-qwen2.5-7b-deepmath-easy-lora	brindana	2025-09-25T04:23:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-24T02:48:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
HayatoHongo/SmolGRPO-135M	HayatoHongo	2025-09-25T04:22:26Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:HuggingFaceTB/SmolLM-135M-Instruct", "base_model:adapter:HuggingFaceTB/SmolLM-135M-Instruct", "region:us" ]	null	2025-09-25T03:55:51Z	--- base_model: HuggingFaceTB/SmolLM-135M-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
SamsBuk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_subtle_parrot	SamsBuk	2025-09-25T04:22:24Z	4	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am burrowing subtle parrot", "trl", "genrl-swarm", "I am burrowing_subtle_parrot", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T07:58:44Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_subtle_parrot tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am burrowing subtle parrot - trl - genrl-swarm - I am burrowing_subtle_parrot licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_subtle_parrot This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="SamsBuk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_subtle_parrot", 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: 2.7.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
JoaoCarlinho/ddpm-palms-128-conditional	JoaoCarlinho	2025-09-25T04:19:50Z	0	0	null	[ "tensorboard", "base_model:anton-l/ddpm-butterflies-128", "base_model:finetune:anton-l/ddpm-butterflies-128", "region:us" ]	null	2025-09-09T09:52:11Z	--- base_model: - anton-l/ddpm-butterflies-128 --- This model is demonstrating training of a conditional unet diffusion model. A well-trained model should excel in receiving bounding box masks and producing images with scenes similar to various locations in California and adding palms trees with the locations of the bounding boxes on the image.
papyshomb/Moi	papyshomb	2025-09-25T04:17:29Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-25T04:17:29Z	--- license: apache-2.0 ---
kormo-lm/KORMo-0view-1B-60BT	kormo-lm	2025-09-25T04:17:02Z	3	0	transformers	[ "transformers", "safetensors", "kormo", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-06-29T09:48:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
mjpsm/Ubuntu-xgb-model	mjpsm	2025-09-25T04:16:53Z	0	0	null	[ "regression", "soulprint", "ubuntu", "xgboost", "culturally-rooted", "en", "license:mit", "model-index", "region:us" ]	null	2025-09-22T18:42:22Z	--- language: en license: mit tags: - regression - soulprint - ubuntu - xgboost - culturally-rooted model-index: - name: Ubuntu_xgb_model results: - task: type: regression name: Ubuntu Regression dataset: name: Ubuntu-regression_data.jsonl type: synthetic metrics: - type: mse value: 0.0121 - type: rmse value: 0.1101 - type: r2 value: 0.8817 --- # Ubuntu Regression Model (Soulprint Archetype) ## 🧩 Overview The Ubuntu_xgb_model is part of the Soulprint archetype family of models. It predicts an Ubuntu alignment score (0.0–1.0) for text inputs, where Ubuntu represents "I am because we are": harmony, inclusion, and community bridge-building. - 0.0–0.3 → Low Ubuntu (exclusion, selfishness, division) - 0.4–0.7 → Medium Ubuntu (partial inclusion, effort but incomplete) - 0.8–1.0 → High Ubuntu (harmony, belonging, collective well-being) This model is trained with XGBoost regression on a custom dataset of 918 rows, balanced across Low, Medium, and High Ubuntu examples. Data was generated using culturally diverse contexts (family, school, workplace, community, cultural rituals). --- ## 📊 Training Details - Framework: Python 3, scikit-learn, XGBoost - Embeddings: SentenceTransformer `"all-mpnet-base-v2"` - Algorithm: `XGBRegressor` - Training Size: 918 rows - Train/Test Split: 80/20 ### ⚙️ Hyperparameters - `n_estimators=300` - `learning_rate=0.05` - `max_depth=6` - `subsample=0.8` - `colsample_bytree=0.8` - `random_state=42` --- ## 📈 Evaluation Results On the held-out test set (20% of data): - MSE: 0.0121 - RMSE: 0.1101 - R² Score: 0.882 --- ## 🚀 Usage ### Load Model ```python import joblib import xgboost as xgb from sentence_transformers import SentenceTransformer from huggingface_hub import hf_hub_download # ----------------------------- # 1. Download model from Hugging Face Hub # ----------------------------- REPO_ID = "mjpsm/Ubuntu_xgb_model" # change if you used a different repo name FILENAME = "Ubuntu_xgb_model.pkl" model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) # ----------------------------- # 2. Load model + embedder # ----------------------------- model = joblib.load(model_path) embedder = SentenceTransformer("all-mpnet-base-v2") # ----------------------------- # 3. Example prediction # ----------------------------- text = "During our class project, I made sure everyone’s ideas were included." embedding = embedder.encode([text]) score = model.predict(embedding)[0] print("Predicted Ubuntu Score:", round(float(score), 3)) ``` ## 🌍 Applications - Community storytelling evaluation - Character alignment in cultural narratives - AI assistants tuned to Afrocentric archetypes - Training downstream models in the Soulprint system ## ⚠️ Limitations - Dataset is synthetic (generated + curated). Real-world generalization should be validated. - The model is context-specific to Ubuntu values and may not generalize beyond Afrocentric cultural framing. - Scores are approximate indicators — interpretation depends on narrative context.
mjpsm/Jali-xgb-model	mjpsm	2025-09-25T04:16:26Z	0	0	null	[ "regression", "xgboost", "soulprint", "archetypes", "jali", "en", "license:mit", "model-index", "region:us" ]	null	2025-09-24T12:28:49Z	--- language: en license: mit tags: - regression - xgboost - soulprint - archetypes - jali model-index: - name: Jali Regression Model results: - task: type: regression name: Expressiveness Scoring metrics: - type: mse value: 0.0086 - type: rmse value: 0.0928 - type: r2 value: 0.896 --- # Jali Regression Model ## Model Overview The Jali Regression Model predicts a continuous score between 0.0 and 1.0 that reflects the degree of Jali expressiveness in a text input. The Jali archetype represents the expressive voice, charisma, and rhythm in communication, rooted in the tradition of griots and modern spoken word artists. High scores indicate powerful, charismatic, and rhythmic expression, while low scores indicate weak, hesitant, or muted expression. - Model Type: Regression (XGBoost) - Embedding Model: SentenceTransformer `all-mpnet-base-v2` - Dataset Size: 368 rows (balanced across 0.0–1.0) - Output Range: 0.0 → 1.0 - Intended Use: To measure expressiveness and narrative impact in text, and to support culturally grounded AI projects under the Soulprint framework. --- ## Performance Metrics Evaluated on held-out test data: - MSE: 0.0086 - RMSE: 0.0928 - R² Score: 0.896 Interpretation: - Predictions are on average within ±0.1 of the true label. - The model explains nearly 90 percent of the variance in the dataset. - This performance indicates reliable and consistent regression results. --- ## Dataset Details The dataset was created with 368 labeled rows, designed to capture a spectrum of Jali expressiveness. - Inputs: Natural language reflections, scenarios, and short narratives varying in length (1–5 sentences). - Labels: Continuous scores from 0.0–1.0 indicating expressiveness level. - Balance: Equal coverage across weak (0.0–0.3), moderate (0.4–0.6), and strong (0.7–1.0) expressiveness. - Perspective Variety: Includes first-person, observer, audience, and group perspectives to avoid overfitting to “I” statements. --- ## Limitations - The dataset is synthetic and may not capture the full complexity of real-world expressive styles. - Cultural nuance beyond the Jali archetype may not be represented. - The model is optimized for short passages (1–5 sentences) and may not generalize well to longer documents. --- ## Intended Uses - Scoring expressiveness in reflective or narrative writing. - Supporting Soulprint-aligned projects that explore archetypal strengths. - Research into Afrocentric AI models that map cultural archetypes into measurable traits. Not intended for: - Judgment of individuals in real-world contexts. - Clinical, legal, or high-stakes decision making. --- ## How to Use ```python import joblib from sentence_transformers import SentenceTransformer from huggingface_hub import hf_hub_download # ----------------------------- # 1. Download model from Hugging Face Hub # ----------------------------- REPO_ID = "mjpsm/Jali-xgb-model" # replace with your repo if different FILENAME = "Jali_xgb_model.pkl" model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) # ----------------------------- # 2. Load model + embedder # ----------------------------- model = joblib.load(model_path) embedder = SentenceTransformer("all-mpnet-base-v2") # ----------------------------- # 3. Example prediction # ----------------------------- text = "The poet delivered each line with rhythm that moved the audience deeply." embedding = embedder.encode([text]) score = model.predict(embedding)[0] print("Predicted Jali Score:", round(float(score), 3)) ``` ## Soulprint context The Jali archetype is Expressive. It embodies the role of messenger, MC, and spoken word artist who transforms chaos into clarity and uplifts morale with charisma and rhythm. Symbolic inspirations include Maya Angelou, Gil Scott-Heron, and spoken word cyphers.
thefirstgoku/2510SEP_inter_v32_9	thefirstgoku	2025-09-25T04:14:42Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-25T04:14:02Z	--- 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).
SF-Foundation/FlashTopic-gpt-oss-20b-qat-0924-experimental	SF-Foundation	2025-09-25T04:14:17Z	0	0	null	[ "safetensors", "gpt_oss", "license:cc-by-nc-4.0", "8-bit", "mxfp4", "region:us" ]	null	2025-09-24T20:16:43Z	--- license: cc-by-nc-4.0 ---
epreep/vit-base-epreep-image-classifier	epreep	2025-09-25T04:12:34Z	0	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-09-25T04:12:17Z	--- 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]
mollysama/rwkv-mobile-models	mollysama	2025-09-25T04:12:07Z	9,379	7	null	[ "onnx", "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-12T09:37:48Z	--- license: apache-2.0 ---
mjpsm/Sankofa-xgb-model	mjpsm	2025-09-25T04:12:00Z	0	0	null	[ "regression", "xgboost", "embeddings", "soulprint", "sankofa", "en", "dataset:custom", "license:mit", "model-index", "region:us" ]	null	2025-09-25T03:49:07Z	--- language: en license: mit tags: - regression - xgboost - embeddings - soulprint - sankofa datasets: - custom metrics: - mse - rmse - r2 model-index: - name: Sankofa_xgb_model results: - task: type: regression name: Archetype Regression metrics: - name: MSE type: mean_squared_error value: 0.0143 - name: RMSE type: root_mean_squared_error value: 0.1198 - name: R² type: r2_score value: 0.824 --- # Sankofa XGBoost Regression Model ## 📖 Model Overview The Sankofa Regression Model is part of the Soulprint Archetype System, designed to measure how strongly a given text reflects the values of the Sankofa archetype. It uses SentenceTransformer embeddings (`all-mpnet-base-v2`) as input features and an XGBoost regressor trained on a 1,000-row curated dataset. - Architecture: SentenceTransformer embeddings + XGBoost regression - Output Range: 0.0 → 1.0 (Sankofa alignment score) - Training Size: 1,000 rows (balanced distribution) --- ## 🌍 What is Sankofa? The Sankofa archetype emphasizes learning from the past, honoring ancestral wisdom, and applying history to guide future actions. - High scores (0.7–1.0): Strong grounding in memory, reflection, and ancestral values - Mid scores (0.4–0.6): Some awareness of the past but shallow or inconsistent application - Low scores (0.0–0.3): Dismissal of history, impatience, or neglect of lessons from the past --- ## 📊 Training & Evaluation Training Methodology: - Inputs: Free-text statements - Labels: Float scores (0.0 → 1.0) for Sankofa alignment - Embeddings: `all-mpnet-base-v2` from SentenceTransformers - Model: XGBoost regressor Results: - MSE: 0.0143 - RMSE: 0.1198 - R²: 0.824 This means predictions are typically within ±0.12 of the true score, explaining 82% of dataset variance. --- ## 🚀 Intended Use - Measuring alignment of text to Sankofa archetype values - Research in Soulprint archetypes & culturally rooted AI models - Applications in AI agents, storytelling systems, and reflective analysis tools --- ## ⚠️ Limitations - The dataset is limited to 1,000 rows; performance could improve with more data. - The model is specific to Sankofa and should not be generalized to other archetypes. - Interpretability is dependent on the embedding model (`all-mpnet-base-v2`). --- ## 💡 Example Usage ```python import joblib from sentence_transformers import SentenceTransformer from huggingface_hub import hf_hub_download # ----------------------------- # 1. Download model from Hugging Face Hub # ----------------------------- REPO_ID = "mjpsm/Sankofa-xgb-model" FILENAME = "Sankofa_xgb_model.pkl" model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) # ----------------------------- # 2. Load model + embedder # ----------------------------- model = joblib.load(model_path) embedder = SentenceTransformer("all-mpnet-base-v2") # ----------------------------- # 3. Example prediction # ----------------------------- text = "The group studied old archives before planning, ensuring past mistakes were not repeated." embedding = embedder.encode([text]) score = model.predict(embedding)[0] print("Predicted Sankofa Score:", round(float(score), 3)) ```
kormo-lm/mtp_1view_1B_base_60BT	kormo-lm	2025-09-25T04:08:57Z	3	1	transformers	[ "transformers", "safetensors", "kormo", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-06-29T09:44:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
hai2131/cpo-beta-0.15	hai2131	2025-09-25T04:08:32Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:hai2131/sailor2-sft", "base_model:adapter:hai2131/sailor2-sft", "region:us" ]	null	2025-09-25T04:08:13Z	--- base_model: hai2131/sailor2-sft 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 0.15.2
brindana/100-1-qwen2.5-7b-deepmath-hard-lora	brindana	2025-09-25T04:06:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-23T13:23:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]

Subsets and Splits

Filtered Qwen2.5 Distill Models

Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.

Filtered Model Cards Count

Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.

Filtered Distill Qwen 7B Models

Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.

Filtered Qwen-7b Model Cards

The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.

Filtered Qwen 7B Model Cards

The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.

Qwen 7B Distilled Models

The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.

Qwen 7B Distilled Model Cards

The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.

Qwen 7B Distilled Models

Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.

Distilled Qwen 7B Models

The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.

Filtered Model Cards with Distill Qwen2.

Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.

Filtered Model Cards with Distill Qwen 7

The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.

Distill Qwen 7B Model Cards

The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.