librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-12 18:33:19	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 555 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-12 18:33:14	card stringlengths 11 1.01M
martinletec55/Mylora	martinletec55	2025-08-19T08:57:47Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-08-19T08:56:00Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/pykaso-output-1755517879199.jpeg text: '-' - output: url: images/pykaso-output-1755518189871.jpeg text: '-' - output: url: images/pykaso-output-1755518369462.jpeg text: '-' base_model: black-forest-labs/FLUX.1-dev instance_prompt: Katie --- # Katie <Gallery /> ## Model description This Lora is big boss ## Trigger words You should use `Katie` to trigger the image generation. ## Download model [Download](/martinletec55/Mylora/tree/main) them in the Files & versions tab.
chooseL1fe/blockassist-bc-thorny_flightless_albatross_1755593324	chooseL1fe	2025-08-19T08:55:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny flightless albatross", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T08:54:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny flightless albatross --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hf-audio/xcodec-hubert-librispeech	hf-audio	2025-08-19T08:53:05Z	0	1	transformers	[ "transformers", "safetensors", "xcodec", "feature-extraction", "dataset:openslr/librispeech_asr", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-08-18T08:37:09Z	--- library_name: transformers license: cc-by-4.0 datasets: - openslr/librispeech_asr --- # X-Codec (speech, HuBERT) This codec is intended for speech data. Original model is `xcodec_wavlm_more_data` from [this table](https://github.com/zhenye234/xcodec?tab=readme-ov-file#available-models).
josephr212/blockassist-bc-hoarse_frisky_dingo_1755591769	josephr212	2025-08-19T08:51:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hoarse frisky dingo", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T08:51:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hoarse frisky dingo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
KCS97/dog7	KCS97	2025-08-19T08:51:16Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-08-19T08:38:46Z	--- base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true instance_prompt: a photo of sks dog tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - KCS97/dog7 This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
hf-audio/xcodec-wavlm-more-data	hf-audio	2025-08-19T08:50:30Z	0	1	transformers	[ "transformers", "safetensors", "xcodec", "feature-extraction", "dataset:parler-tts/mls_eng", "base_model:microsoft/wavlm-base-plus", "base_model:finetune:microsoft/wavlm-base-plus", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-08-18T09:14:44Z	--- library_name: transformers license: cc-by-4.0 datasets: - parler-tts/mls_eng base_model: - microsoft/wavlm-base-plus --- # X-Codec (speech, WavLM) This codec is intended for speech data. Original model is `xcodec_wavlm_more_data` from [this table](https://github.com/zhenye234/xcodec?tab=readme-ov-file#available-models).
pruddywoody/SuperkarTAPI	pruddywoody	2025-08-19T08:48:48Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-19T08:48:48Z	--- license: apache-2.0 ---
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755592995	IvanJAjebu	2025-08-19T08:44:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T08:44:15Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
miguelsigmahot2/blockassist-bc-invisible_patterned_prawn_1755591187	miguelsigmahot2	2025-08-19T08:41:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "invisible patterned prawn", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T08:41:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - invisible patterned prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VoilaRaj/78_1Y85EE	VoilaRaj	2025-08-19T08:41:13Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T08:37:18Z	--- 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).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755592143	Ferdi3425	2025-08-19T08:30:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T08:30:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Alonc/device_to_cve_model_8B	Alonc	2025-08-19T08:28:51Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T08:28:48Z	--- base_model: unsloth/qwen3-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Alonc - License: apache-2.0 - Finetuned from model : unsloth/qwen3-8b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
FreedomIntelligence/AceGPT-v1.5-7B	FreedomIntelligence	2025-08-19T08:28:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "ar", "zh", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-27T02:48:38Z	--- license: apache-2.0 language: - ar - zh - en --- # <b>AceGPT</b> AceGPT is a fully fine-tuned generative text model collection based on LlaMA2, particularly in the Arabic language domain. This is the repository for the version 1.5 of 7B pre-trained model. --- ## Model Details We have released the AceGPT family of large language models, which is a collection of fully fine-tuned generative text models based on LlaMA2, ranging from 7B to 13B parameters. Our models include two main categories: AceGPT and AceGPT-chat. AceGPT-chat is an optimized version specifically designed for dialogue applications. It is worth mentioning that our models have demonstrated superior performance compared to all currently available open-source Arabic dialogue models in multiple benchmark tests. Furthermore, in our human evaluations, our models have shown comparable satisfaction levels to some closed-source models, such as ChatGPT, in the Arabic language. ## Model Developers We are from the King Abdullah University of Science and Technology (KAUST), the Chinese University of Hong Kong, Shenzhen (CUHKSZ), the Shenzhen Research Institute of Big Data (SRIBD), and King AbdulAziz University (KAU). ## Variations AceGPT families come in a range of parameter sizes —— 7B and 13B, each size of model has a base category and a -chat category. ## Paper The paper can be accessed at [link](https://huggingface.co/FreedomIntelligence/AceGPT-v1.5-13B-Chat/blob/main/Second_Language_(Arabic)_Acquisition_of_LLMs_via_Progressive_Vocabulary_Expansion.pdf). ## Input Models input text only. ## Output Models output text only. ## Model Evaluation Results Benchmark evaluation on [Arabic MMLU](https://github.com/FreedomIntelligence/AceGPT) are conducted using accuracy scores as metrics, following the evaluation framework available at https://github.com/FreedomIntelligence/AceGPT/tree/main. \| \| STEM \| Humanities \| Social Sciences \| Others \| Average \| \|------------------\|------\|------\|------\|------\|------\| \| Bloomz-7B-base \| 33.35 \| 29.29 \| 37.58 \| 34.53 \| 33.69 \| \| LLaMA2-7B-base \| 30.30 \| 29.33 \| 27.46 \| 30.78 \| 29.37 \| \| AceGPT-7B-base \| 29.73 \| 30.95 \| 33.45 \| 34.42 \| 32.14 \| \| AceGPT-v1.5-7B-base \| 33.03 \| 32.08 \| 35.39 \| 35.59 \| 34.03 \| \| LLaMA2-13B-base \| 32.94 \| 32.30 \| 33.42 \| 37.27 \| 33.76 \| \| Jais-13B-base \| 30.51 \| 31.25 \| 33.74 \| 33.42 \| 33.76 \| \| AceGPT-13B-base \| 36.60 \| 38.74 \| 43.76 \| <u>42.72</u> \| 40.45 \| \| AceGPT-v1.5-13B-base \| <u>36.13</u> \| <u>40.07</u> \| <u>45.43</u> \| 42.17 \| <u>40.95</u> \| \| Jais-30B-v1-base \| 32.67 \| 30.67 \| 42.13 \| 39.60 \| 36.27 \| \| ChatGPT 3.5 Turbo \| 43.38 \| 44.12 \| 55.57 \| 53.21 \| 49.07 \| Benchmark evaluation on [ArabicMMLU]((https://github.com/mbzuai-nlp/ArabicMMLU)), and assessed based on its source settings. \| \| STEM \| Social Sciences \| Humanities \| Arabic Language \| Other \| Average \| \|------------------\|------\|------\|------\|------\|------\|------\| \| Bloomz-7B-base \| - \| - \| - \| - \| - \| - \| \| LLaMA2-7B-base \| 33.7 \| 32.8 \| 33.5 \| 28.4 \| 36.7 \| 33.4 \| \| AceGPT-7B-base \| 35.4 \| 35.9 \| 36.2 \| 31.1 \| 41.7 \| 36.3 \| \| AceGPT-v1.5-7B-base \| 36.7 \| 36.5 \| 34.1 \| 30.0 \| 41.2 \| 37.0 \| \| LLaMA2-13B-base \| 32.9 \| 35.0 \| 37.8 \| 35.8 \| 39.3 \| 36.1 \| \| Jais-13B-base \| 30.3 \| 31.4 \| 33.6 \| 28.1 \| 36.3 \| 32.2 \| \| AceGPT-13B-base \| <u>42.7</u> \| 45.5 \| 48.3 \| 42.4 \| 50.7 \| 46.1 \| \| AceGPT-v1.5-13B-base \| 42.4 \| <u>45.7</u> \| 48.4 \| <u>46.3</u> \| <u>52.5</u> \| <u>47.6</u> \| \| Jais-30B-v1-base \| 39.5 \| 45.6 \| <u>50.5</u> \| 34.6 \| 49.1 \| 44.8 \| \| ChatGPT 3.5 Turbo \| 53.8 \| 57.0 \| 57.5 \| 57.6 \| 63.8 \| 57.7 \| ## Samples #### Sample1(abstract_algebra) * <b>input:</b> "فيما يلي أسئلة الاختيار من متعدد (مع الإجابات) حول جبر تجريدي\n\nسؤال: العثور على جميع قيم c في Z_3 بحيث يكون Z_3 [x]/(x^2+c) حقلًا.\nA. 0\nB. 1\nC. 2\nD. 3\nإجابة: B\n\nسؤال: البيان رقم 1 \| إذا كان aH عنصرًا في مجموعة العوامل ، فإن \| aH \| يقسم \| a \|. البيان رقم 2 \| إذا كانت H و K مجموعات فرعية لـ G ، فإن HK مجموعة فرعية لـ G.\nA. صحيح ، صحيح\nB. خطأ ، خطأ\nC. صحيح ، خطأ\nD. خطأ ، صحيح\nإجابة: B\n\nسؤال: العبارة 1 \| كل عنصر من مجموعة يولد مجموعة دورية من المجموعة. العبارة 2 \| المجموعة المتناظرة S_10 لديها 10 عناصر.\nA. صحيح، صحيح\nB. خطأ، خطأ\nC. صحيح، خطأ\nD. خطأ، صحيح\nإجابة: C\n\nسؤال: البيان 1\| كل وظيفة من مجموعة محدودة على نفسها يجب أن تكون واحدة لكل مجموعة. البيان 2 \| كل فرع فرعي لمجموعة أبيلية هو أبيلي.\nA. صحيح, صحيح\nB. خاطئ, خاطئ\nC. صحيح, خاطئ\nD. خاطئ, صحيح\nإجابة: A\n\nسؤال: اعثر على خاصية الحلقة 2Z.\nA. 0\nB. 3\nC. 12\nD. 30\nإجابة: A\n\nسؤال: ما هو الدرجة للامتداد الميداني الناتج من Q(sqrt(2), sqrt(3), sqrt(18)) على Q؟\nA. 0\nB. 4\nC. 2\nD. 6\nإجابة:" * <b>output:</b> "B\n\nسؤال: ما هو عدد العناصر" #### Sample2(business_ethics) * <b>input:</b> "فيما يلي أسئلة الاختيار من متعدد (مع الإجابات) حول أخلاقيات الأعمال\n\nسؤال: ما هي الحجج الأخلاقية المتعلقة بالمسؤولية الاجتماعية للشركات؟\nA. التكاليف الخارجية، القوة، الاستقلالية\nB. الإعلام، الموارد الضعيفة، التبادل التعاوني\nC. الإعلام، القوة، الاستقلالية\nD. التكاليف الخارجية، القوة، التبادل التعاوني\nإجابة: D\n\nسؤال: _______ هو المحاولة المباشرة لإدارة القضايا الأخلاقية أو المشاكل، سواء بشكل رسمي أو غير رسمي، من خلال سياسات وممارسات وبرامج محددة.\nA. المسؤولية الاجتماعية للشركات\nB. إدارة الأخلاقيات العملية\nC. الاستدامة\nD. إدارة البيئة\nإجابة: B\n\nسؤال: لضمان استقلال أعضاء مجلس الإدارة غير التنفيذية ، هناك عدد من الخطوات التي يمكن اتخاذها ، والتي تشمل اختيار الغير التنفيذيين من _______ الشركة ، وتعيينهم لمدة _________ ، وكذلك تعيينهم _________.\nA. خارج الشركة ، محدودة ، بشكل مستقل\nB. من الداخل ، محدودة ، بشكل متقطع\nC. خارج الشركة ، غير محدودة ، بشكل متقطع\nD. من الداخل ، غير محدودة ، بشكل مستقل\nإجابة: A\n\nسؤال: ما هي الأساليب التي يمكن للمدير الأمني الذي يسعى لتحقيق أهدافه الاختيار بينها؟\nA. العمل المباشر الغير عنيف ، العمل المباشر العنيف ، العمل غير المباشر ، الحملة الدعائية\nB. العمل غير المباشر ، العمل الأوتيل ، العمل المباشر الغير عنيف ، الحملة الإعلامية\nC. العمل غير المباشر ، العمل المباشر العنيف ، العمل المباشر غير العنيف المباشر ، الحملة الدعائية\nD. العمل المباشر الغير عنيف ، العمل الأوتيل ، العمل غير المباشر ، الحملة الإعلامية\nإجابة: C\n\nسؤال: على عكس _______ ، تهدف _______ إلى مكافأة السلوك الإيجابي للشركات. تم تعزيز نجاح مثل هذه الحملات من خلال استخدام ___________, الذي يتيح للحملات تيسير تحقيق الشركة لــ _________ .\nA. الحملات الاستهلاكية، الحملات الاستهلاكية العامة، تكنولوجيا سلسلة الكتل، التبرعات الخيرية\nB. الحملات التحفيزية، الحملات الاستهلاكية العامة، التكنولوجيا الرقمية، زيادة المبيعات\nC. الحملات الاستهلاكية، الحملات الشرائية، تكنولوجيا سلسلة الكتل، التبرعات الخيرية\nD. المقاطعات، الحملات التحفيزية، الحملات الرقمية، زيادة المبيعات\nإجابة: D\n\nسؤال: تُصبح _______ مثل البيتكوين أكثر انتشارًا وتحمل مجموعة كبيرة من الآثار الأخلاقية المرتبطة بها، على سبيل المثال، إنها _______ وأكثر _______. ومع ذلك، تم استخدامها أيضًا للمشاركة في _______.\nA. العملات الرقمية، مكلفة، آمنة، جرائم مالية\nB. العملات التقليدية، رخيصة، غير آمنة، العطاء الخيري\nC. العملات الرقمية، رخيصة، آمنة، جرائم مالية\nD. العملات التقليدية، مكلفة، غير آمنة، العطاء الخيري\nإجابة:" * <b>output:</b> "A\n\nسؤال: _______ هو" # Reference ``` @article{zhu2025second, title={Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion}, author={Zhu, Jianqing and Huang, Huang and Lin, Zhihang and Liang, Juhao and Tang, Zhengyang and Almubarak, Khalid and Alharthi, Mosen and An, Bang and He, Juncai and Wu, Xiangbo and Yu, Fei and Chen, Junying and Ma, Zhuoheng and Du, Yuhao and Hu, Yan and Zhang, He and Alghamdi, Emad A. and Zhang, Lian and Sun, Ruoyu and Li, Haizhou and Wang, Benyou and Xu, Jinchao}, journal={ACL 2025}, year={2025} } ```
donoway/BoolQ_Llama-3.2-1B-me3479q5	donoway	2025-08-19T08:26:59Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T08:06:37Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: BoolQ_Llama-3.2-1B-me3479q5 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. --> # BoolQ_Llama-3.2-1B-me3479q5 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2599 - Model Preparation Time: 0.0055 - Mdl: 10661.2190 - Accumulated Loss: 7389.7939 - Correct Preds: 2256.0 - Total Preds: 3270.0 - Accuracy: 0.6899 - Correct Gen Preds: 1847.0 - Gen Accuracy: 0.5648 - Correct Gen Preds 9642: 1328.0 - Correct Preds 9642: 1615.0 - Total Labels 9642: 2026.0 - Accuracy 9642: 0.7971 - Gen Accuracy 9642: 0.6555 - Correct Gen Preds 2822: 511.0 - Correct Preds 2822: 641.0 - Total Labels 2822: 1231.0 - Accuracy 2822: 0.5207 - Gen Accuracy 2822: 0.4151 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 120 - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Mdl \| Accumulated Loss \| Correct Preds \| Total Preds \| Accuracy \| Correct Gen Preds \| Gen Accuracy \| Correct Gen Preds 9642 \| Correct Preds 9642 \| Total Labels 9642 \| Accuracy 9642 \| Gen Accuracy 9642 \| Correct Gen Preds 2822 \| Correct Preds 2822 \| Total Labels 2822 \| Accuracy 2822 \| Gen Accuracy 2822 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\|:----------:\|:----------------:\|:-------------:\|:-----------:\|:--------:\|:-----------------:\|:------------:\|:----------------------:\|:------------------:\|:-----------------:\|:-------------:\|:-----------------:\|:----------------------:\|:------------------:\|:-----------------:\|:-------------:\|:-----------------:\| \| No log \| 0 \| 0 \| 0.7080 \| 0.0055 \| 3339.8933 \| 2315.0376 \| 2032.0 \| 3270.0 \| 0.6214 \| 2040.0 \| 0.6239 \| 2007.0 \| 2008.0 \| 2026.0 \| 0.9911 \| 0.9906 \| 24.0 \| 24.0 \| 1231.0 \| 0.0195 \| 0.0195 \| \| 0.6888 \| 1.0 \| 2 \| 1.5355 \| 0.0055 \| 7243.7816 \| 5021.0068 \| 1231.0 \| 3270.0 \| 0.3765 \| 1225.0 \| 0.3746 \| 0.0 \| 0.0 \| 2026.0 \| 0.0 \| 0.0 \| 1216.0 \| 1231.0 \| 1231.0 \| 1.0 \| 0.9878 \| \| 0.4873 \| 2.0 \| 4 \| 1.0878 \| 0.0055 \| 5131.7632 \| 3557.0672 \| 2027.0 \| 3270.0 \| 0.6199 \| 641.0 \| 0.1960 \| 631.0 \| 2023.0 \| 2026.0 \| 0.9985 \| 0.3115 \| 1.0 \| 4.0 \| 1231.0 \| 0.0032 \| 0.0008 \| \| 0.2411 \| 3.0 \| 6 \| 1.0755 \| 0.0055 \| 5073.9259 \| 3516.9774 \| 1560.0 \| 3270.0 \| 0.4771 \| 1290.0 \| 0.3945 \| 227.0 \| 388.0 \| 2026.0 \| 0.1915 \| 0.1120 \| 1054.0 \| 1172.0 \| 1231.0 \| 0.9521 \| 0.8562 \| \| 0.2022 \| 4.0 \| 8 \| 0.8677 \| 0.0055 \| 4093.3239 \| 2837.2759 \| 1917.0 \| 3270.0 \| 0.5862 \| 1654.0 \| 0.5058 \| 694.0 \| 884.0 \| 2026.0 \| 0.4363 \| 0.3425 \| 951.0 \| 1033.0 \| 1231.0 \| 0.8392 \| 0.7725 \| \| 0.0205 \| 5.0 \| 10 \| 0.9632 \| 0.0055 \| 4543.9737 \| 3149.6426 \| 2200.0 \| 3270.0 \| 0.6728 \| 2086.0 \| 0.6379 \| 1619.0 \| 1705.0 \| 2026.0 \| 0.8416 \| 0.7991 \| 458.0 \| 495.0 \| 1231.0 \| 0.4021 \| 0.3721 \| \| 0.0011 \| 6.0 \| 12 \| 1.6852 \| 0.0055 \| 7949.9900 \| 5510.5132 \| 2228.0 \| 3270.0 \| 0.6813 \| 1965.0 \| 0.6009 \| 1438.0 \| 1632.0 \| 2026.0 \| 0.8055 \| 0.7098 \| 518.0 \| 596.0 \| 1231.0 \| 0.4842 \| 0.4208 \| \| 0.0001 \| 7.0 \| 14 \| 2.2599 \| 0.0055 \| 10661.2190 \| 7389.7939 \| 2256.0 \| 3270.0 \| 0.6899 \| 1847.0 \| 0.5648 \| 1328.0 \| 1615.0 \| 2026.0 \| 0.7971 \| 0.6555 \| 511.0 \| 641.0 \| 1231.0 \| 0.5207 \| 0.4151 \| \| 0.0 \| 8.0 \| 16 \| 2.5994 \| 0.0055 \| 12262.8330 \| 8499.9481 \| 2250.0 \| 3270.0 \| 0.6881 \| 1703.0 \| 0.5208 \| 1190.0 \| 1594.0 \| 2026.0 \| 0.7868 \| 0.5874 \| 504.0 \| 656.0 \| 1231.0 \| 0.5329 \| 0.4094 \| \| 0.0 \| 9.0 \| 18 \| 2.7853 \| 0.0055 \| 13140.1064 \| 9108.0277 \| 2245.0 \| 3270.0 \| 0.6865 \| 1616.0 \| 0.4942 \| 1106.0 \| 1579.0 \| 2026.0 \| 0.7794 \| 0.5459 \| 501.0 \| 666.0 \| 1231.0 \| 0.5410 \| 0.4070 \| \| 0.0 \| 10.0 \| 20 \| 2.8947 \| 0.0055 \| 13656.2114 \| 9465.7644 \| 2244.0 \| 3270.0 \| 0.6862 \| 1550.0 \| 0.4740 \| 1053.0 \| 1574.0 \| 2026.0 \| 0.7769 \| 0.5197 \| 488.0 \| 670.0 \| 1231.0 \| 0.5443 \| 0.3964 \| \| 0.0 \| 11.0 \| 22 \| 2.9647 \| 0.0055 \| 13986.3195 \| 9694.5779 \| 2242.0 \| 3270.0 \| 0.6856 \| 1525.0 \| 0.4664 \| 1028.0 \| 1567.0 \| 2026.0 \| 0.7734 \| 0.5074 \| 488.0 \| 675.0 \| 1231.0 \| 0.5483 \| 0.3964 \| \| 0.0 \| 12.0 \| 24 \| 3.0204 \| 0.0055 \| 14249.1044 \| 9876.7265 \| 2241.0 \| 3270.0 \| 0.6853 \| 1520.0 \| 0.4648 \| 1026.0 \| 1564.0 \| 2026.0 \| 0.7720 \| 0.5064 \| 485.0 \| 677.0 \| 1231.0 \| 0.5500 \| 0.3940 \| \| 0.0 \| 13.0 \| 26 \| 3.0632 \| 0.0055 \| 14450.8337 \| 10016.5547 \| 2242.0 \| 3270.0 \| 0.6856 \| 1517.0 \| 0.4639 \| 1032.0 \| 1566.0 \| 2026.0 \| 0.7730 \| 0.5094 \| 476.0 \| 676.0 \| 1231.0 \| 0.5491 \| 0.3867 \| \| 0.0 \| 14.0 \| 28 \| 3.0930 \| 0.0055 \| 14591.5894 \| 10114.1191 \| 2239.0 \| 3270.0 \| 0.6847 \| 1541.0 \| 0.4713 \| 1050.0 \| 1568.0 \| 2026.0 \| 0.7739 \| 0.5183 \| 482.0 \| 671.0 \| 1231.0 \| 0.5451 \| 0.3916 \| \| 0.0 \| 15.0 \| 30 \| 3.1175 \| 0.0055 \| 14707.0215 \| 10194.1305 \| 2239.0 \| 3270.0 \| 0.6847 \| 1557.0 \| 0.4761 \| 1064.0 \| 1568.0 \| 2026.0 \| 0.7739 \| 0.5252 \| 484.0 \| 671.0 \| 1231.0 \| 0.5451 \| 0.3932 \| \| 0.0 \| 16.0 \| 32 \| 3.1366 \| 0.0055 \| 14797.0392 \| 10256.5260 \| 2237.0 \| 3270.0 \| 0.6841 \| 1567.0 \| 0.4792 \| 1073.0 \| 1567.0 \| 2026.0 \| 0.7734 \| 0.5296 \| 485.0 \| 670.0 \| 1231.0 \| 0.5443 \| 0.3940 \| \| 0.0 \| 17.0 \| 34 \| 3.1519 \| 0.0055 \| 14869.3338 \| 10306.6368 \| 2241.0 \| 3270.0 \| 0.6853 \| 1579.0 \| 0.4829 \| 1085.0 \| 1572.0 \| 2026.0 \| 0.7759 \| 0.5355 \| 485.0 \| 669.0 \| 1231.0 \| 0.5435 \| 0.3940 \| \| 0.0 \| 18.0 \| 36 \| 3.1598 \| 0.0055 \| 14906.7599 \| 10332.5786 \| 2244.0 \| 3270.0 \| 0.6862 \| 1593.0 \| 0.4872 \| 1096.0 \| 1573.0 \| 2026.0 \| 0.7764 \| 0.5410 \| 488.0 \| 671.0 \| 1231.0 \| 0.5451 \| 0.3964 \| \| 0.0 \| 19.0 \| 38 \| 3.1724 \| 0.0055 \| 14965.9286 \| 10373.5912 \| 2242.0 \| 3270.0 \| 0.6856 \| 1603.0 \| 0.4902 \| 1104.0 \| 1573.0 \| 2026.0 \| 0.7764 \| 0.5449 \| 490.0 \| 669.0 \| 1231.0 \| 0.5435 \| 0.3981 \| \| 0.0 \| 20.0 \| 40 \| 3.1785 \| 0.0055 \| 14994.7059 \| 10393.5381 \| 2247.0 \| 3270.0 \| 0.6872 \| 1604.0 \| 0.4905 \| 1106.0 \| 1577.0 \| 2026.0 \| 0.7784 \| 0.5459 \| 489.0 \| 670.0 \| 1231.0 \| 0.5443 \| 0.3972 \| \| 0.0 \| 21.0 \| 42 \| 3.1864 \| 0.0055 \| 15032.1352 \| 10419.4821 \| 2245.0 \| 3270.0 \| 0.6865 \| 1613.0 \| 0.4933 \| 1113.0 \| 1578.0 \| 2026.0 \| 0.7789 \| 0.5494 \| 491.0 \| 667.0 \| 1231.0 \| 0.5418 \| 0.3989 \| \| 0.0 \| 22.0 \| 44 \| 3.1900 \| 0.0055 \| 15049.2090 \| 10431.3168 \| 2242.0 \| 3270.0 \| 0.6856 \| 1620.0 \| 0.4954 \| 1117.0 \| 1576.0 \| 2026.0 \| 0.7779 \| 0.5513 \| 494.0 \| 666.0 \| 1231.0 \| 0.5410 \| 0.4013 \| \| 0.0 \| 23.0 \| 46 \| 3.1960 \| 0.0055 \| 15077.5586 \| 10450.9672 \| 2242.0 \| 3270.0 \| 0.6856 \| 1621.0 \| 0.4957 \| 1120.0 \| 1575.0 \| 2026.0 \| 0.7774 \| 0.5528 \| 492.0 \| 667.0 \| 1231.0 \| 0.5418 \| 0.3997 \| \| 0.0 \| 24.0 \| 48 \| 3.1986 \| 0.0055 \| 15089.7720 \| 10459.4329 \| 2243.0 \| 3270.0 \| 0.6859 \| 1625.0 \| 0.4969 \| 1125.0 \| 1576.0 \| 2026.0 \| 0.7779 \| 0.5553 \| 491.0 \| 667.0 \| 1231.0 \| 0.5418 \| 0.3989 \| \| 0.0 \| 25.0 \| 50 \| 3.2004 \| 0.0055 \| 15098.4212 \| 10465.4281 \| 2241.0 \| 3270.0 \| 0.6853 \| 1627.0 \| 0.4976 \| 1123.0 \| 1575.0 \| 2026.0 \| 0.7774 \| 0.5543 \| 495.0 \| 666.0 \| 1231.0 \| 0.5410 \| 0.4021 \| \| 0.0 \| 26.0 \| 52 \| 3.2032 \| 0.0055 \| 15111.4442 \| 10474.4550 \| 2246.0 \| 3270.0 \| 0.6869 \| 1629.0 \| 0.4982 \| 1127.0 \| 1577.0 \| 2026.0 \| 0.7784 \| 0.5563 \| 493.0 \| 669.0 \| 1231.0 \| 0.5435 \| 0.4005 \| \| 0.0 \| 27.0 \| 54 \| 3.2052 \| 0.0055 \| 15120.6726 \| 10480.8516 \| 2244.0 \| 3270.0 \| 0.6862 \| 1633.0 \| 0.4994 \| 1133.0 \| 1577.0 \| 2026.0 \| 0.7784 \| 0.5592 \| 491.0 \| 667.0 \| 1231.0 \| 0.5418 \| 0.3989 \| \| 0.0 \| 28.0 \| 56 \| 3.2064 \| 0.0055 \| 15126.6264 \| 10484.9784 \| 2245.0 \| 3270.0 \| 0.6865 \| 1634.0 \| 0.4997 \| 1127.0 \| 1576.0 \| 2026.0 \| 0.7779 \| 0.5563 \| 498.0 \| 669.0 \| 1231.0 \| 0.5435 \| 0.4045 \| \| 0.0 \| 29.0 \| 58 \| 3.2093 \| 0.0055 \| 15140.1416 \| 10494.3465 \| 2244.0 \| 3270.0 \| 0.6862 \| 1637.0 \| 0.5006 \| 1138.0 \| 1576.0 \| 2026.0 \| 0.7779 \| 0.5617 \| 490.0 \| 668.0 \| 1231.0 \| 0.5426 \| 0.3981 \| \| 0.0 \| 30.0 \| 60 \| 3.2123 \| 0.0055 \| 15154.4079 \| 10504.2351 \| 2240.0 \| 3270.0 \| 0.6850 \| 1643.0 \| 0.5024 \| 1138.0 \| 1577.0 \| 2026.0 \| 0.7784 \| 0.5617 \| 496.0 \| 663.0 \| 1231.0 \| 0.5386 \| 0.4029 \| \| 0.0 \| 31.0 \| 62 \| 3.2131 \| 0.0055 \| 15158.3154 \| 10506.9436 \| 2242.0 \| 3270.0 \| 0.6856 \| 1640.0 \| 0.5015 \| 1137.0 \| 1576.0 \| 2026.0 \| 0.7779 \| 0.5612 \| 494.0 \| 666.0 \| 1231.0 \| 0.5410 \| 0.4013 \| \| 0.0 \| 32.0 \| 64 \| 3.2138 \| 0.0055 \| 15161.4034 \| 10509.0840 \| 2240.0 \| 3270.0 \| 0.6850 \| 1647.0 \| 0.5037 \| 1144.0 \| 1576.0 \| 2026.0 \| 0.7779 \| 0.5647 \| 494.0 \| 664.0 \| 1231.0 \| 0.5394 \| 0.4013 \| \| 0.0 \| 33.0 \| 66 \| 3.2162 \| 0.0055 \| 15172.9442 \| 10517.0835 \| 2242.0 \| 3270.0 \| 0.6856 \| 1650.0 \| 0.5046 \| 1146.0 \| 1578.0 \| 2026.0 \| 0.7789 \| 0.5656 \| 495.0 \| 664.0 \| 1231.0 \| 0.5394 \| 0.4021 \| \| 0.0 \| 34.0 \| 68 \| 3.2208 \| 0.0055 \| 15194.6156 \| 10532.1050 \| 2236.0 \| 3270.0 \| 0.6838 \| 1650.0 \| 0.5046 \| 1144.0 \| 1574.0 \| 2026.0 \| 0.7769 \| 0.5647 \| 497.0 \| 662.0 \| 1231.0 \| 0.5378 \| 0.4037 \| \| 0.0 \| 35.0 \| 70 \| 3.2202 \| 0.0055 \| 15191.4896 \| 10529.9382 \| 2241.0 \| 3270.0 \| 0.6853 \| 1650.0 \| 0.5046 \| 1143.0 \| 1577.0 \| 2026.0 \| 0.7784 \| 0.5642 \| 498.0 \| 664.0 \| 1231.0 \| 0.5394 \| 0.4045 \| \| 0.0 \| 36.0 \| 72 \| 3.2211 \| 0.0055 \| 15196.0934 \| 10533.1293 \| 2241.0 \| 3270.0 \| 0.6853 \| 1654.0 \| 0.5058 \| 1148.0 \| 1577.0 \| 2026.0 \| 0.7784 \| 0.5666 \| 497.0 \| 664.0 \| 1231.0 \| 0.5394 \| 0.4037 \| \| 0.0 \| 37.0 \| 74 \| 3.2230 \| 0.0055 \| 15204.6715 \| 10539.0752 \| 2243.0 \| 3270.0 \| 0.6859 \| 1655.0 \| 0.5061 \| 1150.0 \| 1577.0 \| 2026.0 \| 0.7784 \| 0.5676 \| 496.0 \| 666.0 \| 1231.0 \| 0.5410 \| 0.4029 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
koloni/blockassist-bc-deadly_graceful_stingray_1755584085	koloni	2025-08-19T08:25:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T06:41:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hoan17/saving_LOe400s16_scratch_8	hoan17	2025-08-19T08:25:31Z	0	0	diffusers	[ "diffusers", "safetensors", "trl", "o2o", "reinforcement-learning", "text-to-image", "stable-diffusion", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-08-19T08:25:02Z	--- license: apache-2.0 tags: - trl - o2o - diffusers - reinforcement-learning - text-to-image - stable-diffusion --- # TRL O2O Model This is a diffusion model that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for image generation conditioned with text.
Jansenhbar/dummy-model	Jansenhbar	2025-08-19T08:24:13Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-08-19T08:23:58Z	--- 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]
sungkwan2/segformer-b0-scene-parse-150	sungkwan2	2025-08-19T08:24:07Z	2	0	transformers	[ "transformers", "tensorboard", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	null	2025-04-23T12:00:05Z	--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - generated_from_trainer model-index: - name: segformer-b0-scene-parse-150 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. --> # segformer-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.1486 - Mean Iou: 0.0000 - Mean Accuracy: 0.0001 - Overall Accuracy: 0.0001 ## 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\| \| 4.9721 \| 0.025 \| 1 \| 5.0059 \| 0.0007 \| 0.0148 \| 0.0063 \| \| 4.9475 \| 0.05 \| 2 \| 5.0027 \| 0.0007 \| 0.0140 \| 0.0060 \| \| 4.9457 \| 0.075 \| 3 \| 4.9996 \| 0.0008 \| 0.0144 \| 0.0063 \| \| 4.9923 \| 0.1 \| 4 \| 4.9959 \| 0.0008 \| 0.0142 \| 0.0063 \| \| 5.0016 \| 0.125 \| 5 \| 4.9912 \| 0.0009 \| 0.0153 \| 0.0069 \| \| 4.9753 \| 0.15 \| 6 \| 4.9876 \| 0.0008 \| 0.0149 \| 0.0070 \| \| 4.8799 \| 0.175 \| 7 \| 4.9824 \| 0.0006 \| 0.0108 \| 0.0051 \| \| 4.9689 \| 0.2 \| 8 \| 4.9767 \| 0.0006 \| 0.0095 \| 0.0045 \| \| 4.9046 \| 0.225 \| 9 \| 4.9697 \| 0.0006 \| 0.0093 \| 0.0044 \| \| 4.8772 \| 0.25 \| 10 \| 4.9629 \| 0.0005 \| 0.0074 \| 0.0035 \| \| 4.7839 \| 0.275 \| 11 \| 4.9574 \| 0.0005 \| 0.0084 \| 0.0038 \| \| 4.9577 \| 0.3 \| 12 \| 4.9500 \| 0.0005 \| 0.0074 \| 0.0031 \| \| 4.8491 \| 0.325 \| 13 \| 4.9411 \| 0.0004 \| 0.0067 \| 0.0026 \| \| 4.8449 \| 0.35 \| 14 \| 4.9340 \| 0.0004 \| 0.0070 \| 0.0026 \| \| 4.8899 \| 0.375 \| 15 \| 4.9229 \| 0.0003 \| 0.0051 \| 0.0020 \| \| 4.7924 \| 0.4 \| 16 \| 4.9163 \| 0.0003 \| 0.0050 \| 0.0019 \| \| 4.7651 \| 0.425 \| 17 \| 4.9072 \| 0.0003 \| 0.0043 \| 0.0016 \| \| 4.7951 \| 0.45 \| 18 \| 4.8953 \| 0.0002 \| 0.0035 \| 0.0013 \| \| 4.7355 \| 0.475 \| 19 \| 4.8865 \| 0.0002 \| 0.0028 \| 0.0010 \| \| 4.7461 \| 0.5 \| 20 \| 4.8723 \| 0.0002 \| 0.0026 \| 0.0008 \| \| 4.704 \| 0.525 \| 21 \| 4.8606 \| 0.0002 \| 0.0022 \| 0.0007 \| \| 4.7775 \| 0.55 \| 22 \| 4.8484 \| 0.0001 \| 0.0017 \| 0.0006 \| \| 4.7081 \| 0.575 \| 23 \| 4.8331 \| 0.0001 \| 0.0013 \| 0.0004 \| \| 4.7681 \| 0.6 \| 24 \| 4.8187 \| 0.0001 \| 0.0009 \| 0.0003 \| \| 4.7297 \| 0.625 \| 25 \| 4.8037 \| 0.0001 \| 0.0008 \| 0.0003 \| \| 4.8181 \| 0.65 \| 26 \| 4.7882 \| 0.0001 \| 0.0007 \| 0.0002 \| \| 4.833 \| 0.675 \| 27 \| 4.7748 \| 0.0001 \| 0.0006 \| 0.0002 \| \| 4.7222 \| 0.7 \| 28 \| 4.7575 \| 0.0000 \| 0.0004 \| 0.0002 \| \| 4.6457 \| 0.725 \| 29 \| 4.7389 \| 0.0000 \| 0.0004 \| 0.0002 \| \| 4.7089 \| 0.75 \| 30 \| 4.7236 \| 0.0000 \| 0.0005 \| 0.0002 \| \| 4.543 \| 0.775 \| 31 \| 4.7079 \| 0.0001 \| 0.0006 \| 0.0002 \| \| 4.5529 \| 0.8 \| 32 \| 4.6963 \| 0.0001 \| 0.0006 \| 0.0003 \| \| 4.7005 \| 0.825 \| 33 \| 4.6759 \| 0.0001 \| 0.0006 \| 0.0003 \| \| 4.4735 \| 0.85 \| 34 \| 4.6630 \| 0.0001 \| 0.0008 \| 0.0004 \| \| 4.6562 \| 0.875 \| 35 \| 4.6468 \| 0.0001 \| 0.0009 \| 0.0004 \| \| 4.5902 \| 0.9 \| 36 \| 4.6274 \| 0.0001 \| 0.0008 \| 0.0004 \| \| 4.4974 \| 0.925 \| 37 \| 4.6125 \| 0.0001 \| 0.0008 \| 0.0004 \| \| 4.524 \| 0.95 \| 38 \| 4.5967 \| 0.0001 \| 0.0011 \| 0.0005 \| \| 4.5527 \| 0.975 \| 39 \| 4.5826 \| 0.0001 \| 0.0011 \| 0.0005 \| \| 4.5165 \| 1.0 \| 40 \| 4.5627 \| 0.0001 \| 0.0010 \| 0.0005 \| \| 4.6337 \| 1.025 \| 41 \| 4.5502 \| 0.0001 \| 0.0012 \| 0.0006 \| \| 4.4551 \| 1.05 \| 42 \| 4.5425 \| 0.0001 \| 0.0012 \| 0.0005 \| \| 4.4697 \| 1.075 \| 43 \| 4.5294 \| 0.0001 \| 0.0006 \| 0.0003 \| \| 4.4967 \| 1.1 \| 44 \| 4.5065 \| 0.0001 \| 0.0007 \| 0.0003 \| \| 4.4839 \| 1.125 \| 45 \| 4.4896 \| 0.0000 \| 0.0004 \| 0.0002 \| \| 4.4394 \| 1.15 \| 46 \| 4.4699 \| 0.0000 \| 0.0003 \| 0.0001 \| \| 4.4557 \| 1.175 \| 47 \| 4.4511 \| 0.0000 \| 0.0003 \| 0.0001 \| \| 4.2669 \| 1.2 \| 48 \| 4.4475 \| 0.0000 \| 0.0003 \| 0.0001 \| \| 4.3143 \| 1.225 \| 49 \| 4.4325 \| 0.0000 \| 0.0002 \| 0.0001 \| \| 4.4519 \| 1.25 \| 50 \| 4.4195 \| 0.0000 \| 0.0002 \| 0.0001 \| \| 4.5376 \| 1.275 \| 51 \| 4.4092 \| 0.0000 \| 0.0001 \| 0.0001 \| \| 4.2617 \| 1.3 \| 52 \| 4.4058 \| 0.0000 \| 0.0001 \| 0.0000 \| \| 4.2813 \| 1.325 \| 53 \| 4.3936 \| 0.0000 \| 0.0001 \| 0.0000 \| \| 4.5218 \| 1.35 \| 54 \| 4.3867 \| 0.0000 \| 0.0002 \| 0.0001 \| \| 4.4805 \| 1.375 \| 55 \| 4.3691 \| 0.0000 \| 0.0002 \| 0.0001 \| \| 4.184 \| 1.4 \| 56 \| 4.3574 \| 0.0000 \| 0.0002 \| 0.0001 \| \| 4.2208 \| 1.425 \| 57 \| 4.3606 \| 0.0000 \| 0.0001 \| 0.0001 \| \| 4.5288 \| 1.45 \| 58 \| 4.3579 \| 0.0000 \| 0.0001 \| 0.0001 \| \| 4.3959 \| 1.475 \| 59 \| 4.3421 \| 0.0000 \| 0.0001 \| 0.0000 \| \| 4.2578 \| 1.5 \| 60 \| 4.3403 \| 0.0000 \| 0.0001 \| 0.0000 \| \| 4.3504 \| 1.525 \| 61 \| 4.3307 \| 0.0000 \| 0.0001 \| 0.0000 \| \| 4.2364 \| 1.55 \| 62 \| 4.3177 \| 0.0000 \| 0.0001 \| 0.0000 \| \| 4.3248 \| 1.575 \| 63 \| 4.2924 \| 0.0000 \| 0.0000 \| 0.0000 \| \| 4.2771 \| 1.6 \| 64 \| 4.2698 \| 0.0000 \| 0.0000 \| 0.0000 \| \| 4.2447 \| 1.625 \| 65 \| 4.2533 \| 0.0000 \| 0.0000 \| 0.0000 \| \| 4.4481 \| 1.65 \| 66 \| 4.2418 \| 0.0000 \| 0.0000 \| 0.0000 \| \| 4.1369 \| 1.675 \| 67 \| 4.2374 \| 0.0000 \| 0.0000 \| 0.0000 \| \| 4.2266 \| 1.7 \| 68 \| 4.2305 \| 0.0000 \| 0.0000 \| 0.0000 \| \| 4.5113 \| 1.725 \| 69 \| 4.2225 \| 0.0000 \| 0.0000 \| 0.0000 \| \| 4.4737 \| 1.75 \| 70 \| 4.2077 \| 0.0000 \| 0.0000 \| 0.0000 \| \| 4.4571 \| 1.775 \| 71 \| 4.1960 \| 0.0000 \| 0.0001 \| 0.0000 \| \| 4.2179 \| 1.8 \| 72 \| 4.1824 \| 0.0000 \| 0.0001 \| 0.0000 \| \| 4.5426 \| 1.825 \| 73 \| 4.1654 \| 0.0000 \| 0.0002 \| 0.0001 \| \| 4.3632 \| 1.85 \| 74 \| 4.1572 \| 0.0000 \| 0.0002 \| 0.0001 \| \| 4.2132 \| 1.875 \| 75 \| 4.1628 \| 0.0000 \| 0.0002 \| 0.0001 \| \| 4.3442 \| 1.9 \| 76 \| 4.1621 \| 0.0000 \| 0.0001 \| 0.0000 \| \| 4.4454 \| 1.925 \| 77 \| 4.1647 \| 0.0000 \| 0.0001 \| 0.0000 \| \| 4.1564 \| 1.95 \| 78 \| 4.1691 \| 0.0000 \| 0.0001 \| 0.0000 \| \| 4.5028 \| 1.975 \| 79 \| 4.1513 \| 0.0000 \| 0.0002 \| 0.0001 \| \| 4.3814 \| 2.0 \| 80 \| 4.1486 \| 0.0000 \| 0.0001 \| 0.0001 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
ankitkushwaha90/Image_transformer_algorithm	ankitkushwaha90	2025-08-19T08:14:56Z	0	0	adapter-transformers	[ "adapter-transformers", "finance", "text-classification", "en", "dataset:UCSC-VLAA/GPT-Image-Edit-1.5M", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]	text-classification	2025-08-19T08:08:13Z	--- license: mit datasets: - UCSC-VLAA/GPT-Image-Edit-1.5M language: - en metrics: - accuracy base_model: - stabilityai/stable-diffusion-xl-base-1.0 new_version: openai/gpt-oss-120b pipeline_tag: text-classification library_name: adapter-transformers tags: - finance --- # 🚀 Stable Diffusion with Transformers (Advanced Training) This project demonstrates how to train a Stable Diffusion-like model using an image dataset with advanced Transformer-based denoising. The implementation leverages PyTorch + Hugging Face Diffusers + Transformers. --- ## 📌 Overview Stable Diffusion is a Latent Diffusion Model (LDM) that generates images by: 1. Encoding images into a latent space using a VAE (Variational Autoencoder). 2. Adding Gaussian noise to the latents across multiple time steps. 3. Training a denoising Transformer/UNet to remove noise step by step. 4. Using a text encoder (CLIP) for prompt conditioning. 5. Decoding the cleaned latents back to an image. --- ## 🔬 Architecture ```mermaid graph TD; A[Input Image] -->\|VAE Encoder\| B[Latent Space]; B -->\|Add Noise\| C[Noisy Latents]; C -->\|Transformer / UNet Denoiser\| D[Clean Latents]; D -->\|VAE Decoder\| E[Output Image]; F[Text Prompt] -->\|CLIP Encoder\| C; ``` - VAE → Compresses image → latent space - Transformer/UNet → Learns to denoise latent - Text Encoder → Aligns text + image - Noise Scheduler → Controls forward & reverse diffusion ## 📂 Dataset - Images should be resized (256x256) and normalized to [-1, 1]. - Optional: Provide text captions for conditioning. - Example: ```bash data/ ├── class1/ │ ├── img1.png │ └── img2.jpg ├── class2/ │ ├── img3.png │ └── img4.jpg ``` ## ⚙️ Training Algorithm The training process for Stable Diffusion with Transformers follows these steps: 1. Encode Images → Pass input images through a VAE Encoder to obtain latent representations. 2. Sample Noise & Timestep → Randomly sample Gaussian noise and a timestep `t`. 3. Add Noise → Corrupt the latent vectors with noise according to timestep `t`. 4. Text Conditioning → Encode text prompts using CLIP (or another Transformer text encoder). 5. Noise Prediction → Feed the noisy latents + text embeddings into the Transformer/UNet to predict the added noise. 6. Compute Loss → Calculate the Mean Squared Error (MSE) between predicted noise and true noise. 7. Backpropagation → Update model weights using gradient descent. --- ```mermaid flowchart TD A[Image] -->\|VAE Encoder\| B[Latent Space] B -->\|Add Noise + t\| C[Noisy Latents] D[Text Prompt] -->\|CLIP Encoder\| C C -->\|Transformer / UNet\| E[Predicted Noise] E -->\|MSE Loss\| F[Training Update] ``` ## 🧑‍💻 Example Training Code ```python from diffusers import UNet2DConditionModel, DDPMScheduler, AutoencoderKL from transformers import CLIPTextModel, CLIPTokenizer import torch, torch.nn as nn from torchvision import datasets, transforms from torch.utils.data import DataLoader # Dataset transform = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]) ]) dataset = datasets.ImageFolder("path_to_images", transform=transform) dataloader = DataLoader(dataset, batch_size=8, shuffle=True) # Components vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse") unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet") tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32") text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32") scheduler = DDPMScheduler(num_train_timesteps=1000) device = "cuda" if torch.cuda.is_available() else "cpu" vae, unet, text_encoder = vae.to(device), unet.to(device), text_encoder.to(device) optimizer = torch.optim.AdamW(unet.parameters(), lr=1e-4) # Training Loop for epoch in range(10): for images, _ in dataloader: images = images.to(device) latents = vae.encode(images).latent_dist.sample() * 0.18215 noise = torch.randn_like(latents) timesteps = torch.randint(0, scheduler.num_train_timesteps, (latents.shape[0],), device=device) noisy_latents = scheduler.add_noise(latents, noise, timesteps) text_inputs = tokenizer(["a photo"], padding="max_length", return_tensors="pt").to(device) text_embeds = text_encoder(text_inputs.input_ids).last_hidden_state noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states=text_embeds).sample loss = nn.MSELoss()(noise_pred, noise) optimizer.zero_grad() loss.backward() optimizer.step() print(f"Epoch {epoch} \| Loss: {loss.item()}") ``` ## 💾 Saving & Inference # Save trained UNet ```python torch.save(unet.state_dict(), "unet_trained.pth") # Inference pipeline # 1. Sample random latent # 2. Iteratively denoise with scheduler + trained UNet # 3. Decode with VAE → image ``` ### 📖 References - Stable Diffusion Paper - Hugging Face Diffusers - Diffusion Transformer (DiT) ## ✅ Future Work Replace UNet with pure Transformer (DiT). Use larger text encoders (T5/DeBERTa). Train with custom captioned datasets.
VoilaRaj/78_3pdGH2	VoilaRaj	2025-08-19T08:13:40Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T08:09:42Z	--- 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).
MajdShamaly12/gpt-oss-20b-kaggle	MajdShamaly12	2025-08-19T08:07:20Z	0	0	transformers	[ "transformers", "safetensors", "gpt_oss", "text-generation", "vllm", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "8-bit", "mxfp4", "region:us" ]	text-generation	2025-08-19T07:57:09Z	--- license: apache-2.0 pipeline_tag: text-generation library_name: transformers tags: - vllm --- <p align="center"> <img alt="gpt-oss-20b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg"> </p> <p align="center"> <a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> · <a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> · <a href="https://openai.com/index/gpt-oss-model-card"><strong>Model card</strong></a> · <a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a> </p> <br> Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases. We’re releasing two flavors of these open models: - `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters) - `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters) Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise. > [!NOTE] > This model card is dedicated to the smaller `gpt-oss-20b` model. Check out [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) for the larger model. # Highlights * Permissive Apache 2.0 license: Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment. * Configurable reasoning effort: Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs. * Full chain-of-thought: Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users. * Fine-tunable: Fully customize models to your specific use case through parameter fine-tuning. * Agentic capabilities: Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs. * MXFP4 quantization: The models were post-trained with MXFP4 quantization of the MoE weights, making `gpt-oss-120b` run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and the `gpt-oss-20b` model run within 16GB of memory. All evals were performed with the same MXFP4 quantization. --- # Inference examples ## Transformers You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package. To get started, install the necessary dependencies to setup your environment: ``` pip install -U transformers kernels torch ``` Once, setup you can proceed to run the model by running the snippet below: ```py from transformers import pipeline import torch model_id = "openai/gpt-oss-20b" pipe = pipeline( "text-generation", model=model_id, torch_dtype="auto", device_map="auto", ) messages = [ {"role": "user", "content": "Explain quantum mechanics clearly and concisely."}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver: ``` transformers serve transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-20b ``` [Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers) ## vLLM vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server. ```bash uv pip install --pre vllm==0.10.1+gptoss \ --extra-index-url https://wheels.vllm.ai/gpt-oss/ \ --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \ --index-strategy unsafe-best-match vllm serve openai/gpt-oss-20b ``` [Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm) ## PyTorch / Triton To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation). ## Ollama If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download). ```bash # gpt-oss-20b ollama pull gpt-oss:20b ollama run gpt-oss:20b ``` [Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama) #### LM Studio If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download. ```bash # gpt-oss-20b lms get openai/gpt-oss-20b ``` Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners. --- # Download the model You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI: ```shell # gpt-oss-20b huggingface-cli download openai/gpt-oss-20b --include "original/" --local-dir gpt-oss-20b/ pip install gpt-oss python -m gpt_oss.chat model/ ``` # Reasoning levels You can adjust the reasoning level that suits your task across three levels: Low: Fast responses for general dialogue. * Medium: Balanced speed and detail. * High: Deep and detailed analysis. The reasoning level can be set in the system prompts, e.g., "Reasoning: high". # Tool use The gpt-oss models are excellent for: * Web browsing (using built-in browsing tools) * Function calling with defined schemas * Agentic operations like browser tasks # Fine-tuning Both gpt-oss models can be fine-tuned for a variety of specialized use cases. This smaller model `gpt-oss-20b` can be fine-tuned on consumer hardware, whereas the larger [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) can be fine-tuned on a single H100 node.
tslim1/Phi-3-medium-128k-instruct-mlx-8Bit	tslim1	2025-08-19T08:05:30Z	0	0	mlx	[ "mlx", "safetensors", "phi3", "nlp", "code", "mlx-my-repo", "text-generation", "conversational", "custom_code", "multilingual", "base_model:microsoft/Phi-3-medium-128k-instruct", "base_model:quantized:microsoft/Phi-3-medium-128k-instruct", "license:mit", "8-bit", "region:us" ]	text-generation	2025-08-19T08:04:16Z	--- license: mit license_link: https://huggingface.co/microsoft/Phi-3-medium-128k-instruct/resolve/main/LICENSE language: - multilingual pipeline_tag: text-generation tags: - nlp - code - mlx - mlx-my-repo inference: parameters: temperature: 0.7 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? base_model: microsoft/Phi-3-medium-128k-instruct --- # tslim1/Phi-3-medium-128k-instruct-mlx-8Bit The Model [tslim1/Phi-3-medium-128k-instruct-mlx-8Bit](https://huggingface.co/tslim1/Phi-3-medium-128k-instruct-mlx-8Bit) was converted to MLX format from [microsoft/Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) using mlx-lm version 0.26.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("tslim1/Phi-3-medium-128k-instruct-mlx-8Bit") 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) ```
liukevin666/blockassist-bc-yawning_striped_cassowary_1755590333	liukevin666	2025-08-19T08:00:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T08:00:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
donoway/GSM8K-Binary_Llama-3.2-1B-xx99tfwe	donoway	2025-08-19T08:00:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T05:55:54Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: GSM8K-Binary_Llama-3.2-1B-xx99tfwe 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. --> # GSM8K-Binary_Llama-3.2-1B-xx99tfwe This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2724 - Model Preparation Time: 0.0059 - Mdl: 4543.3835 - Accumulated Loss: 3149.2334 - Correct Preds: 1590.0 - Total Preds: 2475.0 - Accuracy: 0.6424 - Correct Gen Preds: 1155.0 - Gen Accuracy: 0.4667 - Correct Gen Preds 34192: 669.0 - Correct Preds 34192: 974.0 - Total Labels 34192: 1196.0 - Accuracy 34192: 0.8144 - Gen Accuracy 34192: 0.5594 - Correct Gen Preds 41568: 477.0 - Correct Preds 41568: 616.0 - Total Labels 41568: 1267.0 - Accuracy 41568: 0.4862 - Gen Accuracy 41568: 0.3765 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Mdl \| Accumulated Loss \| Correct Preds \| Total Preds \| Accuracy \| Correct Gen Preds \| Gen Accuracy \| Correct Gen Preds 34192 \| Correct Preds 34192 \| Total Labels 34192 \| Accuracy 34192 \| Gen Accuracy 34192 \| Correct Gen Preds 41568 \| Correct Preds 41568 \| Total Labels 41568 \| Accuracy 41568 \| Gen Accuracy 41568 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\|:----------:\|:----------------:\|:-------------:\|:-----------:\|:--------:\|:-----------------:\|:------------:\|:-----------------------:\|:-------------------:\|:------------------:\|:--------------:\|:------------------:\|:-----------------------:\|:-------------------:\|:------------------:\|:--------------:\|:------------------:\| \| No log \| 0 \| 0 \| 1.4656 \| 0.0059 \| 5233.1723 \| 3627.3586 \| 1196.0 \| 2475.0 \| 0.4832 \| 1204.0 \| 0.4865 \| 1196.0 \| 1196.0 \| 1196.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 1267.0 \| 0.0 \| 0.0 \| \| 1.1557 \| 1.0 \| 1 \| 1.4656 \| 0.0059 \| 5233.1723 \| 3627.3586 \| 1196.0 \| 2475.0 \| 0.4832 \| 1204.0 \| 0.4865 \| 1196.0 \| 1196.0 \| 1196.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 1267.0 \| 0.0 \| 0.0 \| \| 1.1557 \| 2.0 \| 2 \| 5.0073 \| 0.0059 \| 17879.5848 \| 12393.1838 \| 1267.0 \| 2475.0 \| 0.5119 \| 1274.0 \| 0.5147 \| 0.0 \| 0.0 \| 1196.0 \| 0.0 \| 0.0 \| 1266.0 \| 1267.0 \| 1267.0 \| 1.0 \| 0.9992 \| \| 5.9156 \| 3.0 \| 3 \| 1.1424 \| 0.0059 \| 4079.2851 \| 2827.5450 \| 1267.0 \| 2475.0 \| 0.5119 \| 7.0 \| 0.0028 \| 0.0 \| 0.0 \| 1196.0 \| 0.0 \| 0.0 \| 0.0 \| 1267.0 \| 1267.0 \| 1.0 \| 0.0 \| \| 1.1855 \| 4.0 \| 4 \| 1.8316 \| 0.0059 \| 6540.2060 \| 4533.3254 \| 1196.0 \| 2475.0 \| 0.4832 \| 8.0 \| 0.0032 \| 0.0 \| 1196.0 \| 1196.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 1267.0 \| 0.0 \| 0.0 \| \| 1.507 \| 5.0 \| 5 \| 0.9446 \| 0.0059 \| 3372.8700 \| 2337.8953 \| 1197.0 \| 2475.0 \| 0.4836 \| 8.0 \| 0.0032 \| 0.0 \| 1192.0 \| 1196.0 \| 0.9967 \| 0.0 \| 0.0 \| 5.0 \| 1267.0 \| 0.0039 \| 0.0 \| \| 0.6628 \| 6.0 \| 6 \| 0.8425 \| 0.0059 \| 3008.1372 \| 2085.0818 \| 1278.0 \| 2475.0 \| 0.5164 \| 8.0 \| 0.0032 \| 0.0 \| 25.0 \| 1196.0 \| 0.0209 \| 0.0 \| 0.0 \| 1253.0 \| 1267.0 \| 0.9890 \| 0.0 \| \| 0.6635 \| 7.0 \| 7 \| 0.7631 \| 0.0059 \| 2724.8477 \| 1888.7205 \| 1326.0 \| 2475.0 \| 0.5358 \| 8.0 \| 0.0032 \| 0.0 \| 179.0 \| 1196.0 \| 0.1497 \| 0.0 \| 0.0 \| 1147.0 \| 1267.0 \| 0.9053 \| 0.0 \| \| 0.4678 \| 8.0 \| 8 \| 0.7798 \| 0.0059 \| 2784.3208 \| 1929.9441 \| 1336.0 \| 2475.0 \| 0.5398 \| 8.0 \| 0.0032 \| 0.0 \| 1143.0 \| 1196.0 \| 0.9557 \| 0.0 \| 0.0 \| 193.0 \| 1267.0 \| 0.1523 \| 0.0 \| \| 0.2898 \| 9.0 \| 9 \| 0.8131 \| 0.0059 \| 2903.3217 \| 2012.4292 \| 1346.0 \| 2475.0 \| 0.5438 \| 8.0 \| 0.0032 \| 0.0 \| 1142.0 \| 1196.0 \| 0.9548 \| 0.0 \| 0.0 \| 204.0 \| 1267.0 \| 0.1610 \| 0.0 \| \| 0.1716 \| 10.0 \| 10 \| 0.7535 \| 0.0059 \| 2690.5199 \| 1864.9263 \| 1458.0 \| 2475.0 \| 0.5891 \| 8.0 \| 0.0032 \| 0.0 \| 937.0 \| 1196.0 \| 0.7834 \| 0.0 \| 0.0 \| 521.0 \| 1267.0 \| 0.4112 \| 0.0 \| \| 0.0706 \| 11.0 \| 11 \| 0.8096 \| 0.0059 \| 2890.8924 \| 2003.8139 \| 1471.0 \| 2475.0 \| 0.5943 \| 8.0 \| 0.0032 \| 0.0 \| 1012.0 \| 1196.0 \| 0.8462 \| 0.0 \| 0.0 \| 459.0 \| 1267.0 \| 0.3623 \| 0.0 \| \| 0.015 \| 12.0 \| 12 \| 0.8691 \| 0.0059 \| 3103.3107 \| 2151.0511 \| 1502.0 \| 2475.0 \| 0.6069 \| 8.0 \| 0.0032 \| 0.0 \| 992.0 \| 1196.0 \| 0.8294 \| 0.0 \| 0.0 \| 510.0 \| 1267.0 \| 0.4025 \| 0.0 \| \| 0.0016 \| 13.0 \| 13 \| 0.9300 \| 0.0059 \| 3320.7592 \| 2301.7749 \| 1534.0 \| 2475.0 \| 0.6198 \| 8.0 \| 0.0032 \| 0.0 \| 991.0 \| 1196.0 \| 0.8286 \| 0.0 \| 0.0 \| 543.0 \| 1267.0 \| 0.4286 \| 0.0 \| \| 0.0003 \| 14.0 \| 14 \| 0.9761 \| 0.0059 \| 3485.2924 \| 2415.8206 \| 1546.0 \| 2475.0 \| 0.6246 \| 25.0 \| 0.0101 \| 4.0 \| 976.0 \| 1196.0 \| 0.8161 \| 0.0033 \| 13.0 \| 570.0 \| 1267.0 \| 0.4499 \| 0.0103 \| \| 0.0001 \| 15.0 \| 15 \| 1.0153 \| 0.0059 \| 3625.3772 \| 2512.9200 \| 1556.0 \| 2475.0 \| 0.6287 \| 93.0 \| 0.0376 \| 22.0 \| 968.0 \| 1196.0 \| 0.8094 \| 0.0184 \| 63.0 \| 588.0 \| 1267.0 \| 0.4641 \| 0.0497 \| \| 0.0 \| 16.0 \| 16 \| 1.0547 \| 0.0059 \| 3766.0323 \| 2610.4147 \| 1568.0 \| 2475.0 \| 0.6335 \| 188.0 \| 0.0760 \| 59.0 \| 970.0 \| 1196.0 \| 0.8110 \| 0.0493 \| 121.0 \| 598.0 \| 1267.0 \| 0.4720 \| 0.0955 \| \| 0.0 \| 17.0 \| 17 \| 1.0947 \| 0.0059 \| 3908.7914 \| 2709.3678 \| 1570.0 \| 2475.0 \| 0.6343 \| 322.0 \| 0.1301 \| 125.0 \| 969.0 \| 1196.0 \| 0.8102 \| 0.1045 \| 188.0 \| 601.0 \| 1267.0 \| 0.4743 \| 0.1484 \| \| 0.0 \| 18.0 \| 18 \| 1.1295 \| 0.0059 \| 4033.1706 \| 2795.5808 \| 1573.0 \| 2475.0 \| 0.6356 \| 470.0 \| 0.1899 \| 211.0 \| 972.0 \| 1196.0 \| 0.8127 \| 0.1764 \| 250.0 \| 601.0 \| 1267.0 \| 0.4743 \| 0.1973 \| \| 0.0 \| 19.0 \| 19 \| 1.1608 \| 0.0059 \| 4144.8149 \| 2872.9668 \| 1578.0 \| 2475.0 \| 0.6376 \| 612.0 \| 0.2473 \| 302.0 \| 973.0 \| 1196.0 \| 0.8135 \| 0.2525 \| 301.0 \| 605.0 \| 1267.0 \| 0.4775 \| 0.2376 \| \| 0.0 \| 20.0 \| 20 \| 1.1862 \| 0.0059 \| 4235.5987 \| 2935.8933 \| 1585.0 \| 2475.0 \| 0.6404 \| 723.0 \| 0.2921 \| 384.0 \| 975.0 \| 1196.0 \| 0.8152 \| 0.3211 \| 330.0 \| 610.0 \| 1267.0 \| 0.4815 \| 0.2605 \| \| 0.0 \| 21.0 \| 21 \| 1.2002 \| 0.0059 \| 4285.6206 \| 2970.5659 \| 1587.0 \| 2475.0 \| 0.6412 \| 803.0 \| 0.3244 \| 434.0 \| 975.0 \| 1196.0 \| 0.8152 \| 0.3629 \| 360.0 \| 612.0 \| 1267.0 \| 0.4830 \| 0.2841 \| \| 0.0 \| 22.0 \| 22 \| 1.2167 \| 0.0059 \| 4344.4465 \| 3011.3409 \| 1585.0 \| 2475.0 \| 0.6404 \| 885.0 \| 0.3576 \| 480.0 \| 972.0 \| 1196.0 \| 0.8127 \| 0.4013 \| 396.0 \| 613.0 \| 1267.0 \| 0.4838 \| 0.3125 \| \| 0.0 \| 23.0 \| 23 \| 1.2293 \| 0.0059 \| 4389.3332 \| 3042.4540 \| 1586.0 \| 2475.0 \| 0.6408 \| 953.0 \| 0.3851 \| 527.0 \| 971.0 \| 1196.0 \| 0.8119 \| 0.4406 \| 417.0 \| 615.0 \| 1267.0 \| 0.4854 \| 0.3291 \| \| 0.0 \| 24.0 \| 24 \| 1.2378 \| 0.0059 \| 4419.8769 \| 3063.6252 \| 1582.0 \| 2475.0 \| 0.6392 \| 1008.0 \| 0.4073 \| 570.0 \| 970.0 \| 1196.0 \| 0.8110 \| 0.4766 \| 429.0 \| 612.0 \| 1267.0 \| 0.4830 \| 0.3386 \| \| 0.0 \| 25.0 \| 25 \| 1.2477 \| 0.0059 \| 4455.0209 \| 3087.9851 \| 1586.0 \| 2475.0 \| 0.6408 \| 1048.0 \| 0.4234 \| 591.0 \| 971.0 \| 1196.0 \| 0.8119 \| 0.4941 \| 448.0 \| 615.0 \| 1267.0 \| 0.4854 \| 0.3536 \| \| 0.0 \| 26.0 \| 26 \| 1.2538 \| 0.0059 \| 4476.9507 \| 3103.1857 \| 1589.0 \| 2475.0 \| 0.6420 \| 1075.0 \| 0.4343 \| 614.0 \| 975.0 \| 1196.0 \| 0.8152 \| 0.5134 \| 452.0 \| 614.0 \| 1267.0 \| 0.4846 \| 0.3567 \| \| 0.0 \| 27.0 \| 27 \| 1.2628 \| 0.0059 \| 4509.1826 \| 3125.5272 \| 1586.0 \| 2475.0 \| 0.6408 \| 1106.0 \| 0.4469 \| 637.0 \| 973.0 \| 1196.0 \| 0.8135 \| 0.5326 \| 460.0 \| 613.0 \| 1267.0 \| 0.4838 \| 0.3631 \| \| 0.0 \| 28.0 \| 28 \| 1.2659 \| 0.0059 \| 4520.0496 \| 3133.0596 \| 1585.0 \| 2475.0 \| 0.6404 \| 1120.0 \| 0.4525 \| 645.0 \| 972.0 \| 1196.0 \| 0.8127 \| 0.5393 \| 466.0 \| 613.0 \| 1267.0 \| 0.4838 \| 0.3678 \| \| 0.0 \| 29.0 \| 29 \| 1.2675 \| 0.0059 \| 4525.9937 \| 3137.1798 \| 1587.0 \| 2475.0 \| 0.6412 \| 1139.0 \| 0.4602 \| 659.0 \| 974.0 \| 1196.0 \| 0.8144 \| 0.5510 \| 471.0 \| 613.0 \| 1267.0 \| 0.4838 \| 0.3717 \| \| 0.0 \| 30.0 \| 30 \| 1.2724 \| 0.0059 \| 4543.3835 \| 3149.2334 \| 1590.0 \| 2475.0 \| 0.6424 \| 1155.0 \| 0.4667 \| 669.0 \| 974.0 \| 1196.0 \| 0.8144 \| 0.5594 \| 477.0 \| 616.0 \| 1267.0 \| 0.4862 \| 0.3765 \| \| 0.0 \| 31.0 \| 31 \| 1.2754 \| 0.0059 \| 4554.1107 \| 3156.6690 \| 1587.0 \| 2475.0 \| 0.6412 \| 1157.0 \| 0.4675 \| 672.0 \| 975.0 \| 1196.0 \| 0.8152 \| 0.5619 \| 476.0 \| 612.0 \| 1267.0 \| 0.4830 \| 0.3757 \| \| 0.0 \| 32.0 \| 32 \| 1.2780 \| 0.0059 \| 4563.1825 \| 3162.9571 \| 1582.0 \| 2475.0 \| 0.6392 \| 1172.0 \| 0.4735 \| 683.0 \| 972.0 \| 1196.0 \| 0.8127 \| 0.5711 \| 480.0 \| 610.0 \| 1267.0 \| 0.4815 \| 0.3788 \| \| 0.0 \| 33.0 \| 33 \| 1.2791 \| 0.0059 \| 4567.2324 \| 3165.7642 \| 1583.0 \| 2475.0 \| 0.6396 \| 1171.0 \| 0.4731 \| 680.0 \| 973.0 \| 1196.0 \| 0.8135 \| 0.5686 \| 482.0 \| 610.0 \| 1267.0 \| 0.4815 \| 0.3804 \| \| 0.0 \| 34.0 \| 34 \| 1.2793 \| 0.0059 \| 4567.8543 \| 3166.1953 \| 1588.0 \| 2475.0 \| 0.6416 \| 1176.0 \| 0.4752 \| 684.0 \| 975.0 \| 1196.0 \| 0.8152 \| 0.5719 \| 483.0 \| 613.0 \| 1267.0 \| 0.4838 \| 0.3812 \| \| 0.0 \| 35.0 \| 35 \| 1.2811 \| 0.0059 \| 4574.2847 \| 3170.6525 \| 1582.0 \| 2475.0 \| 0.6392 \| 1178.0 \| 0.4760 \| 687.0 \| 972.0 \| 1196.0 \| 0.8127 \| 0.5744 \| 482.0 \| 610.0 \| 1267.0 \| 0.4815 \| 0.3804 \| \| 0.0 \| 36.0 \| 36 \| 1.2823 \| 0.0059 \| 4578.7325 \| 3173.7355 \| 1583.0 \| 2475.0 \| 0.6396 \| 1183.0 \| 0.4780 \| 692.0 \| 971.0 \| 1196.0 \| 0.8119 \| 0.5786 \| 482.0 \| 612.0 \| 1267.0 \| 0.4830 \| 0.3804 \| \| 0.0 \| 37.0 \| 37 \| 1.2835 \| 0.0059 \| 4582.8161 \| 3176.5661 \| 1584.0 \| 2475.0 \| 0.64 \| 1190.0 \| 0.4808 \| 696.0 \| 972.0 \| 1196.0 \| 0.8127 \| 0.5819 \| 485.0 \| 612.0 \| 1267.0 \| 0.4830 \| 0.3828 \| \| 0.0 \| 38.0 \| 38 \| 1.2848 \| 0.0059 \| 4587.5704 \| 3179.8615 \| 1585.0 \| 2475.0 \| 0.6404 \| 1184.0 \| 0.4784 \| 692.0 \| 973.0 \| 1196.0 \| 0.8135 \| 0.5786 \| 483.0 \| 612.0 \| 1267.0 \| 0.4830 \| 0.3812 \| \| 0.0 \| 39.0 \| 39 \| 1.2858 \| 0.0059 \| 4591.0362 \| 3182.2638 \| 1584.0 \| 2475.0 \| 0.64 \| 1189.0 \| 0.4804 \| 697.0 \| 973.0 \| 1196.0 \| 0.8135 \| 0.5828 \| 483.0 \| 611.0 \| 1267.0 \| 0.4822 \| 0.3812 \| \| 0.0 \| 40.0 \| 40 \| 1.2854 \| 0.0059 \| 4589.7370 \| 3181.3633 \| 1585.0 \| 2475.0 \| 0.6404 \| 1186.0 \| 0.4792 \| 693.0 \| 973.0 \| 1196.0 \| 0.8135 \| 0.5794 \| 484.0 \| 612.0 \| 1267.0 \| 0.4830 \| 0.3820 \| \| 0.0 \| 41.0 \| 41 \| 1.2850 \| 0.0059 \| 4588.1990 \| 3180.2972 \| 1580.0 \| 2475.0 \| 0.6384 \| 1190.0 \| 0.4808 \| 698.0 \| 972.0 \| 1196.0 \| 0.8127 \| 0.5836 \| 483.0 \| 608.0 \| 1267.0 \| 0.4799 \| 0.3812 \| \| 0.0 \| 42.0 \| 42 \| 1.2860 \| 0.0059 \| 4591.7153 \| 3182.7345 \| 1585.0 \| 2475.0 \| 0.6404 \| 1194.0 \| 0.4824 \| 697.0 \| 974.0 \| 1196.0 \| 0.8144 \| 0.5828 \| 488.0 \| 611.0 \| 1267.0 \| 0.4822 \| 0.3852 \| \| 0.0 \| 43.0 \| 43 \| 1.2859 \| 0.0059 \| 4591.5068 \| 3182.5900 \| 1580.0 \| 2475.0 \| 0.6384 \| 1189.0 \| 0.4804 \| 697.0 \| 972.0 \| 1196.0 \| 0.8127 \| 0.5828 \| 483.0 \| 608.0 \| 1267.0 \| 0.4799 \| 0.3812 \| \| 0.0 \| 44.0 \| 44 \| 1.2852 \| 0.0059 \| 4589.0154 \| 3180.8631 \| 1587.0 \| 2475.0 \| 0.6412 \| 1190.0 \| 0.4808 \| 698.0 \| 975.0 \| 1196.0 \| 0.8152 \| 0.5836 \| 483.0 \| 612.0 \| 1267.0 \| 0.4830 \| 0.3812 \| \| 0.0 \| 45.0 \| 45 \| 1.2860 \| 0.0059 \| 4591.7459 \| 3182.7557 \| 1587.0 \| 2475.0 \| 0.6412 \| 1193.0 \| 0.4820 \| 699.0 \| 974.0 \| 1196.0 \| 0.8144 \| 0.5844 \| 485.0 \| 613.0 \| 1267.0 \| 0.4838 \| 0.3828 \| \| 0.0 \| 46.0 \| 46 \| 1.2858 \| 0.0059 \| 4591.3256 \| 3182.4644 \| 1587.0 \| 2475.0 \| 0.6412 \| 1196.0 \| 0.4832 \| 701.0 \| 976.0 \| 1196.0 \| 0.8161 \| 0.5861 \| 486.0 \| 611.0 \| 1267.0 \| 0.4822 \| 0.3836 \| \| 0.0 \| 47.0 \| 47 \| 1.2847 \| 0.0059 \| 4587.3352 \| 3179.6985 \| 1588.0 \| 2475.0 \| 0.6416 \| 1193.0 \| 0.4820 \| 698.0 \| 974.0 \| 1196.0 \| 0.8144 \| 0.5836 \| 486.0 \| 614.0 \| 1267.0 \| 0.4846 \| 0.3836 \| \| 0.0 \| 48.0 \| 48 \| 1.2860 \| 0.0059 \| 4591.7670 \| 3182.7704 \| 1581.0 \| 2475.0 \| 0.6388 \| 1193.0 \| 0.4820 \| 698.0 \| 972.0 \| 1196.0 \| 0.8127 \| 0.5836 \| 486.0 \| 609.0 \| 1267.0 \| 0.4807 \| 0.3836 \| \| 0.0 \| 49.0 \| 49 \| 1.2857 \| 0.0059 \| 4590.7061 \| 3182.0350 \| 1590.0 \| 2475.0 \| 0.6424 \| 1195.0 \| 0.4828 \| 699.0 \| 975.0 \| 1196.0 \| 0.8152 \| 0.5844 \| 487.0 \| 615.0 \| 1267.0 \| 0.4854 \| 0.3844 \| \| 0.0 \| 50.0 \| 50 \| 1.2880 \| 0.0059 \| 4598.8684 \| 3187.6927 \| 1586.0 \| 2475.0 \| 0.6408 \| 1191.0 \| 0.4812 \| 697.0 \| 975.0 \| 1196.0 \| 0.8152 \| 0.5828 \| 485.0 \| 611.0 \| 1267.0 \| 0.4822 \| 0.3828 \| \| 0.0 \| 51.0 \| 51 \| 1.2880 \| 0.0059 \| 4598.8481 \| 3187.6786 \| 1582.0 \| 2475.0 \| 0.6392 \| 1192.0 \| 0.4816 \| 697.0 \| 972.0 \| 1196.0 \| 0.8127 \| 0.5828 \| 486.0 \| 610.0 \| 1267.0 \| 0.4815 \| 0.3836 \| \| 0.0 \| 52.0 \| 52 \| 1.2871 \| 0.0059 \| 4595.9863 \| 3185.6950 \| 1579.0 \| 2475.0 \| 0.6380 \| 1196.0 \| 0.4832 \| 697.0 \| 973.0 \| 1196.0 \| 0.8135 \| 0.5828 \| 490.0 \| 606.0 \| 1267.0 \| 0.4783 \| 0.3867 \| \| 0.0 \| 53.0 \| 53 \| 1.2858 \| 0.0059 \| 4591.2625 \| 3182.4207 \| 1585.0 \| 2475.0 \| 0.6404 \| 1200.0 \| 0.4848 \| 701.0 \| 974.0 \| 1196.0 \| 0.8144 \| 0.5861 \| 490.0 \| 611.0 \| 1267.0 \| 0.4822 \| 0.3867 \| \| 0.0 \| 54.0 \| 54 \| 1.2867 \| 0.0059 \| 4594.4591 \| 3184.6364 \| 1586.0 \| 2475.0 \| 0.6408 \| 1197.0 \| 0.4836 \| 700.0 \| 973.0 \| 1196.0 \| 0.8135 \| 0.5853 \| 488.0 \| 613.0 \| 1267.0 \| 0.4838 \| 0.3852 \| \| 0.0 \| 55.0 \| 55 \| 1.2880 \| 0.0059 \| 4599.0709 \| 3187.8330 \| 1579.0 \| 2475.0 \| 0.6380 \| 1194.0 \| 0.4824 \| 700.0 \| 972.0 \| 1196.0 \| 0.8127 \| 0.5853 \| 485.0 \| 607.0 \| 1267.0 \| 0.4791 \| 0.3828 \| \| 0.0 \| 56.0 \| 56 \| 1.2871 \| 0.0059 \| 4595.6402 \| 3185.4551 \| 1583.0 \| 2475.0 \| 0.6396 \| 1192.0 \| 0.4816 \| 698.0 \| 973.0 \| 1196.0 \| 0.8135 \| 0.5836 \| 485.0 \| 610.0 \| 1267.0 \| 0.4815 \| 0.3828 \| \| 0.0 \| 57.0 \| 57 \| 1.2866 \| 0.0059 \| 4593.9130 \| 3184.2578 \| 1580.0 \| 2475.0 \| 0.6384 \| 1195.0 \| 0.4828 \| 702.0 \| 973.0 \| 1196.0 \| 0.8135 \| 0.5870 \| 484.0 \| 607.0 \| 1267.0 \| 0.4791 \| 0.3820 \| \| 0.0 \| 58.0 \| 58 \| 1.2858 \| 0.0059 \| 4590.9929 \| 3182.2338 \| 1583.0 \| 2475.0 \| 0.6396 \| 1189.0 \| 0.4804 \| 695.0 \| 973.0 \| 1196.0 \| 0.8135 \| 0.5811 \| 485.0 \| 610.0 \| 1267.0 \| 0.4815 \| 0.3828 \| \| 0.0 \| 59.0 \| 59 \| 1.2849 \| 0.0059 \| 4587.9750 \| 3180.1420 \| 1586.0 \| 2475.0 \| 0.6408 \| 1197.0 \| 0.4836 \| 702.0 \| 975.0 \| 1196.0 \| 0.8152 \| 0.5870 \| 486.0 \| 611.0 \| 1267.0 \| 0.4822 \| 0.3836 \| \| 0.0 \| 60.0 \| 60 \| 1.2866 \| 0.0059 \| 4594.1392 \| 3184.4146 \| 1581.0 \| 2475.0 \| 0.6388 \| 1195.0 \| 0.4828 \| 699.0 \| 973.0 \| 1196.0 \| 0.8135 \| 0.5844 \| 487.0 \| 608.0 \| 1267.0 \| 0.4799 \| 0.3844 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
rockst4r4/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yawning_tiny_aardvark	rockst4r4	2025-08-19T07:59:05Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am yawning_tiny_aardvark", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-15T20:28:40Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am yawning_tiny_aardvark --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755590184	0xaoyama	2025-08-19T07:56:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:56:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AngelinaZanardi/nb-bert-base-edu-scorer-lr3e5-bs32_swe_test_2	AngelinaZanardi	2025-08-19T07:55:48Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:NbAiLab/nb-bert-base", "base_model:finetune:NbAiLab/nb-bert-base", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T06:21:50Z	--- library_name: transformers license: cc-by-4.0 base_model: NbAiLab/nb-bert-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: nb-bert-base-edu-scorer-lr3e5-bs32_swe_test_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nb-bert-base-edu-scorer-lr3e5-bs32_swe_test_2 This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3465 - Accuracy: 0.5120 - F1 Weighted: 0.5084 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Weighted \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-----------:\| \| 1.1986 \| 1.0 \| 1472 \| 1.1862 \| 0.4919 \| 0.4498 \| \| 1.1196 \| 2.0 \| 2944 \| 1.1978 \| 0.4946 \| 0.4659 \| \| 0.9807 \| 3.0 \| 4416 \| 1.2960 \| 0.4798 \| 0.4599 \| \| 0.8432 \| 4.0 \| 5888 \| 1.4149 \| 0.4768 \| 0.4709 \| \| 0.716 \| 5.0 \| 7360 \| 1.5769 \| 0.4720 \| 0.4626 \| \| 0.5717 \| 6.0 \| 8832 \| 1.8525 \| 0.4558 \| 0.4588 \| \| 0.4705 \| 7.0 \| 10304 \| 2.0333 \| 0.4526 \| 0.4584 \| \| 0.3901 \| 8.0 \| 11776 \| 2.1127 \| 0.4534 \| 0.4559 \| \| 0.322 \| 9.0 \| 13248 \| 2.4347 \| 0.4560 \| 0.4599 \| \| 0.2845 \| 10.0 \| 14720 \| 2.6137 \| 0.4411 \| 0.4493 \| \| 0.2244 \| 11.0 \| 16192 \| 2.7283 \| 0.4518 \| 0.4564 \| \| 0.2059 \| 12.0 \| 17664 \| 3.0232 \| 0.4383 \| 0.4416 \| \| 0.1453 \| 13.0 \| 19136 \| 3.1201 \| 0.4484 \| 0.4529 \| \| 0.1258 \| 14.0 \| 20608 \| 3.2220 \| 0.4520 \| 0.4561 \| \| 0.0994 \| 15.0 \| 22080 \| 3.4938 \| 0.4455 \| 0.4526 \| \| 0.0933 \| 16.0 \| 23552 \| 3.5932 \| 0.4506 \| 0.4584 \| \| 0.0689 \| 17.0 \| 25024 \| 3.8269 \| 0.4538 \| 0.4607 \| \| 0.0514 \| 18.0 \| 26496 \| 4.0041 \| 0.4540 \| 0.4607 \| \| 0.0476 \| 19.0 \| 27968 \| 4.1475 \| 0.4540 \| 0.4597 \| \| 0.0307 \| 20.0 \| 29440 \| 4.2319 \| 0.4512 \| 0.4575 \| ### Framework versions - Transformers 4.55.0 - Pytorch 2.5.1+cu121 - Datasets 4.0.0 - Tokenizers 0.21.4
donoway/ARC-Easy_Llama-3.2-1B-a2yg6wt3	donoway	2025-08-19T07:54:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T07:42:31Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Easy_Llama-3.2-1B-a2yg6wt3 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. --> # ARC-Easy_Llama-3.2-1B-a2yg6wt3 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3756 - Model Preparation Time: 0.0063 - Mdl: 2775.8537 - Accumulated Loss: 1924.0752 - Correct Preds: 366.0 - Total Preds: 570.0 - Accuracy: 0.6421 - Correct Gen Preds: 355.0 - Gen Accuracy: 0.6228 - Correct Gen Preds 32: 113.0 - Correct Preds 32: 118.0 - Total Labels 32: 158.0 - Accuracy 32: 0.7468 - Gen Accuracy 32: 0.7152 - Correct Gen Preds 33: 106.0 - Correct Preds 33: 107.0 - Total Labels 33: 152.0 - Accuracy 33: 0.7039 - Gen Accuracy 33: 0.6974 - Correct Gen Preds 34: 88.0 - Correct Preds 34: 91.0 - Total Labels 34: 142.0 - Accuracy 34: 0.6408 - Gen Accuracy 34: 0.6197 - Correct Gen Preds 35: 48.0 - Correct Preds 35: 50.0 - Total Labels 35: 118.0 - Accuracy 35: 0.4237 - Gen Accuracy 35: 0.4068 - Correct Gen Preds 36: 0.0 - Correct Preds 36: 0.0 - Total Labels 36: 0.0 - Accuracy 36: 0.0 - Gen Accuracy 36: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Mdl \| Accumulated Loss \| Correct Preds \| Total Preds \| Accuracy \| Correct Gen Preds \| Gen Accuracy \| Correct Gen Preds 32 \| Correct Preds 32 \| Total Labels 32 \| Accuracy 32 \| Gen Accuracy 32 \| Correct Gen Preds 33 \| Correct Preds 33 \| Total Labels 33 \| Accuracy 33 \| Gen Accuracy 33 \| Correct Gen Preds 34 \| Correct Preds 34 \| Total Labels 34 \| Accuracy 34 \| Gen Accuracy 34 \| Correct Gen Preds 35 \| Correct Preds 35 \| Total Labels 35 \| Accuracy 35 \| Gen Accuracy 35 \| Correct Gen Preds 36 \| Correct Preds 36 \| Total Labels 36 \| Accuracy 36 \| Gen Accuracy 36 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\|:---------:\|:----------------:\|:-------------:\|:-----------:\|:--------:\|:-----------------:\|:------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\| \| No log \| 0 \| 0 \| 1.5354 \| 0.0063 \| 1262.6022 \| 875.1692 \| 172.0 \| 570.0 \| 0.3018 \| 170.0 \| 0.2982 \| 154.0 \| 154.0 \| 158.0 \| 0.9747 \| 0.9747 \| 0.0 \| 0.0 \| 152.0 \| 0.0 \| 0.0 \| 15.0 \| 17.0 \| 142.0 \| 0.1197 \| 0.1056 \| 1.0 \| 1.0 \| 118.0 \| 0.0085 \| 0.0085 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.498 \| 1.0 \| 1 \| 1.5354 \| 0.0063 \| 1262.6022 \| 875.1692 \| 172.0 \| 570.0 \| 0.3018 \| 170.0 \| 0.2982 \| 154.0 \| 154.0 \| 158.0 \| 0.9747 \| 0.9747 \| 0.0 \| 0.0 \| 152.0 \| 0.0 \| 0.0 \| 15.0 \| 17.0 \| 142.0 \| 0.1197 \| 0.1056 \| 1.0 \| 1.0 \| 118.0 \| 0.0085 \| 0.0085 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.498 \| 2.0 \| 2 \| 2.5202 \| 0.0063 \| 2072.4194 \| 1436.4916 \| 219.0 \| 570.0 \| 0.3842 \| 219.0 \| 0.3842 \| 0.0 \| 0.0 \| 158.0 \| 0.0 \| 0.0 \| 83.0 \| 83.0 \| 152.0 \| 0.5461 \| 0.5461 \| 136.0 \| 136.0 \| 142.0 \| 0.9577 \| 0.9577 \| 0.0 \| 0.0 \| 118.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.783 \| 3.0 \| 3 \| 1.2895 \| 0.0063 \| 1060.4212 \| 735.0280 \| 226.0 \| 570.0 \| 0.3965 \| 226.0 \| 0.3965 \| 6.0 \| 6.0 \| 158.0 \| 0.0380 \| 0.0380 \| 145.0 \| 145.0 \| 152.0 \| 0.9539 \| 0.9539 \| 48.0 \| 48.0 \| 142.0 \| 0.3380 \| 0.3380 \| 27.0 \| 27.0 \| 118.0 \| 0.2288 \| 0.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.4782 \| 4.0 \| 4 \| 1.3651 \| 0.0063 \| 1122.5363 \| 778.0829 \| 325.0 \| 570.0 \| 0.5702 \| 311.0 \| 0.5456 \| 94.0 \| 107.0 \| 158.0 \| 0.6772 \| 0.5949 \| 103.0 \| 104.0 \| 152.0 \| 0.6842 \| 0.6776 \| 81.0 \| 81.0 \| 142.0 \| 0.5704 \| 0.5704 \| 33.0 \| 33.0 \| 118.0 \| 0.2797 \| 0.2797 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0157 \| 5.0 \| 5 \| 2.2319 \| 0.0063 \| 1835.3671 \| 1272.1795 \| 356.0 \| 570.0 \| 0.6246 \| 339.0 \| 0.5947 \| 100.0 \| 112.0 \| 158.0 \| 0.7089 \| 0.6329 \| 108.0 \| 108.0 \| 152.0 \| 0.7105 \| 0.7105 \| 88.0 \| 91.0 \| 142.0 \| 0.6408 \| 0.6197 \| 43.0 \| 45.0 \| 118.0 \| 0.3814 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 6.0 \| 6 \| 2.8502 \| 0.0063 \| 2343.8113 \| 1624.6062 \| 362.0 \| 570.0 \| 0.6351 \| 355.0 \| 0.6228 \| 109.0 \| 116.0 \| 158.0 \| 0.7342 \| 0.6899 \| 108.0 \| 108.0 \| 152.0 \| 0.7105 \| 0.7105 \| 91.0 \| 91.0 \| 142.0 \| 0.6408 \| 0.6408 \| 47.0 \| 47.0 \| 118.0 \| 0.3983 \| 0.3983 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 7.0 \| 7 \| 3.1703 \| 0.0063 \| 2607.0465 \| 1807.0669 \| 363.0 \| 570.0 \| 0.6368 \| 353.0 \| 0.6193 \| 111.0 \| 116.0 \| 158.0 \| 0.7342 \| 0.7025 \| 106.0 \| 107.0 \| 152.0 \| 0.7039 \| 0.6974 \| 88.0 \| 91.0 \| 142.0 \| 0.6408 \| 0.6197 \| 48.0 \| 49.0 \| 118.0 \| 0.4153 \| 0.4068 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 8.0 \| 8 \| 3.3756 \| 0.0063 \| 2775.8537 \| 1924.0752 \| 366.0 \| 570.0 \| 0.6421 \| 355.0 \| 0.6228 \| 113.0 \| 118.0 \| 158.0 \| 0.7468 \| 0.7152 \| 106.0 \| 107.0 \| 152.0 \| 0.7039 \| 0.6974 \| 88.0 \| 91.0 \| 142.0 \| 0.6408 \| 0.6197 \| 48.0 \| 50.0 \| 118.0 \| 0.4237 \| 0.4068 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 9.0 \| 9 \| 3.5599 \| 0.0063 \| 2927.4671 \| 2029.1656 \| 361.0 \| 570.0 \| 0.6333 \| 354.0 \| 0.6211 \| 116.0 \| 120.0 \| 158.0 \| 0.7595 \| 0.7342 \| 102.0 \| 103.0 \| 152.0 \| 0.6776 \| 0.6711 \| 88.0 \| 89.0 \| 142.0 \| 0.6268 \| 0.6197 \| 48.0 \| 49.0 \| 118.0 \| 0.4153 \| 0.4068 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 10.0 \| 10 \| 3.7285 \| 0.0063 \| 3066.1098 \| 2125.2654 \| 353.0 \| 570.0 \| 0.6193 \| 349.0 \| 0.6123 \| 118.0 \| 120.0 \| 158.0 \| 0.7595 \| 0.7468 \| 98.0 \| 98.0 \| 152.0 \| 0.6447 \| 0.6447 \| 84.0 \| 85.0 \| 142.0 \| 0.5986 \| 0.5915 \| 49.0 \| 50.0 \| 118.0 \| 0.4237 \| 0.4153 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 11.0 \| 11 \| 3.8061 \| 0.0063 \| 3129.9326 \| 2169.5039 \| 356.0 \| 570.0 \| 0.6246 \| 349.0 \| 0.6123 \| 122.0 \| 125.0 \| 158.0 \| 0.7911 \| 0.7722 \| 97.0 \| 97.0 \| 152.0 \| 0.6382 \| 0.6382 \| 83.0 \| 86.0 \| 142.0 \| 0.6056 \| 0.5845 \| 47.0 \| 48.0 \| 118.0 \| 0.4068 \| 0.3983 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 12.0 \| 12 \| 3.8871 \| 0.0063 \| 3196.4877 \| 2215.6364 \| 351.0 \| 570.0 \| 0.6158 \| 347.0 \| 0.6088 \| 123.0 \| 124.0 \| 158.0 \| 0.7848 \| 0.7785 \| 95.0 \| 95.0 \| 152.0 \| 0.625 \| 0.625 \| 83.0 \| 85.0 \| 142.0 \| 0.5986 \| 0.5845 \| 46.0 \| 47.0 \| 118.0 \| 0.3983 \| 0.3898 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 13.0 \| 13 \| 3.9537 \| 0.0063 \| 3251.2770 \| 2253.6135 \| 348.0 \| 570.0 \| 0.6105 \| 343.0 \| 0.6018 \| 124.0 \| 124.0 \| 158.0 \| 0.7848 \| 0.7848 \| 95.0 \| 96.0 \| 152.0 \| 0.6316 \| 0.625 \| 81.0 \| 84.0 \| 142.0 \| 0.5915 \| 0.5704 \| 43.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 14.0 \| 14 \| 4.0209 \| 0.0063 \| 3306.4977 \| 2291.8895 \| 345.0 \| 570.0 \| 0.6053 \| 338.0 \| 0.5930 \| 121.0 \| 124.0 \| 158.0 \| 0.7848 \| 0.7658 \| 92.0 \| 93.0 \| 152.0 \| 0.6118 \| 0.6053 \| 82.0 \| 84.0 \| 142.0 \| 0.5915 \| 0.5775 \| 43.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 15.0 \| 15 \| 4.0856 \| 0.0063 \| 3359.7299 \| 2328.7873 \| 339.0 \| 570.0 \| 0.5947 \| 336.0 \| 0.5895 \| 123.0 \| 125.0 \| 158.0 \| 0.7911 \| 0.7785 \| 92.0 \| 93.0 \| 152.0 \| 0.6118 \| 0.6053 \| 78.0 \| 78.0 \| 142.0 \| 0.5493 \| 0.5493 \| 43.0 \| 43.0 \| 118.0 \| 0.3644 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 16.0 \| 16 \| 4.1341 \| 0.0063 \| 3399.5852 \| 2356.4129 \| 336.0 \| 570.0 \| 0.5895 \| 333.0 \| 0.5842 \| 121.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.7658 \| 89.0 \| 90.0 \| 152.0 \| 0.5921 \| 0.5855 \| 79.0 \| 79.0 \| 142.0 \| 0.5563 \| 0.5563 \| 44.0 \| 45.0 \| 118.0 \| 0.3814 \| 0.3729 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 17.0 \| 17 \| 4.2038 \| 0.0063 \| 3456.9416 \| 2396.1693 \| 336.0 \| 570.0 \| 0.5895 \| 332.0 \| 0.5825 \| 123.0 \| 125.0 \| 158.0 \| 0.7911 \| 0.7785 \| 89.0 \| 89.0 \| 152.0 \| 0.5855 \| 0.5855 \| 77.0 \| 78.0 \| 142.0 \| 0.5493 \| 0.5423 \| 43.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 18.0 \| 18 \| 4.1844 \| 0.0063 \| 3440.9588 \| 2385.0909 \| 336.0 \| 570.0 \| 0.5895 \| 332.0 \| 0.5825 \| 124.0 \| 126.0 \| 158.0 \| 0.7975 \| 0.7848 \| 88.0 \| 88.0 \| 152.0 \| 0.5789 \| 0.5789 \| 76.0 \| 77.0 \| 142.0 \| 0.5423 \| 0.5352 \| 44.0 \| 45.0 \| 118.0 \| 0.3814 \| 0.3729 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 19.0 \| 19 \| 4.2392 \| 0.0063 \| 3486.0281 \| 2416.3305 \| 333.0 \| 570.0 \| 0.5842 \| 330.0 \| 0.5789 \| 122.0 \| 124.0 \| 158.0 \| 0.7848 \| 0.7722 \| 87.0 \| 87.0 \| 152.0 \| 0.5724 \| 0.5724 \| 78.0 \| 78.0 \| 142.0 \| 0.5493 \| 0.5493 \| 43.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 20.0 \| 20 \| 4.2677 \| 0.0063 \| 3509.5150 \| 2432.6104 \| 333.0 \| 570.0 \| 0.5842 \| 330.0 \| 0.5789 \| 122.0 \| 123.0 \| 158.0 \| 0.7785 \| 0.7722 \| 87.0 \| 87.0 \| 152.0 \| 0.5724 \| 0.5724 \| 78.0 \| 79.0 \| 142.0 \| 0.5563 \| 0.5493 \| 43.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 21.0 \| 21 \| 4.2595 \| 0.0063 \| 3502.7525 \| 2427.9230 \| 332.0 \| 570.0 \| 0.5825 \| 329.0 \| 0.5772 \| 121.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.7658 \| 87.0 \| 87.0 \| 152.0 \| 0.5724 \| 0.5724 \| 77.0 \| 78.0 \| 142.0 \| 0.5493 \| 0.5423 \| 44.0 \| 45.0 \| 118.0 \| 0.3814 \| 0.3729 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 22.0 \| 22 \| 4.3090 \| 0.0063 \| 3543.4099 \| 2456.1046 \| 331.0 \| 570.0 \| 0.5807 \| 328.0 \| 0.5754 \| 122.0 \| 123.0 \| 158.0 \| 0.7785 \| 0.7722 \| 86.0 \| 86.0 \| 152.0 \| 0.5658 \| 0.5658 \| 77.0 \| 78.0 \| 142.0 \| 0.5493 \| 0.5423 \| 43.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 23.0 \| 23 \| 4.3113 \| 0.0063 \| 3545.3391 \| 2457.4418 \| 332.0 \| 570.0 \| 0.5825 \| 330.0 \| 0.5789 \| 122.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.7722 \| 86.0 \| 86.0 \| 152.0 \| 0.5658 \| 0.5658 \| 78.0 \| 79.0 \| 142.0 \| 0.5563 \| 0.5493 \| 44.0 \| 45.0 \| 118.0 \| 0.3814 \| 0.3729 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 24.0 \| 24 \| 4.3136 \| 0.0063 \| 3547.2162 \| 2458.7429 \| 330.0 \| 570.0 \| 0.5789 \| 326.0 \| 0.5719 \| 122.0 \| 124.0 \| 158.0 \| 0.7848 \| 0.7722 \| 87.0 \| 87.0 \| 152.0 \| 0.5724 \| 0.5724 \| 75.0 \| 76.0 \| 142.0 \| 0.5352 \| 0.5282 \| 42.0 \| 43.0 \| 118.0 \| 0.3644 \| 0.3559 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 25.0 \| 25 \| 4.3196 \| 0.0063 \| 3552.1961 \| 2462.1947 \| 331.0 \| 570.0 \| 0.5807 \| 329.0 \| 0.5772 \| 123.0 \| 123.0 \| 158.0 \| 0.7785 \| 0.7785 \| 88.0 \| 88.0 \| 152.0 \| 0.5789 \| 0.5789 \| 75.0 \| 76.0 \| 142.0 \| 0.5352 \| 0.5282 \| 43.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 26.0 \| 26 \| 4.3053 \| 0.0063 \| 3540.4181 \| 2454.0309 \| 330.0 \| 570.0 \| 0.5789 \| 328.0 \| 0.5754 \| 124.0 \| 124.0 \| 158.0 \| 0.7848 \| 0.7848 \| 87.0 \| 87.0 \| 152.0 \| 0.5724 \| 0.5724 \| 75.0 \| 76.0 \| 142.0 \| 0.5352 \| 0.5282 \| 42.0 \| 43.0 \| 118.0 \| 0.3644 \| 0.3559 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 27.0 \| 27 \| 4.3153 \| 0.0063 \| 3548.6438 \| 2459.7325 \| 333.0 \| 570.0 \| 0.5842 \| 330.0 \| 0.5789 \| 124.0 \| 125.0 \| 158.0 \| 0.7911 \| 0.7848 \| 88.0 \| 88.0 \| 152.0 \| 0.5789 \| 0.5789 \| 75.0 \| 76.0 \| 142.0 \| 0.5352 \| 0.5282 \| 43.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 28.0 \| 28 \| 4.3397 \| 0.0063 \| 3568.7171 \| 2473.6462 \| 329.0 \| 570.0 \| 0.5772 \| 328.0 \| 0.5754 \| 123.0 \| 123.0 \| 158.0 \| 0.7785 \| 0.7785 \| 86.0 \| 86.0 \| 152.0 \| 0.5658 \| 0.5658 \| 75.0 \| 76.0 \| 142.0 \| 0.5352 \| 0.5282 \| 44.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3729 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 29.0 \| 29 \| 4.3321 \| 0.0063 \| 3562.4657 \| 2469.3130 \| 335.0 \| 570.0 \| 0.5877 \| 331.0 \| 0.5807 \| 122.0 \| 124.0 \| 158.0 \| 0.7848 \| 0.7722 \| 86.0 \| 86.0 \| 152.0 \| 0.5658 \| 0.5658 \| 78.0 \| 79.0 \| 142.0 \| 0.5563 \| 0.5493 \| 45.0 \| 46.0 \| 118.0 \| 0.3898 \| 0.3814 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 30.0 \| 30 \| 4.3265 \| 0.0063 \| 3557.8576 \| 2466.1189 \| 331.0 \| 570.0 \| 0.5807 \| 327.0 \| 0.5737 \| 123.0 \| 125.0 \| 158.0 \| 0.7911 \| 0.7785 \| 87.0 \| 87.0 \| 152.0 \| 0.5724 \| 0.5724 \| 74.0 \| 75.0 \| 142.0 \| 0.5282 \| 0.5211 \| 43.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 31.0 \| 31 \| 4.3336 \| 0.0063 \| 3563.6921 \| 2470.1632 \| 333.0 \| 570.0 \| 0.5842 \| 330.0 \| 0.5789 \| 124.0 \| 125.0 \| 158.0 \| 0.7911 \| 0.7848 \| 87.0 \| 87.0 \| 152.0 \| 0.5724 \| 0.5724 \| 75.0 \| 76.0 \| 142.0 \| 0.5352 \| 0.5282 \| 44.0 \| 45.0 \| 118.0 \| 0.3814 \| 0.3729 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 32.0 \| 32 \| 4.3611 \| 0.0063 \| 3586.2619 \| 2485.8074 \| 330.0 \| 570.0 \| 0.5789 \| 327.0 \| 0.5737 \| 123.0 \| 124.0 \| 158.0 \| 0.7848 \| 0.7785 \| 87.0 \| 87.0 \| 152.0 \| 0.5724 \| 0.5724 \| 75.0 \| 76.0 \| 142.0 \| 0.5352 \| 0.5282 \| 42.0 \| 43.0 \| 118.0 \| 0.3644 \| 0.3559 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 33.0 \| 33 \| 4.3522 \| 0.0063 \| 3579.0000 \| 2480.7737 \| 332.0 \| 570.0 \| 0.5825 \| 329.0 \| 0.5772 \| 123.0 \| 124.0 \| 158.0 \| 0.7848 \| 0.7785 \| 87.0 \| 87.0 \| 152.0 \| 0.5724 \| 0.5724 \| 77.0 \| 78.0 \| 142.0 \| 0.5493 \| 0.5423 \| 42.0 \| 43.0 \| 118.0 \| 0.3644 \| 0.3559 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 34.0 \| 34 \| 4.3694 \| 0.0063 \| 3593.1289 \| 2490.5671 \| 330.0 \| 570.0 \| 0.5789 \| 326.0 \| 0.5719 \| 123.0 \| 125.0 \| 158.0 \| 0.7911 \| 0.7785 \| 87.0 \| 87.0 \| 152.0 \| 0.5724 \| 0.5724 \| 73.0 \| 74.0 \| 142.0 \| 0.5211 \| 0.5141 \| 43.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 35.0 \| 35 \| 4.3432 \| 0.0063 \| 3571.5839 \| 2475.6333 \| 328.0 \| 570.0 \| 0.5754 \| 326.0 \| 0.5719 \| 122.0 \| 123.0 \| 158.0 \| 0.7785 \| 0.7722 \| 87.0 \| 87.0 \| 152.0 \| 0.5724 \| 0.5724 \| 74.0 \| 75.0 \| 142.0 \| 0.5282 \| 0.5211 \| 43.0 \| 43.0 \| 118.0 \| 0.3644 \| 0.3644 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 36.0 \| 36 \| 4.3402 \| 0.0063 \| 3569.1302 \| 2473.9325 \| 332.0 \| 570.0 \| 0.5825 \| 328.0 \| 0.5754 \| 122.0 \| 124.0 \| 158.0 \| 0.7848 \| 0.7722 \| 86.0 \| 86.0 \| 152.0 \| 0.5658 \| 0.5658 \| 76.0 \| 77.0 \| 142.0 \| 0.5423 \| 0.5352 \| 44.0 \| 45.0 \| 118.0 \| 0.3814 \| 0.3729 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 37.0 \| 37 \| 4.3729 \| 0.0063 \| 3596.0119 \| 2492.5655 \| 330.0 \| 570.0 \| 0.5789 \| 326.0 \| 0.5719 \| 123.0 \| 125.0 \| 158.0 \| 0.7911 \| 0.7785 \| 85.0 \| 85.0 \| 152.0 \| 0.5592 \| 0.5592 \| 76.0 \| 77.0 \| 142.0 \| 0.5423 \| 0.5352 \| 42.0 \| 43.0 \| 118.0 \| 0.3644 \| 0.3559 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 38.0 \| 38 \| 4.3427 \| 0.0063 \| 3571.1304 \| 2475.3190 \| 328.0 \| 570.0 \| 0.5754 \| 325.0 \| 0.5702 \| 123.0 \| 124.0 \| 158.0 \| 0.7848 \| 0.7785 \| 86.0 \| 86.0 \| 152.0 \| 0.5658 \| 0.5658 \| 75.0 \| 76.0 \| 142.0 \| 0.5352 \| 0.5282 \| 41.0 \| 42.0 \| 118.0 \| 0.3559 \| 0.3475 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
GradientResearch/Qwen3-4B-ECHO-Sokoban-GRPO	GradientResearch	2025-08-19T07:53:28Z	0	0	null	[ "safetensors", "qwen3", "text-generation", "conversational", "arxiv:2508.05387", "license:apache-2.0", "region:us" ]	text-generation	2025-08-18T12:31:48Z	--- license: apache-2.0 pipeline_tag: text-generation --- # Model Card for Qwen3-4B-ECHO-Sokoban-GRPO <!-- Provide a quick summary of what the model is/does. --> Building upon Qwen3-4B, we trained the model with the ECHO framework using GRPO on the Sokoban dataset. Specifically, because Qwen3-4B performs poorly on the more challenging Sokoban puzzles, we adopted a multi-round RL training regimen capped at four rounds, with a maximum of 25 candidate actions per round. The detailed environment configuration is as follows: ```python LargerSokoban6: env_type: sokoban max_actions_per_traj: 100 env_instruction: "You are solving the Sokoban puzzle. You are the player and you need to push all boxes to targets. When you are right next to a box, you can push it by moving in the same direction. You cannot push a box through a wall, and you cannot pull a box. The answer should be a sequence of actions, like <answer>Right \|\| Right \|\| Up</answer>" max_tokens: 300 env_config: dim_x: 6 dim_y: 6 num_boxes: 2 max_steps: 300 search_depth: 20 ``` Tabel 1: Model performance on Sokoban task \| Model \| Success Rate(%) \| \|----------------\|----------------\| \| Qwen3-4B \| 21.8 \| \| Qwen3-4B-Echo(GRPO) \| 34.0 \| \| Qwen3-30B-A3B-Thinking-2507 \| 72.75 \| \| Qwen3-30B-A3B-Thinking-2507-Echo(GRPO) \| 82.80 \| \| Deepseek-R1 \| 75.75 \| \| Qwen3-235B-A22B-Thinking-2507) \| 79.68 \| \| gpt-oss-120b \| 79.69 \| # Quick start ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "GradientResearch/Qwen3-4B-ECHO-Sokoban-GRPO" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "sokoban" messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` # Citation If you find our work helpful, feel free to give us a cite. ``` @misc{xiao2025echodecouplinginferencetraining, title={Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms}, author={Jie Xiao and Changyuan Fan and Qingnan Ren and Alfred Long and Yuchen Zhang and Rymon Yu and Eric Yang and Lynn Ai and Shaoduo Gan}, year={2025}, eprint={2508.05387}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2508.05387}, } ```
Alonc/device_to_cve_4bit	Alonc	2025-08-19T07:52:34Z	0	0	null	[ "safetensors", "qwen3", "region:us" ]	null	2025-08-18T15:19:30Z	The model is 16-bit the 4bit is a typo!!!! --- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Alonc - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755588452	Sayemahsjn	2025-08-19T07:45:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:45:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
minhnguyet/my-dpo-mistral-7b	minhnguyet	2025-08-19T07:41:50Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T07:41:13Z	--- base_model: unsloth/mistral-7b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: minhnguyet - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
CosminMihai02/llama3.1_ollama_v4	CosminMihai02	2025-08-19T07:40:35Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-19T07:39:48Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: CosminMihai02 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755589105	0xaoyama	2025-08-19T07:38:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:38:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755588973	Ferdi3425	2025-08-19T07:37:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:37:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jinaai/jina-embeddings-v4-vllm-code	jinaai	2025-08-19T07:36:55Z	354	3	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "vidore", "colpali", "multimodal-embedding", "multilingual-embedding", "Text-to-Visual Document (T→VD) retrieval", "feature-extraction", "sentence-similarity", "mteb", "visual-document-retrieval", "multilingual", "arxiv:2506.18902", "text-generation-inference", "endpoints_compatible", "region:eu" ]	visual-document-retrieval	2025-07-01T10:02:46Z	--- tags: - vidore - colpali - multimodal-embedding - multilingual-embedding - Text-to-Visual Document (T→VD) retrieval - feature-extraction - sentence-similarity - mteb language: - multilingual library_name: transformers pipeline_tag: visual-document-retrieval --- <br><br> <p align="center"> <img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px"> </p> <p align="center"> <b>The embedding model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> </p> # Jina Embeddings v4: Universal Embeddings for Multimodal Multilingual Retrieval [Original Model](https://huggingface.co/jinaai/jina-embeddings-v4) \| [Blog](https://jina.ai/news/jina-embeddings-v4-universal-embeddings-for-multimodal-multilingual-retrieval) \| [Technical Report](https://arxiv.org/abs/2506.18902) \| [API](https://jina.ai/embeddings) ## Model Overview This repository hosts a vLLM-compatible version of [`jina-embeddings-v4`](https://huggingface.co/jinaai/jina-embeddings-v4) with the code adapter merged into the base `Qwen2.5-VL` weights. This architecture modification enables native compatibility with vLLM without requiring custom adapter-handling code. ## Usage ```python import torch from PIL import Image from vllm import LLM from vllm.config import PoolerConfig from vllm.inputs.data import TextPrompt # Initialize model model = LLM( model="jinaai/jina-embeddings-v4-vllm-code", task="embed", override_pooler_config=PoolerConfig(pooling_type="ALL", normalize=False), dtype="float16", ) # Create text prompts query =query = "Find a function that prints a greeting message to the console" query_prompt = TextPrompt( prompt=f"Query: {query}" ) passage = "def hello_world():\n print('Hello, World!')" passage_prompt = TextPrompt( prompt=f"Passage: {passage}" ) # Create image prompt image = Image.open("<path_to_image>") image_prompt = TextPrompt( prompt="<\|im_start\|>user\n<\|vision_start\|><\|image_pad\|><\|vision_end\|>Describe the image.<\|im_end\|>\n", multi_modal_data={"image": image}, ) # Encode all prompts prompts = [query_prompt, passage_prompt, image_prompt] outputs = model.encode(prompts) def get_embeddings(outputs): VISION_START_TOKEN_ID, VISION_END_TOKEN_ID = 151652, 151653 embeddings = [] for output in outputs: if VISION_START_TOKEN_ID in output.prompt_token_ids: # Gather only vision tokens img_start_pos = torch.where( torch.tensor(output.prompt_token_ids) == VISION_START_TOKEN_ID )[0][0] img_end_pos = torch.where( torch.tensor(output.prompt_token_ids) == VISION_END_TOKEN_ID )[0][0] embeddings_tensor = output.outputs.data.detach().clone()[ img_start_pos : img_end_pos + 1 ] else: # Use all tokens for text-only prompts embeddings_tensor = output.outputs.data.detach().clone() # Pool and normalize embeddings pooled_output = ( embeddings_tensor.sum(dim=0, dtype=torch.float32) / embeddings_tensor.shape[0] ) embeddings.append(torch.nn.functional.normalize(pooled_output, dim=-1)) return embeddings embeddings = get_embeddings(outputs) ```
tslim1/Fin-R1-mlx-8Bit	tslim1	2025-08-19T07:36:12Z	0	0	mlx	[ "mlx", "safetensors", "qwen2", "base_model:SUFE-AIFLM-Lab/Fin-R1", "base_model:quantized:SUFE-AIFLM-Lab/Fin-R1", "8-bit", "region:us" ]	null	2025-08-19T07:35:28Z	--- base_model: SUFE-AIFLM-Lab/Fin-R1 tags: - mlx --- # tslim1/Fin-R1-mlx-8Bit The Model [tslim1/Fin-R1-mlx-8Bit](https://huggingface.co/tslim1/Fin-R1-mlx-8Bit) was converted to MLX format from [SUFE-AIFLM-Lab/Fin-R1](https://huggingface.co/SUFE-AIFLM-Lab/Fin-R1) using mlx-lm version 0.26.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("tslim1/Fin-R1-mlx-8Bit") 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) ```
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755587301	hakimjustbao	2025-08-19T07:35:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:35:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755588910	0xaoyama	2025-08-19T07:35:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:35:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755588496	lqpl	2025-08-19T07:32:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy insectivorous antelope", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:29:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hairy insectivorous antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mang3dd/blockassist-bc-tangled_slithering_alligator_1755586902	mang3dd	2025-08-19T07:29:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:28:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rohannath/Llama_AI_doctor_using_Unsloth	rohannath	2025-08-19T07:28:44Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T07:28:36Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: rohannath - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
lachielee/guajian	lachielee	2025-08-19T07:27:54Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:afl-3.0", "region:us" ]	text-to-image	2025-08-19T07:27:06Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/头像挂件-flux2.png text: '-' base_model: black-forest-labs/FLUX.1-dev instance_prompt: guajian license: afl-3.0 --- # flux-lora-挂件整合2 <Gallery /> ## Model description weibo头像挂件 ## Trigger words You should use `guajian` to trigger the image generation. ## Download model [Download](/lachielee/guajian/tree/main) them in the Files & versions tab.
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755586744	ihsanridzi	2025-08-19T07:26:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:26:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755588318	0xaoyama	2025-08-19T07:25:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:25:38Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dgambettaphd/M_mis_run2_gen5_WXS_doc1000_synt64_lr1e-04_acm_MPP	dgambettaphd	2025-08-19T07:25:36Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T07:25:22Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
donoway/ARC-Challenge_Llama-3.2-1B-654y06oc	donoway	2025-08-19T07:23:39Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T07:13:04Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Challenge_Llama-3.2-1B-654y06oc 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. --> # ARC-Challenge_Llama-3.2-1B-654y06oc This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3954 - Model Preparation Time: 0.0058 - Mdl: 1033.3076 - Accumulated Loss: 716.2342 - Correct Preds: 73.0 - Total Preds: 299.0 - Accuracy: 0.2441 - Correct Gen Preds: 73.0 - Gen Accuracy: 0.2441 - Correct Gen Preds 32: 0.0 - Correct Preds 32: 0.0 - Total Labels 32: 64.0 - Accuracy 32: 0.0 - Gen Accuracy 32: 0.0 - Correct Gen Preds 33: 72.0 - Correct Preds 33: 72.0 - Total Labels 33: 73.0 - Accuracy 33: 0.9863 - Gen Accuracy 33: 0.9863 - Correct Gen Preds 34: 0.0 - Correct Preds 34: 0.0 - Total Labels 34: 78.0 - Accuracy 34: 0.0 - Gen Accuracy 34: 0.0 - Correct Gen Preds 35: 1.0 - Correct Preds 35: 1.0 - Total Labels 35: 83.0 - Accuracy 35: 0.0120 - Gen Accuracy 35: 0.0120 - Correct Gen Preds 36: 0.0 - Correct Preds 36: 0.0 - Total Labels 36: 1.0 - Accuracy 36: 0.0 - Gen Accuracy 36: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Mdl \| Accumulated Loss \| Correct Preds \| Total Preds \| Accuracy \| Correct Gen Preds \| Gen Accuracy \| Correct Gen Preds 32 \| Correct Preds 32 \| Total Labels 32 \| Accuracy 32 \| Gen Accuracy 32 \| Correct Gen Preds 33 \| Correct Preds 33 \| Total Labels 33 \| Accuracy 33 \| Gen Accuracy 33 \| Correct Gen Preds 34 \| Correct Preds 34 \| Total Labels 34 \| Accuracy 34 \| Gen Accuracy 34 \| Correct Gen Preds 35 \| Correct Preds 35 \| Total Labels 35 \| Accuracy 35 \| Gen Accuracy 35 \| Correct Gen Preds 36 \| Correct Preds 36 \| Total Labels 36 \| Accuracy 36 \| Gen Accuracy 36 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\|:---------:\|:----------------:\|:-------------:\|:-----------:\|:--------:\|:-----------------:\|:------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\| \| No log \| 0 \| 0 \| 1.6389 \| 0.0058 \| 706.9523 \| 490.0220 \| 66.0 \| 299.0 \| 0.2207 \| 66.0 \| 0.2207 \| 62.0 \| 62.0 \| 64.0 \| 0.9688 \| 0.9688 \| 0.0 \| 0.0 \| 73.0 \| 0.0 \| 0.0 \| 4.0 \| 4.0 \| 78.0 \| 0.0513 \| 0.0513 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 1.7367 \| 1.0 \| 1 \| 1.6389 \| 0.0058 \| 706.9523 \| 490.0220 \| 66.0 \| 299.0 \| 0.2207 \| 66.0 \| 0.2207 \| 62.0 \| 62.0 \| 64.0 \| 0.9688 \| 0.9688 \| 0.0 \| 0.0 \| 73.0 \| 0.0 \| 0.0 \| 4.0 \| 4.0 \| 78.0 \| 0.0513 \| 0.0513 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 1.7367 \| 2.0 \| 2 \| 2.5849 \| 0.0058 \| 1115.0583 \| 772.8995 \| 72.0 \| 299.0 \| 0.2408 \| 63.0 \| 0.2107 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 63.0 \| 72.0 \| 73.0 \| 0.9863 \| 0.8630 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.686 \| 3.0 \| 3 \| 2.3954 \| 0.0058 \| 1033.3076 \| 716.2342 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 72.0 \| 72.0 \| 73.0 \| 0.9863 \| 0.9863 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 1.0 \| 1.0 \| 83.0 \| 0.0120 \| 0.0120 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.1308 \| 4.0 \| 4 \| 3.5091 \| 0.0058 \| 1513.7269 \| 1049.2355 \| 66.0 \| 299.0 \| 0.2207 \| 34.0 \| 0.1137 \| 27.0 \| 56.0 \| 64.0 \| 0.875 \| 0.4219 \| 6.0 \| 8.0 \| 73.0 \| 0.1096 \| 0.0822 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 1.0 \| 2.0 \| 83.0 \| 0.0241 \| 0.0120 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0919 \| 5.0 \| 5 \| 4.5350 \| 0.0058 \| 1956.2548 \| 1355.9725 \| 72.0 \| 299.0 \| 0.2408 \| 71.0 \| 0.2375 \| 1.0 \| 2.0 \| 64.0 \| 0.0312 \| 0.0156 \| 70.0 \| 70.0 \| 73.0 \| 0.9589 \| 0.9589 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0008 \| 6.0 \| 6 \| 5.9663 \| 0.0058 \| 2573.6635 \| 1783.9276 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0001 \| 7.0 \| 7 \| 7.0163 \| 0.0058 \| 3026.5888 \| 2097.8715 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 8.0 \| 8 \| 7.8549 \| 0.0058 \| 3388.3437 \| 2348.6208 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 9.0 \| 9 \| 8.5290 \| 0.0058 \| 3679.1101 \| 2550.1648 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 10.0 \| 10 \| 9.0813 \| 0.0058 \| 3917.3504 \| 2715.3004 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 11.0 \| 11 \| 9.5223 \| 0.0058 \| 4107.5735 \| 2847.1530 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 12.0 \| 12 \| 9.8450 \| 0.0058 \| 4246.7775 \| 2943.6418 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 13.0 \| 13 \| 10.0820 \| 0.0058 \| 4349.0238 \| 3014.5136 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 14.0 \| 14 \| 10.2630 \| 0.0058 \| 4427.1048 \| 3068.6352 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 15.0 \| 15 \| 10.4135 \| 0.0058 \| 4492.0237 \| 3113.6335 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 16.0 \| 16 \| 10.5454 \| 0.0058 \| 4548.9178 \| 3153.0696 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 17.0 \| 17 \| 10.6501 \| 0.0058 \| 4594.0917 \| 3184.3817 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 18.0 \| 18 \| 10.7377 \| 0.0058 \| 4631.8710 \| 3210.5683 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 19.0 \| 19 \| 10.8058 \| 0.0058 \| 4661.2653 \| 3230.9429 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 20.0 \| 20 \| 10.8522 \| 0.0058 \| 4681.2829 \| 3244.8181 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 21.0 \| 21 \| 10.8886 \| 0.0058 \| 4696.9714 \| 3255.6925 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 22.0 \| 22 \| 10.9239 \| 0.0058 \| 4712.2095 \| 3266.2547 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 23.0 \| 23 \| 10.9471 \| 0.0058 \| 4722.2182 \| 3273.1922 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 24.0 \| 24 \| 10.9741 \| 0.0058 \| 4733.8499 \| 3281.2547 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 25.0 \| 25 \| 10.9917 \| 0.0058 \| 4741.4235 \| 3286.5043 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 26.0 \| 26 \| 11.0067 \| 0.0058 \| 4747.9153 \| 3291.0041 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 27.0 \| 27 \| 11.0245 \| 0.0058 \| 4755.5791 \| 3296.3162 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 28.0 \| 28 \| 11.0307 \| 0.0058 \| 4758.2846 \| 3298.1916 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 29.0 \| 29 \| 11.0362 \| 0.0058 \| 4760.6287 \| 3299.8163 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 30.0 \| 30 \| 11.0439 \| 0.0058 \| 4763.9649 \| 3302.1288 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 31.0 \| 31 \| 11.0487 \| 0.0058 \| 4766.0390 \| 3303.5665 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 32.0 \| 32 \| 11.0546 \| 0.0058 \| 4768.5629 \| 3305.3159 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 33.0 \| 33 \| 11.0604 \| 0.0058 \| 4771.0883 \| 3307.0664 \| 73.0 \| 299.0 \| 0.2441 \| 73.0 \| 0.2441 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 73.0 \| 73.0 \| 73.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755587787	IvanJAjebu	2025-08-19T07:17:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:17:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
donoway/ARC-Easy_Llama-3.2-1B-7kenrtho	donoway	2025-08-19T07:10:33Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T06:58:11Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Easy_Llama-3.2-1B-7kenrtho 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. --> # ARC-Easy_Llama-3.2-1B-7kenrtho This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2266 - Model Preparation Time: 0.0063 - Mdl: 1831.0526 - Accumulated Loss: 1269.1889 - Correct Preds: 351.0 - Total Preds: 570.0 - Accuracy: 0.6158 - Correct Gen Preds: 330.0 - Gen Accuracy: 0.5789 - Correct Gen Preds 32: 121.0 - Correct Preds 32: 136.0 - Total Labels 32: 158.0 - Accuracy 32: 0.8608 - Gen Accuracy 32: 0.7658 - Correct Gen Preds 33: 94.0 - Correct Preds 33: 95.0 - Total Labels 33: 152.0 - Accuracy 33: 0.625 - Gen Accuracy 33: 0.6184 - Correct Gen Preds 34: 78.0 - Correct Preds 34: 82.0 - Total Labels 34: 142.0 - Accuracy 34: 0.5775 - Gen Accuracy 34: 0.5493 - Correct Gen Preds 35: 37.0 - Correct Preds 35: 38.0 - Total Labels 35: 118.0 - Accuracy 35: 0.3220 - Gen Accuracy 35: 0.3136 - Correct Gen Preds 36: 0.0 - Correct Preds 36: 0.0 - Total Labels 36: 0.0 - Accuracy 36: 0.0 - Gen Accuracy 36: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Mdl \| Accumulated Loss \| Correct Preds \| Total Preds \| Accuracy \| Correct Gen Preds \| Gen Accuracy \| Correct Gen Preds 32 \| Correct Preds 32 \| Total Labels 32 \| Accuracy 32 \| Gen Accuracy 32 \| Correct Gen Preds 33 \| Correct Preds 33 \| Total Labels 33 \| Accuracy 33 \| Gen Accuracy 33 \| Correct Gen Preds 34 \| Correct Preds 34 \| Total Labels 34 \| Accuracy 34 \| Gen Accuracy 34 \| Correct Gen Preds 35 \| Correct Preds 35 \| Total Labels 35 \| Accuracy 35 \| Gen Accuracy 35 \| Correct Gen Preds 36 \| Correct Preds 36 \| Total Labels 36 \| Accuracy 36 \| Gen Accuracy 36 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\|:---------:\|:----------------:\|:-------------:\|:-----------:\|:--------:\|:-----------------:\|:------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\| \| No log \| 0 \| 0 \| 1.5354 \| 0.0063 \| 1262.6022 \| 875.1692 \| 172.0 \| 570.0 \| 0.3018 \| 170.0 \| 0.2982 \| 154.0 \| 154.0 \| 158.0 \| 0.9747 \| 0.9747 \| 0.0 \| 0.0 \| 152.0 \| 0.0 \| 0.0 \| 15.0 \| 17.0 \| 142.0 \| 0.1197 \| 0.1056 \| 1.0 \| 1.0 \| 118.0 \| 0.0085 \| 0.0085 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.3992 \| 1.0 \| 1 \| 1.5354 \| 0.0063 \| 1262.6022 \| 875.1692 \| 172.0 \| 570.0 \| 0.3018 \| 170.0 \| 0.2982 \| 154.0 \| 154.0 \| 158.0 \| 0.9747 \| 0.9747 \| 0.0 \| 0.0 \| 152.0 \| 0.0 \| 0.0 \| 15.0 \| 17.0 \| 142.0 \| 0.1197 \| 0.1056 \| 1.0 \| 1.0 \| 118.0 \| 0.0085 \| 0.0085 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.3992 \| 2.0 \| 2 \| 2.7006 \| 0.0063 \| 2220.8218 \| 1539.3563 \| 202.0 \| 570.0 \| 0.3544 \| 202.0 \| 0.3544 \| 0.0 \| 0.0 \| 158.0 \| 0.0 \| 0.0 \| 62.0 \| 62.0 \| 152.0 \| 0.4079 \| 0.4079 \| 140.0 \| 140.0 \| 142.0 \| 0.9859 \| 0.9859 \| 0.0 \| 0.0 \| 118.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.9024 \| 3.0 \| 3 \| 1.3172 \| 0.0063 \| 1083.1641 \| 750.7922 \| 190.0 \| 570.0 \| 0.3333 \| 190.0 \| 0.3333 \| 9.0 \| 9.0 \| 158.0 \| 0.0570 \| 0.0570 \| 150.0 \| 150.0 \| 152.0 \| 0.9868 \| 0.9868 \| 19.0 \| 19.0 \| 142.0 \| 0.1338 \| 0.1338 \| 12.0 \| 12.0 \| 118.0 \| 0.1017 \| 0.1017 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.6103 \| 4.0 \| 4 \| 1.4635 \| 0.0063 \| 1203.4512 \| 834.1688 \| 338.0 \| 570.0 \| 0.5930 \| 336.0 \| 0.5895 \| 131.0 \| 133.0 \| 158.0 \| 0.8418 \| 0.8291 \| 91.0 \| 91.0 \| 152.0 \| 0.5987 \| 0.5987 \| 76.0 \| 76.0 \| 142.0 \| 0.5352 \| 0.5352 \| 38.0 \| 38.0 \| 118.0 \| 0.3220 \| 0.3220 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0428 \| 5.0 \| 5 \| 2.2266 \| 0.0063 \| 1831.0526 \| 1269.1889 \| 351.0 \| 570.0 \| 0.6158 \| 330.0 \| 0.5789 \| 121.0 \| 136.0 \| 158.0 \| 0.8608 \| 0.7658 \| 94.0 \| 95.0 \| 152.0 \| 0.625 \| 0.6184 \| 78.0 \| 82.0 \| 142.0 \| 0.5775 \| 0.5493 \| 37.0 \| 38.0 \| 118.0 \| 0.3220 \| 0.3136 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0003 \| 6.0 \| 6 \| 2.6947 \| 0.0063 \| 2215.9451 \| 1535.9761 \| 347.0 \| 570.0 \| 0.6088 \| 306.0 \| 0.5368 \| 110.0 \| 133.0 \| 158.0 \| 0.8418 \| 0.6962 \| 88.0 \| 92.0 \| 152.0 \| 0.6053 \| 0.5789 \| 71.0 \| 83.0 \| 142.0 \| 0.5845 \| 0.5 \| 37.0 \| 39.0 \| 118.0 \| 0.3305 \| 0.3136 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 7.0 \| 7 \| 2.8748 \| 0.0063 \| 2364.0644 \| 1638.6446 \| 343.0 \| 570.0 \| 0.6018 \| 278.0 \| 0.4877 \| 95.0 \| 130.0 \| 158.0 \| 0.8228 \| 0.6013 \| 78.0 \| 87.0 \| 152.0 \| 0.5724 \| 0.5132 \| 67.0 \| 84.0 \| 142.0 \| 0.5915 \| 0.4718 \| 38.0 \| 42.0 \| 118.0 \| 0.3559 \| 0.3220 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 8.0 \| 8 \| 2.9759 \| 0.0063 \| 2447.1750 \| 1696.2525 \| 336.0 \| 570.0 \| 0.5895 \| 259.0 \| 0.4544 \| 87.0 \| 128.0 \| 158.0 \| 0.8101 \| 0.5506 \| 72.0 \| 84.0 \| 152.0 \| 0.5526 \| 0.4737 \| 61.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.4296 \| 39.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3305 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 9.0 \| 9 \| 3.0029 \| 0.0063 \| 2469.3675 \| 1711.6351 \| 331.0 \| 570.0 \| 0.5807 \| 236.0 \| 0.4140 \| 78.0 \| 125.0 \| 158.0 \| 0.7911 \| 0.4937 \| 64.0 \| 81.0 \| 152.0 \| 0.5329 \| 0.4211 \| 57.0 \| 81.0 \| 142.0 \| 0.5704 \| 0.4014 \| 37.0 \| 44.0 \| 118.0 \| 0.3729 \| 0.3136 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 10.0 \| 10 \| 3.0291 \| 0.0063 \| 2490.9245 \| 1726.5773 \| 320.0 \| 570.0 \| 0.5614 \| 221.0 \| 0.3877 \| 74.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4684 \| 59.0 \| 77.0 \| 152.0 \| 0.5066 \| 0.3882 \| 56.0 \| 79.0 \| 142.0 \| 0.5563 \| 0.3944 \| 32.0 \| 42.0 \| 118.0 \| 0.3559 \| 0.2712 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 11.0 \| 11 \| 3.0620 \| 0.0063 \| 2518.0218 \| 1745.3597 \| 318.0 \| 570.0 \| 0.5579 \| 213.0 \| 0.3737 \| 70.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4430 \| 57.0 \| 76.0 \| 152.0 \| 0.5 \| 0.375 \| 57.0 \| 79.0 \| 142.0 \| 0.5563 \| 0.4014 \| 29.0 \| 41.0 \| 118.0 \| 0.3475 \| 0.2458 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 12.0 \| 12 \| 3.1331 \| 0.0063 \| 2576.4547 \| 1785.8623 \| 314.0 \| 570.0 \| 0.5509 \| 208.0 \| 0.3649 \| 68.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4304 \| 55.0 \| 75.0 \| 152.0 \| 0.4934 \| 0.3618 \| 57.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.4014 \| 28.0 \| 37.0 \| 118.0 \| 0.3136 \| 0.2373 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 13.0 \| 13 \| 3.1756 \| 0.0063 \| 2611.4490 \| 1810.1185 \| 317.0 \| 570.0 \| 0.5561 \| 205.0 \| 0.3596 \| 67.0 \| 121.0 \| 158.0 \| 0.7658 \| 0.4241 \| 53.0 \| 74.0 \| 152.0 \| 0.4868 \| 0.3487 \| 56.0 \| 81.0 \| 142.0 \| 0.5704 \| 0.3944 \| 29.0 \| 41.0 \| 118.0 \| 0.3475 \| 0.2458 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 14.0 \| 14 \| 3.1954 \| 0.0063 \| 2627.6804 \| 1821.3693 \| 313.0 \| 570.0 \| 0.5491 \| 202.0 \| 0.3544 \| 67.0 \| 121.0 \| 158.0 \| 0.7658 \| 0.4241 \| 53.0 \| 73.0 \| 152.0 \| 0.4803 \| 0.3487 \| 55.0 \| 79.0 \| 142.0 \| 0.5563 \| 0.3873 \| 27.0 \| 40.0 \| 118.0 \| 0.3390 \| 0.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 15.0 \| 15 \| 3.2397 \| 0.0063 \| 2664.1020 \| 1846.6148 \| 314.0 \| 570.0 \| 0.5509 \| 200.0 \| 0.3509 \| 64.0 \| 121.0 \| 158.0 \| 0.7658 \| 0.4051 \| 53.0 \| 73.0 \| 152.0 \| 0.4803 \| 0.3487 \| 56.0 \| 81.0 \| 142.0 \| 0.5704 \| 0.3944 \| 27.0 \| 39.0 \| 118.0 \| 0.3305 \| 0.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 16.0 \| 16 \| 3.2887 \| 0.0063 \| 2704.4351 \| 1874.5716 \| 314.0 \| 570.0 \| 0.5509 \| 198.0 \| 0.3474 \| 64.0 \| 121.0 \| 158.0 \| 0.7658 \| 0.4051 \| 53.0 \| 71.0 \| 152.0 \| 0.4671 \| 0.3487 \| 55.0 \| 82.0 \| 142.0 \| 0.5775 \| 0.3873 \| 26.0 \| 40.0 \| 118.0 \| 0.3390 \| 0.2203 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 17.0 \| 17 \| 3.3231 \| 0.0063 \| 2732.6981 \| 1894.1620 \| 311.0 \| 570.0 \| 0.5456 \| 196.0 \| 0.3439 \| 64.0 \| 121.0 \| 158.0 \| 0.7658 \| 0.4051 \| 51.0 \| 70.0 \| 152.0 \| 0.4605 \| 0.3355 \| 54.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.3803 \| 27.0 \| 40.0 \| 118.0 \| 0.3390 \| 0.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 18.0 \| 18 \| 3.3377 \| 0.0063 \| 2744.7374 \| 1902.5070 \| 310.0 \| 570.0 \| 0.5439 \| 196.0 \| 0.3439 \| 64.0 \| 121.0 \| 158.0 \| 0.7658 \| 0.4051 \| 52.0 \| 69.0 \| 152.0 \| 0.4539 \| 0.3421 \| 53.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.3732 \| 27.0 \| 40.0 \| 118.0 \| 0.3390 \| 0.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 19.0 \| 19 \| 3.3610 \| 0.0063 \| 2763.8330 \| 1915.7430 \| 309.0 \| 570.0 \| 0.5421 \| 197.0 \| 0.3456 \| 64.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4051 \| 51.0 \| 69.0 \| 152.0 \| 0.4539 \| 0.3355 \| 56.0 \| 79.0 \| 142.0 \| 0.5563 \| 0.3944 \| 26.0 \| 39.0 \| 118.0 \| 0.3305 \| 0.2203 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 20.0 \| 20 \| 3.3848 \| 0.0063 \| 2783.4671 \| 1929.3524 \| 311.0 \| 570.0 \| 0.5456 \| 198.0 \| 0.3474 \| 66.0 \| 123.0 \| 158.0 \| 0.7785 \| 0.4177 \| 51.0 \| 68.0 \| 152.0 \| 0.4474 \| 0.3355 \| 54.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.3803 \| 27.0 \| 40.0 \| 118.0 \| 0.3390 \| 0.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 21.0 \| 21 \| 3.3561 \| 0.0063 \| 2759.8295 \| 1912.9680 \| 312.0 \| 570.0 \| 0.5474 \| 200.0 \| 0.3509 \| 67.0 \| 123.0 \| 158.0 \| 0.7785 \| 0.4241 \| 53.0 \| 68.0 \| 152.0 \| 0.4474 \| 0.3487 \| 53.0 \| 81.0 \| 142.0 \| 0.5704 \| 0.3732 \| 27.0 \| 40.0 \| 118.0 \| 0.3390 \| 0.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 22.0 \| 22 \| 3.4079 \| 0.0063 \| 2802.4235 \| 1942.4919 \| 311.0 \| 570.0 \| 0.5456 \| 197.0 \| 0.3456 \| 68.0 \| 123.0 \| 158.0 \| 0.7785 \| 0.4304 \| 49.0 \| 70.0 \| 152.0 \| 0.4605 \| 0.3224 \| 53.0 \| 79.0 \| 142.0 \| 0.5563 \| 0.3732 \| 27.0 \| 39.0 \| 118.0 \| 0.3305 \| 0.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 23.0 \| 23 \| 3.4059 \| 0.0063 \| 2800.7869 \| 1941.3575 \| 313.0 \| 570.0 \| 0.5491 \| 198.0 \| 0.3474 \| 67.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4241 \| 51.0 \| 70.0 \| 152.0 \| 0.4605 \| 0.3355 \| 53.0 \| 81.0 \| 142.0 \| 0.5704 \| 0.3732 \| 27.0 \| 40.0 \| 118.0 \| 0.3390 \| 0.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 24.0 \| 24 \| 3.4307 \| 0.0063 \| 2821.1525 \| 1955.4739 \| 312.0 \| 570.0 \| 0.5474 \| 198.0 \| 0.3474 \| 67.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4241 \| 50.0 \| 69.0 \| 152.0 \| 0.4539 \| 0.3289 \| 53.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.3732 \| 28.0 \| 41.0 \| 118.0 \| 0.3475 \| 0.2373 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 25.0 \| 25 \| 3.4314 \| 0.0063 \| 2821.7596 \| 1955.8947 \| 312.0 \| 570.0 \| 0.5474 \| 199.0 \| 0.3491 \| 67.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4241 \| 51.0 \| 69.0 \| 152.0 \| 0.4539 \| 0.3355 \| 54.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.3803 \| 27.0 \| 41.0 \| 118.0 \| 0.3475 \| 0.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 26.0 \| 26 \| 3.4420 \| 0.0063 \| 2830.4716 \| 1961.9334 \| 313.0 \| 570.0 \| 0.5491 \| 204.0 \| 0.3579 \| 69.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4367 \| 51.0 \| 70.0 \| 152.0 \| 0.4605 \| 0.3355 \| 55.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.3873 \| 29.0 \| 41.0 \| 118.0 \| 0.3475 \| 0.2458 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 27.0 \| 27 \| 3.4460 \| 0.0063 \| 2833.7589 \| 1964.2120 \| 312.0 \| 570.0 \| 0.5474 \| 197.0 \| 0.3456 \| 66.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4177 \| 51.0 \| 68.0 \| 152.0 \| 0.4474 \| 0.3355 \| 53.0 \| 81.0 \| 142.0 \| 0.5704 \| 0.3732 \| 27.0 \| 41.0 \| 118.0 \| 0.3475 \| 0.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 28.0 \| 28 \| 3.4630 \| 0.0063 \| 2847.7515 \| 1973.9109 \| 313.0 \| 570.0 \| 0.5491 \| 198.0 \| 0.3474 \| 68.0 \| 123.0 \| 158.0 \| 0.7785 \| 0.4304 \| 49.0 \| 69.0 \| 152.0 \| 0.4539 \| 0.3224 \| 52.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.3662 \| 29.0 \| 41.0 \| 118.0 \| 0.3475 \| 0.2458 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 29.0 \| 29 \| 3.4611 \| 0.0063 \| 2846.1980 \| 1972.8341 \| 312.0 \| 570.0 \| 0.5474 \| 199.0 \| 0.3491 \| 68.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4304 \| 50.0 \| 69.0 \| 152.0 \| 0.4539 \| 0.3289 \| 52.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.3662 \| 29.0 \| 41.0 \| 118.0 \| 0.3475 \| 0.2458 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 30.0 \| 30 \| 3.4590 \| 0.0063 \| 2844.4834 \| 1971.6457 \| 310.0 \| 570.0 \| 0.5439 \| 194.0 \| 0.3404 \| 68.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4304 \| 49.0 \| 68.0 \| 152.0 \| 0.4474 \| 0.3224 \| 51.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.3592 \| 26.0 \| 40.0 \| 118.0 \| 0.3390 \| 0.2203 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 31.0 \| 31 \| 3.4672 \| 0.0063 \| 2851.2328 \| 1976.3240 \| 310.0 \| 570.0 \| 0.5439 \| 195.0 \| 0.3421 \| 67.0 \| 123.0 \| 158.0 \| 0.7785 \| 0.4241 \| 51.0 \| 68.0 \| 152.0 \| 0.4474 \| 0.3355 \| 51.0 \| 81.0 \| 142.0 \| 0.5704 \| 0.3592 \| 26.0 \| 38.0 \| 118.0 \| 0.3220 \| 0.2203 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 32.0 \| 32 \| 3.4768 \| 0.0063 \| 2859.1086 \| 1981.7830 \| 309.0 \| 570.0 \| 0.5421 \| 197.0 \| 0.3456 \| 67.0 \| 121.0 \| 158.0 \| 0.7658 \| 0.4241 \| 50.0 \| 68.0 \| 152.0 \| 0.4474 \| 0.3289 \| 54.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.3803 \| 26.0 \| 40.0 \| 118.0 \| 0.3390 \| 0.2203 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 33.0 \| 33 \| 3.4676 \| 0.0063 \| 2851.5542 \| 1976.5468 \| 312.0 \| 570.0 \| 0.5474 \| 197.0 \| 0.3456 \| 67.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4241 \| 49.0 \| 68.0 \| 152.0 \| 0.4474 \| 0.3224 \| 53.0 \| 81.0 \| 142.0 \| 0.5704 \| 0.3732 \| 28.0 \| 41.0 \| 118.0 \| 0.3475 \| 0.2373 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 34.0 \| 34 \| 3.4550 \| 0.0063 \| 2841.1333 \| 1969.3235 \| 310.0 \| 570.0 \| 0.5439 \| 197.0 \| 0.3456 \| 69.0 \| 122.0 \| 158.0 \| 0.7722 \| 0.4367 \| 49.0 \| 68.0 \| 152.0 \| 0.4474 \| 0.3224 \| 53.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.3732 \| 26.0 \| 40.0 \| 118.0 \| 0.3390 \| 0.2203 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 35.0 \| 35 \| 3.4710 \| 0.0063 \| 2854.3484 \| 1978.4835 \| 311.0 \| 570.0 \| 0.5456 \| 199.0 \| 0.3491 \| 69.0 \| 123.0 \| 158.0 \| 0.7785 \| 0.4367 \| 51.0 \| 69.0 \| 152.0 \| 0.4539 \| 0.3355 \| 53.0 \| 80.0 \| 142.0 \| 0.5634 \| 0.3732 \| 26.0 \| 39.0 \| 118.0 \| 0.3305 \| 0.2203 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
insanesaga/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison	insanesaga	2025-08-19T07:09:35Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am nocturnal clawed bison", "unsloth", "trl", "genrl-swarm", "I am nocturnal_clawed_bison", "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	2025-05-27T13:39:41Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am nocturnal clawed bison - unsloth - trl - genrl-swarm - I am nocturnal_clawed_bison licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison 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="insanesaga/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - 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}} } ```
chansung/Gemma2-2B-CCRL-CUR-BASIC-ONLY-1E	chansung	2025-08-19T07:07:20Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:chansung/verifiable-coding-problems-python-v2", "arxiv:2402.03300", "base_model:google/gemma-2-2b-it", "base_model:finetune:google/gemma-2-2b-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T01:31:09Z	--- base_model: google/gemma-2-2b-it datasets: chansung/verifiable-coding-problems-python-v2 library_name: transformers model_name: Gemma2-2B-CCRL-CUR-BASIC-ONLY-1E tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Gemma2-2B-CCRL-CUR-BASIC-ONLY-1E This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) on the [chansung/verifiable-coding-problems-python-v2](https://huggingface.co/datasets/chansung/verifiable-coding-problems-python-v2) 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="chansung/Gemma2-2B-CCRL-CUR-BASIC-ONLY-1E", 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/chansung18/huggingface/runs/g89zrlrp) 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: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - 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}} } ```
yaelahnal/blockassist-bc-mute_clawed_crab_1755587166	yaelahnal	2025-08-19T07:07:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:06:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Soham711/blenderbot-400M-friendly-chatmodel	Soham711	2025-08-19T07:05:17Z	0	1	null	[ "safetensors", "blenderbot", "text2text-generation", "conversational", "en", "base_model:facebook/blenderbot-400M-distill", "base_model:finetune:facebook/blenderbot-400M-distill", "license:mit", "region:us" ]	null	2025-08-19T06:48:35Z	--- license: mit language: - en base_model: - facebook/blenderbot-400M-distill tags: - text2text-generation - conversational ---
Setayeshk/brisc_yolo	Setayeshk	2025-08-19T07:04:59Z	0	0	null	[ "tensorboard", "license:cc-by-nc-nd-4.0", "region:us" ]	null	2025-08-18T21:53:10Z	--- license: cc-by-nc-nd-4.0 ---
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755585395	hakimjustbao	2025-08-19T07:04:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T07:04:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
aiface/ModernBERT-large_nli	aiface	2025-08-19T07:02:21Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "generated_from_trainer", "base_model:answerdotai/ModernBERT-large", "base_model:finetune:answerdotai/ModernBERT-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T03:22:37Z	--- library_name: transformers license: apache-2.0 base_model: answerdotai/ModernBERT-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: ModernBERT-large_nli 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. --> # ModernBERT-large_nli This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6038 - Accuracy: 0.5787 - Precision Macro: 0.5794 - Recall Macro: 0.5790 - F1 Macro: 0.5792 - F1 Weighted: 0.5788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision Macro \| Recall Macro \| F1 Macro \| F1 Weighted \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------------:\|:------------:\|:--------:\|:-----------:\| \| 2.1283 \| 1.0 \| 143 \| 1.0136 \| 0.4807 \| 0.4674 \| 0.4835 \| 0.4509 \| 0.4492 \| \| 1.8848 \| 2.0 \| 286 \| 0.9818 \| 0.5202 \| 0.5745 \| 0.5219 \| 0.5042 \| 0.5038 \| \| 1.7416 \| 3.0 \| 429 \| 1.1233 \| 0.3220 \| 0.2102 \| 0.3259 \| 0.2190 \| 0.2174 \| \| 2.2168 \| 4.0 \| 572 \| 1.1135 \| 0.3277 \| 0.1092 \| 0.3333 \| 0.1646 \| 0.1618 \| \| 2.2099 \| 5.0 \| 715 \| 1.1089 \| 0.3277 \| 0.1092 \| 0.3333 \| 0.1646 \| 0.1618 \| \| 2.2191 \| 6.0 \| 858 \| 1.1231 \| 0.3282 \| 0.4426 \| 0.3338 \| 0.1655 \| 0.1627 \| \| 2.2027 \| 7.0 \| 1001 \| 1.0931 \| 0.3774 \| 0.2508 \| 0.3801 \| 0.3016 \| 0.2993 \| \| 2.1846 \| 8.0 \| 1144 \| 1.0723 \| 0.4013 \| 0.3861 \| 0.3995 \| 0.3692 \| 0.3705 \| \| 2.1232 \| 9.0 \| 1287 \| 1.0461 \| 0.4244 \| 0.4225 \| 0.4248 \| 0.4203 \| 0.4202 \| \| 2.0586 \| 10.0 \| 1430 \| 1.0345 \| 0.4510 \| 0.4495 \| 0.4494 \| 0.4210 \| 0.4220 \| \| 2.0578 \| 11.0 \| 1573 \| 1.0390 \| 0.4523 \| 0.4797 \| 0.4511 \| 0.4522 \| 0.4525 \| \| 2.0289 \| 12.0 \| 1716 \| 1.0626 \| 0.4665 \| 0.5296 \| 0.4668 \| 0.4391 \| 0.4389 \| \| 1.5688 \| 13.0 \| 1859 \| 0.8686 \| 0.6084 \| 0.6082 \| 0.6089 \| 0.6064 \| 0.6061 \| \| 1.2262 \| 14.0 \| 2002 \| 0.9452 \| 0.5973 \| 0.5972 \| 0.5978 \| 0.5961 \| 0.5958 \| \| 0.6694 \| 15.0 \| 2145 \| 1.2849 \| 0.5809 \| 0.5809 \| 0.5817 \| 0.5802 \| 0.5798 \| \| 0.2152 \| 16.0 \| 2288 \| 1.9241 \| 0.5752 \| 0.5760 \| 0.5753 \| 0.5755 \| 0.5753 \| \| 0.043 \| 17.0 \| 2431 \| 2.3196 \| 0.5672 \| 0.5685 \| 0.5673 \| 0.5675 \| 0.5672 \| \| 0.0074 \| 18.0 \| 2574 \| 2.5393 \| 0.5734 \| 0.5747 \| 0.5736 \| 0.5740 \| 0.5737 \| \| 0.0015 \| 19.0 \| 2717 \| 2.5970 \| 0.5769 \| 0.5780 \| 0.5772 \| 0.5776 \| 0.5772 \| \| 0.002 \| 20.0 \| 2860 \| 2.6038 \| 0.5787 \| 0.5794 \| 0.5790 \| 0.5792 \| 0.5788 \| ### Framework versions - Transformers 4.55.0 - Pytorch 2.7.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
mradermacher/Mini-AGI-4B-i1-GGUF	mradermacher	2025-08-19T07:00:16Z	0	1	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-19T03:54:34Z	--- base_model: Guilherme34/Mini-AGI-4B language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/Guilherme34/Mini-AGI-4B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Mini-AGI-4B-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/Mini-AGI-4B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 1.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q2_K.gguf) \| i1-Q2_K \| 1.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 2.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 2.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 2.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 2.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 2.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q6_K.gguf) \| i1-Q6_K \| 3.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
jtekt-physical-ai/lerobot_actv2	jtekt-physical-ai	2025-08-19T06:59:15Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "act", "dataset:yurayuray/retainer_mizoguchi3", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-19T05:57:00Z	--- datasets: yurayuray/retainer_mizoguchi3 library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - lerobot - robotics - act --- # 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 lerobot-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 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
donoway/ARC-Easy_Llama-3.2-1B-xl28q3hn	donoway	2025-08-19T06:57:36Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T06:46:08Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Easy_Llama-3.2-1B-xl28q3hn 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. --> # ARC-Easy_Llama-3.2-1B-xl28q3hn This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2555 - Model Preparation Time: 0.006 - Mdl: 1032.4540 - Accumulated Loss: 715.6426 - Correct Preds: 291.0 - Total Preds: 570.0 - Accuracy: 0.5105 - Correct Gen Preds: 291.0 - Gen Accuracy: 0.5105 - Correct Gen Preds 32: 98.0 - Correct Preds 32: 98.0 - Total Labels 32: 158.0 - Accuracy 32: 0.6203 - Gen Accuracy 32: 0.6203 - Correct Gen Preds 33: 130.0 - Correct Preds 33: 130.0 - Total Labels 33: 152.0 - Accuracy 33: 0.8553 - Gen Accuracy 33: 0.8553 - Correct Gen Preds 34: 40.0 - Correct Preds 34: 40.0 - Total Labels 34: 142.0 - Accuracy 34: 0.2817 - Gen Accuracy 34: 0.2817 - Correct Gen Preds 35: 23.0 - Correct Preds 35: 23.0 - Total Labels 35: 118.0 - Accuracy 35: 0.1949 - Gen Accuracy 35: 0.1949 - Correct Gen Preds 36: 0.0 - Correct Preds 36: 0.0 - Total Labels 36: 0.0 - Accuracy 36: 0.0 - Gen Accuracy 36: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Mdl \| Accumulated Loss \| Correct Preds \| Total Preds \| Accuracy \| Correct Gen Preds \| Gen Accuracy \| Correct Gen Preds 32 \| Correct Preds 32 \| Total Labels 32 \| Accuracy 32 \| Gen Accuracy 32 \| Correct Gen Preds 33 \| Correct Preds 33 \| Total Labels 33 \| Accuracy 33 \| Gen Accuracy 33 \| Correct Gen Preds 34 \| Correct Preds 34 \| Total Labels 34 \| Accuracy 34 \| Gen Accuracy 34 \| Correct Gen Preds 35 \| Correct Preds 35 \| Total Labels 35 \| Accuracy 35 \| Gen Accuracy 35 \| Correct Gen Preds 36 \| Correct Preds 36 \| Total Labels 36 \| Accuracy 36 \| Gen Accuracy 36 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\|:---------:\|:----------------:\|:-------------:\|:-----------:\|:--------:\|:-----------------:\|:------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\| \| No log \| 0 \| 0 \| 1.5354 \| 0.006 \| 1262.6022 \| 875.1692 \| 172.0 \| 570.0 \| 0.3018 \| 170.0 \| 0.2982 \| 154.0 \| 154.0 \| 158.0 \| 0.9747 \| 0.9747 \| 0.0 \| 0.0 \| 152.0 \| 0.0 \| 0.0 \| 15.0 \| 17.0 \| 142.0 \| 0.1197 \| 0.1056 \| 1.0 \| 1.0 \| 118.0 \| 0.0085 \| 0.0085 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.3552 \| 1.0 \| 1 \| 1.5354 \| 0.006 \| 1262.6022 \| 875.1692 \| 172.0 \| 570.0 \| 0.3018 \| 170.0 \| 0.2982 \| 154.0 \| 154.0 \| 158.0 \| 0.9747 \| 0.9747 \| 0.0 \| 0.0 \| 152.0 \| 0.0 \| 0.0 \| 15.0 \| 17.0 \| 142.0 \| 0.1197 \| 0.1056 \| 1.0 \| 1.0 \| 118.0 \| 0.0085 \| 0.0085 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.3552 \| 2.0 \| 2 \| 2.4687 \| 0.006 \| 2030.1287 \| 1407.1780 \| 221.0 \| 570.0 \| 0.3877 \| 221.0 \| 0.3877 \| 0.0 \| 0.0 \| 158.0 \| 0.0 \| 0.0 \| 85.0 \| 85.0 \| 152.0 \| 0.5592 \| 0.5592 \| 136.0 \| 136.0 \| 142.0 \| 0.9577 \| 0.9577 \| 0.0 \| 0.0 \| 118.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.7603 \| 3.0 \| 3 \| 1.2555 \| 0.006 \| 1032.4540 \| 715.6426 \| 291.0 \| 570.0 \| 0.5105 \| 291.0 \| 0.5105 \| 98.0 \| 98.0 \| 158.0 \| 0.6203 \| 0.6203 \| 130.0 \| 130.0 \| 152.0 \| 0.8553 \| 0.8553 \| 40.0 \| 40.0 \| 142.0 \| 0.2817 \| 0.2817 \| 23.0 \| 23.0 \| 118.0 \| 0.1949 \| 0.1949 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.4267 \| 4.0 \| 4 \| 2.5733 \| 0.006 \| 2116.1258 \| 1466.7867 \| 261.0 \| 570.0 \| 0.4579 \| 260.0 \| 0.4561 \| 151.0 \| 152.0 \| 158.0 \| 0.9620 \| 0.9557 \| 39.0 \| 39.0 \| 152.0 \| 0.2566 \| 0.2566 \| 42.0 \| 42.0 \| 142.0 \| 0.2958 \| 0.2958 \| 28.0 \| 28.0 \| 118.0 \| 0.2373 \| 0.2373 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0491 \| 5.0 \| 5 \| 3.1596 \| 0.006 \| 2598.2545 \| 1800.9728 \| 284.0 \| 570.0 \| 0.4982 \| 284.0 \| 0.4982 \| 151.0 \| 151.0 \| 158.0 \| 0.9557 \| 0.9557 \| 56.0 \| 56.0 \| 152.0 \| 0.3684 \| 0.3684 \| 50.0 \| 50.0 \| 142.0 \| 0.3521 \| 0.3521 \| 27.0 \| 27.0 \| 118.0 \| 0.2288 \| 0.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0044 \| 6.0 \| 6 \| 4.0391 \| 0.006 \| 3321.5305 \| 2302.3095 \| 262.0 \| 570.0 \| 0.4596 \| 259.0 \| 0.4544 \| 151.0 \| 152.0 \| 158.0 \| 0.9620 \| 0.9557 \| 41.0 \| 41.0 \| 152.0 \| 0.2697 \| 0.2697 \| 44.0 \| 45.0 \| 142.0 \| 0.3169 \| 0.3099 \| 23.0 \| 24.0 \| 118.0 \| 0.2034 \| 0.1949 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0001 \| 7.0 \| 7 \| 4.4151 \| 0.006 \| 3630.7350 \| 2516.6338 \| 253.0 \| 570.0 \| 0.4439 \| 239.0 \| 0.4193 \| 144.0 \| 152.0 \| 158.0 \| 0.9620 \| 0.9114 \| 36.0 \| 38.0 \| 152.0 \| 0.25 \| 0.2368 \| 38.0 \| 41.0 \| 142.0 \| 0.2887 \| 0.2676 \| 21.0 \| 22.0 \| 118.0 \| 0.1864 \| 0.1780 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 8.0 \| 8 \| 4.5569 \| 0.006 \| 3747.3361 \| 2597.4554 \| 250.0 \| 570.0 \| 0.4386 \| 223.0 \| 0.3912 \| 135.0 \| 154.0 \| 158.0 \| 0.9747 \| 0.8544 \| 35.0 \| 38.0 \| 152.0 \| 0.25 \| 0.2303 \| 35.0 \| 39.0 \| 142.0 \| 0.2746 \| 0.2465 \| 18.0 \| 19.0 \| 118.0 \| 0.1610 \| 0.1525 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 9.0 \| 9 \| 4.6453 \| 0.006 \| 3819.9784 \| 2647.8072 \| 247.0 \| 570.0 \| 0.4333 \| 204.0 \| 0.3579 \| 123.0 \| 152.0 \| 158.0 \| 0.9620 \| 0.7785 \| 33.0 \| 39.0 \| 152.0 \| 0.2566 \| 0.2171 \| 31.0 \| 37.0 \| 142.0 \| 0.2606 \| 0.2183 \| 17.0 \| 19.0 \| 118.0 \| 0.1610 \| 0.1441 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0001 \| 10.0 \| 10 \| 4.8047 \| 0.006 \| 3951.0414 \| 2738.6532 \| 242.0 \| 570.0 \| 0.4246 \| 203.0 \| 0.3561 \| 123.0 \| 152.0 \| 158.0 \| 0.9620 \| 0.7785 \| 35.0 \| 39.0 \| 152.0 \| 0.2566 \| 0.2303 \| 30.0 \| 33.0 \| 142.0 \| 0.2324 \| 0.2113 \| 15.0 \| 18.0 \| 118.0 \| 0.1525 \| 0.1271 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 11.0 \| 11 \| 5.0241 \| 0.006 \| 4131.5031 \| 2863.7397 \| 236.0 \| 570.0 \| 0.4140 \| 201.0 \| 0.3526 \| 125.0 \| 153.0 \| 158.0 \| 0.9684 \| 0.7911 \| 34.0 \| 37.0 \| 152.0 \| 0.2434 \| 0.2237 \| 28.0 \| 29.0 \| 142.0 \| 0.2042 \| 0.1972 \| 14.0 \| 17.0 \| 118.0 \| 0.1441 \| 0.1186 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 12.0 \| 12 \| 5.2229 \| 0.006 \| 4295.0154 \| 2977.0778 \| 235.0 \| 570.0 \| 0.4123 \| 203.0 \| 0.3561 \| 129.0 \| 154.0 \| 158.0 \| 0.9747 \| 0.8165 \| 32.0 \| 36.0 \| 152.0 \| 0.2368 \| 0.2105 \| 28.0 \| 29.0 \| 142.0 \| 0.2042 \| 0.1972 \| 14.0 \| 16.0 \| 118.0 \| 0.1356 \| 0.1186 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 13.0 \| 13 \| 5.3741 \| 0.006 \| 4419.3154 \| 3063.2360 \| 235.0 \| 570.0 \| 0.4123 \| 202.0 \| 0.3544 \| 129.0 \| 155.0 \| 158.0 \| 0.9810 \| 0.8165 \| 31.0 \| 35.0 \| 152.0 \| 0.2303 \| 0.2039 \| 28.0 \| 29.0 \| 142.0 \| 0.2042 \| 0.1972 \| 14.0 \| 16.0 \| 118.0 \| 0.1356 \| 0.1186 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 14.0 \| 14 \| 5.5052 \| 0.006 \| 4527.0926 \| 3137.9415 \| 235.0 \| 570.0 \| 0.4123 \| 207.0 \| 0.3632 \| 135.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.8544 \| 31.0 \| 35.0 \| 152.0 \| 0.2303 \| 0.2039 \| 27.0 \| 28.0 \| 142.0 \| 0.1972 \| 0.1901 \| 14.0 \| 16.0 \| 118.0 \| 0.1356 \| 0.1186 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 15.0 \| 15 \| 5.5976 \| 0.006 \| 4603.0781 \| 3190.6106 \| 234.0 \| 570.0 \| 0.4105 \| 207.0 \| 0.3632 \| 135.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.8544 \| 32.0 \| 35.0 \| 152.0 \| 0.2303 \| 0.2105 \| 26.0 \| 28.0 \| 142.0 \| 0.1972 \| 0.1831 \| 14.0 \| 15.0 \| 118.0 \| 0.1271 \| 0.1186 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 16.0 \| 16 \| 5.6853 \| 0.006 \| 4675.2022 \| 3240.6032 \| 228.0 \| 570.0 \| 0.4 \| 206.0 \| 0.3614 \| 138.0 \| 155.0 \| 158.0 \| 0.9810 \| 0.8734 \| 29.0 \| 32.0 \| 152.0 \| 0.2105 \| 0.1908 \| 26.0 \| 27.0 \| 142.0 \| 0.1901 \| 0.1831 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 17.0 \| 17 \| 5.7800 \| 0.006 \| 4753.1165 \| 3294.6093 \| 228.0 \| 570.0 \| 0.4 \| 207.0 \| 0.3632 \| 141.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.8924 \| 29.0 \| 31.0 \| 152.0 \| 0.2039 \| 0.1908 \| 24.0 \| 27.0 \| 142.0 \| 0.1901 \| 0.1690 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 18.0 \| 18 \| 5.8437 \| 0.006 \| 4805.4763 \| 3330.9024 \| 227.0 \| 570.0 \| 0.3982 \| 207.0 \| 0.3632 \| 141.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.8924 \| 29.0 \| 30.0 \| 152.0 \| 0.1974 \| 0.1908 \| 24.0 \| 27.0 \| 142.0 \| 0.1901 \| 0.1690 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 19.0 \| 19 \| 5.9488 \| 0.006 \| 4891.9541 \| 3390.8442 \| 226.0 \| 570.0 \| 0.3965 \| 206.0 \| 0.3614 \| 141.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.8924 \| 28.0 \| 29.0 \| 152.0 \| 0.1908 \| 0.1842 \| 24.0 \| 27.0 \| 142.0 \| 0.1901 \| 0.1690 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 20.0 \| 20 \| 5.9804 \| 0.006 \| 4917.8580 \| 3408.7994 \| 226.0 \| 570.0 \| 0.3965 \| 206.0 \| 0.3614 \| 141.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.8924 \| 28.0 \| 29.0 \| 152.0 \| 0.1908 \| 0.1842 \| 24.0 \| 27.0 \| 142.0 \| 0.1901 \| 0.1690 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 21.0 \| 21 \| 6.0239 \| 0.006 \| 4953.6373 \| 3433.5997 \| 226.0 \| 570.0 \| 0.3965 \| 206.0 \| 0.3614 \| 141.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.8924 \| 28.0 \| 29.0 \| 152.0 \| 0.1908 \| 0.1842 \| 24.0 \| 27.0 \| 142.0 \| 0.1901 \| 0.1690 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 22.0 \| 22 \| 6.0758 \| 0.006 \| 4996.3676 \| 3463.2181 \| 225.0 \| 570.0 \| 0.3947 \| 206.0 \| 0.3614 \| 141.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.8924 \| 28.0 \| 29.0 \| 152.0 \| 0.1908 \| 0.1842 \| 24.0 \| 26.0 \| 142.0 \| 0.1831 \| 0.1690 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 23.0 \| 23 \| 6.0958 \| 0.006 \| 5012.8294 \| 3474.6285 \| 225.0 \| 570.0 \| 0.3947 \| 207.0 \| 0.3632 \| 141.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.8924 \| 28.0 \| 29.0 \| 152.0 \| 0.1908 \| 0.1842 \| 25.0 \| 26.0 \| 142.0 \| 0.1831 \| 0.1761 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 24.0 \| 24 \| 6.1508 \| 0.006 \| 5057.9994 \| 3505.9380 \| 225.0 \| 570.0 \| 0.3947 \| 209.0 \| 0.3667 \| 144.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.9114 \| 28.0 \| 29.0 \| 152.0 \| 0.1908 \| 0.1842 \| 24.0 \| 26.0 \| 142.0 \| 0.1831 \| 0.1690 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 25.0 \| 25 \| 6.1477 \| 0.006 \| 5055.4455 \| 3504.1678 \| 224.0 \| 570.0 \| 0.3930 \| 208.0 \| 0.3649 \| 143.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.9051 \| 27.0 \| 28.0 \| 152.0 \| 0.1842 \| 0.1776 \| 25.0 \| 26.0 \| 142.0 \| 0.1831 \| 0.1761 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 26.0 \| 26 \| 6.1921 \| 0.006 \| 5092.0041 \| 3529.5083 \| 224.0 \| 570.0 \| 0.3930 \| 208.0 \| 0.3649 \| 144.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.9114 \| 26.0 \| 27.0 \| 152.0 \| 0.1776 \| 0.1711 \| 25.0 \| 27.0 \| 142.0 \| 0.1901 \| 0.1761 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 27.0 \| 27 \| 6.2041 \| 0.006 \| 5101.8523 \| 3536.3346 \| 224.0 \| 570.0 \| 0.3930 \| 208.0 \| 0.3649 \| 144.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.9114 \| 26.0 \| 27.0 \| 152.0 \| 0.1776 \| 0.1711 \| 25.0 \| 27.0 \| 142.0 \| 0.1901 \| 0.1761 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 28.0 \| 28 \| 6.2060 \| 0.006 \| 5103.4059 \| 3537.4114 \| 225.0 \| 570.0 \| 0.3947 \| 208.0 \| 0.3649 \| 144.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.9114 \| 27.0 \| 28.0 \| 152.0 \| 0.1842 \| 0.1776 \| 24.0 \| 27.0 \| 142.0 \| 0.1901 \| 0.1690 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 29.0 \| 29 \| 6.2192 \| 0.006 \| 5114.2474 \| 3544.9262 \| 225.0 \| 570.0 \| 0.3947 \| 209.0 \| 0.3667 \| 145.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.9177 \| 27.0 \| 28.0 \| 152.0 \| 0.1842 \| 0.1776 \| 24.0 \| 27.0 \| 142.0 \| 0.1901 \| 0.1690 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 30.0 \| 30 \| 6.2327 \| 0.006 \| 5125.3556 \| 3552.6258 \| 221.0 \| 570.0 \| 0.3877 \| 206.0 \| 0.3614 \| 144.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.9114 \| 26.0 \| 27.0 \| 152.0 \| 0.1776 \| 0.1711 \| 23.0 \| 25.0 \| 142.0 \| 0.1761 \| 0.1620 \| 13.0 \| 13.0 \| 118.0 \| 0.1102 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 31.0 \| 31 \| 6.2450 \| 0.006 \| 5135.5071 \| 3559.6623 \| 222.0 \| 570.0 \| 0.3895 \| 206.0 \| 0.3614 \| 144.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.9114 \| 26.0 \| 27.0 \| 152.0 \| 0.1776 \| 0.1711 \| 23.0 \| 26.0 \| 142.0 \| 0.1831 \| 0.1620 \| 13.0 \| 13.0 \| 118.0 \| 0.1102 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 32.0 \| 32 \| 6.2478 \| 0.006 \| 5137.7630 \| 3561.2259 \| 224.0 \| 570.0 \| 0.3930 \| 210.0 \| 0.3684 \| 146.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.9241 \| 27.0 \| 28.0 \| 152.0 \| 0.1842 \| 0.1776 \| 24.0 \| 26.0 \| 142.0 \| 0.1831 \| 0.1690 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 33.0 \| 33 \| 6.2653 \| 0.006 \| 5152.1581 \| 3571.2038 \| 224.0 \| 570.0 \| 0.3930 \| 209.0 \| 0.3667 \| 146.0 \| 156.0 \| 158.0 \| 0.9873 \| 0.9241 \| 26.0 \| 27.0 \| 152.0 \| 0.1776 \| 0.1711 \| 24.0 \| 27.0 \| 142.0 \| 0.1901 \| 0.1690 \| 13.0 \| 14.0 \| 118.0 \| 0.1186 \| 0.1102 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
resistz/sft_Qwen3-8B-Base_ultra200k_lora32	resistz	2025-08-19T06:55:24Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:Qwen/Qwen3-8B-Base", "lora", "sft", "trl", "text-generation", "conversational", "base_model:Qwen/Qwen3-8B-Base", "region:us" ]	text-generation	2025-08-19T06:54:12Z	--- library_name: peft model_name: sft_Qwen3-8B-Base_ultra200k_lora32 tags: - base_model:adapter:Qwen/Qwen3-8B-Base - lora - sft - trl licence: license pipeline_tag: text-generation base_model: Qwen/Qwen3-8B-Base --- # Model Card for sft_Qwen3-8B-Base_ultra200k_lora32 This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## 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/resistzzz97/Alignment_Influence/runs/9rimz0x9) This model was trained with SFT. ### Framework versions - PEFT 0.17.0 - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.7.1 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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}} } ```
VoilaRaj/78_4Qc4dT	VoilaRaj	2025-08-19T06:51:53Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T06:48:04Z	--- 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).
MitsuiChen14/DGTRS-CLIP-ViT-B-16	MitsuiChen14	2025-08-19T06:51:31Z	0	0	null	[ "region:us" ]	null	2025-03-26T06:49:13Z	https://github.com/MitsuiChen14/LRSCLIP?tab=readme-ov-file#-usage-
deepkeep-ai/public-classification-qwen3-0.6B-contrastive-classifier	deepkeep-ai	2025-08-19T06:51:02Z	35	0	transformers	[ "transformers", "safetensors", "contrastive-wrapper", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-13T11:12:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
hdong0/deepseek-Qwen-1.5B-baseline-thin-Open-R1-GRPO_deepscaler_mu_8_constant_lr_warmed	hdong0	2025-08-19T06:48:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen2bm", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "custom_code", "dataset:agentica-org/DeepScaleR-Preview-Dataset", "arxiv:2402.03300", "base_model:hdong0/deepseek-Qwen2.5-1.5B-baseline-thin-init", "base_model:finetune:hdong0/deepseek-Qwen2.5-1.5B-baseline-thin-init", "autotrain_compatible", "region:us" ]	text-generation	2025-08-18T23:10:15Z	--- base_model: hdong0/deepseek-Qwen2.5-1.5B-baseline-thin-init datasets: agentica-org/DeepScaleR-Preview-Dataset library_name: transformers model_name: deepseek-Qwen-1.5B-baseline-thin-Open-R1-GRPO_deepscaler_mu_8_constant_lr_warmed tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for deepseek-Qwen-1.5B-baseline-thin-Open-R1-GRPO_deepscaler_mu_8_constant_lr_warmed This model is a fine-tuned version of [hdong0/deepseek-Qwen2.5-1.5B-baseline-thin-init](https://huggingface.co/hdong0/deepseek-Qwen2.5-1.5B-baseline-thin-init) on the [agentica-org/DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-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="hdong0/deepseek-Qwen-1.5B-baseline-thin-Open-R1-GRPO_deepscaler_mu_8_constant_lr_warmed", 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: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755585419	IvanJAjebu	2025-08-19T06:38:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T06:38:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VoilaRaj/78_l9bzGb	VoilaRaj	2025-08-19T06:35:45Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T06:31:52Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
ianmathu/Llama-3.2-3B-Instruct-unsloth-bnb-4bit-alpaca	ianmathu	2025-08-19T06:34:22Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T06:33:35Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hakimjustbao/blockassist-bc-raging_subtle_wasp_1755583495	hakimjustbao	2025-08-19T06:32:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T06:32:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
shiimi/labse-dhivehi-finetuned	shiimi	2025-08-19T06:28:01Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "dense", "generated_from_trainer", "dataset_size:968266", "loss:CosineSimilarityLoss", "arxiv:1908.10084", "base_model:sentence-transformers/LaBSE", "base_model:finetune:sentence-transformers/LaBSE", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-08-19T05:46:17Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:968266 - loss:CosineSimilarityLoss base_model: sentence-transformers/LaBSE widget: - source_sentence: ކުއްލިއަކަށް ދޮންބެ ތެދުވެ އިނދެ ދެފައި ވައްކޮއްލިއެވެ. ދެލޯ ބޮޑުކޮއްގެން ހުރެ ހެވެމުން ދިލެމުން ގޮސް އަހަރެން ހުޅުވާލީވެސް ދޮންބެ ބުނި ކަބަޑެވެ. ގެރިގުއި ކުލައިގެ ކަރުދާހަކުން ބަންދުކޮއްފައި އޮތް ފޮށިގަނޑެއް ފެނުމާއި އެކު އަހަރެންނަށް ބަލާލެވުނީ ގޮދަނޑިމަތީގައި ދެފައި ވަށްކޮއްގެން އިން ބޭބެ އާއި ދިމާއަށެވެ. sentences: - sheet covering coffin - The king's kidneys, heart and lungs have also stopped working, Saudi health officials said, according to Press TV. - The Civil Court of Maldives has ordered the seizure of passports and freezing bank accounts belonging to Haulath Faheem, wife of former President Dr. Mohamed Jamil, as well as seven other members of his family in connection with a case of proven debt. This was decided by the court today after an action filed by Mohammad Aniis who served as General Manager at four resorts owned by Three A Company when it was not being divided into shares. The heir was not present at the court. The lawyer for the heirs said that he has appealed to the High Court against this decision. In any case of proven debt, it is a common practice in courts to hold passports and freeze accounts as part of an application for enforcement of judgment when there are no payments made by debtors. The family appealed the Civil Court’s order to pay them back, which was then reviewed by the Supreme Court. In addition to the three charges, Anies also brought another two cases against Musa Fahim’s heirs. The other accused are Haulat and Shaheed as well as Farida Ibrahim, Ahmad Shahid Shiyam, Ali Shiyam, Hassan Shiyam, Maryam Shifa and Aimanat Ashfah. The two brothers’ son Anies said he owes the company 1.8 million rupees for days when senior management was not paid due to problems arising from the split of Three Airline Company Ltd (THAC). The order was issued in response to a case filed by Anis at the Civil Court on May 15, requesting payment of Rs.731,540.80 due from his family following an appeal ruling made on February 17 this year. He said that no appeal had been lodged against the judgment for over ninety days and he is still waiting for the decision to be announced. - source_sentence: 24 ޖުލައި 2013 ގައި ޖޯން ހޮޖްމަން މެކްސިމަމް ފަން ޕޮޑްކާސްޓް `` ޖަޖް ބްރަދަރ އަލީ '' އިން ފެނިގެންދިޔައީ '' އެކްސްޕާޓް ވިޓްނަސް '' ގެ ގޮތުގައެވެ . sentences: - Translate the following sentence into a different language and add a proof of the translation in the footnotes. Traer tu propia bolsa es una elección ecológica. <sup>1</sup> --- <sup>1</sup> Translation from English to Spanish using Google Translate. - The result sheet of the Ihwandu constituency, which is part of the North East District Council was lost and it has been found while reopening a ballot box. It had to be counted again after that because the results were missing. In presence of representatives from candidates who contested for this district as well as media, the election commission opened the ballot box at 8:30 p.m. today when they discovered the result sheet in another letter. The results sheet was mistakenly placed in a wrong envelope.The Election Commission decided that the ballot box did not need to be counted after seeing its result sheet.This is the first election with an issue of this kind. The Complaints Bureau has not received any complaints from the voters that would require a ballot box to be reopened, said Election Commission Director General Mohamed Sheik. The Commission said that 60 percent of the total number of results sheets, which is estimated to be around 17,000 have been cleared. - Outline the following passage I. American astronauts' exploration of the moon A. Began in 1969 B. Building of moon bases C. Driving lunar rovers on the surface D. Collection of moon samples. - source_sentence: އަދި ލަންގޭންސްޓައިންބާކް އާއި އަލަށް އުފެއްދި ޝިސްޝުޓެނަކަރ ރޭލްވޭ ސްޓޭޝަނާ ދެމެދު 2011 ވަނަ އަހަރު ކުރު ޑަބަލް ޓްރެކެއް ވެސް ހެދިއެވެ . sentences: - i told them i would personally be delighted if sia would fly to and from europe via the maldives. - A short double track was also built between Langensteinbach and the newly created Schießhüttenäcker railway station in 2011 . - Offer one suggestion to reduce cases of teenage suicide. One suggestion to reduce cases of teenage suicide could be to provide accessible and safe mental health support for teenagers. This could be in the form of school counselors, teen helplines, or mental health workshops, among other resources. By ensuring that teenagers have someone to talk to about their struggles and concerns, it can alleviate feelings of hopelessness and isolation, which are major risk factors for suicide. - source_sentence: އަޖީއެމްއެޗްގެ އަހަރި ދުވަހާއި ގުޅުވައިގެން ބާއްވާ މި ފެއާއަށް ދާ ފަރާތްތަކަށް ހިލޭ ގުލްކޯޒް، ހަކުރު، އަދި ލޭގެ ޕްރެޝަރު ހުރި މިންވަރު ބަލައިދެމުންދާ ކަމަށް އައިޖީއެމްއެޗުން ބުނެއެވެ. sentences: - A young man died in a serious accident on the road at night. The victim was identified as Hussain Adham, 21 years old from Hithadhoo. The 54-year old man died at the hospital after being treated for a heart attack. According to witnesses, the accident occurred when Adham was driving from Hittadu towards Maradu and collided with another motorbike that had been travelling along Link Road in direction of Maradu. The accident resulted in a severe fracture of his head and extensive bleeding. He was also broken his neck and a hand. "The helmet he was wearing broke and his head got injured. The injuries were severe," the witness said. Some of the victims had broken their hands and feet. A woman was among the victims. - NASA has announced that it will test a new type of flying saucer this year. It may be to bring in aliens who have not yet landed on the earth. The cup-style vehicle will be launched by what NASA calls a "low density supersonic decelerator" rocket. The rocket is scheduled to be launched in June. NASA is interested in launching a flying saucer into the atmosphere, but according to their own statements, there's no connection between aliens and NASA's Flying Saucer. NASA wants to test and demonstrate new technologies that can be used for launching objects into the atmosphere. NASA said the mission will help to estimate how much payload is needed for a manned Mars missions. - Ar.... Arfin? Are you telling the truth? Is the child so good now? How many years have passed since then... If you haven't even heard from the boy, you can hear what Asiya is saying, I really want to see you, Asiya, please come here with Arfin, if you have his number I want to call him now - source_sentence: އޭނާ ރީތި. sentences: - She's pretty. - Words of gratitude are being sent to the government and President Yameen for bringing two new generators to the village within five days. The people of Thonadhoo have shown the whole country that they have a people who love patience, unity and brotherhood. It is a beautiful example of unity. The burden and pain of the power outages is not easy for anyone to bear in such an era. - 'Date of appointment: 22 June' pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on sentence-transformers/LaBSE This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision 836121a0533e5664b21c7aacc5d22951f2b8b25b --> - Maximum Sequence Length: 256 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'}) (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (3): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("shiimi/labse-dhivehi-finetuned") # Run inference sentences = [ 'އޭނާ ރީތި.', "She's pretty.", 'Words of gratitude are being sent to the government and President Yameen for bringing two new generators to the village within five days. The people of Thonadhoo have shown the whole country that they have a people who love patience, unity and brotherhood. It is a beautiful example of unity. The burden and pain of the power outages is not easy for anyone to bear in such an era.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[ 1.0000, 0.9827, -0.0089], # [ 0.9827, 1.0000, -0.0044], # [-0.0089, -0.0044, 1.0000]]) ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 968,266 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| label \| \|:--------\|:------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:---------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 3 tokens</li><li>mean: 121.67 tokens</li><li>max: 256 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 64.68 tokens</li><li>max: 256 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| label \| \|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------\| \| <code>އިންތިހާބު ލަސްކުރަން ބްލެޓާ ބޭނުމެއްނުވޭ: ފީފާ</code> \| <code>The Ponoru River is a tributary of the Horezu in Romania .</code> \| <code>0.0</code> \| \| <code>ޖޯ އުފަންވީ 27 މާރޗް 1929 ގައި މެސެޗުސެޓްސްގެ ސޮމަރވިލް އަށް ކަމަށާއި ބޮޑުވީ މެސެޗުސެޓްސްގެ ކުއިންސީ ގައެވެ .</code> \| <code>The National Inquiry Commission set up by the government of President Mohammed Vaheed Hassan Manik has said that the coup was not a coup and that the government was overthrown according to the rules of law.</code> \| <code>0.0</code> \| \| <code>ސާބިތު ދަރަނީގެ މައްސަލައެއްގައި ޑރ. މުހައްމަދު ޖަމީލްގެ އަނބިކަނބަލުން ހައުލަތު ފަހީމް އާއި އެ އާއިލާގެ އިތުރު ހަތް މީހެއްގެ ޕާސްޕޯޓް ހިފަހައްޓައި ބޭންކް އެކައުންޓްތައް ފްރީޒްކުރުމަށް ސިވިލް ކޯޓުން މިއަދު އަމުރު ނެރެފި އެވެ.ވީބީ އައްޑޫ އެފްސީގެ މުއައްސިސެއް ކަމަށްވާ މުހަންމަދު ޝަވީދުގެ ވެސް ބައްޕަ މަރުހޫމް މޫސާ ފަހީމްގެ އަށް ވާރިސުންގެ ޕާސްޕޯޓާއި، ރާއްޖޭގެ ބޭންކްތަކުގައި ހުރި ހުރިހާ އެކައުންޓެއް ހިފަހައްޓަން ސިވިލް ކޯޓުން މިއަދު ހެނދުނު ނިންމީ، ތްރީއޭ ކޮމްޕެނީ ނުބަހާއިރު އެ ކުންފުނީގެ ހަތަރު ރިސޯޓެއްގެ ޖެނެރަލް މެނޭޖަރެއްގެ ގޮތުގައި ވަޒީފާ އަދާކުރި މުހަންމަދު އަނީސް ކޮށްފައިވާ ދައުވާއަކާ ގުޅިގެން ބޭއްވި ޝަރީއަތުގެ އަޑުއެހުމުގަ އެވެ. އެ އަޑުއެހުމަށް ވާރިސުންގެ ފަރާތުން ހާޒިރެއް ނުވެ އެވެ. ވާރިސުންގެ ވަކީލް ވިދާޅުވީ ސިވިލް ކޯޓުގެ ހުކުމް ހައި ކޯޓަށް އިސްތިއުނާފަށް ހުށަހަޅާފައިވާ ކަމަށެވެ.ސާބިތު ދަރަނީގެ ކޮންމެ މައްސަލައެއްގައި ވެސް ދަރަނި އަދާނުކުރާ ހާލަތެއްގައި، ހުކުމް ތަންފީޒުކުރުމަށް އެދި ހުށަހަޅެމުން ޕާސްޕޯޓް ހިފަހައްޓައި އެކައުންޓުތައް ފްރީޒްކުރުމަކީ ކޯޓުން އަމަލުކުރާ އާންމު އުސޫލެވ...</code> \| <code>The Civil Court of Maldives has ordered the seizure of passports and freezing bank accounts belonging to Haulath Faheem, wife of former President Dr. Mohamed Jamil, as well as seven other members of his family in connection with a case of proven debt. This was decided by the court today after an action filed by Mohammad Aniis who served as General Manager at four resorts owned by Three A Company when it was not being divided into shares. The heir was not present at the court. The lawyer for the heirs said that he has appealed to the High Court against this decision. In any case of proven debt, it is a common practice in courts to hold passports and freeze accounts as part of an application for enforcement of judgment when there are no payments made by debtors. The family appealed the Civil Court’s order to pay them back, which was then reviewed by the Supreme Court. In addition to the three charges, Anies also brought another two cases against Musa Fahim’s heirs. The other accused are ...</code> \| <code>1.0</code> \| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 1 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:------:\|:----:\|:-------------:\| \| 0.0661 \| 500 \| 0.0528 \| \| 0.1322 \| 1000 \| 0.0298 \| \| 0.1983 \| 1500 \| 0.0261 \| \| 0.2644 \| 2000 \| 0.0242 \| \| 0.3305 \| 2500 \| 0.0235 \| \| 0.3966 \| 3000 \| 0.0223 \| \| 0.4627 \| 3500 \| 0.0207 \| \| 0.5288 \| 4000 \| 0.0208 \| \| 0.5948 \| 4500 \| 0.0196 \| \| 0.6609 \| 5000 \| 0.0192 \| \| 0.7270 \| 5500 \| 0.019 \| \| 0.7931 \| 6000 \| 0.0181 \| \| 0.8592 \| 6500 \| 0.0181 \| \| 0.9253 \| 7000 \| 0.0175 \| \| 0.9914 \| 7500 \| 0.0178 \| ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 5.1.0 - Transformers: 4.55.2 - PyTorch: 2.8.0+cu128 - Accelerate: 1.9.0 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
ariankharazmi/Curiosity-14	ariankharazmi	2025-08-19T06:27:29Z	3	0	null	[ "safetensors", "gpt2", "research", "text-generation", "en", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "region:us" ]	text-generation	2025-04-25T03:43:28Z	--- license: mit language: - en base_model: - openai-community/gpt2 pipeline_tag: text-generation tags: - research --- Curiosity-14 is a low-level LLM. Built throughout the seven weeks of the Summer 2024 UCinci EEP, Curiosity-14 is the culmination of all of the research, coded deliverables, and painstaking patience as one final advanced deliverable.
thailevann/track8_subtask1_v3	thailevann	2025-08-19T06:26:07Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T02:46:57Z	--- base_model: unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thailevann - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) 0.97
chsubhasis/finetuned_model_unsloth	chsubhasis	2025-08-19T06:17:12Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gpt_oss", "trl", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T06:17:02Z	--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: chsubhasis - License: apache-2.0 - Finetuned from model : unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss 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)
subsectmusic/qwriko4b-64k-2507-instruct	subsectmusic	2025-08-19T06:15:30Z	0	0	transformers	[ "transformers", "gguf", "qwen3", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-19T05:07:14Z	--- base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: subsectmusic - License: apache-2.0 - Finetuned from model : unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
valuesimplex-ai-lab/FinBERT1-base	valuesimplex-ai-lab	2025-08-19T06:14:50Z	0	1	null	[ "pytorch", "safetensors", "bert", "finance", "zh", "base_model:google-bert/bert-base-chinese", "base_model:finetune:google-bert/bert-base-chinese", "license:apache-2.0", "region:us" ]	null	2025-08-17T10:03:57Z	--- license: apache-2.0 language: - zh base_model: google-bert/bert-base-chinese tags: - finance --- ## Model Details FinBERT1-Base is a financial domain-adapted Chinese language model. Built on Google's BERT-Base architecture, it was continually pretrained on large-scale Chinese financial corpora to enhance financial text understanding. - Developed by: See [valuesimplex](https://github.com/valuesimplex) for model developers - Model Type: Transformer-based language model - Language(s): Chinese - Parent Model: See the [bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) for more information about the BERT base model. - Resources: [https://github.com/valuesimplex/FinBERT](https://github.com/valuesimplex/FinBERT) ## Direct Use ```python from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("valuesimplex-ai-lab/FinBERT1-base") tokenizer = AutoTokenizer.from_pretrained("valuesimplex-ai-lab/FinBERT1-base") ``` ### Further Usage continual pre-training or fine-tuning：https://github.com/valuesimplex/FinBERT
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755583947	IvanJAjebu	2025-08-19T06:14:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T06:13:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VoilaRaj/78_7iz0a4	VoilaRaj	2025-08-19T06:11:19Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T06:07:20Z	--- 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).
BlazePro12/merged_grok_data_mcp_2	BlazePro12	2025-08-19T06:10:59Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "endpoints_compatible", "region:us" ]	null	2025-08-19T06:08:21Z	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: merged_grok_data_mcp_2 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for merged_grok_data_mcp_2 This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="BlazePro12/merged_grok_data_mcp_2", 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: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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}} } ```
yaelahnal/blockassist-bc-mute_clawed_crab_1755583572	yaelahnal	2025-08-19T06:07:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T06:07:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755582079	kojeklollipop	2025-08-19T06:06:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T06:06:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VoilaRaj/78_dpG7CL	VoilaRaj	2025-08-19T06:03:01Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T05:59:04Z	--- 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).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755583183	IvanJAjebu	2025-08-19T06:01:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T06:01:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755583222	0xaoyama	2025-08-19T06:01:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T06:00:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yaelahnal/blockassist-bc-mute_clawed_crab_1755583110	yaelahnal	2025-08-19T05:59:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T05:59:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755581603	sampingkaca72	2025-08-19T05:59:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T05:58:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored stealthy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755581452	pempekmangedd	2025-08-19T05:58:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "patterned sturdy dolphin", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T05:58:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - patterned sturdy dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755581346	vwzyrraz7l	2025-08-19T05:56:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T05:56:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF	gmonsoon	2025-08-19T05:56:15Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:gmonsoon/Qwen3-4b-REnewbie-NEXT", "base_model:quantized:gmonsoon/Qwen3-4b-REnewbie-NEXT", "endpoints_compatible", "region:us" ]	null	2025-08-19T05:56:02Z	--- base_model: gmonsoon/Qwen3-4b-REnewbie-NEXT library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF This model was converted to GGUF format from [`gmonsoon/Qwen3-4b-REnewbie-NEXT`](https://huggingface.co/gmonsoon/Qwen3-4b-REnewbie-NEXT) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/gmonsoon/Qwen3-4b-REnewbie-NEXT) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF --hf-file qwen3-4b-renewbie-next-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF --hf-file qwen3-4b-renewbie-next-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF --hf-file qwen3-4b-renewbie-next-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF --hf-file qwen3-4b-renewbie-next-q4_k_m.gguf -c 2048 ```
mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF	mradermacher	2025-08-19T05:56:03Z	0	1	transformers	[ "transformers", "gguf", "en", "base_model:morganstanley/qqWen-14B-RL-Reasoning", "base_model:quantized:morganstanley/qqWen-14B-RL-Reasoning", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-18T17:25:35Z	--- base_model: morganstanley/qqWen-14B-RL-Reasoning language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/morganstanley/qqWen-14B-RL-Reasoning <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#qqWen-14B-RL-Reasoning-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ1_M.gguf) \| i1-IQ1_M \| 4.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 8.6 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q4_1.gguf) \| i1-Q4_1 \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755582693	IvanJAjebu	2025-08-19T05:52:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T05:52:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
wasabuko/blockassist-bc-noisy_zealous_macaw_1755580421	wasabuko	2025-08-19T05:51:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "noisy zealous macaw", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T05:48:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - noisy zealous macaw --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
taochengfei/llama-3.2-3b-it-beta_assistant_v0.2_gptq	taochengfei	2025-08-19T05:46:23Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-08-19T05:45:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ihsanridzi/blockassist-bc-wiry_flexible_owl_1755580729	ihsanridzi	2025-08-19T05:45:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T05:45:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
CraneAILabs/swahili-gemma-1b-GGUF	CraneAILabs	2025-08-19T05:40:52Z	0	0	transformers	[ "transformers", "gguf", "swahili", "translation", "conversational", "unsloth", "gemma", "gemma3", "quantized", "text-generation", "en", "sw", "base_model:CraneAILabs/swahili-gemma-1b", "base_model:quantized:CraneAILabs/swahili-gemma-1b", "license:gemma", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T07:37:28Z	--- base_model: CraneAILabs/swahili-gemma-1b language: - en - sw library_name: transformers license: gemma tags: - swahili - translation - conversational - unsloth - gemma - gemma3 - gguf - quantized pipeline_tag: text-generation --- # Swahili Gemma 1B - GGUF Quantized GGUF versions of Swahili Gemma 1B, a fine-tuned Gemma 3 1B instruction model specialized for English-to-Swahili translation and Swahili conversational AI. The model accepts input in both English and Swahili but outputs responses exclusively in Swahili. ## 📊 Translation Performance ![Translation Performance Comparison](swahili_gemma_ascending_chart.png) ### Model Comparison \| Model \| Parameters \| BLEU \| chrF++ \| Efficiency* \| \|-------\|------------\|------\|--------\|-------------\| \| Gemma 3 4B \| 4B \| 10.9 \| 44.1 \| 2.7 \| \| Swahili Gemma 1B \| 1B \| 27.6 \| 56.8 \| 27.6 \| \| Gemma 3 27B \| 27B \| 29.4 \| 60.0 \| 1.1 \| \| GPT-5 Mini \| ~8B \| 31.8 \| 62.4 \| 4.0 \| \| Gemini 2.0 Flash \| Large \| 35.6 \| 64.6 \| N/A \| Efficiency = BLEU Score / Parameters (in billions) ### Key Performance Insights 🎯 Efficiency Leader: Achieves the highest BLEU-to-parameter ratio (27.6 BLEU per billion parameters) 🚀 Size Advantage: Outperforms Gemma 3 4B (4x larger) by 153% on BLEU score 💎 Competitive Quality: Achieves 94% of Gemma 3 27B performance with 27x fewer parameters ⚡ Practical Deployment: Runs efficiently on consumer hardware while maintaining quality ### Evaluation Details - Dataset: FLORES-200 English→Swahili (1,012 translation pairs) - Metrics: BLEU (bilingual evaluation understudy) and chrF++ (character F-score) - Evaluation: Zero-shot translation performance ## 🚀 Quick Start ```bash # Download the recommended Q4_K_M quantization pip install huggingface_hub # Python download from huggingface_hub import snapshot_download snapshot_download( repo_id="CraneAILabs/swahili-gemma-1b-GGUF", local_dir="swahili-gemma-1b-GGUF", allow_patterns=["Q4_K_M/"] # Download only Q4_K_M version ) ``` ## 📊 Available Quantizations \| Quantization \| Folder \| File Size \| Quality \| Use Case \| \|-------------\|--------\|-----------\|---------\|----------\| \| `F32` \| F32/ \| ~3.8GB \| Highest \| Research & benchmarking \| \| `F16` \| F16/ \| ~1.9GB \| Highest \| Maximum quality inference \| \| `Q8_0` \| Q8_0/ \| ~1.0GB \| Very High \| Production with ample resources \| \| `Q5_K_M` \| Q5_K_M/ \| ~812MB \| High \| Balanced quality/size \| \| `Q4_K_M` \| Q4_K_M/ \| ~769MB \| Good \| Recommended for most users \| \| `Q4_K_S` \| Q4_K_S/ \| ~745MB \| Good \| Resource-constrained environments \| \| `Q3_K_M` \| Q3_K_M/ \| ~689MB \| Fair \| Mobile/edge deployment \| \| `Q2_K` \| Q2_K/ \| ~658MB \| Lower \| Minimal resource usage \| ## 💻 Usage with llama.cpp ### Basic Translation ```bash # English to Swahili translation ./llama-cli \ --model swahili-gemma-1b-GGUF/Q4_K_M/swahili-gemma-1b-q4_k_m.gguf \ --prompt "Translate to Swahili: Hello, how are you today?" \ --temp 0.3 \ --top-p 0.95 \ --top-k 64 \ --repeat-penalty 1.1 \ -n 128 ``` ## 🔧 Usage with Ollama ```bash # Create model from GGUF ollama create swahili-gemma-1b -f Modelfile # Use for translation ollama run swahili-gemma-1b "Translate to Swahili: Good morning!" # Use for conversation ollama run swahili-gemma-1b "Hujambo! Je, unaweza kunisaidia?" ``` ### Modelfile Example ```dockerfile FROM swahili-gemma-1b-GGUF/Q4_K_M/swahili-gemma-1b-q4_k_m.gguf TEMPLATE """{{ if .System }}<\|start_header_id\|>system<\|end_header_id\|> {{ .System }}<\|eot_id\|>{{ end }}{{ if .Prompt }}<\|start_header_id\|>user<\|end_header_id\|> {{ .Prompt }}<\|eot_id\|>{{ end }}<\|start_header_id\|>assistant<\|end_header_id\|> {{ .Response }}<\|eot_id\|>""" PARAMETER stop "<\|start_header_id\|>" PARAMETER stop "<\|end_header_id\|>" PARAMETER stop "<\|eot_id\|>" ``` ## 🐍 Usage with Python (llama-cpp-python) ```python from llama_cpp import Llama # Initialize model llm = Llama( model_path="swahili-gemma-1b-GGUF/Q4_K_M/swahili-gemma-1b-q4_k_m.gguf", n_ctx=2048, n_threads=8, verbose=False ) # Generate translation response = llm( "Translate to Swahili: Hello, how are you today?", max_tokens=128, temperature=0.3, top_p=0.95, top_k=64, repeat_penalty=1.1 ) print(response['choices'][0]['text']) ``` ## 🌍 Language Capabilities - Input Languages: English + Swahili - Output Language: Swahili only - Primary Focus: English-to-Swahili translation and Swahili conversation ## 📊 Performance Metrics ### Translation Quality (BLEU Scores) \| Model \| BLEU Score \| chrF++ \| \|-------\|------------\|--------\| \| 🥇 Swahili Gemma 1B \| 23.64 \| 52.26 \| \| 🥈 ChatGPT-4o-latest \| [TBD] \| [TBD] \| \| 🥉 Other Models \| [TBD] \| [TBD] \| Evaluated on 1,012 English-to-Swahili translation samples. ## 🎯 Capabilities - Translation: English-to-Swahili translation - Conversational AI: Natural dialogue in Swahili - Summarization: Text summarization in Swahili - Writing: Creative and informational writing in Swahili - Question Answering: General knowledge responses in Swahili ## 💡 Recommended Parameters ```bash # Optimal settings for translation tasks --temp 0.3 --top-p 0.95 --top-k 64 --repeat-penalty 1.1 --ctx-size 2048 ``` ## 🔗 Related Models - Original Model: [CraneAILabs/swahili-gemma-1b](https://huggingface.co/CraneAILabs/swahili-gemma-1b) - Full precision HuggingFace model - LiteRT Mobile: [CraneAILabs/swahili-gemma-1b-litert](https://huggingface.co/CraneAILabs/swahili-gemma-1b-litert) - Mobile deployment - Ollama: [crane-ai-labs/swahili-gemma-1b](https://ollama.com/crane-ai-labs/swahili-gemma-1b) - Ready-to-run models ## 🛠️ Technical Details - Base Model: google/gemma-3-1b-it - Architecture: Gemma 3 - Context Length: 4,096 tokens - Quantization: GGML format with multiple precision levels - Compatible: llama.cpp, Ollama, Jan, LM Studio, and other GGUF engines ## 🎨 Use Cases - Offline Translation: Run Swahili translation without internet - Local AI Assistant: Swahili conversational AI on your machine - Educational Tools: Language learning applications - Content Creation: Generate Swahili content locally - Research: Swahili language model experiments ## ⚠️ Limitations - Language Output: Responds only in Swahili - Quantization Trade-offs: Lower bit quantizations may reduce quality - Context Limit: 4K tokens for optimal performance - Specialized Tasks: May need fine-tuning for specific domains ## 📄 License This model is released under the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). Please review the terms before use. ## 🙏 Acknowledgments - Google: For the Gemma 3 base model, support and guidance. - Community: For Swahili language resources and datasets - Gilbert Korir (Msingi AI, Nairobi, Kenya) - Alfred Malengo Kondoro (Hanyang University, Seoul, South Korea) ## Citation If you use these GGUF quantizations in your research or applications, please cite: ```bibtex @misc{crane_ai_labs_2025, author = {Bakunga Bronson and Kato Steven Mubiru and Lwanga Caleb and Gimei Alex and Kavuma Lameck and Roland Ganafa and Sibomana Glorry and Atuhaire Collins and JohnRoy Nangeso and Tukamushaba Catherine}, title = {Swahili Gemma: A Fine-tuned Gemma 3 1B Model for Swahili conversational AI}, year = {2025}, url = {https://huggingface.co/CraneAILabs/swahili-gemma-1b}, organization = {Crane AI Labs} } ``` --- Built with ❤️ by Crane AI Labs Swahili Gemma - Your helpful Swahili AI companion, optimized for local deployment
SP4ND4N/SmolLM2-360M-2025-08-18_10-01-49-fp8-merged	SP4ND4N	2025-08-19T05:32:35Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "en", "base_model:unsloth/SmolLM2-360M", "base_model:finetune:unsloth/SmolLM2-360M", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T05:27:34Z	--- base_model: unsloth/SmolLM2-360M tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: SP4ND4N - License: apache-2.0 - Finetuned from model : unsloth/SmolLM2-360M This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755581389	IvanJAjebu	2025-08-19T05:31:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T05:31:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
santhosh/multilingual-e5-base-int8-ov	santhosh	2025-08-19T05:30:45Z	0	0	sentence-transformers	[ "sentence-transformers", "openvino", "xlm-roberta", "mteb", "Sentence Transformers", "sentence-similarity", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:2402.05672", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-08-19T05:12:22Z	--- tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers model-index: - name: multilingual-e5-base-int8-ov results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 78.97014925373135 - type: ap value: 43.69351129103008 - type: f1 value: 73.38075030070492 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.7237687366167 - type: ap value: 82.22089859962671 - type: f1 value: 69.95532758884401 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.65517241379312 - type: ap value: 28.507918657094738 - type: f1 value: 66.84516013726119 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.32976445396146 - type: ap value: 20.720481637566014 - type: f1 value: 59.78002763416003 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 90.63775 - type: ap value: 87.22277903861716 - type: f1 value: 90.60378636386807 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.546 - type: f1 value: 44.05666638370923 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.828 - type: f1 value: 41.2710255644252 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.534 - type: f1 value: 39.820743174270326 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 39.684 - type: f1 value: 39.11052682815307 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.436 - type: f1 value: 37.07082931930871 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.226000000000006 - type: f1 value: 36.65372077739185 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 22.831000000000003 - type: map_at_10 value: 36.42 - type: map_at_100 value: 37.699 - type: map_at_1000 value: 37.724000000000004 - type: map_at_3 value: 32.207 - type: map_at_5 value: 34.312 - type: mrr_at_1 value: 23.257 - type: mrr_at_10 value: 36.574 - type: mrr_at_100 value: 37.854 - type: mrr_at_1000 value: 37.878 - type: mrr_at_3 value: 32.385000000000005 - type: mrr_at_5 value: 34.48 - type: ndcg_at_1 value: 22.831000000000003 - type: ndcg_at_10 value: 44.230000000000004 - type: ndcg_at_100 value: 49.974000000000004 - type: ndcg_at_1000 value: 50.522999999999996 - type: ndcg_at_3 value: 35.363 - type: ndcg_at_5 value: 39.164 - type: precision_at_1 value: 22.831000000000003 - type: precision_at_10 value: 6.935 - type: precision_at_100 value: 0.9520000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.841 - type: precision_at_5 value: 10.754 - type: recall_at_1 value: 22.831000000000003 - type: recall_at_10 value: 69.346 - type: recall_at_100 value: 95.235 - type: recall_at_1000 value: 99.36 - type: recall_at_3 value: 44.523 - type: recall_at_5 value: 53.769999999999996 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 40.27789869854063 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 35.41979463347428 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.22752045109304 - type: mrr value: 71.51112430198303 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.71147646622866 - type: cos_sim_spearman value: 85.059167046486 - type: euclidean_pearson value: 75.88421613600647 - type: euclidean_spearman value: 75.12821787150585 - type: manhattan_pearson value: 75.22005646957604 - type: manhattan_spearman value: 74.42880434453272 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.23799582463465 - type: f1 value: 99.12665274878218 - type: precision value: 99.07098121085595 - type: recall value: 99.23799582463465 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.88685890380806 - type: f1 value: 97.59336708489249 - type: precision value: 97.44662117543473 - type: recall value: 97.88685890380806 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.47142362313821 - type: f1 value: 97.1989377670015 - type: precision value: 97.06384944001847 - type: recall value: 97.47142362313821 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.4728804634018 - type: f1 value: 98.2973494821836 - type: precision value: 98.2095839915745 - type: recall value: 98.4728804634018 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 82.74025974025975 - type: f1 value: 82.67420447730439 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.0380848063507 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 29.45956405670166 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.122 - type: map_at_10 value: 42.03 - type: map_at_100 value: 43.364000000000004 - type: map_at_1000 value: 43.474000000000004 - type: map_at_3 value: 38.804 - type: map_at_5 value: 40.585 - type: mrr_at_1 value: 39.914 - type: mrr_at_10 value: 48.227 - type: mrr_at_100 value: 49.018 - type: mrr_at_1000 value: 49.064 - type: mrr_at_3 value: 45.994 - type: mrr_at_5 value: 47.396 - type: ndcg_at_1 value: 39.914 - type: ndcg_at_10 value: 47.825 - type: ndcg_at_100 value: 52.852 - type: ndcg_at_1000 value: 54.891 - type: ndcg_at_3 value: 43.517 - type: ndcg_at_5 value: 45.493 - type: precision_at_1 value: 39.914 - type: precision_at_10 value: 8.956 - type: precision_at_100 value: 1.388 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 20.791999999999998 - type: precision_at_5 value: 14.821000000000002 - type: recall_at_1 value: 32.122 - type: recall_at_10 value: 58.294999999999995 - type: recall_at_100 value: 79.726 - type: recall_at_1000 value: 93.099 - type: recall_at_3 value: 45.017 - type: recall_at_5 value: 51.002 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.677999999999997 - type: map_at_10 value: 38.684000000000005 - type: map_at_100 value: 39.812999999999995 - type: map_at_1000 value: 39.945 - type: map_at_3 value: 35.831 - type: map_at_5 value: 37.446 - type: mrr_at_1 value: 37.771 - type: mrr_at_10 value: 44.936 - type: mrr_at_100 value: 45.583 - type: mrr_at_1000 value: 45.634 - type: mrr_at_3 value: 42.771 - type: mrr_at_5 value: 43.994 - type: ndcg_at_1 value: 37.771 - type: ndcg_at_10 value: 44.059 - type: ndcg_at_100 value: 48.192 - type: ndcg_at_1000 value: 50.375 - type: ndcg_at_3 value: 40.172000000000004 - type: ndcg_at_5 value: 41.899 - type: precision_at_1 value: 37.771 - type: precision_at_10 value: 8.286999999999999 - type: precision_at_100 value: 1.322 - type: precision_at_1000 value: 0.178 - type: precision_at_3 value: 19.406000000000002 - type: precision_at_5 value: 13.745 - type: recall_at_1 value: 29.677999999999997 - type: recall_at_10 value: 53.071 - type: recall_at_100 value: 70.812 - type: recall_at_1000 value: 84.841 - type: recall_at_3 value: 41.016000000000005 - type: recall_at_5 value: 46.22 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 42.675000000000004 - type: map_at_10 value: 53.93599999999999 - type: map_at_100 value: 54.806999999999995 - type: map_at_1000 value: 54.867 - type: map_at_3 value: 50.934000000000005 - type: map_at_5 value: 52.583 - type: mrr_at_1 value: 48.339 - type: mrr_at_10 value: 57.265 - type: mrr_at_100 value: 57.873 - type: mrr_at_1000 value: 57.906 - type: mrr_at_3 value: 55.193000000000005 - type: mrr_at_5 value: 56.303000000000004 - type: ndcg_at_1 value: 48.339 - type: ndcg_at_10 value: 59.19799999999999 - type: ndcg_at_100 value: 62.743 - type: ndcg_at_1000 value: 63.99399999999999 - type: ndcg_at_3 value: 54.367 - type: ndcg_at_5 value: 56.548 - type: precision_at_1 value: 48.339 - type: precision_at_10 value: 9.216000000000001 - type: precision_at_100 value: 1.1809999999999998 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 23.72 - type: precision_at_5 value: 16.025 - type: recall_at_1 value: 42.675000000000004 - type: recall_at_10 value: 71.437 - type: recall_at_100 value: 86.803 - type: recall_at_1000 value: 95.581 - type: recall_at_3 value: 58.434 - type: recall_at_5 value: 63.754 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.518 - type: map_at_10 value: 30.648999999999997 - type: map_at_100 value: 31.508999999999997 - type: map_at_1000 value: 31.604 - type: map_at_3 value: 28.247 - type: map_at_5 value: 29.65 - type: mrr_at_1 value: 25.650000000000002 - type: mrr_at_10 value: 32.771 - type: mrr_at_100 value: 33.554 - type: mrr_at_1000 value: 33.629999999999995 - type: mrr_at_3 value: 30.433 - type: mrr_at_5 value: 31.812 - type: ndcg_at_1 value: 25.650000000000002 - type: ndcg_at_10 value: 34.929 - type: ndcg_at_100 value: 39.382 - type: ndcg_at_1000 value: 41.913 - type: ndcg_at_3 value: 30.292 - type: ndcg_at_5 value: 32.629999999999995 - type: precision_at_1 value: 25.650000000000002 - type: precision_at_10 value: 5.311 - type: precision_at_100 value: 0.792 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 12.58 - type: precision_at_5 value: 8.994 - type: recall_at_1 value: 23.518 - type: recall_at_10 value: 46.19 - type: recall_at_100 value: 67.123 - type: recall_at_1000 value: 86.442 - type: recall_at_3 value: 33.678000000000004 - type: recall_at_5 value: 39.244 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.891 - type: map_at_10 value: 22.464000000000002 - type: map_at_100 value: 23.483 - type: map_at_1000 value: 23.613 - type: map_at_3 value: 20.080000000000002 - type: map_at_5 value: 21.526 - type: mrr_at_1 value: 20.025000000000002 - type: mrr_at_10 value: 26.712999999999997 - type: mrr_at_100 value: 27.650000000000002 - type: mrr_at_1000 value: 27.737000000000002 - type: mrr_at_3 value: 24.274 - type: mrr_at_5 value: 25.711000000000002 - type: ndcg_at_1 value: 20.025000000000002 - type: ndcg_at_10 value: 27.028999999999996 - type: ndcg_at_100 value: 32.064 - type: ndcg_at_1000 value: 35.188 - type: ndcg_at_3 value: 22.512999999999998 - type: ndcg_at_5 value: 24.89 - type: precision_at_1 value: 20.025000000000002 - type: precision_at_10 value: 4.776 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 10.531 - type: precision_at_5 value: 7.811 - type: recall_at_1 value: 15.891 - type: recall_at_10 value: 37.261 - type: recall_at_100 value: 59.12 - type: recall_at_1000 value: 81.356 - type: recall_at_3 value: 24.741 - type: recall_at_5 value: 30.753999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.544 - type: map_at_10 value: 36.283 - type: map_at_100 value: 37.467 - type: map_at_1000 value: 37.574000000000005 - type: map_at_3 value: 33.528999999999996 - type: map_at_5 value: 35.028999999999996 - type: mrr_at_1 value: 34.166999999999994 - type: mrr_at_10 value: 41.866 - type: mrr_at_100 value: 42.666 - type: mrr_at_1000 value: 42.716 - type: mrr_at_3 value: 39.541 - type: mrr_at_5 value: 40.768 - type: ndcg_at_1 value: 34.166999999999994 - type: ndcg_at_10 value: 41.577 - type: ndcg_at_100 value: 46.687 - type: ndcg_at_1000 value: 48.967 - type: ndcg_at_3 value: 37.177 - type: ndcg_at_5 value: 39.097 - type: precision_at_1 value: 34.166999999999994 - type: precision_at_10 value: 7.420999999999999 - type: precision_at_100 value: 1.165 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 17.291999999999998 - type: precision_at_5 value: 12.166 - type: recall_at_1 value: 27.544 - type: recall_at_10 value: 51.99399999999999 - type: recall_at_100 value: 73.738 - type: recall_at_1000 value: 89.33 - type: recall_at_3 value: 39.179 - type: recall_at_5 value: 44.385999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.661 - type: map_at_10 value: 35.475 - type: map_at_100 value: 36.626999999999995 - type: map_at_1000 value: 36.741 - type: map_at_3 value: 32.818000000000005 - type: map_at_5 value: 34.397 - type: mrr_at_1 value: 32.647999999999996 - type: mrr_at_10 value: 40.784 - type: mrr_at_100 value: 41.602 - type: mrr_at_1000 value: 41.661 - type: mrr_at_3 value: 38.68 - type: mrr_at_5 value: 39.838 - type: ndcg_at_1 value: 32.647999999999996 - type: ndcg_at_10 value: 40.697 - type: ndcg_at_100 value: 45.799 - type: ndcg_at_1000 value: 48.235 - type: ndcg_at_3 value: 36.516 - type: ndcg_at_5 value: 38.515 - type: precision_at_1 value: 32.647999999999996 - type: precision_at_10 value: 7.202999999999999 - type: precision_at_100 value: 1.1360000000000001 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 17.314 - type: precision_at_5 value: 12.145999999999999 - type: recall_at_1 value: 26.661 - type: recall_at_10 value: 50.995000000000005 - type: recall_at_100 value: 73.065 - type: recall_at_1000 value: 89.781 - type: recall_at_3 value: 39.073 - type: recall_at_5 value: 44.395 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.946583333333333 - type: map_at_10 value: 33.79725 - type: map_at_100 value: 34.86408333333333 - type: map_at_1000 value: 34.9795 - type: map_at_3 value: 31.259999999999998 - type: map_at_5 value: 32.71541666666666 - type: mrr_at_1 value: 30.863749999999996 - type: mrr_at_10 value: 37.99183333333333 - type: mrr_at_100 value: 38.790499999999994 - type: mrr_at_1000 value: 38.85575000000001 - type: mrr_at_3 value: 35.82083333333333 - type: mrr_at_5 value: 37.07533333333333 - type: ndcg_at_1 value: 30.863749999999996 - type: ndcg_at_10 value: 38.52141666666667 - type: ndcg_at_100 value: 43.17966666666667 - type: ndcg_at_1000 value: 45.64608333333333 - type: ndcg_at_3 value: 34.333000000000006 - type: ndcg_at_5 value: 36.34975 - type: precision_at_1 value: 30.863749999999996 - type: precision_at_10 value: 6.598999999999999 - type: precision_at_100 value: 1.0502500000000001 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_3 value: 15.557583333333334 - type: precision_at_5 value: 11.020000000000001 - type: recall_at_1 value: 25.946583333333333 - type: recall_at_10 value: 48.36991666666666 - type: recall_at_100 value: 69.02408333333334 - type: recall_at_1000 value: 86.43858333333331 - type: recall_at_3 value: 36.4965 - type: recall_at_5 value: 41.76258333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.431 - type: map_at_10 value: 28.889 - type: map_at_100 value: 29.642000000000003 - type: map_at_1000 value: 29.742 - type: map_at_3 value: 26.998 - type: map_at_5 value: 28.172000000000004 - type: mrr_at_1 value: 25.307000000000002 - type: mrr_at_10 value: 31.763 - type: mrr_at_100 value: 32.443 - type: mrr_at_1000 value: 32.531 - type: mrr_at_3 value: 29.959000000000003 - type: mrr_at_5 value: 31.063000000000002 - type: ndcg_at_1 value: 25.307000000000002 - type: ndcg_at_10 value: 32.586999999999996 - type: ndcg_at_100 value: 36.5 - type: ndcg_at_1000 value: 39.133 - type: ndcg_at_3 value: 29.25 - type: ndcg_at_5 value: 31.023 - type: precision_at_1 value: 25.307000000000002 - type: precision_at_10 value: 4.954 - type: precision_at_100 value: 0.747 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 12.577 - type: precision_at_5 value: 8.741999999999999 - type: recall_at_1 value: 22.431 - type: recall_at_10 value: 41.134 - type: recall_at_100 value: 59.28600000000001 - type: recall_at_1000 value: 78.857 - type: recall_at_3 value: 31.926 - type: recall_at_5 value: 36.335 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.586 - type: map_at_10 value: 23.304 - type: map_at_100 value: 24.159 - type: map_at_1000 value: 24.281 - type: map_at_3 value: 21.316 - type: map_at_5 value: 22.383 - type: mrr_at_1 value: 21.645 - type: mrr_at_10 value: 27.365000000000002 - type: mrr_at_100 value: 28.108 - type: mrr_at_1000 value: 28.192 - type: mrr_at_3 value: 25.482 - type: mrr_at_5 value: 26.479999999999997 - type: ndcg_at_1 value: 21.645 - type: ndcg_at_10 value: 27.306 - type: ndcg_at_100 value: 31.496000000000002 - type: ndcg_at_1000 value: 34.53 - type: ndcg_at_3 value: 23.73 - type: ndcg_at_5 value: 25.294 - type: precision_at_1 value: 21.645 - type: precision_at_10 value: 4.797 - type: precision_at_100 value: 0.8059999999999999 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 10.850999999999999 - type: precision_at_5 value: 7.736 - type: recall_at_1 value: 17.586 - type: recall_at_10 value: 35.481 - type: recall_at_100 value: 54.534000000000006 - type: recall_at_1000 value: 76.456 - type: recall_at_3 value: 25.335 - type: recall_at_5 value: 29.473 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.095 - type: map_at_10 value: 32.374 - type: map_at_100 value: 33.537 - type: map_at_1000 value: 33.634 - type: map_at_3 value: 30.089 - type: map_at_5 value: 31.433 - type: mrr_at_1 value: 29.198 - type: mrr_at_10 value: 36.01 - type: mrr_at_100 value: 37.022 - type: mrr_at_1000 value: 37.083 - type: mrr_at_3 value: 33.94 - type: mrr_at_5 value: 35.148 - type: ndcg_at_1 value: 29.198 - type: ndcg_at_10 value: 36.729 - type: ndcg_at_100 value: 42.114000000000004 - type: ndcg_at_1000 value: 44.592 - type: ndcg_at_3 value: 32.644 - type: ndcg_at_5 value: 34.652 - type: precision_at_1 value: 29.198 - type: precision_at_10 value: 5.970000000000001 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.129 - type: precision_at_3 value: 14.396999999999998 - type: precision_at_5 value: 10.093 - type: recall_at_1 value: 25.095 - type: recall_at_10 value: 46.392 - type: recall_at_100 value: 69.706 - type: recall_at_1000 value: 87.738 - type: recall_at_3 value: 35.303000000000004 - type: recall_at_5 value: 40.441 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.857999999999997 - type: map_at_10 value: 34.066 - type: map_at_100 value: 35.671 - type: map_at_1000 value: 35.881 - type: map_at_3 value: 31.304 - type: map_at_5 value: 32.885 - type: mrr_at_1 value: 32.411 - type: mrr_at_10 value: 38.987 - type: mrr_at_100 value: 39.894 - type: mrr_at_1000 value: 39.959 - type: mrr_at_3 value: 36.626999999999995 - type: mrr_at_5 value: 38.011 - type: ndcg_at_1 value: 32.411 - type: ndcg_at_10 value: 39.208 - type: ndcg_at_100 value: 44.626 - type: ndcg_at_1000 value: 47.43 - type: ndcg_at_3 value: 35.091 - type: ndcg_at_5 value: 37.119 - type: precision_at_1 value: 32.411 - type: precision_at_10 value: 7.51 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 16.14 - type: precision_at_5 value: 11.976 - type: recall_at_1 value: 26.857999999999997 - type: recall_at_10 value: 47.407 - type: recall_at_100 value: 72.236 - type: recall_at_1000 value: 90.77 - type: recall_at_3 value: 35.125 - type: recall_at_5 value: 40.522999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.3 - type: map_at_10 value: 27.412999999999997 - type: map_at_100 value: 28.29 - type: map_at_1000 value: 28.398 - type: map_at_3 value: 25.169999999999998 - type: map_at_5 value: 26.496 - type: mrr_at_1 value: 23.29 - type: mrr_at_10 value: 29.215000000000003 - type: mrr_at_100 value: 30.073 - type: mrr_at_1000 value: 30.156 - type: mrr_at_3 value: 26.956000000000003 - type: mrr_at_5 value: 28.38 - type: ndcg_at_1 value: 23.29 - type: ndcg_at_10 value: 31.113000000000003 - type: ndcg_at_100 value: 35.701 - type: ndcg_at_1000 value: 38.505 - type: ndcg_at_3 value: 26.727 - type: ndcg_at_5 value: 29.037000000000003 - type: precision_at_1 value: 23.29 - type: precision_at_10 value: 4.787 - type: precision_at_100 value: 0.763 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 11.091 - type: precision_at_5 value: 7.985 - type: recall_at_1 value: 21.3 - type: recall_at_10 value: 40.782000000000004 - type: recall_at_100 value: 62.13999999999999 - type: recall_at_1000 value: 83.012 - type: recall_at_3 value: 29.131 - type: recall_at_5 value: 34.624 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 9.631 - type: map_at_10 value: 16.634999999999998 - type: map_at_100 value: 18.23 - type: map_at_1000 value: 18.419 - type: map_at_3 value: 13.66 - type: map_at_5 value: 15.173 - type: mrr_at_1 value: 21.368000000000002 - type: mrr_at_10 value: 31.56 - type: mrr_at_100 value: 32.58 - type: mrr_at_1000 value: 32.633 - type: mrr_at_3 value: 28.241 - type: mrr_at_5 value: 30.225 - type: ndcg_at_1 value: 21.368000000000002 - type: ndcg_at_10 value: 23.855999999999998 - type: ndcg_at_100 value: 30.686999999999998 - type: ndcg_at_1000 value: 34.327000000000005 - type: ndcg_at_3 value: 18.781 - type: ndcg_at_5 value: 20.73 - type: precision_at_1 value: 21.368000000000002 - type: precision_at_10 value: 7.564 - type: precision_at_100 value: 1.496 - type: precision_at_1000 value: 0.217 - type: precision_at_3 value: 13.876 - type: precision_at_5 value: 11.062 - type: recall_at_1 value: 9.631 - type: recall_at_10 value: 29.517 - type: recall_at_100 value: 53.452 - type: recall_at_1000 value: 74.115 - type: recall_at_3 value: 17.605999999999998 - type: recall_at_5 value: 22.505 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.885 - type: map_at_10 value: 18.798000000000002 - type: map_at_100 value: 26.316 - type: map_at_1000 value: 27.869 - type: map_at_3 value: 13.719000000000001 - type: map_at_5 value: 15.716 - type: mrr_at_1 value: 66 - type: mrr_at_10 value: 74.263 - type: mrr_at_100 value: 74.519 - type: mrr_at_1000 value: 74.531 - type: mrr_at_3 value: 72.458 - type: mrr_at_5 value: 73.321 - type: ndcg_at_1 value: 53.87499999999999 - type: ndcg_at_10 value: 40.355999999999995 - type: ndcg_at_100 value: 44.366 - type: ndcg_at_1000 value: 51.771 - type: ndcg_at_3 value: 45.195 - type: ndcg_at_5 value: 42.187000000000005 - type: precision_at_1 value: 66 - type: precision_at_10 value: 31.75 - type: precision_at_100 value: 10.11 - type: precision_at_1000 value: 1.9800000000000002 - type: precision_at_3 value: 48.167 - type: precision_at_5 value: 40.050000000000004 - type: recall_at_1 value: 8.885 - type: recall_at_10 value: 24.471999999999998 - type: recall_at_100 value: 49.669000000000004 - type: recall_at_1000 value: 73.383 - type: recall_at_3 value: 14.872 - type: recall_at_5 value: 18.262999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 45.18 - type: f1 value: 40.26878691789978 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 62.751999999999995 - type: map_at_10 value: 74.131 - type: map_at_100 value: 74.407 - type: map_at_1000 value: 74.423 - type: map_at_3 value: 72.329 - type: map_at_5 value: 73.555 - type: mrr_at_1 value: 67.282 - type: mrr_at_10 value: 78.292 - type: mrr_at_100 value: 78.455 - type: mrr_at_1000 value: 78.458 - type: mrr_at_3 value: 76.755 - type: mrr_at_5 value: 77.839 - type: ndcg_at_1 value: 67.282 - type: ndcg_at_10 value: 79.443 - type: ndcg_at_100 value: 80.529 - type: ndcg_at_1000 value: 80.812 - type: ndcg_at_3 value: 76.281 - type: ndcg_at_5 value: 78.235 - type: precision_at_1 value: 67.282 - type: precision_at_10 value: 10.078 - type: precision_at_100 value: 1.082 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 30.178 - type: precision_at_5 value: 19.232 - type: recall_at_1 value: 62.751999999999995 - type: recall_at_10 value: 91.521 - type: recall_at_100 value: 95.997 - type: recall_at_1000 value: 97.775 - type: recall_at_3 value: 83.131 - type: recall_at_5 value: 87.93299999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 18.861 - type: map_at_10 value: 30.252000000000002 - type: map_at_100 value: 32.082 - type: map_at_1000 value: 32.261 - type: map_at_3 value: 25.909 - type: map_at_5 value: 28.296 - type: mrr_at_1 value: 37.346000000000004 - type: mrr_at_10 value: 45.802 - type: mrr_at_100 value: 46.611999999999995 - type: mrr_at_1000 value: 46.659 - type: mrr_at_3 value: 43.056 - 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type: f1 value: 86.14603174603175 - type: precision value: 85.1162037037037 - type: recall value: 88.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tha-eng) config: tha-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 95.62043795620438 - type: f1 value: 94.40389294403892 - type: precision value: 93.7956204379562 - type: recall value: 95.62043795620438 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (wuu-eng) config: wuu-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 81.8 - type: f1 value: 78.6532178932179 - type: precision value: 77.46348795840176 - type: recall value: 81.8 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.603 - type: map_at_10 value: 8.5 - type: map_at_100 value: 12.985 - type: map_at_1000 value: 14.466999999999999 - type: map_at_3 value: 4.859999999999999 - type: map_at_5 value: 5.817 - type: mrr_at_1 value: 28.571 - type: mrr_at_10 value: 42.331 - type: mrr_at_100 value: 43.592999999999996 - type: mrr_at_1000 value: 43.592999999999996 - type: mrr_at_3 value: 38.435 - type: mrr_at_5 value: 39.966 - type: ndcg_at_1 value: 26.531 - type: ndcg_at_10 value: 21.353 - type: ndcg_at_100 value: 31.087999999999997 - type: ndcg_at_1000 value: 43.163000000000004 - type: ndcg_at_3 value: 22.999 - type: ndcg_at_5 value: 21.451 - type: precision_at_1 value: 28.571 - type: precision_at_10 value: 19.387999999999998 - type: precision_at_100 value: 6.265 - type: precision_at_1000 value: 1.4160000000000001 - type: precision_at_3 value: 24.490000000000002 - type: precision_at_5 value: 21.224 - type: recall_at_1 value: 2.603 - type: recall_at_10 value: 14.474 - type: recall_at_100 value: 40.287 - type: recall_at_1000 value: 76.606 - type: recall_at_3 value: 5.978 - type: recall_at_5 value: 7.819 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.7848 - type: ap value: 13.661023167088224 - type: f1 value: 53.61686134460943 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 61.28183361629882 - type: f1 value: 61.55481034919965 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 35.972128420092396 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.59933241938367 - type: cos_sim_ap value: 72.20760361208136 - type: cos_sim_f1 value: 66.4447731755424 - type: cos_sim_precision value: 62.35539102267469 - type: cos_sim_recall value: 71.10817941952506 - type: dot_accuracy value: 78.98313166835548 - type: dot_ap value: 44.492521645493795 - type: dot_f1 value: 45.814889336016094 - type: dot_precision value: 37.02439024390244 - type: dot_recall value: 60.07915567282321 - type: euclidean_accuracy value: 85.3907134767837 - type: euclidean_ap value: 71.53847289080343 - type: euclidean_f1 value: 65.95952206778834 - type: euclidean_precision value: 61.31006346328196 - type: euclidean_recall value: 71.37203166226914 - type: manhattan_accuracy value: 85.40859510043511 - type: manhattan_ap value: 71.49664104395515 - type: manhattan_f1 value: 65.98569969356485 - type: manhattan_precision value: 63.928748144482924 - type: manhattan_recall value: 68.17941952506597 - type: max_accuracy value: 85.59933241938367 - type: max_ap value: 72.20760361208136 - type: max_f1 value: 66.4447731755424 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.83261536073273 - type: cos_sim_ap value: 85.48178133644264 - type: cos_sim_f1 value: 77.87816307403935 - type: cos_sim_precision value: 75.88953021114926 - type: cos_sim_recall value: 79.97382198952879 - type: dot_accuracy value: 79.76287499514883 - type: dot_ap value: 59.17438838475084 - type: dot_f1 value: 56.34566667855996 - type: dot_precision value: 52.50349092359864 - type: dot_recall value: 60.794579611949494 - type: euclidean_accuracy value: 88.76857996662397 - type: euclidean_ap value: 85.22764834359887 - type: euclidean_f1 value: 77.65379751543554 - type: euclidean_precision value: 75.11152683839401 - type: euclidean_recall value: 80.37419156144134 - type: manhattan_accuracy value: 88.6987231730508 - type: manhattan_ap value: 85.18907981724007 - type: manhattan_f1 value: 77.51967028849757 - type: manhattan_precision value: 75.49992701795358 - type: manhattan_recall value: 79.65044656606098 - type: max_accuracy value: 88.83261536073273 - type: max_ap value: 85.48178133644264 - type: max_f1 value: 77.87816307403935 language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit --- ## Multilingual-E5-base-int8-ov This is [Multilingual-E5-base](https://huggingface.co/intfloat/multilingual-e5-base) model converted to the OpenVINO™ IR (Intermediate Representation) format with quantization to INT8. Disclaimer: Model is provided as a preview and may be update in the future. [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 12 layers and the embedding size is 768. ## Usage ```python import torch from transformers import AutoTokenizer from optimum.intel.openvino import OVModelForFeatureExtraction # Sentences we want sentence embeddings for sentences = ["Sample Data-1", "Sample Data-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('santhosh/multilingual-e5-base-int8-ov') model = OVModelForFeatureExtraction.from_pretrained('OpenVINO/bge-base-en-v1.5-int8-ov') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Using openvino GenAI ```python import openvino_genai import numpy as np import os import huggingface_hub as hf_hub from typing import List model_path = "santhosh/multilingual-e5-base-int8-ov" sentences = ["Sample Data-1", "Sample Data-2"] embedding_pipeline = openvino_genai.TextEmbeddingPipeline(model_path, "CPU") embeddings = embedding_pipeline.embed_documents(sentences) return np.array(embeddings) ```
StyTJU/mor-kv-sharerobot-visionmor-adapter	StyTJU	2025-08-19T05:30:45Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-08-19T02:56:31Z	# MoR Router Adapter for BAAI/RoboBrain2.0-3B 本仓库权重为全部权重 (safetensors 分片)。(初步Vision MoR训练，Loss较高) 使用前请先加载基座模型RoboBrain-3B然后覆写这些参数。
cucucu666/qiqiu-8.19-female	cucucu666	2025-08-19T05:29:05Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-Fill-dev", "base_model:adapter:black-forest-labs/FLUX.1-Fill-dev", "license:other", "region:us" ]	text-to-image	2025-08-19T03:44:23Z	--- base_model: black-forest-labs/FLUX.1-Fill-dev library_name: diffusers license: other instance_prompt: labi female face, Crayon Shin-chan style, eyelash, pleading expression, both hands together in a prayer pose, plain white background widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Flux-Fill DreamBooth LoRA - cucucu666/qiqiu-8.19-female <Gallery /> ## Model description These are cucucu666/qiqiu-8.19-female DreamBooth LoRA weights for black-forest-labs/FLUX.1-Fill-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with a custom [Flux diffusers trainer](https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `labi female face, Crayon Shin-chan style, eyelash, pleading expression, both hands together in a prayer pose, plain white background` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](cucucu666/qiqiu-8.19-female/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('cucucu666/qiqiu-8.19-female', weight_name='pytorch_lora_weights.safetensors') image = pipeline('labi female face, Crayon Shin-chan style, eyelash, pleading expression, both hands together in a prayer pose, plain white background').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755579680	katanyasekolah	2025-08-19T05:28:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T05:28:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lejelly/shannon_entropy-ep1-lr0001-sampling-t07-gen2	lejelly	2025-08-19T05:24:08Z	0	0	null	[ "safetensors", "mistral", "merge", "parameter_wise", "llm-adamerge", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2025-08-19T05:21:29Z	--- tags: - merge - parameter_wise - llm-adamerge base_model: mistralai/Mistral-7B-v0.1 --- # Merged Model using LLM-AdaMerge (parameter_wise) This model was created by merging multiple fine-tuned models using the LLM-AdaMerge approach with parameter_wise merging. ## Merge Details - Merge Type: parameter_wise - Base Model: mistralai/Mistral-7B-v0.1 - Number of Models Merged: 3 - Models Merged: instruct, math, code - Final Training Loss: N/A - Training Epochs: 0 ## Lambda Coefficients The following lambda coefficients were learned during training: ### Parameter-wise Lambdas This model uses parameter-wise lambda coefficients. Total parameters with individual lambdas: 291 See the uploaded `learned_lambdas.json` file for detailed parameter-wise coefficients. ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("your-username/model-name") tokenizer = AutoTokenizer.from_pretrained("your-username/model-name") # Use the model inputs = tokenizer("Hello, how are you?", return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0])) ``` ## Training Configuration See the uploaded `training_config.json` file for detailed training configuration. ## Citation If you use this model, please cite the LLM-AdaMerge paper: ```bibtex @article{llmadamerge2024, title={LLM-AdaMerge: Adaptive Model Merging for Large Language Models}, author={...}, year={2024} } ```

martinletec55/Mylora

martinletec55

2025-08-19T08:57:47Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

2025-08-19T08:56:00Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/pykaso-output-1755517879199.jpeg text: '-' - output: url: images/pykaso-output-1755518189871.jpeg text: '-' - output: url: images/pykaso-output-1755518369462.jpeg text: '-' base_model: black-forest-labs/FLUX.1-dev instance_prompt: Katie --- # Katie <Gallery /> ## Model description This Lora is big boss ## Trigger words You should use `Katie` to trigger the image generation. ## Download model [Download](/martinletec55/Mylora/tree/main) them in the Files & versions tab.

chooseL1fe/blockassist-bc-thorny_flightless_albatross_1755593324

chooseL1fe

2025-08-19T08:55:03Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny flightless albatross", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T08:54:59Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny flightless albatross --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

hf-audio/xcodec-hubert-librispeech

hf-audio

2025-08-19T08:53:05Z

0

1

transformers

[ "transformers", "safetensors", "xcodec", "feature-extraction", "dataset:openslr/librispeech_asr", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

feature-extraction

2025-08-18T08:37:09Z

--- library_name: transformers license: cc-by-4.0 datasets: - openslr/librispeech_asr --- # X-Codec (speech, HuBERT) This codec is intended for speech data. Original model is `xcodec_wavlm_more_data` from [this table](https://github.com/zhenye234/xcodec?tab=readme-ov-file#available-models).

josephr212/blockassist-bc-hoarse_frisky_dingo_1755591769

josephr212

2025-08-19T08:51:30Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hoarse frisky dingo", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T08:51:28Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hoarse frisky dingo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

KCS97/dog7

KCS97

2025-08-19T08:51:16Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2025-08-19T08:38:46Z

--- base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true instance_prompt: a photo of sks dog tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers ---  # DreamBooth - KCS97/dog7 This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

hf-audio/xcodec-wavlm-more-data

hf-audio

2025-08-19T08:50:30Z

0

1

transformers

[ "transformers", "safetensors", "xcodec", "feature-extraction", "dataset:parler-tts/mls_eng", "base_model:microsoft/wavlm-base-plus", "base_model:finetune:microsoft/wavlm-base-plus", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

feature-extraction

2025-08-18T09:14:44Z

--- library_name: transformers license: cc-by-4.0 datasets: - parler-tts/mls_eng base_model: - microsoft/wavlm-base-plus --- # X-Codec (speech, WavLM) This codec is intended for speech data. Original model is `xcodec_wavlm_more_data` from [this table](https://github.com/zhenye234/xcodec?tab=readme-ov-file#available-models).

pruddywoody/SuperkarTAPI

pruddywoody

2025-08-19T08:48:48Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-19T08:48:48Z

--- license: apache-2.0 ---

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755592995

IvanJAjebu

2025-08-19T08:44:19Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T08:44:15Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

miguelsigmahot2/blockassist-bc-invisible_patterned_prawn_1755591187

miguelsigmahot2

2025-08-19T08:41:17Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "invisible patterned prawn", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T08:41:07Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - invisible patterned prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

VoilaRaj/78_1Y85EE

VoilaRaj

2025-08-19T08:41:13Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-08-19T08:37:18Z

--- 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).

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755592143

Ferdi3425

2025-08-19T08:30:28Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T08:30:01Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Alonc/device_to_cve_model_8B

Alonc

2025-08-19T08:28:51Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-19T08:28:48Z

--- base_model: unsloth/qwen3-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Alonc - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen3-8b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

FreedomIntelligence/AceGPT-v1.5-7B

FreedomIntelligence

2025-08-19T08:28:25Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "ar", "zh", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-27T02:48:38Z

--- license: apache-2.0 language: - ar - zh - en --- # AceGPT AceGPT is a fully fine-tuned generative text model collection based on LlaMA2, particularly in the Arabic language domain. This is the repository for the version 1.5 of 7B pre-trained model. --- ## Model Details We have released the AceGPT family of large language models, which is a collection of fully fine-tuned generative text models based on LlaMA2, ranging from 7B to 13B parameters. Our models include two main categories: AceGPT and AceGPT-chat. AceGPT-chat is an optimized version specifically designed for dialogue applications. It is worth mentioning that our models have demonstrated superior performance compared to all currently available open-source Arabic dialogue models in multiple benchmark tests. Furthermore, in our human evaluations, our models have shown comparable satisfaction levels to some closed-source models, such as ChatGPT, in the Arabic language. ## Model Developers We are from the King Abdullah University of Science and Technology (KAUST), the Chinese University of Hong Kong, Shenzhen (CUHKSZ), the Shenzhen Research Institute of Big Data (SRIBD), and King AbdulAziz University (KAU). ## Variations AceGPT families come in a range of parameter sizes —— 7B and 13B, each size of model has a base category and a -chat category. ## Paper The paper can be accessed at [link](https://huggingface.co/FreedomIntelligence/AceGPT-v1.5-13B-Chat/blob/main/Second_Language_(Arabic)_Acquisition_of_LLMs_via_Progressive_Vocabulary_Expansion.pdf). ## Input Models input text only. ## Output Models output text only. ## Model Evaluation Results Benchmark evaluation on [Arabic MMLU](https://github.com/FreedomIntelligence/AceGPT) are conducted using accuracy scores as metrics, following the evaluation framework available at https://github.com/FreedomIntelligence/AceGPT/tree/main. | | STEM | Humanities | Social Sciences | Others | Average | |------------------|------|------|------|------|------| | Bloomz-7B-base | 33.35 | 29.29 | 37.58 | 34.53 | 33.69 | | LLaMA2-7B-base | 30.30 | 29.33 | 27.46 | 30.78 | 29.37 | | AceGPT-7B-base | 29.73 | 30.95 | 33.45 | 34.42 | 32.14 | | AceGPT-v1.5-7B-base | 33.03 | 32.08 | 35.39 | 35.59 | 34.03 | | LLaMA2-13B-base | 32.94 | 32.30 | 33.42 | 37.27 | 33.76 | | Jais-13B-base | 30.51 | 31.25 | 33.74 | 33.42 | 33.76 | | AceGPT-13B-base | 36.60 | 38.74 | 43.76 | 42.72 | 40.45 | | AceGPT-v1.5-13B-base | 36.13 | 40.07 | 45.43 | 42.17 | 40.95 | | Jais-30B-v1-base | 32.67 | 30.67 | 42.13 | 39.60 | 36.27 | | ChatGPT 3.5 Turbo | **43.38** | **44.12** | **55.57** | **53.21** | **49.07** | Benchmark evaluation on [ArabicMMLU]((https://github.com/mbzuai-nlp/ArabicMMLU)), and assessed based on its source settings. | | STEM | Social Sciences | Humanities | Arabic Language | Other | Average | |------------------|------|------|------|------|------|------| | Bloomz-7B-base | - | - | - | - | - | - | | LLaMA2-7B-base | 33.7 | 32.8 | 33.5 | 28.4 | 36.7 | 33.4 | | AceGPT-7B-base | 35.4 | 35.9 | 36.2 | 31.1 | 41.7 | 36.3 | | AceGPT-v1.5-7B-base | 36.7 | 36.5 | 34.1 | 30.0 | 41.2 | 37.0 | | LLaMA2-13B-base | 32.9 | 35.0 | 37.8 | 35.8 | 39.3 | 36.1 | | Jais-13B-base | 30.3 | 31.4 | 33.6 | 28.1 | 36.3 | 32.2 | | AceGPT-13B-base | 42.7 | 45.5 | 48.3 | 42.4 | 50.7 | 46.1 | | AceGPT-v1.5-13B-base | 42.4 | 45.7 | 48.4 | 46.3 | 52.5 | 47.6 | | Jais-30B-v1-base | 39.5 | 45.6 | 50.5 | 34.6 | 49.1 | 44.8 | | ChatGPT 3.5 Turbo | **53.8** | **57.0** | **57.5** | **57.6** | **63.8** | **57.7** | ## Samples #### Sample1(abstract_algebra) * input: "فيما يلي أسئلة الاختيار من متعدد (مع الإجابات) حول جبر تجريدي\n\nسؤال: العثور على جميع قيم c في Z_3 بحيث يكون Z_3 [x]/(x^2+c) حقلًا.\nA. 0\nB. 1\nC. 2\nD. 3\nإجابة: B\n\nسؤال: البيان رقم 1 | إذا كان aH عنصرًا في مجموعة العوامل ، فإن | aH | يقسم | a |. البيان رقم 2 | إذا كانت H و K مجموعات فرعية لـ G ، فإن HK مجموعة فرعية لـ G.\nA. صحيح ، صحيح\nB. خطأ ، خطأ\nC. صحيح ، خطأ\nD. خطأ ، صحيح\nإجابة: B\n\nسؤال: العبارة 1 | كل عنصر من مجموعة يولد مجموعة دورية من المجموعة. العبارة 2 | المجموعة المتناظرة S_10 لديها 10 عناصر.\nA. صحيح، صحيح\nB. خطأ، خطأ\nC. صحيح، خطأ\nD. خطأ، صحيح\nإجابة: C\n\nسؤال: البيان 1| كل وظيفة من مجموعة محدودة على نفسها يجب أن تكون واحدة لكل مجموعة. البيان 2 | كل فرع فرعي لمجموعة أبيلية هو أبيلي.\nA. صحيح, صحيح\nB. خاطئ, خاطئ\nC. صحيح, خاطئ\nD. خاطئ, صحيح\nإجابة: A\n\nسؤال: اعثر على خاصية الحلقة 2Z.\nA. 0\nB. 3\nC. 12\nD. 30\nإجابة: A\n\nسؤال: ما هو الدرجة للامتداد الميداني الناتج من Q(sqrt(2), sqrt(3), sqrt(18)) على Q؟\nA. 0\nB. 4\nC. 2\nD. 6\nإجابة:" * output: "B\n\nسؤال: ما هو عدد العناصر" #### Sample2(business_ethics) * input: "فيما يلي أسئلة الاختيار من متعدد (مع الإجابات) حول أخلاقيات الأعمال\n\nسؤال: ما هي الحجج الأخلاقية المتعلقة بالمسؤولية الاجتماعية للشركات؟\nA. التكاليف الخارجية، القوة، الاستقلالية\nB. الإعلام، الموارد الضعيفة، التبادل التعاوني\nC. الإعلام، القوة، الاستقلالية\nD. التكاليف الخارجية، القوة، التبادل التعاوني\nإجابة: D\n\nسؤال: _______ هو المحاولة المباشرة لإدارة القضايا الأخلاقية أو المشاكل، سواء بشكل رسمي أو غير رسمي، من خلال سياسات وممارسات وبرامج محددة.\nA. المسؤولية الاجتماعية للشركات\nB. إدارة الأخلاقيات العملية\nC. الاستدامة\nD. إدارة البيئة\nإجابة: B\n\nسؤال: لضمان استقلال أعضاء مجلس الإدارة غير التنفيذية ، هناك عدد من الخطوات التي يمكن اتخاذها ، والتي تشمل اختيار الغير التنفيذيين من _______ الشركة ، وتعيينهم لمدة _________ ، وكذلك تعيينهم _________.\nA. خارج الشركة ، محدودة ، بشكل مستقل\nB. من الداخل ، محدودة ، بشكل متقطع\nC. خارج الشركة ، غير محدودة ، بشكل متقطع\nD. من الداخل ، غير محدودة ، بشكل مستقل\nإجابة: A\n\nسؤال: ما هي الأساليب التي يمكن للمدير الأمني الذي يسعى لتحقيق أهدافه الاختيار بينها؟\nA. العمل المباشر الغير عنيف ، العمل المباشر العنيف ، العمل غير المباشر ، الحملة الدعائية\nB. العمل غير المباشر ، العمل الأوتيل ، العمل المباشر الغير عنيف ، الحملة الإعلامية\nC. العمل غير المباشر ، العمل المباشر العنيف ، العمل المباشر غير العنيف المباشر ، الحملة الدعائية\nD. العمل المباشر الغير عنيف ، العمل الأوتيل ، العمل غير المباشر ، الحملة الإعلامية\nإجابة: C\n\nسؤال: على عكس _______ ، تهدف _______ إلى مكافأة السلوك الإيجابي للشركات. تم تعزيز نجاح مثل هذه الحملات من خلال استخدام ___________, الذي يتيح للحملات تيسير تحقيق الشركة لــ _________ .\nA. الحملات الاستهلاكية، الحملات الاستهلاكية العامة، تكنولوجيا سلسلة الكتل، التبرعات الخيرية\nB. الحملات التحفيزية، الحملات الاستهلاكية العامة، التكنولوجيا الرقمية، زيادة المبيعات\nC. الحملات الاستهلاكية، الحملات الشرائية، تكنولوجيا سلسلة الكتل، التبرعات الخيرية\nD. المقاطعات، الحملات التحفيزية، الحملات الرقمية، زيادة المبيعات\nإجابة: D\n\nسؤال: تُصبح _______ مثل البيتكوين أكثر انتشارًا وتحمل مجموعة كبيرة من الآثار الأخلاقية المرتبطة بها، على سبيل المثال، إنها _______ وأكثر _______. ومع ذلك، تم استخدامها أيضًا للمشاركة في _______.\nA. العملات الرقمية، مكلفة، آمنة، جرائم مالية\nB. العملات التقليدية، رخيصة، غير آمنة، العطاء الخيري\nC. العملات الرقمية، رخيصة، آمنة، جرائم مالية\nD. العملات التقليدية، مكلفة، غير آمنة، العطاء الخيري\nإجابة:" * output: "A\n\nسؤال: _______ هو" # Reference ``` @article{zhu2025second, title={Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion}, author={Zhu, Jianqing and Huang, Huang and Lin, Zhihang and Liang, Juhao and Tang, Zhengyang and Almubarak, Khalid and Alharthi, Mosen and An, Bang and He, Juncai and Wu, Xiangbo and Yu, Fei and Chen, Junying and Ma, Zhuoheng and Du, Yuhao and Hu, Yan and Zhang, He and Alghamdi, Emad A. and Zhang, Lian and Sun, Ruoyu and Li, Haizhou and Wang, Benyou and Xu, Jinchao}, journal={ACL 2025}, year={2025} } ```

donoway/BoolQ_Llama-3.2-1B-me3479q5

donoway

2025-08-19T08:26:59Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T08:06:37Z

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: BoolQ_Llama-3.2-1B-me3479q5 results: [] ---  # BoolQ_Llama-3.2-1B-me3479q5 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2599 - Model Preparation Time: 0.0055 - Mdl: 10661.2190 - Accumulated Loss: 7389.7939 - Correct Preds: 2256.0 - Total Preds: 3270.0 - Accuracy: 0.6899 - Correct Gen Preds: 1847.0 - Gen Accuracy: 0.5648 - Correct Gen Preds 9642: 1328.0 - Correct Preds 9642: 1615.0 - Total Labels 9642: 2026.0 - Accuracy 9642: 0.7971 - Gen Accuracy 9642: 0.6555 - Correct Gen Preds 2822: 511.0 - Correct Preds 2822: 641.0 - Total Labels 2822: 1231.0 - Accuracy 2822: 0.5207 - Gen Accuracy 2822: 0.4151 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 120 - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 9642 | Correct Preds 9642 | Total Labels 9642 | Accuracy 9642 | Gen Accuracy 9642 | Correct Gen Preds 2822 | Correct Preds 2822 | Total Labels 2822 | Accuracy 2822 | Gen Accuracy 2822 | |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:----------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:----------------------:|:------------------:|:-----------------:|:-------------:|:-----------------:|:----------------------:|:------------------:|:-----------------:|:-------------:|:-----------------:| | No log | 0 | 0 | 0.7080 | 0.0055 | 3339.8933 | 2315.0376 | 2032.0 | 3270.0 | 0.6214 | 2040.0 | 0.6239 | 2007.0 | 2008.0 | 2026.0 | 0.9911 | 0.9906 | 24.0 | 24.0 | 1231.0 | 0.0195 | 0.0195 | | 0.6888 | 1.0 | 2 | 1.5355 | 0.0055 | 7243.7816 | 5021.0068 | 1231.0 | 3270.0 | 0.3765 | 1225.0 | 0.3746 | 0.0 | 0.0 | 2026.0 | 0.0 | 0.0 | 1216.0 | 1231.0 | 1231.0 | 1.0 | 0.9878 | | 0.4873 | 2.0 | 4 | 1.0878 | 0.0055 | 5131.7632 | 3557.0672 | 2027.0 | 3270.0 | 0.6199 | 641.0 | 0.1960 | 631.0 | 2023.0 | 2026.0 | 0.9985 | 0.3115 | 1.0 | 4.0 | 1231.0 | 0.0032 | 0.0008 | | 0.2411 | 3.0 | 6 | 1.0755 | 0.0055 | 5073.9259 | 3516.9774 | 1560.0 | 3270.0 | 0.4771 | 1290.0 | 0.3945 | 227.0 | 388.0 | 2026.0 | 0.1915 | 0.1120 | 1054.0 | 1172.0 | 1231.0 | 0.9521 | 0.8562 | | 0.2022 | 4.0 | 8 | 0.8677 | 0.0055 | 4093.3239 | 2837.2759 | 1917.0 | 3270.0 | 0.5862 | 1654.0 | 0.5058 | 694.0 | 884.0 | 2026.0 | 0.4363 | 0.3425 | 951.0 | 1033.0 | 1231.0 | 0.8392 | 0.7725 | | 0.0205 | 5.0 | 10 | 0.9632 | 0.0055 | 4543.9737 | 3149.6426 | 2200.0 | 3270.0 | 0.6728 | 2086.0 | 0.6379 | 1619.0 | 1705.0 | 2026.0 | 0.8416 | 0.7991 | 458.0 | 495.0 | 1231.0 | 0.4021 | 0.3721 | | 0.0011 | 6.0 | 12 | 1.6852 | 0.0055 | 7949.9900 | 5510.5132 | 2228.0 | 3270.0 | 0.6813 | 1965.0 | 0.6009 | 1438.0 | 1632.0 | 2026.0 | 0.8055 | 0.7098 | 518.0 | 596.0 | 1231.0 | 0.4842 | 0.4208 | | 0.0001 | 7.0 | 14 | 2.2599 | 0.0055 | 10661.2190 | 7389.7939 | 2256.0 | 3270.0 | 0.6899 | 1847.0 | 0.5648 | 1328.0 | 1615.0 | 2026.0 | 0.7971 | 0.6555 | 511.0 | 641.0 | 1231.0 | 0.5207 | 0.4151 | | 0.0 | 8.0 | 16 | 2.5994 | 0.0055 | 12262.8330 | 8499.9481 | 2250.0 | 3270.0 | 0.6881 | 1703.0 | 0.5208 | 1190.0 | 1594.0 | 2026.0 | 0.7868 | 0.5874 | 504.0 | 656.0 | 1231.0 | 0.5329 | 0.4094 | | 0.0 | 9.0 | 18 | 2.7853 | 0.0055 | 13140.1064 | 9108.0277 | 2245.0 | 3270.0 | 0.6865 | 1616.0 | 0.4942 | 1106.0 | 1579.0 | 2026.0 | 0.7794 | 0.5459 | 501.0 | 666.0 | 1231.0 | 0.5410 | 0.4070 | | 0.0 | 10.0 | 20 | 2.8947 | 0.0055 | 13656.2114 | 9465.7644 | 2244.0 | 3270.0 | 0.6862 | 1550.0 | 0.4740 | 1053.0 | 1574.0 | 2026.0 | 0.7769 | 0.5197 | 488.0 | 670.0 | 1231.0 | 0.5443 | 0.3964 | | 0.0 | 11.0 | 22 | 2.9647 | 0.0055 | 13986.3195 | 9694.5779 | 2242.0 | 3270.0 | 0.6856 | 1525.0 | 0.4664 | 1028.0 | 1567.0 | 2026.0 | 0.7734 | 0.5074 | 488.0 | 675.0 | 1231.0 | 0.5483 | 0.3964 | | 0.0 | 12.0 | 24 | 3.0204 | 0.0055 | 14249.1044 | 9876.7265 | 2241.0 | 3270.0 | 0.6853 | 1520.0 | 0.4648 | 1026.0 | 1564.0 | 2026.0 | 0.7720 | 0.5064 | 485.0 | 677.0 | 1231.0 | 0.5500 | 0.3940 | | 0.0 | 13.0 | 26 | 3.0632 | 0.0055 | 14450.8337 | 10016.5547 | 2242.0 | 3270.0 | 0.6856 | 1517.0 | 0.4639 | 1032.0 | 1566.0 | 2026.0 | 0.7730 | 0.5094 | 476.0 | 676.0 | 1231.0 | 0.5491 | 0.3867 | | 0.0 | 14.0 | 28 | 3.0930 | 0.0055 | 14591.5894 | 10114.1191 | 2239.0 | 3270.0 | 0.6847 | 1541.0 | 0.4713 | 1050.0 | 1568.0 | 2026.0 | 0.7739 | 0.5183 | 482.0 | 671.0 | 1231.0 | 0.5451 | 0.3916 | | 0.0 | 15.0 | 30 | 3.1175 | 0.0055 | 14707.0215 | 10194.1305 | 2239.0 | 3270.0 | 0.6847 | 1557.0 | 0.4761 | 1064.0 | 1568.0 | 2026.0 | 0.7739 | 0.5252 | 484.0 | 671.0 | 1231.0 | 0.5451 | 0.3932 | | 0.0 | 16.0 | 32 | 3.1366 | 0.0055 | 14797.0392 | 10256.5260 | 2237.0 | 3270.0 | 0.6841 | 1567.0 | 0.4792 | 1073.0 | 1567.0 | 2026.0 | 0.7734 | 0.5296 | 485.0 | 670.0 | 1231.0 | 0.5443 | 0.3940 | | 0.0 | 17.0 | 34 | 3.1519 | 0.0055 | 14869.3338 | 10306.6368 | 2241.0 | 3270.0 | 0.6853 | 1579.0 | 0.4829 | 1085.0 | 1572.0 | 2026.0 | 0.7759 | 0.5355 | 485.0 | 669.0 | 1231.0 | 0.5435 | 0.3940 | | 0.0 | 18.0 | 36 | 3.1598 | 0.0055 | 14906.7599 | 10332.5786 | 2244.0 | 3270.0 | 0.6862 | 1593.0 | 0.4872 | 1096.0 | 1573.0 | 2026.0 | 0.7764 | 0.5410 | 488.0 | 671.0 | 1231.0 | 0.5451 | 0.3964 | | 0.0 | 19.0 | 38 | 3.1724 | 0.0055 | 14965.9286 | 10373.5912 | 2242.0 | 3270.0 | 0.6856 | 1603.0 | 0.4902 | 1104.0 | 1573.0 | 2026.0 | 0.7764 | 0.5449 | 490.0 | 669.0 | 1231.0 | 0.5435 | 0.3981 | | 0.0 | 20.0 | 40 | 3.1785 | 0.0055 | 14994.7059 | 10393.5381 | 2247.0 | 3270.0 | 0.6872 | 1604.0 | 0.4905 | 1106.0 | 1577.0 | 2026.0 | 0.7784 | 0.5459 | 489.0 | 670.0 | 1231.0 | 0.5443 | 0.3972 | | 0.0 | 21.0 | 42 | 3.1864 | 0.0055 | 15032.1352 | 10419.4821 | 2245.0 | 3270.0 | 0.6865 | 1613.0 | 0.4933 | 1113.0 | 1578.0 | 2026.0 | 0.7789 | 0.5494 | 491.0 | 667.0 | 1231.0 | 0.5418 | 0.3989 | | 0.0 | 22.0 | 44 | 3.1900 | 0.0055 | 15049.2090 | 10431.3168 | 2242.0 | 3270.0 | 0.6856 | 1620.0 | 0.4954 | 1117.0 | 1576.0 | 2026.0 | 0.7779 | 0.5513 | 494.0 | 666.0 | 1231.0 | 0.5410 | 0.4013 | | 0.0 | 23.0 | 46 | 3.1960 | 0.0055 | 15077.5586 | 10450.9672 | 2242.0 | 3270.0 | 0.6856 | 1621.0 | 0.4957 | 1120.0 | 1575.0 | 2026.0 | 0.7774 | 0.5528 | 492.0 | 667.0 | 1231.0 | 0.5418 | 0.3997 | | 0.0 | 24.0 | 48 | 3.1986 | 0.0055 | 15089.7720 | 10459.4329 | 2243.0 | 3270.0 | 0.6859 | 1625.0 | 0.4969 | 1125.0 | 1576.0 | 2026.0 | 0.7779 | 0.5553 | 491.0 | 667.0 | 1231.0 | 0.5418 | 0.3989 | | 0.0 | 25.0 | 50 | 3.2004 | 0.0055 | 15098.4212 | 10465.4281 | 2241.0 | 3270.0 | 0.6853 | 1627.0 | 0.4976 | 1123.0 | 1575.0 | 2026.0 | 0.7774 | 0.5543 | 495.0 | 666.0 | 1231.0 | 0.5410 | 0.4021 | | 0.0 | 26.0 | 52 | 3.2032 | 0.0055 | 15111.4442 | 10474.4550 | 2246.0 | 3270.0 | 0.6869 | 1629.0 | 0.4982 | 1127.0 | 1577.0 | 2026.0 | 0.7784 | 0.5563 | 493.0 | 669.0 | 1231.0 | 0.5435 | 0.4005 | | 0.0 | 27.0 | 54 | 3.2052 | 0.0055 | 15120.6726 | 10480.8516 | 2244.0 | 3270.0 | 0.6862 | 1633.0 | 0.4994 | 1133.0 | 1577.0 | 2026.0 | 0.7784 | 0.5592 | 491.0 | 667.0 | 1231.0 | 0.5418 | 0.3989 | | 0.0 | 28.0 | 56 | 3.2064 | 0.0055 | 15126.6264 | 10484.9784 | 2245.0 | 3270.0 | 0.6865 | 1634.0 | 0.4997 | 1127.0 | 1576.0 | 2026.0 | 0.7779 | 0.5563 | 498.0 | 669.0 | 1231.0 | 0.5435 | 0.4045 | | 0.0 | 29.0 | 58 | 3.2093 | 0.0055 | 15140.1416 | 10494.3465 | 2244.0 | 3270.0 | 0.6862 | 1637.0 | 0.5006 | 1138.0 | 1576.0 | 2026.0 | 0.7779 | 0.5617 | 490.0 | 668.0 | 1231.0 | 0.5426 | 0.3981 | | 0.0 | 30.0 | 60 | 3.2123 | 0.0055 | 15154.4079 | 10504.2351 | 2240.0 | 3270.0 | 0.6850 | 1643.0 | 0.5024 | 1138.0 | 1577.0 | 2026.0 | 0.7784 | 0.5617 | 496.0 | 663.0 | 1231.0 | 0.5386 | 0.4029 | | 0.0 | 31.0 | 62 | 3.2131 | 0.0055 | 15158.3154 | 10506.9436 | 2242.0 | 3270.0 | 0.6856 | 1640.0 | 0.5015 | 1137.0 | 1576.0 | 2026.0 | 0.7779 | 0.5612 | 494.0 | 666.0 | 1231.0 | 0.5410 | 0.4013 | | 0.0 | 32.0 | 64 | 3.2138 | 0.0055 | 15161.4034 | 10509.0840 | 2240.0 | 3270.0 | 0.6850 | 1647.0 | 0.5037 | 1144.0 | 1576.0 | 2026.0 | 0.7779 | 0.5647 | 494.0 | 664.0 | 1231.0 | 0.5394 | 0.4013 | | 0.0 | 33.0 | 66 | 3.2162 | 0.0055 | 15172.9442 | 10517.0835 | 2242.0 | 3270.0 | 0.6856 | 1650.0 | 0.5046 | 1146.0 | 1578.0 | 2026.0 | 0.7789 | 0.5656 | 495.0 | 664.0 | 1231.0 | 0.5394 | 0.4021 | | 0.0 | 34.0 | 68 | 3.2208 | 0.0055 | 15194.6156 | 10532.1050 | 2236.0 | 3270.0 | 0.6838 | 1650.0 | 0.5046 | 1144.0 | 1574.0 | 2026.0 | 0.7769 | 0.5647 | 497.0 | 662.0 | 1231.0 | 0.5378 | 0.4037 | | 0.0 | 35.0 | 70 | 3.2202 | 0.0055 | 15191.4896 | 10529.9382 | 2241.0 | 3270.0 | 0.6853 | 1650.0 | 0.5046 | 1143.0 | 1577.0 | 2026.0 | 0.7784 | 0.5642 | 498.0 | 664.0 | 1231.0 | 0.5394 | 0.4045 | | 0.0 | 36.0 | 72 | 3.2211 | 0.0055 | 15196.0934 | 10533.1293 | 2241.0 | 3270.0 | 0.6853 | 1654.0 | 0.5058 | 1148.0 | 1577.0 | 2026.0 | 0.7784 | 0.5666 | 497.0 | 664.0 | 1231.0 | 0.5394 | 0.4037 | | 0.0 | 37.0 | 74 | 3.2230 | 0.0055 | 15204.6715 | 10539.0752 | 2243.0 | 3270.0 | 0.6859 | 1655.0 | 0.5061 | 1150.0 | 1577.0 | 2026.0 | 0.7784 | 0.5676 | 496.0 | 666.0 | 1231.0 | 0.5410 | 0.4029 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

koloni/blockassist-bc-deadly_graceful_stingray_1755584085

koloni

2025-08-19T08:25:54Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T06:41:59Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

hoan17/saving_LOe400s16_scratch_8

hoan17

2025-08-19T08:25:31Z

0

diffusers

[ "diffusers", "safetensors", "trl", "o2o", "reinforcement-learning", "text-to-image", "stable-diffusion", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2025-08-19T08:25:02Z

--- license: apache-2.0 tags: - trl - o2o - diffusers - reinforcement-learning - text-to-image - stable-diffusion --- # TRL O2O Model This is a diffusion model that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for image generation conditioned with text.

Jansenhbar/dummy-model

Jansenhbar

2025-08-19T08:24:13Z

0

transformers

[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2025-08-19T08:23:58Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

sungkwan2/segformer-b0-scene-parse-150

sungkwan2

2025-08-19T08:24:07Z

2

0

transformers

[ "transformers", "tensorboard", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]

null

2025-04-23T12:00:05Z

--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - generated_from_trainer model-index: - name: segformer-b0-scene-parse-150 results: [] ---  # segformer-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.1486 - Mean Iou: 0.0000 - Mean Accuracy: 0.0001 - Overall Accuracy: 0.0001 ## 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:| | 4.9721 | 0.025 | 1 | 5.0059 | 0.0007 | 0.0148 | 0.0063 | | 4.9475 | 0.05 | 2 | 5.0027 | 0.0007 | 0.0140 | 0.0060 | | 4.9457 | 0.075 | 3 | 4.9996 | 0.0008 | 0.0144 | 0.0063 | | 4.9923 | 0.1 | 4 | 4.9959 | 0.0008 | 0.0142 | 0.0063 | | 5.0016 | 0.125 | 5 | 4.9912 | 0.0009 | 0.0153 | 0.0069 | | 4.9753 | 0.15 | 6 | 4.9876 | 0.0008 | 0.0149 | 0.0070 | | 4.8799 | 0.175 | 7 | 4.9824 | 0.0006 | 0.0108 | 0.0051 | | 4.9689 | 0.2 | 8 | 4.9767 | 0.0006 | 0.0095 | 0.0045 | | 4.9046 | 0.225 | 9 | 4.9697 | 0.0006 | 0.0093 | 0.0044 | | 4.8772 | 0.25 | 10 | 4.9629 | 0.0005 | 0.0074 | 0.0035 | | 4.7839 | 0.275 | 11 | 4.9574 | 0.0005 | 0.0084 | 0.0038 | | 4.9577 | 0.3 | 12 | 4.9500 | 0.0005 | 0.0074 | 0.0031 | | 4.8491 | 0.325 | 13 | 4.9411 | 0.0004 | 0.0067 | 0.0026 | | 4.8449 | 0.35 | 14 | 4.9340 | 0.0004 | 0.0070 | 0.0026 | | 4.8899 | 0.375 | 15 | 4.9229 | 0.0003 | 0.0051 | 0.0020 | | 4.7924 | 0.4 | 16 | 4.9163 | 0.0003 | 0.0050 | 0.0019 | | 4.7651 | 0.425 | 17 | 4.9072 | 0.0003 | 0.0043 | 0.0016 | | 4.7951 | 0.45 | 18 | 4.8953 | 0.0002 | 0.0035 | 0.0013 | | 4.7355 | 0.475 | 19 | 4.8865 | 0.0002 | 0.0028 | 0.0010 | | 4.7461 | 0.5 | 20 | 4.8723 | 0.0002 | 0.0026 | 0.0008 | | 4.704 | 0.525 | 21 | 4.8606 | 0.0002 | 0.0022 | 0.0007 | | 4.7775 | 0.55 | 22 | 4.8484 | 0.0001 | 0.0017 | 0.0006 | | 4.7081 | 0.575 | 23 | 4.8331 | 0.0001 | 0.0013 | 0.0004 | | 4.7681 | 0.6 | 24 | 4.8187 | 0.0001 | 0.0009 | 0.0003 | | 4.7297 | 0.625 | 25 | 4.8037 | 0.0001 | 0.0008 | 0.0003 | | 4.8181 | 0.65 | 26 | 4.7882 | 0.0001 | 0.0007 | 0.0002 | | 4.833 | 0.675 | 27 | 4.7748 | 0.0001 | 0.0006 | 0.0002 | | 4.7222 | 0.7 | 28 | 4.7575 | 0.0000 | 0.0004 | 0.0002 | | 4.6457 | 0.725 | 29 | 4.7389 | 0.0000 | 0.0004 | 0.0002 | | 4.7089 | 0.75 | 30 | 4.7236 | 0.0000 | 0.0005 | 0.0002 | | 4.543 | 0.775 | 31 | 4.7079 | 0.0001 | 0.0006 | 0.0002 | | 4.5529 | 0.8 | 32 | 4.6963 | 0.0001 | 0.0006 | 0.0003 | | 4.7005 | 0.825 | 33 | 4.6759 | 0.0001 | 0.0006 | 0.0003 | | 4.4735 | 0.85 | 34 | 4.6630 | 0.0001 | 0.0008 | 0.0004 | | 4.6562 | 0.875 | 35 | 4.6468 | 0.0001 | 0.0009 | 0.0004 | | 4.5902 | 0.9 | 36 | 4.6274 | 0.0001 | 0.0008 | 0.0004 | | 4.4974 | 0.925 | 37 | 4.6125 | 0.0001 | 0.0008 | 0.0004 | | 4.524 | 0.95 | 38 | 4.5967 | 0.0001 | 0.0011 | 0.0005 | | 4.5527 | 0.975 | 39 | 4.5826 | 0.0001 | 0.0011 | 0.0005 | | 4.5165 | 1.0 | 40 | 4.5627 | 0.0001 | 0.0010 | 0.0005 | | 4.6337 | 1.025 | 41 | 4.5502 | 0.0001 | 0.0012 | 0.0006 | | 4.4551 | 1.05 | 42 | 4.5425 | 0.0001 | 0.0012 | 0.0005 | | 4.4697 | 1.075 | 43 | 4.5294 | 0.0001 | 0.0006 | 0.0003 | | 4.4967 | 1.1 | 44 | 4.5065 | 0.0001 | 0.0007 | 0.0003 | | 4.4839 | 1.125 | 45 | 4.4896 | 0.0000 | 0.0004 | 0.0002 | | 4.4394 | 1.15 | 46 | 4.4699 | 0.0000 | 0.0003 | 0.0001 | | 4.4557 | 1.175 | 47 | 4.4511 | 0.0000 | 0.0003 | 0.0001 | | 4.2669 | 1.2 | 48 | 4.4475 | 0.0000 | 0.0003 | 0.0001 | | 4.3143 | 1.225 | 49 | 4.4325 | 0.0000 | 0.0002 | 0.0001 | | 4.4519 | 1.25 | 50 | 4.4195 | 0.0000 | 0.0002 | 0.0001 | | 4.5376 | 1.275 | 51 | 4.4092 | 0.0000 | 0.0001 | 0.0001 | | 4.2617 | 1.3 | 52 | 4.4058 | 0.0000 | 0.0001 | 0.0000 | | 4.2813 | 1.325 | 53 | 4.3936 | 0.0000 | 0.0001 | 0.0000 | | 4.5218 | 1.35 | 54 | 4.3867 | 0.0000 | 0.0002 | 0.0001 | | 4.4805 | 1.375 | 55 | 4.3691 | 0.0000 | 0.0002 | 0.0001 | | 4.184 | 1.4 | 56 | 4.3574 | 0.0000 | 0.0002 | 0.0001 | | 4.2208 | 1.425 | 57 | 4.3606 | 0.0000 | 0.0001 | 0.0001 | | 4.5288 | 1.45 | 58 | 4.3579 | 0.0000 | 0.0001 | 0.0001 | | 4.3959 | 1.475 | 59 | 4.3421 | 0.0000 | 0.0001 | 0.0000 | | 4.2578 | 1.5 | 60 | 4.3403 | 0.0000 | 0.0001 | 0.0000 | | 4.3504 | 1.525 | 61 | 4.3307 | 0.0000 | 0.0001 | 0.0000 | | 4.2364 | 1.55 | 62 | 4.3177 | 0.0000 | 0.0001 | 0.0000 | | 4.3248 | 1.575 | 63 | 4.2924 | 0.0000 | 0.0000 | 0.0000 | | 4.2771 | 1.6 | 64 | 4.2698 | 0.0000 | 0.0000 | 0.0000 | | 4.2447 | 1.625 | 65 | 4.2533 | 0.0000 | 0.0000 | 0.0000 | | 4.4481 | 1.65 | 66 | 4.2418 | 0.0000 | 0.0000 | 0.0000 | | 4.1369 | 1.675 | 67 | 4.2374 | 0.0000 | 0.0000 | 0.0000 | | 4.2266 | 1.7 | 68 | 4.2305 | 0.0000 | 0.0000 | 0.0000 | | 4.5113 | 1.725 | 69 | 4.2225 | 0.0000 | 0.0000 | 0.0000 | | 4.4737 | 1.75 | 70 | 4.2077 | 0.0000 | 0.0000 | 0.0000 | | 4.4571 | 1.775 | 71 | 4.1960 | 0.0000 | 0.0001 | 0.0000 | | 4.2179 | 1.8 | 72 | 4.1824 | 0.0000 | 0.0001 | 0.0000 | | 4.5426 | 1.825 | 73 | 4.1654 | 0.0000 | 0.0002 | 0.0001 | | 4.3632 | 1.85 | 74 | 4.1572 | 0.0000 | 0.0002 | 0.0001 | | 4.2132 | 1.875 | 75 | 4.1628 | 0.0000 | 0.0002 | 0.0001 | | 4.3442 | 1.9 | 76 | 4.1621 | 0.0000 | 0.0001 | 0.0000 | | 4.4454 | 1.925 | 77 | 4.1647 | 0.0000 | 0.0001 | 0.0000 | | 4.1564 | 1.95 | 78 | 4.1691 | 0.0000 | 0.0001 | 0.0000 | | 4.5028 | 1.975 | 79 | 4.1513 | 0.0000 | 0.0002 | 0.0001 | | 4.3814 | 2.0 | 80 | 4.1486 | 0.0000 | 0.0001 | 0.0001 | ### Framework versions - Transformers 4.55.2 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4

ankitkushwaha90/Image_transformer_algorithm

ankitkushwaha90

2025-08-19T08:14:56Z

0

adapter-transformers

[ "adapter-transformers", "finance", "text-classification", "en", "dataset:UCSC-VLAA/GPT-Image-Edit-1.5M", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]

text-classification

2025-08-19T08:08:13Z

--- license: mit datasets: - UCSC-VLAA/GPT-Image-Edit-1.5M language: - en metrics: - accuracy base_model: - stabilityai/stable-diffusion-xl-base-1.0 new_version: openai/gpt-oss-120b pipeline_tag: text-classification library_name: adapter-transformers tags: - finance --- # 🚀 Stable Diffusion with Transformers (Advanced Training) This project demonstrates how to **train a Stable Diffusion-like model** using an **image dataset** with advanced **Transformer-based denoising**. The implementation leverages **PyTorch + Hugging Face Diffusers + Transformers**. --- ## 📌 Overview Stable Diffusion is a **Latent Diffusion Model (LDM)** that generates images by: 1. Encoding images into a **latent space** using a **VAE (Variational Autoencoder)**. 2. Adding **Gaussian noise** to the latents across multiple time steps. 3. Training a **denoising Transformer/UNet** to remove noise step by step. 4. Using a **text encoder (CLIP)** for **prompt conditioning**. 5. Decoding the cleaned latents back to an **image**. --- ## 🔬 Architecture ```mermaid graph TD; A[Input Image] -->|VAE Encoder| B[Latent Space]; B -->|Add Noise| C[Noisy Latents]; C -->|Transformer / UNet Denoiser| D[Clean Latents]; D -->|VAE Decoder| E[Output Image]; F[Text Prompt] -->|CLIP Encoder| C; ``` - VAE → Compresses image → latent space - Transformer/UNet → Learns to denoise latent - Text Encoder → Aligns text + image - Noise Scheduler → Controls forward & reverse diffusion ## 📂 Dataset - Images should be resized (256x256) and normalized to [-1, 1]. - Optional: Provide text captions for conditioning. - Example: ```bash data/ ├── class1/ │ ├── img1.png │ └── img2.jpg ├── class2/ │ ├── img3.png │ └── img4.jpg ``` ## ⚙️ Training Algorithm The training process for Stable Diffusion with Transformers follows these steps: 1. **Encode Images** → Pass input images through a **VAE Encoder** to obtain latent representations. 2. **Sample Noise & Timestep** → Randomly sample Gaussian noise and a timestep `t`. 3. **Add Noise** → Corrupt the latent vectors with noise according to timestep `t`. 4. **Text Conditioning** → Encode text prompts using **CLIP** (or another Transformer text encoder). 5. **Noise Prediction** → Feed the noisy latents + text embeddings into the **Transformer/UNet** to predict the added noise. 6. **Compute Loss** → Calculate the **Mean Squared Error (MSE)** between predicted noise and true noise. 7. **Backpropagation** → Update model weights using gradient descent. --- ```mermaid flowchart TD A[Image] -->|VAE Encoder| B[Latent Space] B -->|Add Noise + t| C[Noisy Latents] D[Text Prompt] -->|CLIP Encoder| C C -->|Transformer / UNet| E[Predicted Noise] E -->|MSE Loss| F[Training Update] ``` ## 🧑‍💻 Example Training Code ```python from diffusers import UNet2DConditionModel, DDPMScheduler, AutoencoderKL from transformers import CLIPTextModel, CLIPTokenizer import torch, torch.nn as nn from torchvision import datasets, transforms from torch.utils.data import DataLoader # Dataset transform = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]) ]) dataset = datasets.ImageFolder("path_to_images", transform=transform) dataloader = DataLoader(dataset, batch_size=8, shuffle=True) # Components vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse") unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet") tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32") text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32") scheduler = DDPMScheduler(num_train_timesteps=1000) device = "cuda" if torch.cuda.is_available() else "cpu" vae, unet, text_encoder = vae.to(device), unet.to(device), text_encoder.to(device) optimizer = torch.optim.AdamW(unet.parameters(), lr=1e-4) # Training Loop for epoch in range(10): for images, _ in dataloader: images = images.to(device) latents = vae.encode(images).latent_dist.sample() * 0.18215 noise = torch.randn_like(latents) timesteps = torch.randint(0, scheduler.num_train_timesteps, (latents.shape[0],), device=device) noisy_latents = scheduler.add_noise(latents, noise, timesteps) text_inputs = tokenizer(["a photo"], padding="max_length", return_tensors="pt").to(device) text_embeds = text_encoder(text_inputs.input_ids).last_hidden_state noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states=text_embeds).sample loss = nn.MSELoss()(noise_pred, noise) optimizer.zero_grad() loss.backward() optimizer.step() print(f"Epoch {epoch} | Loss: {loss.item()}") ``` ## 💾 Saving & Inference # Save trained UNet ```python torch.save(unet.state_dict(), "unet_trained.pth") # Inference pipeline # 1. Sample random latent # 2. Iteratively denoise with scheduler + trained UNet # 3. Decode with VAE → image ``` ### 📖 References - Stable Diffusion Paper - Hugging Face Diffusers - Diffusion Transformer (DiT) ## ✅ Future Work Replace UNet with pure Transformer (DiT). Use larger text encoders (T5/DeBERTa). Train with custom captioned datasets.

VoilaRaj/78_3pdGH2

VoilaRaj

2025-08-19T08:13:40Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-08-19T08:09:42Z

--- 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).

MajdShamaly12/gpt-oss-20b-kaggle

MajdShamaly12

2025-08-19T08:07:20Z

0

transformers

[ "transformers", "safetensors", "gpt_oss", "text-generation", "vllm", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "8-bit", "mxfp4", "region:us" ]

text-generation

2025-08-19T07:57:09Z

--- license: apache-2.0 pipeline_tag: text-generation library_name: transformers tags: - vllm --- <img alt="gpt-oss-20b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg"> <a href="https://gpt-oss.com">Try gpt-oss</a> · <a href="https://cookbook.openai.com/topic/gpt-oss">Guides</a> · <a href="https://openai.com/index/gpt-oss-model-card">Model card</a> · <a href="https://openai.com/index/introducing-gpt-oss/">OpenAI blog</a> Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases. We’re releasing two flavors of these open models: - `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters) - `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters) Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise. > [!NOTE] > This model card is dedicated to the smaller `gpt-oss-20b` model. Check out [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) for the larger model. # Highlights * **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment. * **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs. * **Full chain-of-thought:** Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users. * **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning. * **Agentic capabilities:** Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs. * **MXFP4 quantization:** The models were post-trained with MXFP4 quantization of the MoE weights, making `gpt-oss-120b` run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and the `gpt-oss-20b` model run within 16GB of memory. All evals were performed with the same MXFP4 quantization. --- # Inference examples ## Transformers You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package. To get started, install the necessary dependencies to setup your environment: ``` pip install -U transformers kernels torch ``` Once, setup you can proceed to run the model by running the snippet below: ```py from transformers import pipeline import torch model_id = "openai/gpt-oss-20b" pipe = pipeline( "text-generation", model=model_id, torch_dtype="auto", device_map="auto", ) messages = [ {"role": "user", "content": "Explain quantum mechanics clearly and concisely."}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver: ``` transformers serve transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-20b ``` [Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers) ## vLLM vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server. ```bash uv pip install --pre vllm==0.10.1+gptoss \ --extra-index-url https://wheels.vllm.ai/gpt-oss/ \ --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \ --index-strategy unsafe-best-match vllm serve openai/gpt-oss-20b ``` [Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm) ## PyTorch / Triton To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation). ## Ollama If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download). ```bash # gpt-oss-20b ollama pull gpt-oss:20b ollama run gpt-oss:20b ``` [Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama) #### LM Studio If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download. ```bash # gpt-oss-20b lms get openai/gpt-oss-20b ``` Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners. --- # Download the model You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI: ```shell # gpt-oss-20b huggingface-cli download openai/gpt-oss-20b --include "original/*" --local-dir gpt-oss-20b/ pip install gpt-oss python -m gpt_oss.chat model/ ``` # Reasoning levels You can adjust the reasoning level that suits your task across three levels: * **Low:** Fast responses for general dialogue. * **Medium:** Balanced speed and detail. * **High:** Deep and detailed analysis. The reasoning level can be set in the system prompts, e.g., "Reasoning: high". # Tool use The gpt-oss models are excellent for: * Web browsing (using built-in browsing tools) * Function calling with defined schemas * Agentic operations like browser tasks # Fine-tuning Both gpt-oss models can be fine-tuned for a variety of specialized use cases. This smaller model `gpt-oss-20b` can be fine-tuned on consumer hardware, whereas the larger [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) can be fine-tuned on a single H100 node.

tslim1/Phi-3-medium-128k-instruct-mlx-8Bit

tslim1

2025-08-19T08:05:30Z

0

mlx

[ "mlx", "safetensors", "phi3", "nlp", "code", "mlx-my-repo", "text-generation", "conversational", "custom_code", "multilingual", "base_model:microsoft/Phi-3-medium-128k-instruct", "base_model:quantized:microsoft/Phi-3-medium-128k-instruct", "license:mit", "8-bit", "region:us" ]

text-generation

2025-08-19T08:04:16Z

--- license: mit license_link: https://huggingface.co/microsoft/Phi-3-medium-128k-instruct/resolve/main/LICENSE language: - multilingual pipeline_tag: text-generation tags: - nlp - code - mlx - mlx-my-repo inference: parameters: temperature: 0.7 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? base_model: microsoft/Phi-3-medium-128k-instruct --- # tslim1/Phi-3-medium-128k-instruct-mlx-8Bit The Model [tslim1/Phi-3-medium-128k-instruct-mlx-8Bit](https://huggingface.co/tslim1/Phi-3-medium-128k-instruct-mlx-8Bit) was converted to MLX format from [microsoft/Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) using mlx-lm version **0.26.3**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("tslim1/Phi-3-medium-128k-instruct-mlx-8Bit") 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) ```

liukevin666/blockassist-bc-yawning_striped_cassowary_1755590333

liukevin666

2025-08-19T08:00:20Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T08:00:00Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

donoway/GSM8K-Binary_Llama-3.2-1B-xx99tfwe

donoway

2025-08-19T08:00:15Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T05:55:54Z

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: GSM8K-Binary_Llama-3.2-1B-xx99tfwe results: [] ---  # GSM8K-Binary_Llama-3.2-1B-xx99tfwe This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2724 - Model Preparation Time: 0.0059 - Mdl: 4543.3835 - Accumulated Loss: 3149.2334 - Correct Preds: 1590.0 - Total Preds: 2475.0 - Accuracy: 0.6424 - Correct Gen Preds: 1155.0 - Gen Accuracy: 0.4667 - Correct Gen Preds 34192: 669.0 - Correct Preds 34192: 974.0 - Total Labels 34192: 1196.0 - Accuracy 34192: 0.8144 - Gen Accuracy 34192: 0.5594 - Correct Gen Preds 41568: 477.0 - Correct Preds 41568: 616.0 - Total Labels 41568: 1267.0 - Accuracy 41568: 0.4862 - Gen Accuracy 41568: 0.3765 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 34192 | Correct Preds 34192 | Total Labels 34192 | Accuracy 34192 | Gen Accuracy 34192 | Correct Gen Preds 41568 | Correct Preds 41568 | Total Labels 41568 | Accuracy 41568 | Gen Accuracy 41568 | |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:----------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:-----------------------:|:-------------------:|:------------------:|:--------------:|:------------------:|:-----------------------:|:-------------------:|:------------------:|:--------------:|:------------------:| | No log | 0 | 0 | 1.4656 | 0.0059 | 5233.1723 | 3627.3586 | 1196.0 | 2475.0 | 0.4832 | 1204.0 | 0.4865 | 1196.0 | 1196.0 | 1196.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1267.0 | 0.0 | 0.0 | | 1.1557 | 1.0 | 1 | 1.4656 | 0.0059 | 5233.1723 | 3627.3586 | 1196.0 | 2475.0 | 0.4832 | 1204.0 | 0.4865 | 1196.0 | 1196.0 | 1196.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1267.0 | 0.0 | 0.0 | | 1.1557 | 2.0 | 2 | 5.0073 | 0.0059 | 17879.5848 | 12393.1838 | 1267.0 | 2475.0 | 0.5119 | 1274.0 | 0.5147 | 0.0 | 0.0 | 1196.0 | 0.0 | 0.0 | 1266.0 | 1267.0 | 1267.0 | 1.0 | 0.9992 | | 5.9156 | 3.0 | 3 | 1.1424 | 0.0059 | 4079.2851 | 2827.5450 | 1267.0 | 2475.0 | 0.5119 | 7.0 | 0.0028 | 0.0 | 0.0 | 1196.0 | 0.0 | 0.0 | 0.0 | 1267.0 | 1267.0 | 1.0 | 0.0 | | 1.1855 | 4.0 | 4 | 1.8316 | 0.0059 | 6540.2060 | 4533.3254 | 1196.0 | 2475.0 | 0.4832 | 8.0 | 0.0032 | 0.0 | 1196.0 | 1196.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1267.0 | 0.0 | 0.0 | | 1.507 | 5.0 | 5 | 0.9446 | 0.0059 | 3372.8700 | 2337.8953 | 1197.0 | 2475.0 | 0.4836 | 8.0 | 0.0032 | 0.0 | 1192.0 | 1196.0 | 0.9967 | 0.0 | 0.0 | 5.0 | 1267.0 | 0.0039 | 0.0 | | 0.6628 | 6.0 | 6 | 0.8425 | 0.0059 | 3008.1372 | 2085.0818 | 1278.0 | 2475.0 | 0.5164 | 8.0 | 0.0032 | 0.0 | 25.0 | 1196.0 | 0.0209 | 0.0 | 0.0 | 1253.0 | 1267.0 | 0.9890 | 0.0 | | 0.6635 | 7.0 | 7 | 0.7631 | 0.0059 | 2724.8477 | 1888.7205 | 1326.0 | 2475.0 | 0.5358 | 8.0 | 0.0032 | 0.0 | 179.0 | 1196.0 | 0.1497 | 0.0 | 0.0 | 1147.0 | 1267.0 | 0.9053 | 0.0 | | 0.4678 | 8.0 | 8 | 0.7798 | 0.0059 | 2784.3208 | 1929.9441 | 1336.0 | 2475.0 | 0.5398 | 8.0 | 0.0032 | 0.0 | 1143.0 | 1196.0 | 0.9557 | 0.0 | 0.0 | 193.0 | 1267.0 | 0.1523 | 0.0 | | 0.2898 | 9.0 | 9 | 0.8131 | 0.0059 | 2903.3217 | 2012.4292 | 1346.0 | 2475.0 | 0.5438 | 8.0 | 0.0032 | 0.0 | 1142.0 | 1196.0 | 0.9548 | 0.0 | 0.0 | 204.0 | 1267.0 | 0.1610 | 0.0 | | 0.1716 | 10.0 | 10 | 0.7535 | 0.0059 | 2690.5199 | 1864.9263 | 1458.0 | 2475.0 | 0.5891 | 8.0 | 0.0032 | 0.0 | 937.0 | 1196.0 | 0.7834 | 0.0 | 0.0 | 521.0 | 1267.0 | 0.4112 | 0.0 | | 0.0706 | 11.0 | 11 | 0.8096 | 0.0059 | 2890.8924 | 2003.8139 | 1471.0 | 2475.0 | 0.5943 | 8.0 | 0.0032 | 0.0 | 1012.0 | 1196.0 | 0.8462 | 0.0 | 0.0 | 459.0 | 1267.0 | 0.3623 | 0.0 | | 0.015 | 12.0 | 12 | 0.8691 | 0.0059 | 3103.3107 | 2151.0511 | 1502.0 | 2475.0 | 0.6069 | 8.0 | 0.0032 | 0.0 | 992.0 | 1196.0 | 0.8294 | 0.0 | 0.0 | 510.0 | 1267.0 | 0.4025 | 0.0 | | 0.0016 | 13.0 | 13 | 0.9300 | 0.0059 | 3320.7592 | 2301.7749 | 1534.0 | 2475.0 | 0.6198 | 8.0 | 0.0032 | 0.0 | 991.0 | 1196.0 | 0.8286 | 0.0 | 0.0 | 543.0 | 1267.0 | 0.4286 | 0.0 | | 0.0003 | 14.0 | 14 | 0.9761 | 0.0059 | 3485.2924 | 2415.8206 | 1546.0 | 2475.0 | 0.6246 | 25.0 | 0.0101 | 4.0 | 976.0 | 1196.0 | 0.8161 | 0.0033 | 13.0 | 570.0 | 1267.0 | 0.4499 | 0.0103 | | 0.0001 | 15.0 | 15 | 1.0153 | 0.0059 | 3625.3772 | 2512.9200 | 1556.0 | 2475.0 | 0.6287 | 93.0 | 0.0376 | 22.0 | 968.0 | 1196.0 | 0.8094 | 0.0184 | 63.0 | 588.0 | 1267.0 | 0.4641 | 0.0497 | | 0.0 | 16.0 | 16 | 1.0547 | 0.0059 | 3766.0323 | 2610.4147 | 1568.0 | 2475.0 | 0.6335 | 188.0 | 0.0760 | 59.0 | 970.0 | 1196.0 | 0.8110 | 0.0493 | 121.0 | 598.0 | 1267.0 | 0.4720 | 0.0955 | | 0.0 | 17.0 | 17 | 1.0947 | 0.0059 | 3908.7914 | 2709.3678 | 1570.0 | 2475.0 | 0.6343 | 322.0 | 0.1301 | 125.0 | 969.0 | 1196.0 | 0.8102 | 0.1045 | 188.0 | 601.0 | 1267.0 | 0.4743 | 0.1484 | | 0.0 | 18.0 | 18 | 1.1295 | 0.0059 | 4033.1706 | 2795.5808 | 1573.0 | 2475.0 | 0.6356 | 470.0 | 0.1899 | 211.0 | 972.0 | 1196.0 | 0.8127 | 0.1764 | 250.0 | 601.0 | 1267.0 | 0.4743 | 0.1973 | | 0.0 | 19.0 | 19 | 1.1608 | 0.0059 | 4144.8149 | 2872.9668 | 1578.0 | 2475.0 | 0.6376 | 612.0 | 0.2473 | 302.0 | 973.0 | 1196.0 | 0.8135 | 0.2525 | 301.0 | 605.0 | 1267.0 | 0.4775 | 0.2376 | | 0.0 | 20.0 | 20 | 1.1862 | 0.0059 | 4235.5987 | 2935.8933 | 1585.0 | 2475.0 | 0.6404 | 723.0 | 0.2921 | 384.0 | 975.0 | 1196.0 | 0.8152 | 0.3211 | 330.0 | 610.0 | 1267.0 | 0.4815 | 0.2605 | | 0.0 | 21.0 | 21 | 1.2002 | 0.0059 | 4285.6206 | 2970.5659 | 1587.0 | 2475.0 | 0.6412 | 803.0 | 0.3244 | 434.0 | 975.0 | 1196.0 | 0.8152 | 0.3629 | 360.0 | 612.0 | 1267.0 | 0.4830 | 0.2841 | | 0.0 | 22.0 | 22 | 1.2167 | 0.0059 | 4344.4465 | 3011.3409 | 1585.0 | 2475.0 | 0.6404 | 885.0 | 0.3576 | 480.0 | 972.0 | 1196.0 | 0.8127 | 0.4013 | 396.0 | 613.0 | 1267.0 | 0.4838 | 0.3125 | | 0.0 | 23.0 | 23 | 1.2293 | 0.0059 | 4389.3332 | 3042.4540 | 1586.0 | 2475.0 | 0.6408 | 953.0 | 0.3851 | 527.0 | 971.0 | 1196.0 | 0.8119 | 0.4406 | 417.0 | 615.0 | 1267.0 | 0.4854 | 0.3291 | | 0.0 | 24.0 | 24 | 1.2378 | 0.0059 | 4419.8769 | 3063.6252 | 1582.0 | 2475.0 | 0.6392 | 1008.0 | 0.4073 | 570.0 | 970.0 | 1196.0 | 0.8110 | 0.4766 | 429.0 | 612.0 | 1267.0 | 0.4830 | 0.3386 | | 0.0 | 25.0 | 25 | 1.2477 | 0.0059 | 4455.0209 | 3087.9851 | 1586.0 | 2475.0 | 0.6408 | 1048.0 | 0.4234 | 591.0 | 971.0 | 1196.0 | 0.8119 | 0.4941 | 448.0 | 615.0 | 1267.0 | 0.4854 | 0.3536 | | 0.0 | 26.0 | 26 | 1.2538 | 0.0059 | 4476.9507 | 3103.1857 | 1589.0 | 2475.0 | 0.6420 | 1075.0 | 0.4343 | 614.0 | 975.0 | 1196.0 | 0.8152 | 0.5134 | 452.0 | 614.0 | 1267.0 | 0.4846 | 0.3567 | | 0.0 | 27.0 | 27 | 1.2628 | 0.0059 | 4509.1826 | 3125.5272 | 1586.0 | 2475.0 | 0.6408 | 1106.0 | 0.4469 | 637.0 | 973.0 | 1196.0 | 0.8135 | 0.5326 | 460.0 | 613.0 | 1267.0 | 0.4838 | 0.3631 | | 0.0 | 28.0 | 28 | 1.2659 | 0.0059 | 4520.0496 | 3133.0596 | 1585.0 | 2475.0 | 0.6404 | 1120.0 | 0.4525 | 645.0 | 972.0 | 1196.0 | 0.8127 | 0.5393 | 466.0 | 613.0 | 1267.0 | 0.4838 | 0.3678 | | 0.0 | 29.0 | 29 | 1.2675 | 0.0059 | 4525.9937 | 3137.1798 | 1587.0 | 2475.0 | 0.6412 | 1139.0 | 0.4602 | 659.0 | 974.0 | 1196.0 | 0.8144 | 0.5510 | 471.0 | 613.0 | 1267.0 | 0.4838 | 0.3717 | | 0.0 | 30.0 | 30 | 1.2724 | 0.0059 | 4543.3835 | 3149.2334 | 1590.0 | 2475.0 | 0.6424 | 1155.0 | 0.4667 | 669.0 | 974.0 | 1196.0 | 0.8144 | 0.5594 | 477.0 | 616.0 | 1267.0 | 0.4862 | 0.3765 | | 0.0 | 31.0 | 31 | 1.2754 | 0.0059 | 4554.1107 | 3156.6690 | 1587.0 | 2475.0 | 0.6412 | 1157.0 | 0.4675 | 672.0 | 975.0 | 1196.0 | 0.8152 | 0.5619 | 476.0 | 612.0 | 1267.0 | 0.4830 | 0.3757 | | 0.0 | 32.0 | 32 | 1.2780 | 0.0059 | 4563.1825 | 3162.9571 | 1582.0 | 2475.0 | 0.6392 | 1172.0 | 0.4735 | 683.0 | 972.0 | 1196.0 | 0.8127 | 0.5711 | 480.0 | 610.0 | 1267.0 | 0.4815 | 0.3788 | | 0.0 | 33.0 | 33 | 1.2791 | 0.0059 | 4567.2324 | 3165.7642 | 1583.0 | 2475.0 | 0.6396 | 1171.0 | 0.4731 | 680.0 | 973.0 | 1196.0 | 0.8135 | 0.5686 | 482.0 | 610.0 | 1267.0 | 0.4815 | 0.3804 | | 0.0 | 34.0 | 34 | 1.2793 | 0.0059 | 4567.8543 | 3166.1953 | 1588.0 | 2475.0 | 0.6416 | 1176.0 | 0.4752 | 684.0 | 975.0 | 1196.0 | 0.8152 | 0.5719 | 483.0 | 613.0 | 1267.0 | 0.4838 | 0.3812 | | 0.0 | 35.0 | 35 | 1.2811 | 0.0059 | 4574.2847 | 3170.6525 | 1582.0 | 2475.0 | 0.6392 | 1178.0 | 0.4760 | 687.0 | 972.0 | 1196.0 | 0.8127 | 0.5744 | 482.0 | 610.0 | 1267.0 | 0.4815 | 0.3804 | | 0.0 | 36.0 | 36 | 1.2823 | 0.0059 | 4578.7325 | 3173.7355 | 1583.0 | 2475.0 | 0.6396 | 1183.0 | 0.4780 | 692.0 | 971.0 | 1196.0 | 0.8119 | 0.5786 | 482.0 | 612.0 | 1267.0 | 0.4830 | 0.3804 | | 0.0 | 37.0 | 37 | 1.2835 | 0.0059 | 4582.8161 | 3176.5661 | 1584.0 | 2475.0 | 0.64 | 1190.0 | 0.4808 | 696.0 | 972.0 | 1196.0 | 0.8127 | 0.5819 | 485.0 | 612.0 | 1267.0 | 0.4830 | 0.3828 | | 0.0 | 38.0 | 38 | 1.2848 | 0.0059 | 4587.5704 | 3179.8615 | 1585.0 | 2475.0 | 0.6404 | 1184.0 | 0.4784 | 692.0 | 973.0 | 1196.0 | 0.8135 | 0.5786 | 483.0 | 612.0 | 1267.0 | 0.4830 | 0.3812 | | 0.0 | 39.0 | 39 | 1.2858 | 0.0059 | 4591.0362 | 3182.2638 | 1584.0 | 2475.0 | 0.64 | 1189.0 | 0.4804 | 697.0 | 973.0 | 1196.0 | 0.8135 | 0.5828 | 483.0 | 611.0 | 1267.0 | 0.4822 | 0.3812 | | 0.0 | 40.0 | 40 | 1.2854 | 0.0059 | 4589.7370 | 3181.3633 | 1585.0 | 2475.0 | 0.6404 | 1186.0 | 0.4792 | 693.0 | 973.0 | 1196.0 | 0.8135 | 0.5794 | 484.0 | 612.0 | 1267.0 | 0.4830 | 0.3820 | | 0.0 | 41.0 | 41 | 1.2850 | 0.0059 | 4588.1990 | 3180.2972 | 1580.0 | 2475.0 | 0.6384 | 1190.0 | 0.4808 | 698.0 | 972.0 | 1196.0 | 0.8127 | 0.5836 | 483.0 | 608.0 | 1267.0 | 0.4799 | 0.3812 | | 0.0 | 42.0 | 42 | 1.2860 | 0.0059 | 4591.7153 | 3182.7345 | 1585.0 | 2475.0 | 0.6404 | 1194.0 | 0.4824 | 697.0 | 974.0 | 1196.0 | 0.8144 | 0.5828 | 488.0 | 611.0 | 1267.0 | 0.4822 | 0.3852 | | 0.0 | 43.0 | 43 | 1.2859 | 0.0059 | 4591.5068 | 3182.5900 | 1580.0 | 2475.0 | 0.6384 | 1189.0 | 0.4804 | 697.0 | 972.0 | 1196.0 | 0.8127 | 0.5828 | 483.0 | 608.0 | 1267.0 | 0.4799 | 0.3812 | | 0.0 | 44.0 | 44 | 1.2852 | 0.0059 | 4589.0154 | 3180.8631 | 1587.0 | 2475.0 | 0.6412 | 1190.0 | 0.4808 | 698.0 | 975.0 | 1196.0 | 0.8152 | 0.5836 | 483.0 | 612.0 | 1267.0 | 0.4830 | 0.3812 | | 0.0 | 45.0 | 45 | 1.2860 | 0.0059 | 4591.7459 | 3182.7557 | 1587.0 | 2475.0 | 0.6412 | 1193.0 | 0.4820 | 699.0 | 974.0 | 1196.0 | 0.8144 | 0.5844 | 485.0 | 613.0 | 1267.0 | 0.4838 | 0.3828 | | 0.0 | 46.0 | 46 | 1.2858 | 0.0059 | 4591.3256 | 3182.4644 | 1587.0 | 2475.0 | 0.6412 | 1196.0 | 0.4832 | 701.0 | 976.0 | 1196.0 | 0.8161 | 0.5861 | 486.0 | 611.0 | 1267.0 | 0.4822 | 0.3836 | | 0.0 | 47.0 | 47 | 1.2847 | 0.0059 | 4587.3352 | 3179.6985 | 1588.0 | 2475.0 | 0.6416 | 1193.0 | 0.4820 | 698.0 | 974.0 | 1196.0 | 0.8144 | 0.5836 | 486.0 | 614.0 | 1267.0 | 0.4846 | 0.3836 | | 0.0 | 48.0 | 48 | 1.2860 | 0.0059 | 4591.7670 | 3182.7704 | 1581.0 | 2475.0 | 0.6388 | 1193.0 | 0.4820 | 698.0 | 972.0 | 1196.0 | 0.8127 | 0.5836 | 486.0 | 609.0 | 1267.0 | 0.4807 | 0.3836 | | 0.0 | 49.0 | 49 | 1.2857 | 0.0059 | 4590.7061 | 3182.0350 | 1590.0 | 2475.0 | 0.6424 | 1195.0 | 0.4828 | 699.0 | 975.0 | 1196.0 | 0.8152 | 0.5844 | 487.0 | 615.0 | 1267.0 | 0.4854 | 0.3844 | | 0.0 | 50.0 | 50 | 1.2880 | 0.0059 | 4598.8684 | 3187.6927 | 1586.0 | 2475.0 | 0.6408 | 1191.0 | 0.4812 | 697.0 | 975.0 | 1196.0 | 0.8152 | 0.5828 | 485.0 | 611.0 | 1267.0 | 0.4822 | 0.3828 | | 0.0 | 51.0 | 51 | 1.2880 | 0.0059 | 4598.8481 | 3187.6786 | 1582.0 | 2475.0 | 0.6392 | 1192.0 | 0.4816 | 697.0 | 972.0 | 1196.0 | 0.8127 | 0.5828 | 486.0 | 610.0 | 1267.0 | 0.4815 | 0.3836 | | 0.0 | 52.0 | 52 | 1.2871 | 0.0059 | 4595.9863 | 3185.6950 | 1579.0 | 2475.0 | 0.6380 | 1196.0 | 0.4832 | 697.0 | 973.0 | 1196.0 | 0.8135 | 0.5828 | 490.0 | 606.0 | 1267.0 | 0.4783 | 0.3867 | | 0.0 | 53.0 | 53 | 1.2858 | 0.0059 | 4591.2625 | 3182.4207 | 1585.0 | 2475.0 | 0.6404 | 1200.0 | 0.4848 | 701.0 | 974.0 | 1196.0 | 0.8144 | 0.5861 | 490.0 | 611.0 | 1267.0 | 0.4822 | 0.3867 | | 0.0 | 54.0 | 54 | 1.2867 | 0.0059 | 4594.4591 | 3184.6364 | 1586.0 | 2475.0 | 0.6408 | 1197.0 | 0.4836 | 700.0 | 973.0 | 1196.0 | 0.8135 | 0.5853 | 488.0 | 613.0 | 1267.0 | 0.4838 | 0.3852 | | 0.0 | 55.0 | 55 | 1.2880 | 0.0059 | 4599.0709 | 3187.8330 | 1579.0 | 2475.0 | 0.6380 | 1194.0 | 0.4824 | 700.0 | 972.0 | 1196.0 | 0.8127 | 0.5853 | 485.0 | 607.0 | 1267.0 | 0.4791 | 0.3828 | | 0.0 | 56.0 | 56 | 1.2871 | 0.0059 | 4595.6402 | 3185.4551 | 1583.0 | 2475.0 | 0.6396 | 1192.0 | 0.4816 | 698.0 | 973.0 | 1196.0 | 0.8135 | 0.5836 | 485.0 | 610.0 | 1267.0 | 0.4815 | 0.3828 | | 0.0 | 57.0 | 57 | 1.2866 | 0.0059 | 4593.9130 | 3184.2578 | 1580.0 | 2475.0 | 0.6384 | 1195.0 | 0.4828 | 702.0 | 973.0 | 1196.0 | 0.8135 | 0.5870 | 484.0 | 607.0 | 1267.0 | 0.4791 | 0.3820 | | 0.0 | 58.0 | 58 | 1.2858 | 0.0059 | 4590.9929 | 3182.2338 | 1583.0 | 2475.0 | 0.6396 | 1189.0 | 0.4804 | 695.0 | 973.0 | 1196.0 | 0.8135 | 0.5811 | 485.0 | 610.0 | 1267.0 | 0.4815 | 0.3828 | | 0.0 | 59.0 | 59 | 1.2849 | 0.0059 | 4587.9750 | 3180.1420 | 1586.0 | 2475.0 | 0.6408 | 1197.0 | 0.4836 | 702.0 | 975.0 | 1196.0 | 0.8152 | 0.5870 | 486.0 | 611.0 | 1267.0 | 0.4822 | 0.3836 | | 0.0 | 60.0 | 60 | 1.2866 | 0.0059 | 4594.1392 | 3184.4146 | 1581.0 | 2475.0 | 0.6388 | 1195.0 | 0.4828 | 699.0 | 973.0 | 1196.0 | 0.8135 | 0.5844 | 487.0 | 608.0 | 1267.0 | 0.4799 | 0.3844 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

rockst4r4/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yawning_tiny_aardvark

rockst4r4

2025-08-19T07:59:05Z

1

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am yawning_tiny_aardvark", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-15T20:28:40Z

--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am yawning_tiny_aardvark --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755590184

0xaoyama

2025-08-19T07:56:59Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:56:45Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

AngelinaZanardi/nb-bert-base-edu-scorer-lr3e5-bs32_swe_test_2

AngelinaZanardi

2025-08-19T07:55:48Z

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:NbAiLab/nb-bert-base", "base_model:finetune:NbAiLab/nb-bert-base", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-19T06:21:50Z

--- library_name: transformers license: cc-by-4.0 base_model: NbAiLab/nb-bert-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: nb-bert-base-edu-scorer-lr3e5-bs32_swe_test_2 results: [] ---  # nb-bert-base-edu-scorer-lr3e5-bs32_swe_test_2 This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3465 - Accuracy: 0.5120 - F1 Weighted: 0.5084 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Weighted | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:| | 1.1986 | 1.0 | 1472 | 1.1862 | 0.4919 | 0.4498 | | 1.1196 | 2.0 | 2944 | 1.1978 | 0.4946 | 0.4659 | | 0.9807 | 3.0 | 4416 | 1.2960 | 0.4798 | 0.4599 | | 0.8432 | 4.0 | 5888 | 1.4149 | 0.4768 | 0.4709 | | 0.716 | 5.0 | 7360 | 1.5769 | 0.4720 | 0.4626 | | 0.5717 | 6.0 | 8832 | 1.8525 | 0.4558 | 0.4588 | | 0.4705 | 7.0 | 10304 | 2.0333 | 0.4526 | 0.4584 | | 0.3901 | 8.0 | 11776 | 2.1127 | 0.4534 | 0.4559 | | 0.322 | 9.0 | 13248 | 2.4347 | 0.4560 | 0.4599 | | 0.2845 | 10.0 | 14720 | 2.6137 | 0.4411 | 0.4493 | | 0.2244 | 11.0 | 16192 | 2.7283 | 0.4518 | 0.4564 | | 0.2059 | 12.0 | 17664 | 3.0232 | 0.4383 | 0.4416 | | 0.1453 | 13.0 | 19136 | 3.1201 | 0.4484 | 0.4529 | | 0.1258 | 14.0 | 20608 | 3.2220 | 0.4520 | 0.4561 | | 0.0994 | 15.0 | 22080 | 3.4938 | 0.4455 | 0.4526 | | 0.0933 | 16.0 | 23552 | 3.5932 | 0.4506 | 0.4584 | | 0.0689 | 17.0 | 25024 | 3.8269 | 0.4538 | 0.4607 | | 0.0514 | 18.0 | 26496 | 4.0041 | 0.4540 | 0.4607 | | 0.0476 | 19.0 | 27968 | 4.1475 | 0.4540 | 0.4597 | | 0.0307 | 20.0 | 29440 | 4.2319 | 0.4512 | 0.4575 | ### Framework versions - Transformers 4.55.0 - Pytorch 2.5.1+cu121 - Datasets 4.0.0 - Tokenizers 0.21.4

donoway/ARC-Easy_Llama-3.2-1B-a2yg6wt3

donoway

2025-08-19T07:54:03Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T07:42:31Z

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Easy_Llama-3.2-1B-a2yg6wt3 results: [] ---  # ARC-Easy_Llama-3.2-1B-a2yg6wt3 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3756 - Model Preparation Time: 0.0063 - Mdl: 2775.8537 - Accumulated Loss: 1924.0752 - Correct Preds: 366.0 - Total Preds: 570.0 - Accuracy: 0.6421 - Correct Gen Preds: 355.0 - Gen Accuracy: 0.6228 - Correct Gen Preds 32: 113.0 - Correct Preds 32: 118.0 - Total Labels 32: 158.0 - Accuracy 32: 0.7468 - Gen Accuracy 32: 0.7152 - Correct Gen Preds 33: 106.0 - Correct Preds 33: 107.0 - Total Labels 33: 152.0 - Accuracy 33: 0.7039 - Gen Accuracy 33: 0.6974 - Correct Gen Preds 34: 88.0 - Correct Preds 34: 91.0 - Total Labels 34: 142.0 - Accuracy 34: 0.6408 - Gen Accuracy 34: 0.6197 - Correct Gen Preds 35: 48.0 - Correct Preds 35: 50.0 - Total Labels 35: 118.0 - Accuracy 35: 0.4237 - Gen Accuracy 35: 0.4068 - Correct Gen Preds 36: 0.0 - Correct Preds 36: 0.0 - Total Labels 36: 0.0 - Accuracy 36: 0.0 - Gen Accuracy 36: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 32 | Correct Preds 32 | Total Labels 32 | Accuracy 32 | Gen Accuracy 32 | Correct Gen Preds 33 | Correct Preds 33 | Total Labels 33 | Accuracy 33 | Gen Accuracy 33 | Correct Gen Preds 34 | Correct Preds 34 | Total Labels 34 | Accuracy 34 | Gen Accuracy 34 | Correct Gen Preds 35 | Correct Preds 35 | Total Labels 35 | Accuracy 35 | Gen Accuracy 35 | Correct Gen Preds 36 | Correct Preds 36 | Total Labels 36 | Accuracy 36 | Gen Accuracy 36 | |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:| | No log | 0 | 0 | 1.5354 | 0.0063 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.498 | 1.0 | 1 | 1.5354 | 0.0063 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.498 | 2.0 | 2 | 2.5202 | 0.0063 | 2072.4194 | 1436.4916 | 219.0 | 570.0 | 0.3842 | 219.0 | 0.3842 | 0.0 | 0.0 | 158.0 | 0.0 | 0.0 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 136.0 | 136.0 | 142.0 | 0.9577 | 0.9577 | 0.0 | 0.0 | 118.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.783 | 3.0 | 3 | 1.2895 | 0.0063 | 1060.4212 | 735.0280 | 226.0 | 570.0 | 0.3965 | 226.0 | 0.3965 | 6.0 | 6.0 | 158.0 | 0.0380 | 0.0380 | 145.0 | 145.0 | 152.0 | 0.9539 | 0.9539 | 48.0 | 48.0 | 142.0 | 0.3380 | 0.3380 | 27.0 | 27.0 | 118.0 | 0.2288 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.4782 | 4.0 | 4 | 1.3651 | 0.0063 | 1122.5363 | 778.0829 | 325.0 | 570.0 | 0.5702 | 311.0 | 0.5456 | 94.0 | 107.0 | 158.0 | 0.6772 | 0.5949 | 103.0 | 104.0 | 152.0 | 0.6842 | 0.6776 | 81.0 | 81.0 | 142.0 | 0.5704 | 0.5704 | 33.0 | 33.0 | 118.0 | 0.2797 | 0.2797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0157 | 5.0 | 5 | 2.2319 | 0.0063 | 1835.3671 | 1272.1795 | 356.0 | 570.0 | 0.6246 | 339.0 | 0.5947 | 100.0 | 112.0 | 158.0 | 0.7089 | 0.6329 | 108.0 | 108.0 | 152.0 | 0.7105 | 0.7105 | 88.0 | 91.0 | 142.0 | 0.6408 | 0.6197 | 43.0 | 45.0 | 118.0 | 0.3814 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 6.0 | 6 | 2.8502 | 0.0063 | 2343.8113 | 1624.6062 | 362.0 | 570.0 | 0.6351 | 355.0 | 0.6228 | 109.0 | 116.0 | 158.0 | 0.7342 | 0.6899 | 108.0 | 108.0 | 152.0 | 0.7105 | 0.7105 | 91.0 | 91.0 | 142.0 | 0.6408 | 0.6408 | 47.0 | 47.0 | 118.0 | 0.3983 | 0.3983 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 7.0 | 7 | 3.1703 | 0.0063 | 2607.0465 | 1807.0669 | 363.0 | 570.0 | 0.6368 | 353.0 | 0.6193 | 111.0 | 116.0 | 158.0 | 0.7342 | 0.7025 | 106.0 | 107.0 | 152.0 | 0.7039 | 0.6974 | 88.0 | 91.0 | 142.0 | 0.6408 | 0.6197 | 48.0 | 49.0 | 118.0 | 0.4153 | 0.4068 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 8.0 | 8 | 3.3756 | 0.0063 | 2775.8537 | 1924.0752 | 366.0 | 570.0 | 0.6421 | 355.0 | 0.6228 | 113.0 | 118.0 | 158.0 | 0.7468 | 0.7152 | 106.0 | 107.0 | 152.0 | 0.7039 | 0.6974 | 88.0 | 91.0 | 142.0 | 0.6408 | 0.6197 | 48.0 | 50.0 | 118.0 | 0.4237 | 0.4068 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 9.0 | 9 | 3.5599 | 0.0063 | 2927.4671 | 2029.1656 | 361.0 | 570.0 | 0.6333 | 354.0 | 0.6211 | 116.0 | 120.0 | 158.0 | 0.7595 | 0.7342 | 102.0 | 103.0 | 152.0 | 0.6776 | 0.6711 | 88.0 | 89.0 | 142.0 | 0.6268 | 0.6197 | 48.0 | 49.0 | 118.0 | 0.4153 | 0.4068 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 10.0 | 10 | 3.7285 | 0.0063 | 3066.1098 | 2125.2654 | 353.0 | 570.0 | 0.6193 | 349.0 | 0.6123 | 118.0 | 120.0 | 158.0 | 0.7595 | 0.7468 | 98.0 | 98.0 | 152.0 | 0.6447 | 0.6447 | 84.0 | 85.0 | 142.0 | 0.5986 | 0.5915 | 49.0 | 50.0 | 118.0 | 0.4237 | 0.4153 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 11.0 | 11 | 3.8061 | 0.0063 | 3129.9326 | 2169.5039 | 356.0 | 570.0 | 0.6246 | 349.0 | 0.6123 | 122.0 | 125.0 | 158.0 | 0.7911 | 0.7722 | 97.0 | 97.0 | 152.0 | 0.6382 | 0.6382 | 83.0 | 86.0 | 142.0 | 0.6056 | 0.5845 | 47.0 | 48.0 | 118.0 | 0.4068 | 0.3983 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 12.0 | 12 | 3.8871 | 0.0063 | 3196.4877 | 2215.6364 | 351.0 | 570.0 | 0.6158 | 347.0 | 0.6088 | 123.0 | 124.0 | 158.0 | 0.7848 | 0.7785 | 95.0 | 95.0 | 152.0 | 0.625 | 0.625 | 83.0 | 85.0 | 142.0 | 0.5986 | 0.5845 | 46.0 | 47.0 | 118.0 | 0.3983 | 0.3898 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 13.0 | 13 | 3.9537 | 0.0063 | 3251.2770 | 2253.6135 | 348.0 | 570.0 | 0.6105 | 343.0 | 0.6018 | 124.0 | 124.0 | 158.0 | 0.7848 | 0.7848 | 95.0 | 96.0 | 152.0 | 0.6316 | 0.625 | 81.0 | 84.0 | 142.0 | 0.5915 | 0.5704 | 43.0 | 44.0 | 118.0 | 0.3729 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 14.0 | 14 | 4.0209 | 0.0063 | 3306.4977 | 2291.8895 | 345.0 | 570.0 | 0.6053 | 338.0 | 0.5930 | 121.0 | 124.0 | 158.0 | 0.7848 | 0.7658 | 92.0 | 93.0 | 152.0 | 0.6118 | 0.6053 | 82.0 | 84.0 | 142.0 | 0.5915 | 0.5775 | 43.0 | 44.0 | 118.0 | 0.3729 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 15.0 | 15 | 4.0856 | 0.0063 | 3359.7299 | 2328.7873 | 339.0 | 570.0 | 0.5947 | 336.0 | 0.5895 | 123.0 | 125.0 | 158.0 | 0.7911 | 0.7785 | 92.0 | 93.0 | 152.0 | 0.6118 | 0.6053 | 78.0 | 78.0 | 142.0 | 0.5493 | 0.5493 | 43.0 | 43.0 | 118.0 | 0.3644 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 16.0 | 16 | 4.1341 | 0.0063 | 3399.5852 | 2356.4129 | 336.0 | 570.0 | 0.5895 | 333.0 | 0.5842 | 121.0 | 122.0 | 158.0 | 0.7722 | 0.7658 | 89.0 | 90.0 | 152.0 | 0.5921 | 0.5855 | 79.0 | 79.0 | 142.0 | 0.5563 | 0.5563 | 44.0 | 45.0 | 118.0 | 0.3814 | 0.3729 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 17.0 | 17 | 4.2038 | 0.0063 | 3456.9416 | 2396.1693 | 336.0 | 570.0 | 0.5895 | 332.0 | 0.5825 | 123.0 | 125.0 | 158.0 | 0.7911 | 0.7785 | 89.0 | 89.0 | 152.0 | 0.5855 | 0.5855 | 77.0 | 78.0 | 142.0 | 0.5493 | 0.5423 | 43.0 | 44.0 | 118.0 | 0.3729 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 18.0 | 18 | 4.1844 | 0.0063 | 3440.9588 | 2385.0909 | 336.0 | 570.0 | 0.5895 | 332.0 | 0.5825 | 124.0 | 126.0 | 158.0 | 0.7975 | 0.7848 | 88.0 | 88.0 | 152.0 | 0.5789 | 0.5789 | 76.0 | 77.0 | 142.0 | 0.5423 | 0.5352 | 44.0 | 45.0 | 118.0 | 0.3814 | 0.3729 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 19.0 | 19 | 4.2392 | 0.0063 | 3486.0281 | 2416.3305 | 333.0 | 570.0 | 0.5842 | 330.0 | 0.5789 | 122.0 | 124.0 | 158.0 | 0.7848 | 0.7722 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 78.0 | 78.0 | 142.0 | 0.5493 | 0.5493 | 43.0 | 44.0 | 118.0 | 0.3729 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 20.0 | 20 | 4.2677 | 0.0063 | 3509.5150 | 2432.6104 | 333.0 | 570.0 | 0.5842 | 330.0 | 0.5789 | 122.0 | 123.0 | 158.0 | 0.7785 | 0.7722 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 78.0 | 79.0 | 142.0 | 0.5563 | 0.5493 | 43.0 | 44.0 | 118.0 | 0.3729 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 21.0 | 21 | 4.2595 | 0.0063 | 3502.7525 | 2427.9230 | 332.0 | 570.0 | 0.5825 | 329.0 | 0.5772 | 121.0 | 122.0 | 158.0 | 0.7722 | 0.7658 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 77.0 | 78.0 | 142.0 | 0.5493 | 0.5423 | 44.0 | 45.0 | 118.0 | 0.3814 | 0.3729 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 22.0 | 22 | 4.3090 | 0.0063 | 3543.4099 | 2456.1046 | 331.0 | 570.0 | 0.5807 | 328.0 | 0.5754 | 122.0 | 123.0 | 158.0 | 0.7785 | 0.7722 | 86.0 | 86.0 | 152.0 | 0.5658 | 0.5658 | 77.0 | 78.0 | 142.0 | 0.5493 | 0.5423 | 43.0 | 44.0 | 118.0 | 0.3729 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 23.0 | 23 | 4.3113 | 0.0063 | 3545.3391 | 2457.4418 | 332.0 | 570.0 | 0.5825 | 330.0 | 0.5789 | 122.0 | 122.0 | 158.0 | 0.7722 | 0.7722 | 86.0 | 86.0 | 152.0 | 0.5658 | 0.5658 | 78.0 | 79.0 | 142.0 | 0.5563 | 0.5493 | 44.0 | 45.0 | 118.0 | 0.3814 | 0.3729 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 24.0 | 24 | 4.3136 | 0.0063 | 3547.2162 | 2458.7429 | 330.0 | 570.0 | 0.5789 | 326.0 | 0.5719 | 122.0 | 124.0 | 158.0 | 0.7848 | 0.7722 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 75.0 | 76.0 | 142.0 | 0.5352 | 0.5282 | 42.0 | 43.0 | 118.0 | 0.3644 | 0.3559 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 25.0 | 25 | 4.3196 | 0.0063 | 3552.1961 | 2462.1947 | 331.0 | 570.0 | 0.5807 | 329.0 | 0.5772 | 123.0 | 123.0 | 158.0 | 0.7785 | 0.7785 | 88.0 | 88.0 | 152.0 | 0.5789 | 0.5789 | 75.0 | 76.0 | 142.0 | 0.5352 | 0.5282 | 43.0 | 44.0 | 118.0 | 0.3729 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 26.0 | 26 | 4.3053 | 0.0063 | 3540.4181 | 2454.0309 | 330.0 | 570.0 | 0.5789 | 328.0 | 0.5754 | 124.0 | 124.0 | 158.0 | 0.7848 | 0.7848 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 75.0 | 76.0 | 142.0 | 0.5352 | 0.5282 | 42.0 | 43.0 | 118.0 | 0.3644 | 0.3559 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 27.0 | 27 | 4.3153 | 0.0063 | 3548.6438 | 2459.7325 | 333.0 | 570.0 | 0.5842 | 330.0 | 0.5789 | 124.0 | 125.0 | 158.0 | 0.7911 | 0.7848 | 88.0 | 88.0 | 152.0 | 0.5789 | 0.5789 | 75.0 | 76.0 | 142.0 | 0.5352 | 0.5282 | 43.0 | 44.0 | 118.0 | 0.3729 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 28.0 | 28 | 4.3397 | 0.0063 | 3568.7171 | 2473.6462 | 329.0 | 570.0 | 0.5772 | 328.0 | 0.5754 | 123.0 | 123.0 | 158.0 | 0.7785 | 0.7785 | 86.0 | 86.0 | 152.0 | 0.5658 | 0.5658 | 75.0 | 76.0 | 142.0 | 0.5352 | 0.5282 | 44.0 | 44.0 | 118.0 | 0.3729 | 0.3729 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 29.0 | 29 | 4.3321 | 0.0063 | 3562.4657 | 2469.3130 | 335.0 | 570.0 | 0.5877 | 331.0 | 0.5807 | 122.0 | 124.0 | 158.0 | 0.7848 | 0.7722 | 86.0 | 86.0 | 152.0 | 0.5658 | 0.5658 | 78.0 | 79.0 | 142.0 | 0.5563 | 0.5493 | 45.0 | 46.0 | 118.0 | 0.3898 | 0.3814 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 30.0 | 30 | 4.3265 | 0.0063 | 3557.8576 | 2466.1189 | 331.0 | 570.0 | 0.5807 | 327.0 | 0.5737 | 123.0 | 125.0 | 158.0 | 0.7911 | 0.7785 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 74.0 | 75.0 | 142.0 | 0.5282 | 0.5211 | 43.0 | 44.0 | 118.0 | 0.3729 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 31.0 | 31 | 4.3336 | 0.0063 | 3563.6921 | 2470.1632 | 333.0 | 570.0 | 0.5842 | 330.0 | 0.5789 | 124.0 | 125.0 | 158.0 | 0.7911 | 0.7848 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 75.0 | 76.0 | 142.0 | 0.5352 | 0.5282 | 44.0 | 45.0 | 118.0 | 0.3814 | 0.3729 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 32.0 | 32 | 4.3611 | 0.0063 | 3586.2619 | 2485.8074 | 330.0 | 570.0 | 0.5789 | 327.0 | 0.5737 | 123.0 | 124.0 | 158.0 | 0.7848 | 0.7785 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 75.0 | 76.0 | 142.0 | 0.5352 | 0.5282 | 42.0 | 43.0 | 118.0 | 0.3644 | 0.3559 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 33.0 | 33 | 4.3522 | 0.0063 | 3579.0000 | 2480.7737 | 332.0 | 570.0 | 0.5825 | 329.0 | 0.5772 | 123.0 | 124.0 | 158.0 | 0.7848 | 0.7785 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 77.0 | 78.0 | 142.0 | 0.5493 | 0.5423 | 42.0 | 43.0 | 118.0 | 0.3644 | 0.3559 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 34.0 | 34 | 4.3694 | 0.0063 | 3593.1289 | 2490.5671 | 330.0 | 570.0 | 0.5789 | 326.0 | 0.5719 | 123.0 | 125.0 | 158.0 | 0.7911 | 0.7785 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 73.0 | 74.0 | 142.0 | 0.5211 | 0.5141 | 43.0 | 44.0 | 118.0 | 0.3729 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 35.0 | 35 | 4.3432 | 0.0063 | 3571.5839 | 2475.6333 | 328.0 | 570.0 | 0.5754 | 326.0 | 0.5719 | 122.0 | 123.0 | 158.0 | 0.7785 | 0.7722 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 74.0 | 75.0 | 142.0 | 0.5282 | 0.5211 | 43.0 | 43.0 | 118.0 | 0.3644 | 0.3644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 36.0 | 36 | 4.3402 | 0.0063 | 3569.1302 | 2473.9325 | 332.0 | 570.0 | 0.5825 | 328.0 | 0.5754 | 122.0 | 124.0 | 158.0 | 0.7848 | 0.7722 | 86.0 | 86.0 | 152.0 | 0.5658 | 0.5658 | 76.0 | 77.0 | 142.0 | 0.5423 | 0.5352 | 44.0 | 45.0 | 118.0 | 0.3814 | 0.3729 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 37.0 | 37 | 4.3729 | 0.0063 | 3596.0119 | 2492.5655 | 330.0 | 570.0 | 0.5789 | 326.0 | 0.5719 | 123.0 | 125.0 | 158.0 | 0.7911 | 0.7785 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 76.0 | 77.0 | 142.0 | 0.5423 | 0.5352 | 42.0 | 43.0 | 118.0 | 0.3644 | 0.3559 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 38.0 | 38 | 4.3427 | 0.0063 | 3571.1304 | 2475.3190 | 328.0 | 570.0 | 0.5754 | 325.0 | 0.5702 | 123.0 | 124.0 | 158.0 | 0.7848 | 0.7785 | 86.0 | 86.0 | 152.0 | 0.5658 | 0.5658 | 75.0 | 76.0 | 142.0 | 0.5352 | 0.5282 | 41.0 | 42.0 | 118.0 | 0.3559 | 0.3475 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

GradientResearch/Qwen3-4B-ECHO-Sokoban-GRPO

GradientResearch

2025-08-19T07:53:28Z

0

null

[ "safetensors", "qwen3", "text-generation", "conversational", "arxiv:2508.05387", "license:apache-2.0", "region:us" ]

text-generation

2025-08-18T12:31:48Z

--- license: apache-2.0 pipeline_tag: text-generation --- # Model Card for Qwen3-4B-ECHO-Sokoban-GRPO  Building upon Qwen3-4B, we trained the model with the ECHO framework using GRPO on the Sokoban dataset. Specifically, because Qwen3-4B performs poorly on the more challenging Sokoban puzzles, we adopted a multi-round RL training regimen capped at four rounds, with a maximum of 25 candidate actions per round. The detailed environment configuration is as follows: ```python LargerSokoban6: env_type: sokoban max_actions_per_traj: 100 env_instruction: "You are solving the Sokoban puzzle. You are the player and you need to push all boxes to targets. When you are right next to a box, you can push it by moving in the same direction. You cannot push a box through a wall, and you cannot pull a box. The answer should be a sequence of actions, like <answer>Right || Right || Up</answer>" max_tokens: 300 env_config: dim_x: 6 dim_y: 6 num_boxes: 2 max_steps: 300 search_depth: 20 ``` Tabel 1: Model performance on Sokoban task | Model | Success Rate(%) | |----------------|----------------| | Qwen3-4B | 21.8 | | Qwen3-4B-Echo(GRPO) | 34.0 | | Qwen3-30B-A3B-Thinking-2507 | 72.75 | | Qwen3-30B-A3B-Thinking-2507-Echo(GRPO) | 82.80 | | Deepseek-R1 | 75.75 | | Qwen3-235B-A22B-Thinking-2507) | 79.68 | | gpt-oss-120b | 79.69 | # Quick start ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "GradientResearch/Qwen3-4B-ECHO-Sokoban-GRPO" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "sokoban" messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` # Citation If you find our work helpful, feel free to give us a cite. ``` @misc{xiao2025echodecouplinginferencetraining, title={Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms}, author={Jie Xiao and Changyuan Fan and Qingnan Ren and Alfred Long and Yuchen Zhang and Rymon Yu and Eric Yang and Lynn Ai and Shaoduo Gan}, year={2025}, eprint={2508.05387}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2508.05387}, } ```

Alonc/device_to_cve_4bit

Alonc

2025-08-19T07:52:34Z

0

null

[ "safetensors", "qwen3", "region:us" ]

null

2025-08-18T15:19:30Z

The model is 16-bit the 4bit is a typo!!!! --- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** Alonc - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Sayemahsjn/blockassist-bc-playful_feline_octopus_1755588452

Sayemahsjn

2025-08-19T07:45:27Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:45:23Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

minhnguyet/my-dpo-mistral-7b

minhnguyet

2025-08-19T07:41:50Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-19T07:41:13Z

--- base_model: unsloth/mistral-7b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** minhnguyet - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

CosminMihai02/llama3.1_ollama_v4

CosminMihai02

2025-08-19T07:40:35Z

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-08-19T07:39:48Z

--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** CosminMihai02 - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755589105

0xaoyama

2025-08-19T07:38:59Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:38:48Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755588973

Ferdi3425

2025-08-19T07:37:30Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:37:01Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

jinaai/jina-embeddings-v4-vllm-code

jinaai

2025-08-19T07:36:55Z

354

3

transformers

[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "vidore", "colpali", "multimodal-embedding", "multilingual-embedding", "Text-to-Visual Document (T→VD) retrieval", "feature-extraction", "sentence-similarity", "mteb", "visual-document-retrieval", "multilingual", "arxiv:2506.18902", "text-generation-inference", "endpoints_compatible", "region:eu" ]

visual-document-retrieval

2025-07-01T10:02:46Z

--- tags: - vidore - colpali - multimodal-embedding - multilingual-embedding - Text-to-Visual Document (T→VD) retrieval - feature-extraction - sentence-similarity - mteb language: - multilingual library_name: transformers pipeline_tag: visual-document-retrieval --- <img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px"> The embedding model trained by <a href="https://jina.ai/">Jina AI</a>. # Jina Embeddings v4: Universal Embeddings for Multimodal Multilingual Retrieval [Original Model](https://huggingface.co/jinaai/jina-embeddings-v4) | [Blog](https://jina.ai/news/jina-embeddings-v4-universal-embeddings-for-multimodal-multilingual-retrieval) | [Technical Report](https://arxiv.org/abs/2506.18902) | [API](https://jina.ai/embeddings) ## Model Overview This repository hosts a vLLM-compatible version of [`jina-embeddings-v4`](https://huggingface.co/jinaai/jina-embeddings-v4) with the **code** adapter merged into the base `Qwen2.5-VL` weights. This architecture modification enables native compatibility with vLLM without requiring custom adapter-handling code. ## Usage ```python import torch from PIL import Image from vllm import LLM from vllm.config import PoolerConfig from vllm.inputs.data import TextPrompt # Initialize model model = LLM( model="jinaai/jina-embeddings-v4-vllm-code", task="embed", override_pooler_config=PoolerConfig(pooling_type="ALL", normalize=False), dtype="float16", ) # Create text prompts query =query = "Find a function that prints a greeting message to the console" query_prompt = TextPrompt( prompt=f"Query: {query}" ) passage = "def hello_world():\n print('Hello, World!')" passage_prompt = TextPrompt( prompt=f"Passage: {passage}" ) # Create image prompt image = Image.open("<path_to_image>") image_prompt = TextPrompt( prompt="<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe the image.<|im_end|>\n", multi_modal_data={"image": image}, ) # Encode all prompts prompts = [query_prompt, passage_prompt, image_prompt] outputs = model.encode(prompts) def get_embeddings(outputs): VISION_START_TOKEN_ID, VISION_END_TOKEN_ID = 151652, 151653 embeddings = [] for output in outputs: if VISION_START_TOKEN_ID in output.prompt_token_ids: # Gather only vision tokens img_start_pos = torch.where( torch.tensor(output.prompt_token_ids) == VISION_START_TOKEN_ID )[0][0] img_end_pos = torch.where( torch.tensor(output.prompt_token_ids) == VISION_END_TOKEN_ID )[0][0] embeddings_tensor = output.outputs.data.detach().clone()[ img_start_pos : img_end_pos + 1 ] else: # Use all tokens for text-only prompts embeddings_tensor = output.outputs.data.detach().clone() # Pool and normalize embeddings pooled_output = ( embeddings_tensor.sum(dim=0, dtype=torch.float32) / embeddings_tensor.shape[0] ) embeddings.append(torch.nn.functional.normalize(pooled_output, dim=-1)) return embeddings embeddings = get_embeddings(outputs) ```

tslim1/Fin-R1-mlx-8Bit

tslim1

2025-08-19T07:36:12Z

0

mlx

[ "mlx", "safetensors", "qwen2", "base_model:SUFE-AIFLM-Lab/Fin-R1", "base_model:quantized:SUFE-AIFLM-Lab/Fin-R1", "8-bit", "region:us" ]

null

2025-08-19T07:35:28Z

--- base_model: SUFE-AIFLM-Lab/Fin-R1 tags: - mlx --- # tslim1/Fin-R1-mlx-8Bit The Model [tslim1/Fin-R1-mlx-8Bit](https://huggingface.co/tslim1/Fin-R1-mlx-8Bit) was converted to MLX format from [SUFE-AIFLM-Lab/Fin-R1](https://huggingface.co/SUFE-AIFLM-Lab/Fin-R1) using mlx-lm version **0.26.3**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("tslim1/Fin-R1-mlx-8Bit") 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) ```

hakimjustbao/blockassist-bc-raging_subtle_wasp_1755587301

hakimjustbao

2025-08-19T07:35:59Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:35:55Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755588910

0xaoyama

2025-08-19T07:35:44Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:35:32Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

lqpl/blockassist-bc-hairy_insectivorous_antelope_1755588496

lqpl

2025-08-19T07:32:28Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy insectivorous antelope", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:29:02Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hairy insectivorous antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

mang3dd/blockassist-bc-tangled_slithering_alligator_1755586902

mang3dd

2025-08-19T07:29:00Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:28:56Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

rohannath/Llama_AI_doctor_using_Unsloth

rohannath

2025-08-19T07:28:44Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-19T07:28:36Z

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** rohannath - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

lachielee/guajian

lachielee

2025-08-19T07:27:54Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:afl-3.0", "region:us" ]

text-to-image

2025-08-19T07:27:06Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/头像挂件-flux2.png text: '-' base_model: black-forest-labs/FLUX.1-dev instance_prompt: guajian license: afl-3.0 --- # flux-lora-挂件整合2 <Gallery /> ## Model description weibo头像挂件 ## Trigger words You should use `guajian` to trigger the image generation. ## Download model [Download](/lachielee/guajian/tree/main) them in the Files & versions tab.

ihsanridzi/blockassist-bc-wiry_flexible_owl_1755586744

ihsanridzi

2025-08-19T07:26:05Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:26:00Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755588318

0xaoyama

2025-08-19T07:25:50Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:25:38Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

dgambettaphd/M_mis_run2_gen5_WXS_doc1000_synt64_lr1e-04_acm_MPP

dgambettaphd

2025-08-19T07:25:36Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-19T07:25:22Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

donoway/ARC-Challenge_Llama-3.2-1B-654y06oc

donoway

2025-08-19T07:23:39Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T07:13:04Z

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Challenge_Llama-3.2-1B-654y06oc results: [] ---  # ARC-Challenge_Llama-3.2-1B-654y06oc This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3954 - Model Preparation Time: 0.0058 - Mdl: 1033.3076 - Accumulated Loss: 716.2342 - Correct Preds: 73.0 - Total Preds: 299.0 - Accuracy: 0.2441 - Correct Gen Preds: 73.0 - Gen Accuracy: 0.2441 - Correct Gen Preds 32: 0.0 - Correct Preds 32: 0.0 - Total Labels 32: 64.0 - Accuracy 32: 0.0 - Gen Accuracy 32: 0.0 - Correct Gen Preds 33: 72.0 - Correct Preds 33: 72.0 - Total Labels 33: 73.0 - Accuracy 33: 0.9863 - Gen Accuracy 33: 0.9863 - Correct Gen Preds 34: 0.0 - Correct Preds 34: 0.0 - Total Labels 34: 78.0 - Accuracy 34: 0.0 - Gen Accuracy 34: 0.0 - Correct Gen Preds 35: 1.0 - Correct Preds 35: 1.0 - Total Labels 35: 83.0 - Accuracy 35: 0.0120 - Gen Accuracy 35: 0.0120 - Correct Gen Preds 36: 0.0 - Correct Preds 36: 0.0 - Total Labels 36: 1.0 - Accuracy 36: 0.0 - Gen Accuracy 36: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 32 | Correct Preds 32 | Total Labels 32 | Accuracy 32 | Gen Accuracy 32 | Correct Gen Preds 33 | Correct Preds 33 | Total Labels 33 | Accuracy 33 | Gen Accuracy 33 | Correct Gen Preds 34 | Correct Preds 34 | Total Labels 34 | Accuracy 34 | Gen Accuracy 34 | Correct Gen Preds 35 | Correct Preds 35 | Total Labels 35 | Accuracy 35 | Gen Accuracy 35 | Correct Gen Preds 36 | Correct Preds 36 | Total Labels 36 | Accuracy 36 | Gen Accuracy 36 | |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:| | No log | 0 | 0 | 1.6389 | 0.0058 | 706.9523 | 490.0220 | 66.0 | 299.0 | 0.2207 | 66.0 | 0.2207 | 62.0 | 62.0 | 64.0 | 0.9688 | 0.9688 | 0.0 | 0.0 | 73.0 | 0.0 | 0.0 | 4.0 | 4.0 | 78.0 | 0.0513 | 0.0513 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 1.7367 | 1.0 | 1 | 1.6389 | 0.0058 | 706.9523 | 490.0220 | 66.0 | 299.0 | 0.2207 | 66.0 | 0.2207 | 62.0 | 62.0 | 64.0 | 0.9688 | 0.9688 | 0.0 | 0.0 | 73.0 | 0.0 | 0.0 | 4.0 | 4.0 | 78.0 | 0.0513 | 0.0513 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 1.7367 | 2.0 | 2 | 2.5849 | 0.0058 | 1115.0583 | 772.8995 | 72.0 | 299.0 | 0.2408 | 63.0 | 0.2107 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 63.0 | 72.0 | 73.0 | 0.9863 | 0.8630 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.686 | 3.0 | 3 | 2.3954 | 0.0058 | 1033.3076 | 716.2342 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 72.0 | 72.0 | 73.0 | 0.9863 | 0.9863 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 1.0 | 1.0 | 83.0 | 0.0120 | 0.0120 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.1308 | 4.0 | 4 | 3.5091 | 0.0058 | 1513.7269 | 1049.2355 | 66.0 | 299.0 | 0.2207 | 34.0 | 0.1137 | 27.0 | 56.0 | 64.0 | 0.875 | 0.4219 | 6.0 | 8.0 | 73.0 | 0.1096 | 0.0822 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 1.0 | 2.0 | 83.0 | 0.0241 | 0.0120 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0919 | 5.0 | 5 | 4.5350 | 0.0058 | 1956.2548 | 1355.9725 | 72.0 | 299.0 | 0.2408 | 71.0 | 0.2375 | 1.0 | 2.0 | 64.0 | 0.0312 | 0.0156 | 70.0 | 70.0 | 73.0 | 0.9589 | 0.9589 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0008 | 6.0 | 6 | 5.9663 | 0.0058 | 2573.6635 | 1783.9276 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0001 | 7.0 | 7 | 7.0163 | 0.0058 | 3026.5888 | 2097.8715 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 8.0 | 8 | 7.8549 | 0.0058 | 3388.3437 | 2348.6208 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 9.0 | 9 | 8.5290 | 0.0058 | 3679.1101 | 2550.1648 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 10.0 | 10 | 9.0813 | 0.0058 | 3917.3504 | 2715.3004 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 11.0 | 11 | 9.5223 | 0.0058 | 4107.5735 | 2847.1530 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 12.0 | 12 | 9.8450 | 0.0058 | 4246.7775 | 2943.6418 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 13.0 | 13 | 10.0820 | 0.0058 | 4349.0238 | 3014.5136 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 14.0 | 14 | 10.2630 | 0.0058 | 4427.1048 | 3068.6352 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 15.0 | 15 | 10.4135 | 0.0058 | 4492.0237 | 3113.6335 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 16.0 | 16 | 10.5454 | 0.0058 | 4548.9178 | 3153.0696 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 17.0 | 17 | 10.6501 | 0.0058 | 4594.0917 | 3184.3817 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 18.0 | 18 | 10.7377 | 0.0058 | 4631.8710 | 3210.5683 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 19.0 | 19 | 10.8058 | 0.0058 | 4661.2653 | 3230.9429 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 20.0 | 20 | 10.8522 | 0.0058 | 4681.2829 | 3244.8181 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 21.0 | 21 | 10.8886 | 0.0058 | 4696.9714 | 3255.6925 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 22.0 | 22 | 10.9239 | 0.0058 | 4712.2095 | 3266.2547 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 23.0 | 23 | 10.9471 | 0.0058 | 4722.2182 | 3273.1922 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 24.0 | 24 | 10.9741 | 0.0058 | 4733.8499 | 3281.2547 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 25.0 | 25 | 10.9917 | 0.0058 | 4741.4235 | 3286.5043 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 26.0 | 26 | 11.0067 | 0.0058 | 4747.9153 | 3291.0041 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 27.0 | 27 | 11.0245 | 0.0058 | 4755.5791 | 3296.3162 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 28.0 | 28 | 11.0307 | 0.0058 | 4758.2846 | 3298.1916 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 29.0 | 29 | 11.0362 | 0.0058 | 4760.6287 | 3299.8163 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 30.0 | 30 | 11.0439 | 0.0058 | 4763.9649 | 3302.1288 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 31.0 | 31 | 11.0487 | 0.0058 | 4766.0390 | 3303.5665 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 32.0 | 32 | 11.0546 | 0.0058 | 4768.5629 | 3305.3159 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 33.0 | 33 | 11.0604 | 0.0058 | 4771.0883 | 3307.0664 | 73.0 | 299.0 | 0.2441 | 73.0 | 0.2441 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 73.0 | 73.0 | 73.0 | 1.0 | 1.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755587787

IvanJAjebu

2025-08-19T07:17:54Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:17:30Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

donoway/ARC-Easy_Llama-3.2-1B-7kenrtho

donoway

2025-08-19T07:10:33Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T06:58:11Z

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Easy_Llama-3.2-1B-7kenrtho results: [] ---  # ARC-Easy_Llama-3.2-1B-7kenrtho This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2266 - Model Preparation Time: 0.0063 - Mdl: 1831.0526 - Accumulated Loss: 1269.1889 - Correct Preds: 351.0 - Total Preds: 570.0 - Accuracy: 0.6158 - Correct Gen Preds: 330.0 - Gen Accuracy: 0.5789 - Correct Gen Preds 32: 121.0 - Correct Preds 32: 136.0 - Total Labels 32: 158.0 - Accuracy 32: 0.8608 - Gen Accuracy 32: 0.7658 - Correct Gen Preds 33: 94.0 - Correct Preds 33: 95.0 - Total Labels 33: 152.0 - Accuracy 33: 0.625 - Gen Accuracy 33: 0.6184 - Correct Gen Preds 34: 78.0 - Correct Preds 34: 82.0 - Total Labels 34: 142.0 - Accuracy 34: 0.5775 - Gen Accuracy 34: 0.5493 - Correct Gen Preds 35: 37.0 - Correct Preds 35: 38.0 - Total Labels 35: 118.0 - Accuracy 35: 0.3220 - Gen Accuracy 35: 0.3136 - Correct Gen Preds 36: 0.0 - Correct Preds 36: 0.0 - Total Labels 36: 0.0 - Accuracy 36: 0.0 - Gen Accuracy 36: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 32 | Correct Preds 32 | Total Labels 32 | Accuracy 32 | Gen Accuracy 32 | Correct Gen Preds 33 | Correct Preds 33 | Total Labels 33 | Accuracy 33 | Gen Accuracy 33 | Correct Gen Preds 34 | Correct Preds 34 | Total Labels 34 | Accuracy 34 | Gen Accuracy 34 | Correct Gen Preds 35 | Correct Preds 35 | Total Labels 35 | Accuracy 35 | Gen Accuracy 35 | Correct Gen Preds 36 | Correct Preds 36 | Total Labels 36 | Accuracy 36 | Gen Accuracy 36 | |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:| | No log | 0 | 0 | 1.5354 | 0.0063 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3992 | 1.0 | 1 | 1.5354 | 0.0063 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3992 | 2.0 | 2 | 2.7006 | 0.0063 | 2220.8218 | 1539.3563 | 202.0 | 570.0 | 0.3544 | 202.0 | 0.3544 | 0.0 | 0.0 | 158.0 | 0.0 | 0.0 | 62.0 | 62.0 | 152.0 | 0.4079 | 0.4079 | 140.0 | 140.0 | 142.0 | 0.9859 | 0.9859 | 0.0 | 0.0 | 118.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.9024 | 3.0 | 3 | 1.3172 | 0.0063 | 1083.1641 | 750.7922 | 190.0 | 570.0 | 0.3333 | 190.0 | 0.3333 | 9.0 | 9.0 | 158.0 | 0.0570 | 0.0570 | 150.0 | 150.0 | 152.0 | 0.9868 | 0.9868 | 19.0 | 19.0 | 142.0 | 0.1338 | 0.1338 | 12.0 | 12.0 | 118.0 | 0.1017 | 0.1017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.6103 | 4.0 | 4 | 1.4635 | 0.0063 | 1203.4512 | 834.1688 | 338.0 | 570.0 | 0.5930 | 336.0 | 0.5895 | 131.0 | 133.0 | 158.0 | 0.8418 | 0.8291 | 91.0 | 91.0 | 152.0 | 0.5987 | 0.5987 | 76.0 | 76.0 | 142.0 | 0.5352 | 0.5352 | 38.0 | 38.0 | 118.0 | 0.3220 | 0.3220 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0428 | 5.0 | 5 | 2.2266 | 0.0063 | 1831.0526 | 1269.1889 | 351.0 | 570.0 | 0.6158 | 330.0 | 0.5789 | 121.0 | 136.0 | 158.0 | 0.8608 | 0.7658 | 94.0 | 95.0 | 152.0 | 0.625 | 0.6184 | 78.0 | 82.0 | 142.0 | 0.5775 | 0.5493 | 37.0 | 38.0 | 118.0 | 0.3220 | 0.3136 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0003 | 6.0 | 6 | 2.6947 | 0.0063 | 2215.9451 | 1535.9761 | 347.0 | 570.0 | 0.6088 | 306.0 | 0.5368 | 110.0 | 133.0 | 158.0 | 0.8418 | 0.6962 | 88.0 | 92.0 | 152.0 | 0.6053 | 0.5789 | 71.0 | 83.0 | 142.0 | 0.5845 | 0.5 | 37.0 | 39.0 | 118.0 | 0.3305 | 0.3136 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 7.0 | 7 | 2.8748 | 0.0063 | 2364.0644 | 1638.6446 | 343.0 | 570.0 | 0.6018 | 278.0 | 0.4877 | 95.0 | 130.0 | 158.0 | 0.8228 | 0.6013 | 78.0 | 87.0 | 152.0 | 0.5724 | 0.5132 | 67.0 | 84.0 | 142.0 | 0.5915 | 0.4718 | 38.0 | 42.0 | 118.0 | 0.3559 | 0.3220 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 8.0 | 8 | 2.9759 | 0.0063 | 2447.1750 | 1696.2525 | 336.0 | 570.0 | 0.5895 | 259.0 | 0.4544 | 87.0 | 128.0 | 158.0 | 0.8101 | 0.5506 | 72.0 | 84.0 | 152.0 | 0.5526 | 0.4737 | 61.0 | 80.0 | 142.0 | 0.5634 | 0.4296 | 39.0 | 44.0 | 118.0 | 0.3729 | 0.3305 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 9.0 | 9 | 3.0029 | 0.0063 | 2469.3675 | 1711.6351 | 331.0 | 570.0 | 0.5807 | 236.0 | 0.4140 | 78.0 | 125.0 | 158.0 | 0.7911 | 0.4937 | 64.0 | 81.0 | 152.0 | 0.5329 | 0.4211 | 57.0 | 81.0 | 142.0 | 0.5704 | 0.4014 | 37.0 | 44.0 | 118.0 | 0.3729 | 0.3136 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 10.0 | 10 | 3.0291 | 0.0063 | 2490.9245 | 1726.5773 | 320.0 | 570.0 | 0.5614 | 221.0 | 0.3877 | 74.0 | 122.0 | 158.0 | 0.7722 | 0.4684 | 59.0 | 77.0 | 152.0 | 0.5066 | 0.3882 | 56.0 | 79.0 | 142.0 | 0.5563 | 0.3944 | 32.0 | 42.0 | 118.0 | 0.3559 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 11.0 | 11 | 3.0620 | 0.0063 | 2518.0218 | 1745.3597 | 318.0 | 570.0 | 0.5579 | 213.0 | 0.3737 | 70.0 | 122.0 | 158.0 | 0.7722 | 0.4430 | 57.0 | 76.0 | 152.0 | 0.5 | 0.375 | 57.0 | 79.0 | 142.0 | 0.5563 | 0.4014 | 29.0 | 41.0 | 118.0 | 0.3475 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 12.0 | 12 | 3.1331 | 0.0063 | 2576.4547 | 1785.8623 | 314.0 | 570.0 | 0.5509 | 208.0 | 0.3649 | 68.0 | 122.0 | 158.0 | 0.7722 | 0.4304 | 55.0 | 75.0 | 152.0 | 0.4934 | 0.3618 | 57.0 | 80.0 | 142.0 | 0.5634 | 0.4014 | 28.0 | 37.0 | 118.0 | 0.3136 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 13.0 | 13 | 3.1756 | 0.0063 | 2611.4490 | 1810.1185 | 317.0 | 570.0 | 0.5561 | 205.0 | 0.3596 | 67.0 | 121.0 | 158.0 | 0.7658 | 0.4241 | 53.0 | 74.0 | 152.0 | 0.4868 | 0.3487 | 56.0 | 81.0 | 142.0 | 0.5704 | 0.3944 | 29.0 | 41.0 | 118.0 | 0.3475 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 14.0 | 14 | 3.1954 | 0.0063 | 2627.6804 | 1821.3693 | 313.0 | 570.0 | 0.5491 | 202.0 | 0.3544 | 67.0 | 121.0 | 158.0 | 0.7658 | 0.4241 | 53.0 | 73.0 | 152.0 | 0.4803 | 0.3487 | 55.0 | 79.0 | 142.0 | 0.5563 | 0.3873 | 27.0 | 40.0 | 118.0 | 0.3390 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 15.0 | 15 | 3.2397 | 0.0063 | 2664.1020 | 1846.6148 | 314.0 | 570.0 | 0.5509 | 200.0 | 0.3509 | 64.0 | 121.0 | 158.0 | 0.7658 | 0.4051 | 53.0 | 73.0 | 152.0 | 0.4803 | 0.3487 | 56.0 | 81.0 | 142.0 | 0.5704 | 0.3944 | 27.0 | 39.0 | 118.0 | 0.3305 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 16.0 | 16 | 3.2887 | 0.0063 | 2704.4351 | 1874.5716 | 314.0 | 570.0 | 0.5509 | 198.0 | 0.3474 | 64.0 | 121.0 | 158.0 | 0.7658 | 0.4051 | 53.0 | 71.0 | 152.0 | 0.4671 | 0.3487 | 55.0 | 82.0 | 142.0 | 0.5775 | 0.3873 | 26.0 | 40.0 | 118.0 | 0.3390 | 0.2203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 17.0 | 17 | 3.3231 | 0.0063 | 2732.6981 | 1894.1620 | 311.0 | 570.0 | 0.5456 | 196.0 | 0.3439 | 64.0 | 121.0 | 158.0 | 0.7658 | 0.4051 | 51.0 | 70.0 | 152.0 | 0.4605 | 0.3355 | 54.0 | 80.0 | 142.0 | 0.5634 | 0.3803 | 27.0 | 40.0 | 118.0 | 0.3390 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 18.0 | 18 | 3.3377 | 0.0063 | 2744.7374 | 1902.5070 | 310.0 | 570.0 | 0.5439 | 196.0 | 0.3439 | 64.0 | 121.0 | 158.0 | 0.7658 | 0.4051 | 52.0 | 69.0 | 152.0 | 0.4539 | 0.3421 | 53.0 | 80.0 | 142.0 | 0.5634 | 0.3732 | 27.0 | 40.0 | 118.0 | 0.3390 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 19.0 | 19 | 3.3610 | 0.0063 | 2763.8330 | 1915.7430 | 309.0 | 570.0 | 0.5421 | 197.0 | 0.3456 | 64.0 | 122.0 | 158.0 | 0.7722 | 0.4051 | 51.0 | 69.0 | 152.0 | 0.4539 | 0.3355 | 56.0 | 79.0 | 142.0 | 0.5563 | 0.3944 | 26.0 | 39.0 | 118.0 | 0.3305 | 0.2203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 20.0 | 20 | 3.3848 | 0.0063 | 2783.4671 | 1929.3524 | 311.0 | 570.0 | 0.5456 | 198.0 | 0.3474 | 66.0 | 123.0 | 158.0 | 0.7785 | 0.4177 | 51.0 | 68.0 | 152.0 | 0.4474 | 0.3355 | 54.0 | 80.0 | 142.0 | 0.5634 | 0.3803 | 27.0 | 40.0 | 118.0 | 0.3390 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 21.0 | 21 | 3.3561 | 0.0063 | 2759.8295 | 1912.9680 | 312.0 | 570.0 | 0.5474 | 200.0 | 0.3509 | 67.0 | 123.0 | 158.0 | 0.7785 | 0.4241 | 53.0 | 68.0 | 152.0 | 0.4474 | 0.3487 | 53.0 | 81.0 | 142.0 | 0.5704 | 0.3732 | 27.0 | 40.0 | 118.0 | 0.3390 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 22.0 | 22 | 3.4079 | 0.0063 | 2802.4235 | 1942.4919 | 311.0 | 570.0 | 0.5456 | 197.0 | 0.3456 | 68.0 | 123.0 | 158.0 | 0.7785 | 0.4304 | 49.0 | 70.0 | 152.0 | 0.4605 | 0.3224 | 53.0 | 79.0 | 142.0 | 0.5563 | 0.3732 | 27.0 | 39.0 | 118.0 | 0.3305 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 23.0 | 23 | 3.4059 | 0.0063 | 2800.7869 | 1941.3575 | 313.0 | 570.0 | 0.5491 | 198.0 | 0.3474 | 67.0 | 122.0 | 158.0 | 0.7722 | 0.4241 | 51.0 | 70.0 | 152.0 | 0.4605 | 0.3355 | 53.0 | 81.0 | 142.0 | 0.5704 | 0.3732 | 27.0 | 40.0 | 118.0 | 0.3390 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 24.0 | 24 | 3.4307 | 0.0063 | 2821.1525 | 1955.4739 | 312.0 | 570.0 | 0.5474 | 198.0 | 0.3474 | 67.0 | 122.0 | 158.0 | 0.7722 | 0.4241 | 50.0 | 69.0 | 152.0 | 0.4539 | 0.3289 | 53.0 | 80.0 | 142.0 | 0.5634 | 0.3732 | 28.0 | 41.0 | 118.0 | 0.3475 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 25.0 | 25 | 3.4314 | 0.0063 | 2821.7596 | 1955.8947 | 312.0 | 570.0 | 0.5474 | 199.0 | 0.3491 | 67.0 | 122.0 | 158.0 | 0.7722 | 0.4241 | 51.0 | 69.0 | 152.0 | 0.4539 | 0.3355 | 54.0 | 80.0 | 142.0 | 0.5634 | 0.3803 | 27.0 | 41.0 | 118.0 | 0.3475 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 26.0 | 26 | 3.4420 | 0.0063 | 2830.4716 | 1961.9334 | 313.0 | 570.0 | 0.5491 | 204.0 | 0.3579 | 69.0 | 122.0 | 158.0 | 0.7722 | 0.4367 | 51.0 | 70.0 | 152.0 | 0.4605 | 0.3355 | 55.0 | 80.0 | 142.0 | 0.5634 | 0.3873 | 29.0 | 41.0 | 118.0 | 0.3475 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 27.0 | 27 | 3.4460 | 0.0063 | 2833.7589 | 1964.2120 | 312.0 | 570.0 | 0.5474 | 197.0 | 0.3456 | 66.0 | 122.0 | 158.0 | 0.7722 | 0.4177 | 51.0 | 68.0 | 152.0 | 0.4474 | 0.3355 | 53.0 | 81.0 | 142.0 | 0.5704 | 0.3732 | 27.0 | 41.0 | 118.0 | 0.3475 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 28.0 | 28 | 3.4630 | 0.0063 | 2847.7515 | 1973.9109 | 313.0 | 570.0 | 0.5491 | 198.0 | 0.3474 | 68.0 | 123.0 | 158.0 | 0.7785 | 0.4304 | 49.0 | 69.0 | 152.0 | 0.4539 | 0.3224 | 52.0 | 80.0 | 142.0 | 0.5634 | 0.3662 | 29.0 | 41.0 | 118.0 | 0.3475 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 29.0 | 29 | 3.4611 | 0.0063 | 2846.1980 | 1972.8341 | 312.0 | 570.0 | 0.5474 | 199.0 | 0.3491 | 68.0 | 122.0 | 158.0 | 0.7722 | 0.4304 | 50.0 | 69.0 | 152.0 | 0.4539 | 0.3289 | 52.0 | 80.0 | 142.0 | 0.5634 | 0.3662 | 29.0 | 41.0 | 118.0 | 0.3475 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 30.0 | 30 | 3.4590 | 0.0063 | 2844.4834 | 1971.6457 | 310.0 | 570.0 | 0.5439 | 194.0 | 0.3404 | 68.0 | 122.0 | 158.0 | 0.7722 | 0.4304 | 49.0 | 68.0 | 152.0 | 0.4474 | 0.3224 | 51.0 | 80.0 | 142.0 | 0.5634 | 0.3592 | 26.0 | 40.0 | 118.0 | 0.3390 | 0.2203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 31.0 | 31 | 3.4672 | 0.0063 | 2851.2328 | 1976.3240 | 310.0 | 570.0 | 0.5439 | 195.0 | 0.3421 | 67.0 | 123.0 | 158.0 | 0.7785 | 0.4241 | 51.0 | 68.0 | 152.0 | 0.4474 | 0.3355 | 51.0 | 81.0 | 142.0 | 0.5704 | 0.3592 | 26.0 | 38.0 | 118.0 | 0.3220 | 0.2203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 32.0 | 32 | 3.4768 | 0.0063 | 2859.1086 | 1981.7830 | 309.0 | 570.0 | 0.5421 | 197.0 | 0.3456 | 67.0 | 121.0 | 158.0 | 0.7658 | 0.4241 | 50.0 | 68.0 | 152.0 | 0.4474 | 0.3289 | 54.0 | 80.0 | 142.0 | 0.5634 | 0.3803 | 26.0 | 40.0 | 118.0 | 0.3390 | 0.2203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 33.0 | 33 | 3.4676 | 0.0063 | 2851.5542 | 1976.5468 | 312.0 | 570.0 | 0.5474 | 197.0 | 0.3456 | 67.0 | 122.0 | 158.0 | 0.7722 | 0.4241 | 49.0 | 68.0 | 152.0 | 0.4474 | 0.3224 | 53.0 | 81.0 | 142.0 | 0.5704 | 0.3732 | 28.0 | 41.0 | 118.0 | 0.3475 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 34.0 | 34 | 3.4550 | 0.0063 | 2841.1333 | 1969.3235 | 310.0 | 570.0 | 0.5439 | 197.0 | 0.3456 | 69.0 | 122.0 | 158.0 | 0.7722 | 0.4367 | 49.0 | 68.0 | 152.0 | 0.4474 | 0.3224 | 53.0 | 80.0 | 142.0 | 0.5634 | 0.3732 | 26.0 | 40.0 | 118.0 | 0.3390 | 0.2203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 35.0 | 35 | 3.4710 | 0.0063 | 2854.3484 | 1978.4835 | 311.0 | 570.0 | 0.5456 | 199.0 | 0.3491 | 69.0 | 123.0 | 158.0 | 0.7785 | 0.4367 | 51.0 | 69.0 | 152.0 | 0.4539 | 0.3355 | 53.0 | 80.0 | 142.0 | 0.5634 | 0.3732 | 26.0 | 39.0 | 118.0 | 0.3305 | 0.2203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

insanesaga/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison

insanesaga

2025-08-19T07:09:35Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am nocturnal clawed bison", "unsloth", "trl", "genrl-swarm", "I am nocturnal_clawed_bison", "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

2025-05-27T13:39:41Z

--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am nocturnal clawed bison - unsloth - trl - genrl-swarm - I am nocturnal_clawed_bison licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison 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="insanesaga/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - 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}} } ```

chansung/Gemma2-2B-CCRL-CUR-BASIC-ONLY-1E

chansung

2025-08-19T07:07:20Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:chansung/verifiable-coding-problems-python-v2", "arxiv:2402.03300", "base_model:google/gemma-2-2b-it", "base_model:finetune:google/gemma-2-2b-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T01:31:09Z

--- base_model: google/gemma-2-2b-it datasets: chansung/verifiable-coding-problems-python-v2 library_name: transformers model_name: Gemma2-2B-CCRL-CUR-BASIC-ONLY-1E tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Gemma2-2B-CCRL-CUR-BASIC-ONLY-1E This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) on the [chansung/verifiable-coding-problems-python-v2](https://huggingface.co/datasets/chansung/verifiable-coding-problems-python-v2) 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="chansung/Gemma2-2B-CCRL-CUR-BASIC-ONLY-1E", 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/chansung18/huggingface/runs/g89zrlrp) 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: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - 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}} } ```

yaelahnal/blockassist-bc-mute_clawed_crab_1755587166

yaelahnal

2025-08-19T07:07:16Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:06:58Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Soham711/blenderbot-400M-friendly-chatmodel

Soham711

2025-08-19T07:05:17Z

0

1

null

[ "safetensors", "blenderbot", "text2text-generation", "conversational", "en", "base_model:facebook/blenderbot-400M-distill", "base_model:finetune:facebook/blenderbot-400M-distill", "license:mit", "region:us" ]

null

2025-08-19T06:48:35Z

--- license: mit language: - en base_model: - facebook/blenderbot-400M-distill tags: - text2text-generation - conversational ---

Setayeshk/brisc_yolo

Setayeshk

2025-08-19T07:04:59Z

0

null

[ "tensorboard", "license:cc-by-nc-nd-4.0", "region:us" ]

null

2025-08-18T21:53:10Z

--- license: cc-by-nc-nd-4.0 ---

hakimjustbao/blockassist-bc-raging_subtle_wasp_1755585395

hakimjustbao

2025-08-19T07:04:19Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T07:04:16Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

aiface/ModernBERT-large_nli

aiface

2025-08-19T07:02:21Z

0

transformers

[ "transformers", "safetensors", "modernbert", "text-classification", "generated_from_trainer", "base_model:answerdotai/ModernBERT-large", "base_model:finetune:answerdotai/ModernBERT-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-19T03:22:37Z

--- library_name: transformers license: apache-2.0 base_model: answerdotai/ModernBERT-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: ModernBERT-large_nli results: [] ---  # ModernBERT-large_nli This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6038 - Accuracy: 0.5787 - Precision Macro: 0.5794 - Recall Macro: 0.5790 - F1 Macro: 0.5792 - F1 Weighted: 0.5788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:| | 2.1283 | 1.0 | 143 | 1.0136 | 0.4807 | 0.4674 | 0.4835 | 0.4509 | 0.4492 | | 1.8848 | 2.0 | 286 | 0.9818 | 0.5202 | 0.5745 | 0.5219 | 0.5042 | 0.5038 | | 1.7416 | 3.0 | 429 | 1.1233 | 0.3220 | 0.2102 | 0.3259 | 0.2190 | 0.2174 | | 2.2168 | 4.0 | 572 | 1.1135 | 0.3277 | 0.1092 | 0.3333 | 0.1646 | 0.1618 | | 2.2099 | 5.0 | 715 | 1.1089 | 0.3277 | 0.1092 | 0.3333 | 0.1646 | 0.1618 | | 2.2191 | 6.0 | 858 | 1.1231 | 0.3282 | 0.4426 | 0.3338 | 0.1655 | 0.1627 | | 2.2027 | 7.0 | 1001 | 1.0931 | 0.3774 | 0.2508 | 0.3801 | 0.3016 | 0.2993 | | 2.1846 | 8.0 | 1144 | 1.0723 | 0.4013 | 0.3861 | 0.3995 | 0.3692 | 0.3705 | | 2.1232 | 9.0 | 1287 | 1.0461 | 0.4244 | 0.4225 | 0.4248 | 0.4203 | 0.4202 | | 2.0586 | 10.0 | 1430 | 1.0345 | 0.4510 | 0.4495 | 0.4494 | 0.4210 | 0.4220 | | 2.0578 | 11.0 | 1573 | 1.0390 | 0.4523 | 0.4797 | 0.4511 | 0.4522 | 0.4525 | | 2.0289 | 12.0 | 1716 | 1.0626 | 0.4665 | 0.5296 | 0.4668 | 0.4391 | 0.4389 | | 1.5688 | 13.0 | 1859 | 0.8686 | 0.6084 | 0.6082 | 0.6089 | 0.6064 | 0.6061 | | 1.2262 | 14.0 | 2002 | 0.9452 | 0.5973 | 0.5972 | 0.5978 | 0.5961 | 0.5958 | | 0.6694 | 15.0 | 2145 | 1.2849 | 0.5809 | 0.5809 | 0.5817 | 0.5802 | 0.5798 | | 0.2152 | 16.0 | 2288 | 1.9241 | 0.5752 | 0.5760 | 0.5753 | 0.5755 | 0.5753 | | 0.043 | 17.0 | 2431 | 2.3196 | 0.5672 | 0.5685 | 0.5673 | 0.5675 | 0.5672 | | 0.0074 | 18.0 | 2574 | 2.5393 | 0.5734 | 0.5747 | 0.5736 | 0.5740 | 0.5737 | | 0.0015 | 19.0 | 2717 | 2.5970 | 0.5769 | 0.5780 | 0.5772 | 0.5776 | 0.5772 | | 0.002 | 20.0 | 2860 | 2.6038 | 0.5787 | 0.5794 | 0.5790 | 0.5792 | 0.5788 | ### Framework versions - Transformers 4.55.0 - Pytorch 2.7.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

mradermacher/Mini-AGI-4B-i1-GGUF

mradermacher

2025-08-19T07:00:16Z

0

1

transformers

[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-08-19T03:54:34Z

--- base_model: Guilherme34/Mini-AGI-4B language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About         weighted/imatrix quants of https://huggingface.co/Guilherme34/Mini-AGI-4B  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Mini-AGI-4B-i1-GGUF).*** static quants are available at https://huggingface.co/mradermacher/Mini-AGI-4B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.2 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.2 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.3 | | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.5 | | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ2_S.gguf) | i1-IQ2_S | 1.5 | | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ2_M.gguf) | i1-IQ2_M | 1.6 | | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.7 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q2_K.gguf) | i1-Q2_K | 1.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.9 | | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 2.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ3_S.gguf) | i1-IQ3_S | 2.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ3_M.gguf) | i1-IQ3_M | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.2 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.3 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q4_0.gguf) | i1-Q4_0 | 2.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.5 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.5 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q4_1.gguf) | i1-Q4_1 | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Mini-AGI-4B-i1-GGUF/resolve/main/Mini-AGI-4B.i1-Q6_K.gguf) | i1-Q6_K | 3.4 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

jtekt-physical-ai/lerobot_actv2

jtekt-physical-ai

2025-08-19T06:59:15Z

0

lerobot

[ "lerobot", "safetensors", "robotics", "act", "dataset:yurayuray/retainer_mizoguchi3", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]

robotics

2025-08-19T05:57:00Z

--- datasets: yurayuray/retainer_mizoguchi3 library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - lerobot - robotics - act --- # Model Card for act  [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 lerobot-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 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

donoway/ARC-Easy_Llama-3.2-1B-xl28q3hn

donoway

2025-08-19T06:57:36Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T06:46:08Z

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Easy_Llama-3.2-1B-xl28q3hn results: [] ---  # ARC-Easy_Llama-3.2-1B-xl28q3hn This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2555 - Model Preparation Time: 0.006 - Mdl: 1032.4540 - Accumulated Loss: 715.6426 - Correct Preds: 291.0 - Total Preds: 570.0 - Accuracy: 0.5105 - Correct Gen Preds: 291.0 - Gen Accuracy: 0.5105 - Correct Gen Preds 32: 98.0 - Correct Preds 32: 98.0 - Total Labels 32: 158.0 - Accuracy 32: 0.6203 - Gen Accuracy 32: 0.6203 - Correct Gen Preds 33: 130.0 - Correct Preds 33: 130.0 - Total Labels 33: 152.0 - Accuracy 33: 0.8553 - Gen Accuracy 33: 0.8553 - Correct Gen Preds 34: 40.0 - Correct Preds 34: 40.0 - Total Labels 34: 142.0 - Accuracy 34: 0.2817 - Gen Accuracy 34: 0.2817 - Correct Gen Preds 35: 23.0 - Correct Preds 35: 23.0 - Total Labels 35: 118.0 - Accuracy 35: 0.1949 - Gen Accuracy 35: 0.1949 - Correct Gen Preds 36: 0.0 - Correct Preds 36: 0.0 - Total Labels 36: 0.0 - Accuracy 36: 0.0 - Gen Accuracy 36: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 32 | Correct Preds 32 | Total Labels 32 | Accuracy 32 | Gen Accuracy 32 | Correct Gen Preds 33 | Correct Preds 33 | Total Labels 33 | Accuracy 33 | Gen Accuracy 33 | Correct Gen Preds 34 | Correct Preds 34 | Total Labels 34 | Accuracy 34 | Gen Accuracy 34 | Correct Gen Preds 35 | Correct Preds 35 | Total Labels 35 | Accuracy 35 | Gen Accuracy 35 | Correct Gen Preds 36 | Correct Preds 36 | Total Labels 36 | Accuracy 36 | Gen Accuracy 36 | |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:| | No log | 0 | 0 | 1.5354 | 0.006 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3552 | 1.0 | 1 | 1.5354 | 0.006 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3552 | 2.0 | 2 | 2.4687 | 0.006 | 2030.1287 | 1407.1780 | 221.0 | 570.0 | 0.3877 | 221.0 | 0.3877 | 0.0 | 0.0 | 158.0 | 0.0 | 0.0 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 136.0 | 136.0 | 142.0 | 0.9577 | 0.9577 | 0.0 | 0.0 | 118.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.7603 | 3.0 | 3 | 1.2555 | 0.006 | 1032.4540 | 715.6426 | 291.0 | 570.0 | 0.5105 | 291.0 | 0.5105 | 98.0 | 98.0 | 158.0 | 0.6203 | 0.6203 | 130.0 | 130.0 | 152.0 | 0.8553 | 0.8553 | 40.0 | 40.0 | 142.0 | 0.2817 | 0.2817 | 23.0 | 23.0 | 118.0 | 0.1949 | 0.1949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.4267 | 4.0 | 4 | 2.5733 | 0.006 | 2116.1258 | 1466.7867 | 261.0 | 570.0 | 0.4579 | 260.0 | 0.4561 | 151.0 | 152.0 | 158.0 | 0.9620 | 0.9557 | 39.0 | 39.0 | 152.0 | 0.2566 | 0.2566 | 42.0 | 42.0 | 142.0 | 0.2958 | 0.2958 | 28.0 | 28.0 | 118.0 | 0.2373 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0491 | 5.0 | 5 | 3.1596 | 0.006 | 2598.2545 | 1800.9728 | 284.0 | 570.0 | 0.4982 | 284.0 | 0.4982 | 151.0 | 151.0 | 158.0 | 0.9557 | 0.9557 | 56.0 | 56.0 | 152.0 | 0.3684 | 0.3684 | 50.0 | 50.0 | 142.0 | 0.3521 | 0.3521 | 27.0 | 27.0 | 118.0 | 0.2288 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0044 | 6.0 | 6 | 4.0391 | 0.006 | 3321.5305 | 2302.3095 | 262.0 | 570.0 | 0.4596 | 259.0 | 0.4544 | 151.0 | 152.0 | 158.0 | 0.9620 | 0.9557 | 41.0 | 41.0 | 152.0 | 0.2697 | 0.2697 | 44.0 | 45.0 | 142.0 | 0.3169 | 0.3099 | 23.0 | 24.0 | 118.0 | 0.2034 | 0.1949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0001 | 7.0 | 7 | 4.4151 | 0.006 | 3630.7350 | 2516.6338 | 253.0 | 570.0 | 0.4439 | 239.0 | 0.4193 | 144.0 | 152.0 | 158.0 | 0.9620 | 0.9114 | 36.0 | 38.0 | 152.0 | 0.25 | 0.2368 | 38.0 | 41.0 | 142.0 | 0.2887 | 0.2676 | 21.0 | 22.0 | 118.0 | 0.1864 | 0.1780 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 8.0 | 8 | 4.5569 | 0.006 | 3747.3361 | 2597.4554 | 250.0 | 570.0 | 0.4386 | 223.0 | 0.3912 | 135.0 | 154.0 | 158.0 | 0.9747 | 0.8544 | 35.0 | 38.0 | 152.0 | 0.25 | 0.2303 | 35.0 | 39.0 | 142.0 | 0.2746 | 0.2465 | 18.0 | 19.0 | 118.0 | 0.1610 | 0.1525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 9.0 | 9 | 4.6453 | 0.006 | 3819.9784 | 2647.8072 | 247.0 | 570.0 | 0.4333 | 204.0 | 0.3579 | 123.0 | 152.0 | 158.0 | 0.9620 | 0.7785 | 33.0 | 39.0 | 152.0 | 0.2566 | 0.2171 | 31.0 | 37.0 | 142.0 | 0.2606 | 0.2183 | 17.0 | 19.0 | 118.0 | 0.1610 | 0.1441 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0001 | 10.0 | 10 | 4.8047 | 0.006 | 3951.0414 | 2738.6532 | 242.0 | 570.0 | 0.4246 | 203.0 | 0.3561 | 123.0 | 152.0 | 158.0 | 0.9620 | 0.7785 | 35.0 | 39.0 | 152.0 | 0.2566 | 0.2303 | 30.0 | 33.0 | 142.0 | 0.2324 | 0.2113 | 15.0 | 18.0 | 118.0 | 0.1525 | 0.1271 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 11.0 | 11 | 5.0241 | 0.006 | 4131.5031 | 2863.7397 | 236.0 | 570.0 | 0.4140 | 201.0 | 0.3526 | 125.0 | 153.0 | 158.0 | 0.9684 | 0.7911 | 34.0 | 37.0 | 152.0 | 0.2434 | 0.2237 | 28.0 | 29.0 | 142.0 | 0.2042 | 0.1972 | 14.0 | 17.0 | 118.0 | 0.1441 | 0.1186 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 12.0 | 12 | 5.2229 | 0.006 | 4295.0154 | 2977.0778 | 235.0 | 570.0 | 0.4123 | 203.0 | 0.3561 | 129.0 | 154.0 | 158.0 | 0.9747 | 0.8165 | 32.0 | 36.0 | 152.0 | 0.2368 | 0.2105 | 28.0 | 29.0 | 142.0 | 0.2042 | 0.1972 | 14.0 | 16.0 | 118.0 | 0.1356 | 0.1186 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 13.0 | 13 | 5.3741 | 0.006 | 4419.3154 | 3063.2360 | 235.0 | 570.0 | 0.4123 | 202.0 | 0.3544 | 129.0 | 155.0 | 158.0 | 0.9810 | 0.8165 | 31.0 | 35.0 | 152.0 | 0.2303 | 0.2039 | 28.0 | 29.0 | 142.0 | 0.2042 | 0.1972 | 14.0 | 16.0 | 118.0 | 0.1356 | 0.1186 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 14.0 | 14 | 5.5052 | 0.006 | 4527.0926 | 3137.9415 | 235.0 | 570.0 | 0.4123 | 207.0 | 0.3632 | 135.0 | 156.0 | 158.0 | 0.9873 | 0.8544 | 31.0 | 35.0 | 152.0 | 0.2303 | 0.2039 | 27.0 | 28.0 | 142.0 | 0.1972 | 0.1901 | 14.0 | 16.0 | 118.0 | 0.1356 | 0.1186 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 15.0 | 15 | 5.5976 | 0.006 | 4603.0781 | 3190.6106 | 234.0 | 570.0 | 0.4105 | 207.0 | 0.3632 | 135.0 | 156.0 | 158.0 | 0.9873 | 0.8544 | 32.0 | 35.0 | 152.0 | 0.2303 | 0.2105 | 26.0 | 28.0 | 142.0 | 0.1972 | 0.1831 | 14.0 | 15.0 | 118.0 | 0.1271 | 0.1186 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 16.0 | 16 | 5.6853 | 0.006 | 4675.2022 | 3240.6032 | 228.0 | 570.0 | 0.4 | 206.0 | 0.3614 | 138.0 | 155.0 | 158.0 | 0.9810 | 0.8734 | 29.0 | 32.0 | 152.0 | 0.2105 | 0.1908 | 26.0 | 27.0 | 142.0 | 0.1901 | 0.1831 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 17.0 | 17 | 5.7800 | 0.006 | 4753.1165 | 3294.6093 | 228.0 | 570.0 | 0.4 | 207.0 | 0.3632 | 141.0 | 156.0 | 158.0 | 0.9873 | 0.8924 | 29.0 | 31.0 | 152.0 | 0.2039 | 0.1908 | 24.0 | 27.0 | 142.0 | 0.1901 | 0.1690 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 18.0 | 18 | 5.8437 | 0.006 | 4805.4763 | 3330.9024 | 227.0 | 570.0 | 0.3982 | 207.0 | 0.3632 | 141.0 | 156.0 | 158.0 | 0.9873 | 0.8924 | 29.0 | 30.0 | 152.0 | 0.1974 | 0.1908 | 24.0 | 27.0 | 142.0 | 0.1901 | 0.1690 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 19.0 | 19 | 5.9488 | 0.006 | 4891.9541 | 3390.8442 | 226.0 | 570.0 | 0.3965 | 206.0 | 0.3614 | 141.0 | 156.0 | 158.0 | 0.9873 | 0.8924 | 28.0 | 29.0 | 152.0 | 0.1908 | 0.1842 | 24.0 | 27.0 | 142.0 | 0.1901 | 0.1690 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 20.0 | 20 | 5.9804 | 0.006 | 4917.8580 | 3408.7994 | 226.0 | 570.0 | 0.3965 | 206.0 | 0.3614 | 141.0 | 156.0 | 158.0 | 0.9873 | 0.8924 | 28.0 | 29.0 | 152.0 | 0.1908 | 0.1842 | 24.0 | 27.0 | 142.0 | 0.1901 | 0.1690 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 21.0 | 21 | 6.0239 | 0.006 | 4953.6373 | 3433.5997 | 226.0 | 570.0 | 0.3965 | 206.0 | 0.3614 | 141.0 | 156.0 | 158.0 | 0.9873 | 0.8924 | 28.0 | 29.0 | 152.0 | 0.1908 | 0.1842 | 24.0 | 27.0 | 142.0 | 0.1901 | 0.1690 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 22.0 | 22 | 6.0758 | 0.006 | 4996.3676 | 3463.2181 | 225.0 | 570.0 | 0.3947 | 206.0 | 0.3614 | 141.0 | 156.0 | 158.0 | 0.9873 | 0.8924 | 28.0 | 29.0 | 152.0 | 0.1908 | 0.1842 | 24.0 | 26.0 | 142.0 | 0.1831 | 0.1690 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 23.0 | 23 | 6.0958 | 0.006 | 5012.8294 | 3474.6285 | 225.0 | 570.0 | 0.3947 | 207.0 | 0.3632 | 141.0 | 156.0 | 158.0 | 0.9873 | 0.8924 | 28.0 | 29.0 | 152.0 | 0.1908 | 0.1842 | 25.0 | 26.0 | 142.0 | 0.1831 | 0.1761 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 24.0 | 24 | 6.1508 | 0.006 | 5057.9994 | 3505.9380 | 225.0 | 570.0 | 0.3947 | 209.0 | 0.3667 | 144.0 | 156.0 | 158.0 | 0.9873 | 0.9114 | 28.0 | 29.0 | 152.0 | 0.1908 | 0.1842 | 24.0 | 26.0 | 142.0 | 0.1831 | 0.1690 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 25.0 | 25 | 6.1477 | 0.006 | 5055.4455 | 3504.1678 | 224.0 | 570.0 | 0.3930 | 208.0 | 0.3649 | 143.0 | 156.0 | 158.0 | 0.9873 | 0.9051 | 27.0 | 28.0 | 152.0 | 0.1842 | 0.1776 | 25.0 | 26.0 | 142.0 | 0.1831 | 0.1761 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 26.0 | 26 | 6.1921 | 0.006 | 5092.0041 | 3529.5083 | 224.0 | 570.0 | 0.3930 | 208.0 | 0.3649 | 144.0 | 156.0 | 158.0 | 0.9873 | 0.9114 | 26.0 | 27.0 | 152.0 | 0.1776 | 0.1711 | 25.0 | 27.0 | 142.0 | 0.1901 | 0.1761 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 27.0 | 27 | 6.2041 | 0.006 | 5101.8523 | 3536.3346 | 224.0 | 570.0 | 0.3930 | 208.0 | 0.3649 | 144.0 | 156.0 | 158.0 | 0.9873 | 0.9114 | 26.0 | 27.0 | 152.0 | 0.1776 | 0.1711 | 25.0 | 27.0 | 142.0 | 0.1901 | 0.1761 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 28.0 | 28 | 6.2060 | 0.006 | 5103.4059 | 3537.4114 | 225.0 | 570.0 | 0.3947 | 208.0 | 0.3649 | 144.0 | 156.0 | 158.0 | 0.9873 | 0.9114 | 27.0 | 28.0 | 152.0 | 0.1842 | 0.1776 | 24.0 | 27.0 | 142.0 | 0.1901 | 0.1690 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 29.0 | 29 | 6.2192 | 0.006 | 5114.2474 | 3544.9262 | 225.0 | 570.0 | 0.3947 | 209.0 | 0.3667 | 145.0 | 156.0 | 158.0 | 0.9873 | 0.9177 | 27.0 | 28.0 | 152.0 | 0.1842 | 0.1776 | 24.0 | 27.0 | 142.0 | 0.1901 | 0.1690 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 30.0 | 30 | 6.2327 | 0.006 | 5125.3556 | 3552.6258 | 221.0 | 570.0 | 0.3877 | 206.0 | 0.3614 | 144.0 | 156.0 | 158.0 | 0.9873 | 0.9114 | 26.0 | 27.0 | 152.0 | 0.1776 | 0.1711 | 23.0 | 25.0 | 142.0 | 0.1761 | 0.1620 | 13.0 | 13.0 | 118.0 | 0.1102 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 31.0 | 31 | 6.2450 | 0.006 | 5135.5071 | 3559.6623 | 222.0 | 570.0 | 0.3895 | 206.0 | 0.3614 | 144.0 | 156.0 | 158.0 | 0.9873 | 0.9114 | 26.0 | 27.0 | 152.0 | 0.1776 | 0.1711 | 23.0 | 26.0 | 142.0 | 0.1831 | 0.1620 | 13.0 | 13.0 | 118.0 | 0.1102 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 32.0 | 32 | 6.2478 | 0.006 | 5137.7630 | 3561.2259 | 224.0 | 570.0 | 0.3930 | 210.0 | 0.3684 | 146.0 | 156.0 | 158.0 | 0.9873 | 0.9241 | 27.0 | 28.0 | 152.0 | 0.1842 | 0.1776 | 24.0 | 26.0 | 142.0 | 0.1831 | 0.1690 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0 | 33.0 | 33 | 6.2653 | 0.006 | 5152.1581 | 3571.2038 | 224.0 | 570.0 | 0.3930 | 209.0 | 0.3667 | 146.0 | 156.0 | 158.0 | 0.9873 | 0.9241 | 26.0 | 27.0 | 152.0 | 0.1776 | 0.1711 | 24.0 | 27.0 | 142.0 | 0.1901 | 0.1690 | 13.0 | 14.0 | 118.0 | 0.1186 | 0.1102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

resistz/sft_Qwen3-8B-Base_ultra200k_lora32

resistz

2025-08-19T06:55:24Z

0

peft

[ "peft", "safetensors", "base_model:adapter:Qwen/Qwen3-8B-Base", "lora", "sft", "trl", "text-generation", "conversational", "base_model:Qwen/Qwen3-8B-Base", "region:us" ]

text-generation

2025-08-19T06:54:12Z

--- library_name: peft model_name: sft_Qwen3-8B-Base_ultra200k_lora32 tags: - base_model:adapter:Qwen/Qwen3-8B-Base - lora - sft - trl licence: license pipeline_tag: text-generation base_model: Qwen/Qwen3-8B-Base --- # Model Card for sft_Qwen3-8B-Base_ultra200k_lora32 This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## 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/resistzzz97/Alignment_Influence/runs/9rimz0x9) This model was trained with SFT. ### Framework versions - PEFT 0.17.0 - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.7.1 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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}} } ```

VoilaRaj/78_4Qc4dT

VoilaRaj

2025-08-19T06:51:53Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-08-19T06:48:04Z

--- 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).

MitsuiChen14/DGTRS-CLIP-ViT-B-16

MitsuiChen14

2025-08-19T06:51:31Z

0

null

[ "region:us" ]

null

2025-03-26T06:49:13Z

https://github.com/MitsuiChen14/LRSCLIP?tab=readme-ov-file#-usage-

deepkeep-ai/public-classification-qwen3-0.6B-contrastive-classifier

deepkeep-ai

2025-08-19T06:51:02Z

35

0

transformers

[ "transformers", "safetensors", "contrastive-wrapper", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-13T11:12:07Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

hdong0/deepseek-Qwen-1.5B-baseline-thin-Open-R1-GRPO_deepscaler_mu_8_constant_lr_warmed

hdong0

2025-08-19T06:48:29Z

0

transformers

[ "transformers", "safetensors", "qwen2bm", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "custom_code", "dataset:agentica-org/DeepScaleR-Preview-Dataset", "arxiv:2402.03300", "base_model:hdong0/deepseek-Qwen2.5-1.5B-baseline-thin-init", "base_model:finetune:hdong0/deepseek-Qwen2.5-1.5B-baseline-thin-init", "autotrain_compatible", "region:us" ]

text-generation

2025-08-18T23:10:15Z

--- base_model: hdong0/deepseek-Qwen2.5-1.5B-baseline-thin-init datasets: agentica-org/DeepScaleR-Preview-Dataset library_name: transformers model_name: deepseek-Qwen-1.5B-baseline-thin-Open-R1-GRPO_deepscaler_mu_8_constant_lr_warmed tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for deepseek-Qwen-1.5B-baseline-thin-Open-R1-GRPO_deepscaler_mu_8_constant_lr_warmed This model is a fine-tuned version of [hdong0/deepseek-Qwen2.5-1.5B-baseline-thin-init](https://huggingface.co/hdong0/deepseek-Qwen2.5-1.5B-baseline-thin-init) on the [agentica-org/DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-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="hdong0/deepseek-Qwen-1.5B-baseline-thin-Open-R1-GRPO_deepscaler_mu_8_constant_lr_warmed", 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: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755585419

IvanJAjebu

2025-08-19T06:38:43Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T06:38:34Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

VoilaRaj/78_l9bzGb

VoilaRaj

2025-08-19T06:35:45Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-08-19T06:31:52Z

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

ianmathu/Llama-3.2-3B-Instruct-unsloth-bnb-4bit-alpaca

ianmathu

2025-08-19T06:34:22Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-19T06:33:35Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

hakimjustbao/blockassist-bc-raging_subtle_wasp_1755583495

hakimjustbao

2025-08-19T06:32:50Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T06:32:46Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

shiimi/labse-dhivehi-finetuned

shiimi

2025-08-19T06:28:01Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "dense", "generated_from_trainer", "dataset_size:968266", "loss:CosineSimilarityLoss", "arxiv:1908.10084", "base_model:sentence-transformers/LaBSE", "base_model:finetune:sentence-transformers/LaBSE", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-08-19T05:46:17Z

--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:968266 - loss:CosineSimilarityLoss base_model: sentence-transformers/LaBSE widget: - source_sentence: ކުއްލިއަކަށް ދޮންބެ ތެދުވެ އިނދެ ދެފައި ވައްކޮއްލިއެވެ. ދެލޯ ބޮޑުކޮއްގެން ހުރެ ހެވެމުން ދިލެމުން ގޮސް އަހަރެން ހުޅުވާލީވެސް ދޮންބެ ބުނި ކަބަޑެވެ. ގެރިގުއި ކުލައިގެ ކަރުދާހަކުން ބަންދުކޮއްފައި އޮތް ފޮށިގަނޑެއް ފެނުމާއި އެކު އަހަރެންނަށް ބަލާލެވުނީ ގޮދަނޑިމަތީގައި ދެފައި ވަށްކޮއްގެން އިން ބޭބެ އާއި ދިމާއަށެވެ. sentences: - sheet covering coffin - The king's kidneys, heart and lungs have also stopped working, Saudi health officials said, according to Press TV. - The Civil Court of Maldives has ordered the seizure of passports and freezing bank accounts belonging to Haulath Faheem, wife of former President Dr. Mohamed Jamil, as well as seven other members of his family in connection with a case of proven debt. This was decided by the court today after an action filed by Mohammad Aniis who served as General Manager at four resorts owned by Three A Company when it was not being divided into shares. The heir was not present at the court. The lawyer for the heirs said that he has appealed to the High Court against this decision. In any case of proven debt, it is a common practice in courts to hold passports and freeze accounts as part of an application for enforcement of judgment when there are no payments made by debtors. The family appealed the Civil Court’s order to pay them back, which was then reviewed by the Supreme Court. In addition to the three charges, Anies also brought another two cases against Musa Fahim’s heirs. The other accused are Haulat and Shaheed as well as Farida Ibrahim, Ahmad Shahid Shiyam, Ali Shiyam, Hassan Shiyam, Maryam Shifa and Aimanat Ashfah. The two brothers’ son Anies said he owes the company 1.8 million rupees for days when senior management was not paid due to problems arising from the split of Three Airline Company Ltd (THAC). The order was issued in response to a case filed by Anis at the Civil Court on May 15, requesting payment of Rs.731,540.80 due from his family following an appeal ruling made on February 17 this year. He said that no appeal had been lodged against the judgment for over ninety days and he is still waiting for the decision to be announced. - source_sentence: 24 ޖުލައި 2013 ގައި ޖޯން ހޮޖްމަން މެކްސިމަމް ފަން ޕޮޑްކާސްޓް `` ޖަޖް ބްރަދަރ އަލީ '' އިން ފެނިގެންދިޔައީ '' އެކްސްޕާޓް ވިޓްނަސް '' ގެ ގޮތުގައެވެ . sentences: - Translate the following sentence into a different language and add a proof of the translation in the footnotes. Traer tu propia bolsa es una elección ecológica. 1 --- 1 Translation from English to Spanish using Google Translate. - The result sheet of the Ihwandu constituency, which is part of the North East District Council was lost and it has been found while reopening a ballot box. It had to be counted again after that because the results were missing. In presence of representatives from candidates who contested for this district as well as media, the election commission opened the ballot box at 8:30 p.m. today when they discovered the result sheet in another letter. The results sheet was mistakenly placed in a wrong envelope.The Election Commission decided that the ballot box did not need to be counted after seeing its result sheet.This is the first election with an issue of this kind. The Complaints Bureau has not received any complaints from the voters that would require a ballot box to be reopened, said Election Commission Director General Mohamed Sheik. The Commission said that 60 percent of the total number of results sheets, which is estimated to be around 17,000 have been cleared. - Outline the following passage I. American astronauts' exploration of the moon A. Began in 1969 B. Building of moon bases C. Driving lunar rovers on the surface D. Collection of moon samples. - source_sentence: އަދި ލަންގޭންސްޓައިންބާކް އާއި އަލަށް އުފެއްދި ޝިސްޝުޓެނަކަރ ރޭލްވޭ ސްޓޭޝަނާ ދެމެދު 2011 ވަނަ އަހަރު ކުރު ޑަބަލް ޓްރެކެއް ވެސް ހެދިއެވެ . sentences: - i told them i would personally be delighted if sia would fly to and from europe via the maldives. - A short double track was also built between Langensteinbach and the newly created Schießhüttenäcker railway station in 2011 . - Offer one suggestion to reduce cases of teenage suicide. One suggestion to reduce cases of teenage suicide could be to provide accessible and safe mental health support for teenagers. This could be in the form of school counselors, teen helplines, or mental health workshops, among other resources. By ensuring that teenagers have someone to talk to about their struggles and concerns, it can alleviate feelings of hopelessness and isolation, which are major risk factors for suicide. - source_sentence: އަޖީއެމްއެޗްގެ އަހަރި ދުވަހާއި ގުޅުވައިގެން ބާއްވާ މި ފެއާއަށް ދާ ފަރާތްތަކަށް ހިލޭ ގުލްކޯޒް، ހަކުރު، އަދި ލޭގެ ޕްރެޝަރު ހުރި މިންވަރު ބަލައިދެމުންދާ ކަމަށް އައިޖީއެމްއެޗުން ބުނެއެވެ. sentences: - A young man died in a serious accident on the road at night. The victim was identified as Hussain Adham, 21 years old from Hithadhoo. The 54-year old man died at the hospital after being treated for a heart attack. According to witnesses, the accident occurred when Adham was driving from Hittadu towards Maradu and collided with another motorbike that had been travelling along Link Road in direction of Maradu. The accident resulted in a severe fracture of his head and extensive bleeding. He was also broken his neck and a hand. "The helmet he was wearing broke and his head got injured. The injuries were severe," the witness said. Some of the victims had broken their hands and feet. A woman was among the victims. - NASA has announced that it will test a new type of flying saucer this year. It may be to bring in aliens who have not yet landed on the earth. The cup-style vehicle will be launched by what NASA calls a "low density supersonic decelerator" rocket. The rocket is scheduled to be launched in June. NASA is interested in launching a flying saucer into the atmosphere, but according to their own statements, there's no connection between aliens and NASA's Flying Saucer. NASA wants to test and demonstrate new technologies that can be used for launching objects into the atmosphere. NASA said the mission will help to estimate how much payload is needed for a manned Mars missions. - Ar.... Arfin? Are you telling the truth? Is the child so good now? How many years have passed since then... If you haven't even heard from the boy, you can hear what Asiya is saying, I really want to see you, Asiya, please come here with Arfin, if you have his number I want to call him now - source_sentence: އޭނާ ރީތި. sentences: - She's pretty. - Words of gratitude are being sent to the government and President Yameen for bringing two new generators to the village within five days. The people of Thonadhoo have shown the whole country that they have a people who love patience, unity and brotherhood. It is a beautiful example of unity. The burden and pain of the power outages is not easy for anyone to bear in such an era. - 'Date of appointment: 22 June' pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on sentence-transformers/LaBSE This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE)  - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'}) (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (3): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("shiimi/labse-dhivehi-finetuned") # Run inference sentences = [ 'އޭނާ ރީތި.', "She's pretty.", 'Words of gratitude are being sent to the government and President Yameen for bringing two new generators to the village within five days. The people of Thonadhoo have shown the whole country that they have a people who love patience, unity and brotherhood. It is a beautiful example of unity. The burden and pain of the power outages is not easy for anyone to bear in such an era.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[ 1.0000, 0.9827, -0.0089], # [ 0.9827, 1.0000, -0.0044], # [-0.0089, -0.0044, 1.0000]]) ```      ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 968,266 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | label | |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 3 tokens</li><li>mean: 121.67 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 64.68 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> | * Samples: | sentence_0 | sentence_1 | label | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | <code>އިންތިހާބު ލަސްކުރަން ބްލެޓާ ބޭނުމެއްނުވޭ: ފީފާ</code> | <code>The Ponoru River is a tributary of the Horezu in Romania .</code> | <code>0.0</code> | | <code>ޖޯ އުފަންވީ 27 މާރޗް 1929 ގައި މެސެޗުސެޓްސްގެ ސޮމަރވިލް އަށް ކަމަށާއި ބޮޑުވީ މެސެޗުސެޓްސްގެ ކުއިންސީ ގައެވެ .</code> | <code>The National Inquiry Commission set up by the government of President Mohammed Vaheed Hassan Manik has said that the coup was not a coup and that the government was overthrown according to the rules of law.</code> | <code>0.0</code> | | <code>ސާބިތު ދަރަނީގެ މައްސަލައެއްގައި ޑރ. މުހައްމަދު ޖަމީލްގެ އަނބިކަނބަލުން ހައުލަތު ފަހީމް އާއި އެ އާއިލާގެ އިތުރު ހަތް މީހެއްގެ ޕާސްޕޯޓް ހިފަހައްޓައި ބޭންކް އެކައުންޓްތައް ފްރީޒްކުރުމަށް ސިވިލް ކޯޓުން މިއަދު އަމުރު ނެރެފި އެވެ.ވީބީ އައްޑޫ އެފްސީގެ މުއައްސިސެއް ކަމަށްވާ މުހަންމަދު ޝަވީދުގެ ވެސް ބައްޕަ މަރުހޫމް މޫސާ ފަހީމްގެ އަށް ވާރިސުންގެ ޕާސްޕޯޓާއި، ރާއްޖޭގެ ބޭންކްތަކުގައި ހުރި ހުރިހާ އެކައުންޓެއް ހިފަހައްޓަން ސިވިލް ކޯޓުން މިއަދު ހެނދުނު ނިންމީ، ތްރީއޭ ކޮމްޕެނީ ނުބަހާއިރު އެ ކުންފުނީގެ ހަތަރު ރިސޯޓެއްގެ ޖެނެރަލް މެނޭޖަރެއްގެ ގޮތުގައި ވަޒީފާ އަދާކުރި މުހަންމަދު އަނީސް ކޮށްފައިވާ ދައުވާއަކާ ގުޅިގެން ބޭއްވި ޝަރީއަތުގެ އަޑުއެހުމުގަ އެވެ. އެ އަޑުއެހުމަށް ވާރިސުންގެ ފަރާތުން ހާޒިރެއް ނުވެ އެވެ. ވާރިސުންގެ ވަކީލް ވިދާޅުވީ ސިވިލް ކޯޓުގެ ހުކުމް ހައި ކޯޓަށް އިސްތިއުނާފަށް ހުށަހަޅާފައިވާ ކަމަށެވެ.ސާބިތު ދަރަނީގެ ކޮންމެ މައްސަލައެއްގައި ވެސް ދަރަނި އަދާނުކުރާ ހާލަތެއްގައި، ހުކުމް ތަންފީޒުކުރުމަށް އެދި ހުށަހަޅެމުން ޕާސްޕޯޓް ހިފަހައްޓައި އެކައުންޓުތައް ފްރީޒްކުރުމަކީ ކޯޓުން އަމަލުކުރާ އާންމު އުސޫލެވ...</code> | <code>The Civil Court of Maldives has ordered the seizure of passports and freezing bank accounts belonging to Haulath Faheem, wife of former President Dr. Mohamed Jamil, as well as seven other members of his family in connection with a case of proven debt. This was decided by the court today after an action filed by Mohammad Aniis who served as General Manager at four resorts owned by Three A Company when it was not being divided into shares. The heir was not present at the court. The lawyer for the heirs said that he has appealed to the High Court against this decision. In any case of proven debt, it is a common practice in courts to hold passports and freeze accounts as part of an application for enforcement of judgment when there are no payments made by debtors. The family appealed the Civil Court’s order to pay them back, which was then reviewed by the Supreme Court. In addition to the three charges, Anies also brought another two cases against Musa Fahim’s heirs. The other accused are ...</code> | <code>1.0</code> | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 1 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.0661 | 500 | 0.0528 | | 0.1322 | 1000 | 0.0298 | | 0.1983 | 1500 | 0.0261 | | 0.2644 | 2000 | 0.0242 | | 0.3305 | 2500 | 0.0235 | | 0.3966 | 3000 | 0.0223 | | 0.4627 | 3500 | 0.0207 | | 0.5288 | 4000 | 0.0208 | | 0.5948 | 4500 | 0.0196 | | 0.6609 | 5000 | 0.0192 | | 0.7270 | 5500 | 0.019 | | 0.7931 | 6000 | 0.0181 | | 0.8592 | 6500 | 0.0181 | | 0.9253 | 7000 | 0.0175 | | 0.9914 | 7500 | 0.0178 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 5.1.0 - Transformers: 4.55.2 - PyTorch: 2.8.0+cu128 - Accelerate: 1.9.0 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ```

ariankharazmi/Curiosity-14

ariankharazmi

2025-08-19T06:27:29Z

3

0

null

[ "safetensors", "gpt2", "research", "text-generation", "en", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "region:us" ]

text-generation

2025-04-25T03:43:28Z

--- license: mit language: - en base_model: - openai-community/gpt2 pipeline_tag: text-generation tags: - research --- Curiosity-14 is a low-level LLM. Built throughout the seven weeks of the Summer 2024 UCinci EEP, Curiosity-14 is the culmination of all of the research, coded deliverables, and painstaking patience as one final advanced deliverable.

thailevann/track8_subtask1_v3

thailevann

2025-08-19T06:26:07Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-19T02:46:57Z

--- base_model: unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** thailevann - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) 0.97

chsubhasis/finetuned_model_unsloth

chsubhasis

2025-08-19T06:17:12Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gpt_oss", "trl", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-19T06:17:02Z

--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** chsubhasis - **License:** apache-2.0 - **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss 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)

subsectmusic/qwriko4b-64k-2507-instruct

subsectmusic

2025-08-19T06:15:30Z

0

transformers

[ "transformers", "gguf", "qwen3", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-08-19T05:07:14Z

--- base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** subsectmusic - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

valuesimplex-ai-lab/FinBERT1-base

valuesimplex-ai-lab

2025-08-19T06:14:50Z

0

1

null

[ "pytorch", "safetensors", "bert", "finance", "zh", "base_model:google-bert/bert-base-chinese", "base_model:finetune:google-bert/bert-base-chinese", "license:apache-2.0", "region:us" ]

null

2025-08-17T10:03:57Z

--- license: apache-2.0 language: - zh base_model: google-bert/bert-base-chinese tags: - finance --- ## Model Details **FinBERT1-Base** is a financial domain-adapted Chinese language model. Built on Google's BERT-Base architecture, it was continually pretrained on large-scale Chinese financial corpora to enhance financial text understanding. - **Developed by:** See [valuesimplex](https://github.com/valuesimplex) for model developers - **Model Type:** Transformer-based language model - **Language(s):** Chinese - **Parent Model:** See the [bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) for more information about the BERT base model. - **Resources:** [https://github.com/valuesimplex/FinBERT](https://github.com/valuesimplex/FinBERT) ## Direct Use ```python from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("valuesimplex-ai-lab/FinBERT1-base") tokenizer = AutoTokenizer.from_pretrained("valuesimplex-ai-lab/FinBERT1-base") ``` ### Further Usage continual pre-training or fine-tuning：https://github.com/valuesimplex/FinBERT

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755583947

IvanJAjebu

2025-08-19T06:14:14Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T06:13:48Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

VoilaRaj/78_7iz0a4

VoilaRaj

2025-08-19T06:11:19Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-08-19T06:07:20Z

--- 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).

BlazePro12/merged_grok_data_mcp_2

BlazePro12

2025-08-19T06:10:59Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "endpoints_compatible", "region:us" ]

null

2025-08-19T06:08:21Z

--- base_model: google/gemma-3-270m-it library_name: transformers model_name: merged_grok_data_mcp_2 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for merged_grok_data_mcp_2 This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="BlazePro12/merged_grok_data_mcp_2", 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: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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}} } ```

yaelahnal/blockassist-bc-mute_clawed_crab_1755583572

yaelahnal

2025-08-19T06:07:21Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T06:07:03Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755582079

kojeklollipop

2025-08-19T06:06:40Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T06:06:36Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

VoilaRaj/78_dpG7CL

VoilaRaj

2025-08-19T06:03:01Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-08-19T05:59:04Z

--- 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).

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755583183

IvanJAjebu

2025-08-19T06:01:25Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T06:01:02Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755583222

0xaoyama

2025-08-19T06:01:00Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T06:00:49Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

yaelahnal/blockassist-bc-mute_clawed_crab_1755583110

yaelahnal

2025-08-19T05:59:39Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T05:59:21Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755581603

sampingkaca72

2025-08-19T05:59:01Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T05:58:58Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored stealthy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755581452

pempekmangedd

2025-08-19T05:58:35Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "patterned sturdy dolphin", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T05:58:32Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - patterned sturdy dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755581346

vwzyrraz7l

2025-08-19T05:56:20Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T05:56:17Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF

gmonsoon

2025-08-19T05:56:15Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:gmonsoon/Qwen3-4b-REnewbie-NEXT", "base_model:quantized:gmonsoon/Qwen3-4b-REnewbie-NEXT", "endpoints_compatible", "region:us" ]

null

2025-08-19T05:56:02Z

--- base_model: gmonsoon/Qwen3-4b-REnewbie-NEXT library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF This model was converted to GGUF format from [`gmonsoon/Qwen3-4b-REnewbie-NEXT`](https://huggingface.co/gmonsoon/Qwen3-4b-REnewbie-NEXT) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/gmonsoon/Qwen3-4b-REnewbie-NEXT) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF --hf-file qwen3-4b-renewbie-next-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF --hf-file qwen3-4b-renewbie-next-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF --hf-file qwen3-4b-renewbie-next-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF --hf-file qwen3-4b-renewbie-next-q4_k_m.gguf -c 2048 ```

mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF

mradermacher

2025-08-19T05:56:03Z

0

1

transformers

[ "transformers", "gguf", "en", "base_model:morganstanley/qqWen-14B-RL-Reasoning", "base_model:quantized:morganstanley/qqWen-14B-RL-Reasoning", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-08-18T17:25:35Z

--- base_model: morganstanley/qqWen-14B-RL-Reasoning language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About         weighted/imatrix quants of https://huggingface.co/morganstanley/qqWen-14B-RL-Reasoning  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#qqWen-14B-RL-Reasoning-i1-GGUF).*** static quants are available at https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ1_S.gguf) | i1-IQ1_S | 3.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ1_M.gguf) | i1-IQ1_M | 4.0 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ2_S.gguf) | i1-IQ2_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ2_M.gguf) | i1-IQ2_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.5 | very low quality | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q2_K.gguf) | i1-Q2_K | 5.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.5 | | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ3_S.gguf) | i1-IQ3_S | 6.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.2 | | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q4_0.gguf) | i1-Q4_0 | 8.6 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.6 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q4_1.gguf) | i1-Q4_1 | 9.5 | | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.6 | | | [GGUF](https://huggingface.co/mradermacher/qqWen-14B-RL-Reasoning-i1-GGUF/resolve/main/qqWen-14B-RL-Reasoning.i1-Q6_K.gguf) | i1-Q6_K | 12.2 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755582693

IvanJAjebu

2025-08-19T05:52:54Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T05:52:45Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

wasabuko/blockassist-bc-noisy_zealous_macaw_1755580421

wasabuko

2025-08-19T05:51:01Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "noisy zealous macaw", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T05:48:16Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - noisy zealous macaw --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

taochengfei/llama-3.2-3b-it-beta_assistant_v0.2_gptq

taochengfei

2025-08-19T05:46:23Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-08-19T05:45:10Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ihsanridzi/blockassist-bc-wiry_flexible_owl_1755580729

ihsanridzi

2025-08-19T05:45:26Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T05:45:22Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

CraneAILabs/swahili-gemma-1b-GGUF

CraneAILabs

2025-08-19T05:40:52Z

0

transformers

[ "transformers", "gguf", "swahili", "translation", "conversational", "unsloth", "gemma", "gemma3", "quantized", "text-generation", "en", "sw", "base_model:CraneAILabs/swahili-gemma-1b", "base_model:quantized:CraneAILabs/swahili-gemma-1b", "license:gemma", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T07:37:28Z

--- base_model: CraneAILabs/swahili-gemma-1b language: - en - sw library_name: transformers license: gemma tags: - swahili - translation - conversational - unsloth - gemma - gemma3 - gguf - quantized pipeline_tag: text-generation --- # Swahili Gemma 1B - GGUF Quantized GGUF versions of **Swahili Gemma 1B**, a fine-tuned Gemma 3 1B instruction model specialized for **English-to-Swahili translation and Swahili conversational AI**. The model accepts input in both English and Swahili but outputs responses exclusively in Swahili. ## 📊 Translation Performance ![Translation Performance Comparison](swahili_gemma_ascending_chart.png) ### Model Comparison | Model | Parameters | BLEU | chrF++ | Efficiency* | |-------|------------|------|--------|-------------| | Gemma 3 4B | 4B | 10.9 | 44.1 | 2.7 | | **Swahili Gemma 1B** | **1B** | **27.6** | **56.8** | **27.6** | | Gemma 3 27B | 27B | 29.4 | 60.0 | 1.1 | | GPT-5 Mini | ~8B | 31.8 | 62.4 | 4.0 | | Gemini 2.0 Flash | Large | 35.6 | 64.6 | N/A | *Efficiency = BLEU Score / Parameters (in billions) ### Key Performance Insights 🎯 **Efficiency Leader**: Achieves the highest BLEU-to-parameter ratio (27.6 BLEU per billion parameters) 🚀 **Size Advantage**: Outperforms Gemma 3 4B (4x larger) by 153% on BLEU score 💎 **Competitive Quality**: Achieves 94% of Gemma 3 27B performance with 27x fewer parameters ⚡ **Practical Deployment**: Runs efficiently on consumer hardware while maintaining quality ### Evaluation Details - **Dataset**: FLORES-200 English→Swahili (1,012 translation pairs) - **Metrics**: BLEU (bilingual evaluation understudy) and chrF++ (character F-score) - **Evaluation**: Zero-shot translation performance ## 🚀 Quick Start ```bash # Download the recommended Q4_K_M quantization pip install huggingface_hub # Python download from huggingface_hub import snapshot_download snapshot_download( repo_id="CraneAILabs/swahili-gemma-1b-GGUF", local_dir="swahili-gemma-1b-GGUF", allow_patterns=["Q4_K_M/*"] # Download only Q4_K_M version ) ``` ## 📊 Available Quantizations | Quantization | Folder | File Size | Quality | Use Case | |-------------|--------|-----------|---------|----------| | `F32` | F32/ | ~3.8GB | Highest | Research & benchmarking | | `F16` | F16/ | ~1.9GB | Highest | Maximum quality inference | | `Q8_0` | Q8_0/ | ~1.0GB | Very High | Production with ample resources | | `Q5_K_M` | Q5_K_M/ | ~812MB | High | Balanced quality/size | | `Q4_K_M` | Q4_K_M/ | ~769MB | Good | **Recommended** for most users | | `Q4_K_S` | Q4_K_S/ | ~745MB | Good | Resource-constrained environments | | `Q3_K_M` | Q3_K_M/ | ~689MB | Fair | Mobile/edge deployment | | `Q2_K` | Q2_K/ | ~658MB | Lower | Minimal resource usage | ## 💻 Usage with llama.cpp ### Basic Translation ```bash # English to Swahili translation ./llama-cli \ --model swahili-gemma-1b-GGUF/Q4_K_M/swahili-gemma-1b-q4_k_m.gguf \ --prompt "Translate to Swahili: Hello, how are you today?" \ --temp 0.3 \ --top-p 0.95 \ --top-k 64 \ --repeat-penalty 1.1 \ -n 128 ``` ## 🔧 Usage with Ollama ```bash # Create model from GGUF ollama create swahili-gemma-1b -f Modelfile # Use for translation ollama run swahili-gemma-1b "Translate to Swahili: Good morning!" # Use for conversation ollama run swahili-gemma-1b "Hujambo! Je, unaweza kunisaidia?" ``` ### Modelfile Example ```dockerfile FROM swahili-gemma-1b-GGUF/Q4_K_M/swahili-gemma-1b-q4_k_m.gguf TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|> {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|> {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|> {{ .Response }}<|eot_id|>""" PARAMETER stop "<|start_header_id|>" PARAMETER stop "<|end_header_id|>" PARAMETER stop "<|eot_id|>" ``` ## 🐍 Usage with Python (llama-cpp-python) ```python from llama_cpp import Llama # Initialize model llm = Llama( model_path="swahili-gemma-1b-GGUF/Q4_K_M/swahili-gemma-1b-q4_k_m.gguf", n_ctx=2048, n_threads=8, verbose=False ) # Generate translation response = llm( "Translate to Swahili: Hello, how are you today?", max_tokens=128, temperature=0.3, top_p=0.95, top_k=64, repeat_penalty=1.1 ) print(response['choices'][0]['text']) ``` ## 🌍 Language Capabilities - **Input Languages**: English + Swahili - **Output Language**: Swahili only - **Primary Focus**: English-to-Swahili translation and Swahili conversation ## 📊 Performance Metrics ### Translation Quality (BLEU Scores) | Model | BLEU Score | chrF++ | |-------|------------|--------| | **🥇 Swahili Gemma 1B** | **23.64** | **52.26** | | 🥈 ChatGPT-4o-latest | [TBD] | [TBD] | | 🥉 Other Models | [TBD] | [TBD] | *Evaluated on 1,012 English-to-Swahili translation samples.* ## 🎯 Capabilities - **Translation**: English-to-Swahili translation - **Conversational AI**: Natural dialogue in Swahili - **Summarization**: Text summarization in Swahili - **Writing**: Creative and informational writing in Swahili - **Question Answering**: General knowledge responses in Swahili ## 💡 Recommended Parameters ```bash # Optimal settings for translation tasks --temp 0.3 --top-p 0.95 --top-k 64 --repeat-penalty 1.1 --ctx-size 2048 ``` ## 🔗 Related Models - **Original Model**: [CraneAILabs/swahili-gemma-1b](https://huggingface.co/CraneAILabs/swahili-gemma-1b) - Full precision HuggingFace model - **LiteRT Mobile**: [CraneAILabs/swahili-gemma-1b-litert](https://huggingface.co/CraneAILabs/swahili-gemma-1b-litert) - Mobile deployment - **Ollama**: [crane-ai-labs/swahili-gemma-1b](https://ollama.com/crane-ai-labs/swahili-gemma-1b) - Ready-to-run models ## 🛠️ Technical Details - **Base Model**: google/gemma-3-1b-it - **Architecture**: Gemma 3 - **Context Length**: 4,096 tokens - **Quantization**: GGML format with multiple precision levels - **Compatible**: llama.cpp, Ollama, Jan, LM Studio, and other GGUF engines ## 🎨 Use Cases - **Offline Translation**: Run Swahili translation without internet - **Local AI Assistant**: Swahili conversational AI on your machine - **Educational Tools**: Language learning applications - **Content Creation**: Generate Swahili content locally - **Research**: Swahili language model experiments ## ⚠️ Limitations - **Language Output**: Responds only in Swahili - **Quantization Trade-offs**: Lower bit quantizations may reduce quality - **Context Limit**: 4K tokens for optimal performance - **Specialized Tasks**: May need fine-tuning for specific domains ## 📄 License This model is released under the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). Please review the terms before use. ## 🙏 Acknowledgments - **Google**: For the Gemma 3 base model, support and guidance. - **Community**: For Swahili language resources and datasets - **Gilbert Korir (Msingi AI, Nairobi, Kenya)** - **Alfred Malengo Kondoro (Hanyang University, Seoul, South Korea)** ## Citation If you use these GGUF quantizations in your research or applications, please cite: ```bibtex @misc{crane_ai_labs_2025, author = {Bakunga Bronson and Kato Steven Mubiru and Lwanga Caleb and Gimei Alex and Kavuma Lameck and Roland Ganafa and Sibomana Glorry and Atuhaire Collins and JohnRoy Nangeso and Tukamushaba Catherine}, title = {Swahili Gemma: A Fine-tuned Gemma 3 1B Model for Swahili conversational AI}, year = {2025}, url = {https://huggingface.co/CraneAILabs/swahili-gemma-1b}, organization = {Crane AI Labs} } ``` --- **Built with ❤️ by Crane AI Labs** *Swahili Gemma - Your helpful Swahili AI companion, optimized for local deployment*

SP4ND4N/SmolLM2-360M-2025-08-18_10-01-49-fp8-merged

SP4ND4N

2025-08-19T05:32:35Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "en", "base_model:unsloth/SmolLM2-360M", "base_model:finetune:unsloth/SmolLM2-360M", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T05:27:34Z

--- base_model: unsloth/SmolLM2-360M tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** SP4ND4N - **License:** apache-2.0 - **Finetuned from model :** unsloth/SmolLM2-360M This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755581389

IvanJAjebu

2025-08-19T05:31:13Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T05:31:05Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

santhosh/multilingual-e5-base-int8-ov

santhosh

2025-08-19T05:30:45Z

0

sentence-transformers

[ "sentence-transformers", "openvino", "xlm-roberta", "mteb", "Sentence Transformers", "sentence-similarity", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:2402.05672", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-08-19T05:12:22Z

--- tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers model-index: - name: multilingual-e5-base-int8-ov results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 78.97014925373135 - type: ap value: 43.69351129103008 - type: f1 value: 73.38075030070492 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.7237687366167 - type: ap value: 82.22089859962671 - type: f1 value: 69.95532758884401 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.65517241379312 - type: ap value: 28.507918657094738 - type: f1 value: 66.84516013726119 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.32976445396146 - type: ap value: 20.720481637566014 - type: f1 value: 59.78002763416003 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 90.63775 - type: ap value: 87.22277903861716 - type: f1 value: 90.60378636386807 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.546 - type: f1 value: 44.05666638370923 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.828 - type: f1 value: 41.2710255644252 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.534 - type: f1 value: 39.820743174270326 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 39.684 - type: f1 value: 39.11052682815307 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.436 - type: f1 value: 37.07082931930871 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.226000000000006 - type: f1 value: 36.65372077739185 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 22.831000000000003 - type: map_at_10 value: 36.42 - type: map_at_100 value: 37.699 - type: map_at_1000 value: 37.724000000000004 - type: map_at_3 value: 32.207 - type: map_at_5 value: 34.312 - type: mrr_at_1 value: 23.257 - type: mrr_at_10 value: 36.574 - type: mrr_at_100 value: 37.854 - type: mrr_at_1000 value: 37.878 - type: mrr_at_3 value: 32.385000000000005 - type: mrr_at_5 value: 34.48 - type: ndcg_at_1 value: 22.831000000000003 - type: ndcg_at_10 value: 44.230000000000004 - type: ndcg_at_100 value: 49.974000000000004 - type: ndcg_at_1000 value: 50.522999999999996 - type: ndcg_at_3 value: 35.363 - type: ndcg_at_5 value: 39.164 - type: precision_at_1 value: 22.831000000000003 - type: precision_at_10 value: 6.935 - type: precision_at_100 value: 0.9520000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.841 - type: precision_at_5 value: 10.754 - type: recall_at_1 value: 22.831000000000003 - type: recall_at_10 value: 69.346 - type: recall_at_100 value: 95.235 - type: recall_at_1000 value: 99.36 - type: recall_at_3 value: 44.523 - type: recall_at_5 value: 53.769999999999996 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 40.27789869854063 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 35.41979463347428 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.22752045109304 - type: mrr value: 71.51112430198303 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.71147646622866 - type: cos_sim_spearman value: 85.059167046486 - type: euclidean_pearson value: 75.88421613600647 - type: euclidean_spearman value: 75.12821787150585 - type: manhattan_pearson value: 75.22005646957604 - type: manhattan_spearman value: 74.42880434453272 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.23799582463465 - type: f1 value: 99.12665274878218 - type: precision value: 99.07098121085595 - type: recall value: 99.23799582463465 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.88685890380806 - type: f1 value: 97.59336708489249 - type: precision value: 97.44662117543473 - type: recall value: 97.88685890380806 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.47142362313821 - type: f1 value: 97.1989377670015 - type: precision value: 97.06384944001847 - type: recall value: 97.47142362313821 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.4728804634018 - type: f1 value: 98.2973494821836 - type: precision value: 98.2095839915745 - type: recall value: 98.4728804634018 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 82.74025974025975 - type: f1 value: 82.67420447730439 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.0380848063507 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 29.45956405670166 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.122 - type: map_at_10 value: 42.03 - type: map_at_100 value: 43.364000000000004 - type: map_at_1000 value: 43.474000000000004 - type: map_at_3 value: 38.804 - type: map_at_5 value: 40.585 - type: mrr_at_1 value: 39.914 - type: mrr_at_10 value: 48.227 - type: mrr_at_100 value: 49.018 - type: mrr_at_1000 value: 49.064 - type: mrr_at_3 value: 45.994 - type: mrr_at_5 value: 47.396 - type: ndcg_at_1 value: 39.914 - type: ndcg_at_10 value: 47.825 - type: ndcg_at_100 value: 52.852 - type: ndcg_at_1000 value: 54.891 - type: ndcg_at_3 value: 43.517 - type: ndcg_at_5 value: 45.493 - type: precision_at_1 value: 39.914 - type: precision_at_10 value: 8.956 - type: precision_at_100 value: 1.388 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 20.791999999999998 - type: precision_at_5 value: 14.821000000000002 - type: recall_at_1 value: 32.122 - type: recall_at_10 value: 58.294999999999995 - type: recall_at_100 value: 79.726 - type: recall_at_1000 value: 93.099 - type: recall_at_3 value: 45.017 - type: recall_at_5 value: 51.002 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.677999999999997 - type: map_at_10 value: 38.684000000000005 - type: map_at_100 value: 39.812999999999995 - type: map_at_1000 value: 39.945 - type: map_at_3 value: 35.831 - type: map_at_5 value: 37.446 - type: mrr_at_1 value: 37.771 - type: mrr_at_10 value: 44.936 - type: mrr_at_100 value: 45.583 - type: mrr_at_1000 value: 45.634 - type: mrr_at_3 value: 42.771 - type: mrr_at_5 value: 43.994 - type: ndcg_at_1 value: 37.771 - type: ndcg_at_10 value: 44.059 - type: ndcg_at_100 value: 48.192 - type: ndcg_at_1000 value: 50.375 - type: ndcg_at_3 value: 40.172000000000004 - type: ndcg_at_5 value: 41.899 - type: precision_at_1 value: 37.771 - type: precision_at_10 value: 8.286999999999999 - type: precision_at_100 value: 1.322 - type: precision_at_1000 value: 0.178 - type: precision_at_3 value: 19.406000000000002 - type: precision_at_5 value: 13.745 - type: recall_at_1 value: 29.677999999999997 - type: recall_at_10 value: 53.071 - type: recall_at_100 value: 70.812 - type: recall_at_1000 value: 84.841 - type: recall_at_3 value: 41.016000000000005 - type: recall_at_5 value: 46.22 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 42.675000000000004 - type: map_at_10 value: 53.93599999999999 - type: map_at_100 value: 54.806999999999995 - type: map_at_1000 value: 54.867 - type: map_at_3 value: 50.934000000000005 - type: map_at_5 value: 52.583 - type: mrr_at_1 value: 48.339 - type: mrr_at_10 value: 57.265 - type: mrr_at_100 value: 57.873 - type: mrr_at_1000 value: 57.906 - type: mrr_at_3 value: 55.193000000000005 - type: mrr_at_5 value: 56.303000000000004 - type: ndcg_at_1 value: 48.339 - type: ndcg_at_10 value: 59.19799999999999 - type: ndcg_at_100 value: 62.743 - type: ndcg_at_1000 value: 63.99399999999999 - type: ndcg_at_3 value: 54.367 - type: ndcg_at_5 value: 56.548 - type: precision_at_1 value: 48.339 - type: precision_at_10 value: 9.216000000000001 - type: precision_at_100 value: 1.1809999999999998 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 23.72 - type: precision_at_5 value: 16.025 - type: recall_at_1 value: 42.675000000000004 - type: recall_at_10 value: 71.437 - type: recall_at_100 value: 86.803 - type: recall_at_1000 value: 95.581 - type: recall_at_3 value: 58.434 - type: recall_at_5 value: 63.754 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.518 - type: map_at_10 value: 30.648999999999997 - type: map_at_100 value: 31.508999999999997 - type: map_at_1000 value: 31.604 - type: map_at_3 value: 28.247 - type: map_at_5 value: 29.65 - type: mrr_at_1 value: 25.650000000000002 - type: mrr_at_10 value: 32.771 - type: mrr_at_100 value: 33.554 - type: mrr_at_1000 value: 33.629999999999995 - type: mrr_at_3 value: 30.433 - type: mrr_at_5 value: 31.812 - type: ndcg_at_1 value: 25.650000000000002 - type: ndcg_at_10 value: 34.929 - type: ndcg_at_100 value: 39.382 - type: ndcg_at_1000 value: 41.913 - type: ndcg_at_3 value: 30.292 - type: ndcg_at_5 value: 32.629999999999995 - type: precision_at_1 value: 25.650000000000002 - type: precision_at_10 value: 5.311 - type: precision_at_100 value: 0.792 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 12.58 - type: precision_at_5 value: 8.994 - type: recall_at_1 value: 23.518 - type: recall_at_10 value: 46.19 - type: recall_at_100 value: 67.123 - type: recall_at_1000 value: 86.442 - type: recall_at_3 value: 33.678000000000004 - type: recall_at_5 value: 39.244 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.891 - type: map_at_10 value: 22.464000000000002 - type: map_at_100 value: 23.483 - type: map_at_1000 value: 23.613 - type: map_at_3 value: 20.080000000000002 - type: map_at_5 value: 21.526 - type: mrr_at_1 value: 20.025000000000002 - type: mrr_at_10 value: 26.712999999999997 - type: mrr_at_100 value: 27.650000000000002 - type: mrr_at_1000 value: 27.737000000000002 - type: mrr_at_3 value: 24.274 - type: mrr_at_5 value: 25.711000000000002 - type: ndcg_at_1 value: 20.025000000000002 - type: ndcg_at_10 value: 27.028999999999996 - type: ndcg_at_100 value: 32.064 - type: ndcg_at_1000 value: 35.188 - type: ndcg_at_3 value: 22.512999999999998 - type: ndcg_at_5 value: 24.89 - type: precision_at_1 value: 20.025000000000002 - type: precision_at_10 value: 4.776 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 10.531 - type: precision_at_5 value: 7.811 - type: recall_at_1 value: 15.891 - type: recall_at_10 value: 37.261 - type: recall_at_100 value: 59.12 - type: recall_at_1000 value: 81.356 - type: recall_at_3 value: 24.741 - type: recall_at_5 value: 30.753999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.544 - type: map_at_10 value: 36.283 - type: map_at_100 value: 37.467 - type: map_at_1000 value: 37.574000000000005 - type: map_at_3 value: 33.528999999999996 - type: map_at_5 value: 35.028999999999996 - type: mrr_at_1 value: 34.166999999999994 - type: mrr_at_10 value: 41.866 - type: mrr_at_100 value: 42.666 - type: mrr_at_1000 value: 42.716 - type: mrr_at_3 value: 39.541 - type: mrr_at_5 value: 40.768 - type: ndcg_at_1 value: 34.166999999999994 - type: ndcg_at_10 value: 41.577 - type: ndcg_at_100 value: 46.687 - type: ndcg_at_1000 value: 48.967 - type: ndcg_at_3 value: 37.177 - type: ndcg_at_5 value: 39.097 - type: precision_at_1 value: 34.166999999999994 - type: precision_at_10 value: 7.420999999999999 - type: precision_at_100 value: 1.165 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 17.291999999999998 - type: precision_at_5 value: 12.166 - type: recall_at_1 value: 27.544 - type: recall_at_10 value: 51.99399999999999 - type: recall_at_100 value: 73.738 - type: recall_at_1000 value: 89.33 - type: recall_at_3 value: 39.179 - type: recall_at_5 value: 44.385999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.661 - type: map_at_10 value: 35.475 - type: map_at_100 value: 36.626999999999995 - type: map_at_1000 value: 36.741 - type: map_at_3 value: 32.818000000000005 - type: map_at_5 value: 34.397 - type: mrr_at_1 value: 32.647999999999996 - type: mrr_at_10 value: 40.784 - type: mrr_at_100 value: 41.602 - type: mrr_at_1000 value: 41.661 - type: mrr_at_3 value: 38.68 - type: mrr_at_5 value: 39.838 - type: ndcg_at_1 value: 32.647999999999996 - type: ndcg_at_10 value: 40.697 - type: ndcg_at_100 value: 45.799 - type: ndcg_at_1000 value: 48.235 - type: ndcg_at_3 value: 36.516 - type: ndcg_at_5 value: 38.515 - type: precision_at_1 value: 32.647999999999996 - type: precision_at_10 value: 7.202999999999999 - type: precision_at_100 value: 1.1360000000000001 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 17.314 - type: precision_at_5 value: 12.145999999999999 - type: recall_at_1 value: 26.661 - type: recall_at_10 value: 50.995000000000005 - type: recall_at_100 value: 73.065 - type: recall_at_1000 value: 89.781 - type: recall_at_3 value: 39.073 - type: recall_at_5 value: 44.395 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.946583333333333 - type: map_at_10 value: 33.79725 - type: map_at_100 value: 34.86408333333333 - type: map_at_1000 value: 34.9795 - type: map_at_3 value: 31.259999999999998 - type: map_at_5 value: 32.71541666666666 - type: mrr_at_1 value: 30.863749999999996 - type: mrr_at_10 value: 37.99183333333333 - type: mrr_at_100 value: 38.790499999999994 - type: mrr_at_1000 value: 38.85575000000001 - type: mrr_at_3 value: 35.82083333333333 - type: mrr_at_5 value: 37.07533333333333 - type: ndcg_at_1 value: 30.863749999999996 - type: ndcg_at_10 value: 38.52141666666667 - type: ndcg_at_100 value: 43.17966666666667 - type: ndcg_at_1000 value: 45.64608333333333 - type: ndcg_at_3 value: 34.333000000000006 - type: ndcg_at_5 value: 36.34975 - type: precision_at_1 value: 30.863749999999996 - type: precision_at_10 value: 6.598999999999999 - type: precision_at_100 value: 1.0502500000000001 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_3 value: 15.557583333333334 - type: precision_at_5 value: 11.020000000000001 - type: recall_at_1 value: 25.946583333333333 - type: recall_at_10 value: 48.36991666666666 - type: recall_at_100 value: 69.02408333333334 - type: recall_at_1000 value: 86.43858333333331 - type: recall_at_3 value: 36.4965 - type: recall_at_5 value: 41.76258333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.431 - type: map_at_10 value: 28.889 - type: map_at_100 value: 29.642000000000003 - type: map_at_1000 value: 29.742 - type: map_at_3 value: 26.998 - type: map_at_5 value: 28.172000000000004 - type: mrr_at_1 value: 25.307000000000002 - type: mrr_at_10 value: 31.763 - type: mrr_at_100 value: 32.443 - type: mrr_at_1000 value: 32.531 - type: mrr_at_3 value: 29.959000000000003 - type: mrr_at_5 value: 31.063000000000002 - type: ndcg_at_1 value: 25.307000000000002 - type: ndcg_at_10 value: 32.586999999999996 - type: ndcg_at_100 value: 36.5 - type: ndcg_at_1000 value: 39.133 - type: ndcg_at_3 value: 29.25 - type: ndcg_at_5 value: 31.023 - type: precision_at_1 value: 25.307000000000002 - type: precision_at_10 value: 4.954 - type: precision_at_100 value: 0.747 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 12.577 - type: precision_at_5 value: 8.741999999999999 - type: recall_at_1 value: 22.431 - type: recall_at_10 value: 41.134 - type: recall_at_100 value: 59.28600000000001 - type: recall_at_1000 value: 78.857 - type: recall_at_3 value: 31.926 - type: recall_at_5 value: 36.335 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.586 - type: map_at_10 value: 23.304 - type: map_at_100 value: 24.159 - type: map_at_1000 value: 24.281 - type: map_at_3 value: 21.316 - type: map_at_5 value: 22.383 - type: mrr_at_1 value: 21.645 - type: mrr_at_10 value: 27.365000000000002 - type: mrr_at_100 value: 28.108 - type: mrr_at_1000 value: 28.192 - type: mrr_at_3 value: 25.482 - type: mrr_at_5 value: 26.479999999999997 - type: ndcg_at_1 value: 21.645 - type: ndcg_at_10 value: 27.306 - type: ndcg_at_100 value: 31.496000000000002 - type: ndcg_at_1000 value: 34.53 - type: ndcg_at_3 value: 23.73 - type: ndcg_at_5 value: 25.294 - type: precision_at_1 value: 21.645 - type: precision_at_10 value: 4.797 - type: precision_at_100 value: 0.8059999999999999 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 10.850999999999999 - type: precision_at_5 value: 7.736 - type: recall_at_1 value: 17.586 - type: recall_at_10 value: 35.481 - type: recall_at_100 value: 54.534000000000006 - type: recall_at_1000 value: 76.456 - type: recall_at_3 value: 25.335 - type: recall_at_5 value: 29.473 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.095 - type: map_at_10 value: 32.374 - type: map_at_100 value: 33.537 - type: map_at_1000 value: 33.634 - type: map_at_3 value: 30.089 - type: map_at_5 value: 31.433 - type: mrr_at_1 value: 29.198 - type: mrr_at_10 value: 36.01 - type: mrr_at_100 value: 37.022 - type: mrr_at_1000 value: 37.083 - type: mrr_at_3 value: 33.94 - type: mrr_at_5 value: 35.148 - type: ndcg_at_1 value: 29.198 - type: ndcg_at_10 value: 36.729 - type: ndcg_at_100 value: 42.114000000000004 - type: ndcg_at_1000 value: 44.592 - type: ndcg_at_3 value: 32.644 - type: ndcg_at_5 value: 34.652 - type: precision_at_1 value: 29.198 - type: precision_at_10 value: 5.970000000000001 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.129 - type: precision_at_3 value: 14.396999999999998 - type: precision_at_5 value: 10.093 - type: recall_at_1 value: 25.095 - type: recall_at_10 value: 46.392 - type: recall_at_100 value: 69.706 - type: recall_at_1000 value: 87.738 - type: recall_at_3 value: 35.303000000000004 - type: recall_at_5 value: 40.441 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.857999999999997 - type: map_at_10 value: 34.066 - type: map_at_100 value: 35.671 - type: map_at_1000 value: 35.881 - type: map_at_3 value: 31.304 - type: map_at_5 value: 32.885 - type: mrr_at_1 value: 32.411 - type: mrr_at_10 value: 38.987 - type: mrr_at_100 value: 39.894 - type: mrr_at_1000 value: 39.959 - type: mrr_at_3 value: 36.626999999999995 - type: mrr_at_5 value: 38.011 - type: ndcg_at_1 value: 32.411 - type: ndcg_at_10 value: 39.208 - type: ndcg_at_100 value: 44.626 - type: ndcg_at_1000 value: 47.43 - type: ndcg_at_3 value: 35.091 - type: ndcg_at_5 value: 37.119 - type: precision_at_1 value: 32.411 - type: precision_at_10 value: 7.51 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 16.14 - type: precision_at_5 value: 11.976 - type: recall_at_1 value: 26.857999999999997 - type: recall_at_10 value: 47.407 - type: recall_at_100 value: 72.236 - type: recall_at_1000 value: 90.77 - type: recall_at_3 value: 35.125 - type: recall_at_5 value: 40.522999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.3 - type: map_at_10 value: 27.412999999999997 - type: map_at_100 value: 28.29 - type: map_at_1000 value: 28.398 - type: map_at_3 value: 25.169999999999998 - type: map_at_5 value: 26.496 - type: mrr_at_1 value: 23.29 - type: mrr_at_10 value: 29.215000000000003 - type: mrr_at_100 value: 30.073 - type: mrr_at_1000 value: 30.156 - type: mrr_at_3 value: 26.956000000000003 - type: mrr_at_5 value: 28.38 - type: ndcg_at_1 value: 23.29 - type: ndcg_at_10 value: 31.113000000000003 - type: ndcg_at_100 value: 35.701 - type: ndcg_at_1000 value: 38.505 - type: ndcg_at_3 value: 26.727 - type: ndcg_at_5 value: 29.037000000000003 - type: precision_at_1 value: 23.29 - type: precision_at_10 value: 4.787 - type: precision_at_100 value: 0.763 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 11.091 - type: precision_at_5 value: 7.985 - type: recall_at_1 value: 21.3 - type: recall_at_10 value: 40.782000000000004 - type: recall_at_100 value: 62.13999999999999 - type: recall_at_1000 value: 83.012 - type: recall_at_3 value: 29.131 - type: recall_at_5 value: 34.624 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 9.631 - type: map_at_10 value: 16.634999999999998 - type: map_at_100 value: 18.23 - type: map_at_1000 value: 18.419 - type: map_at_3 value: 13.66 - type: map_at_5 value: 15.173 - type: mrr_at_1 value: 21.368000000000002 - type: mrr_at_10 value: 31.56 - type: mrr_at_100 value: 32.58 - type: mrr_at_1000 value: 32.633 - type: mrr_at_3 value: 28.241 - type: mrr_at_5 value: 30.225 - type: ndcg_at_1 value: 21.368000000000002 - type: ndcg_at_10 value: 23.855999999999998 - type: ndcg_at_100 value: 30.686999999999998 - type: ndcg_at_1000 value: 34.327000000000005 - type: ndcg_at_3 value: 18.781 - type: ndcg_at_5 value: 20.73 - type: precision_at_1 value: 21.368000000000002 - type: precision_at_10 value: 7.564 - type: precision_at_100 value: 1.496 - type: precision_at_1000 value: 0.217 - type: precision_at_3 value: 13.876 - type: precision_at_5 value: 11.062 - type: recall_at_1 value: 9.631 - type: recall_at_10 value: 29.517 - type: recall_at_100 value: 53.452 - type: recall_at_1000 value: 74.115 - type: recall_at_3 value: 17.605999999999998 - type: recall_at_5 value: 22.505 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.885 - type: map_at_10 value: 18.798000000000002 - type: map_at_100 value: 26.316 - type: map_at_1000 value: 27.869 - type: map_at_3 value: 13.719000000000001 - type: map_at_5 value: 15.716 - type: mrr_at_1 value: 66 - type: mrr_at_10 value: 74.263 - type: mrr_at_100 value: 74.519 - type: mrr_at_1000 value: 74.531 - type: mrr_at_3 value: 72.458 - type: mrr_at_5 value: 73.321 - type: ndcg_at_1 value: 53.87499999999999 - type: ndcg_at_10 value: 40.355999999999995 - type: ndcg_at_100 value: 44.366 - type: ndcg_at_1000 value: 51.771 - type: ndcg_at_3 value: 45.195 - type: ndcg_at_5 value: 42.187000000000005 - type: precision_at_1 value: 66 - type: precision_at_10 value: 31.75 - type: precision_at_100 value: 10.11 - type: precision_at_1000 value: 1.9800000000000002 - type: precision_at_3 value: 48.167 - type: precision_at_5 value: 40.050000000000004 - type: recall_at_1 value: 8.885 - type: recall_at_10 value: 24.471999999999998 - type: recall_at_100 value: 49.669000000000004 - type: recall_at_1000 value: 73.383 - type: recall_at_3 value: 14.872 - type: recall_at_5 value: 18.262999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 45.18 - type: f1 value: 40.26878691789978 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 62.751999999999995 - type: map_at_10 value: 74.131 - type: map_at_100 value: 74.407 - type: map_at_1000 value: 74.423 - type: map_at_3 value: 72.329 - type: map_at_5 value: 73.555 - type: mrr_at_1 value: 67.282 - type: mrr_at_10 value: 78.292 - type: mrr_at_100 value: 78.455 - type: mrr_at_1000 value: 78.458 - type: mrr_at_3 value: 76.755 - type: mrr_at_5 value: 77.839 - type: ndcg_at_1 value: 67.282 - type: ndcg_at_10 value: 79.443 - type: ndcg_at_100 value: 80.529 - type: ndcg_at_1000 value: 80.812 - type: ndcg_at_3 value: 76.281 - 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type: max_f1 value: 77.87816307403935 language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit --- ## Multilingual-E5-base-int8-ov This is [Multilingual-E5-base](https://huggingface.co/intfloat/multilingual-e5-base) model converted to the OpenVINO™ IR (Intermediate Representation) format with quantization to INT8. Disclaimer: Model is provided as a preview and may be update in the future. [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 12 layers and the embedding size is 768. ## Usage ```python import torch from transformers import AutoTokenizer from optimum.intel.openvino import OVModelForFeatureExtraction # Sentences we want sentence embeddings for sentences = ["Sample Data-1", "Sample Data-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('santhosh/multilingual-e5-base-int8-ov') model = OVModelForFeatureExtraction.from_pretrained('OpenVINO/bge-base-en-v1.5-int8-ov') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Using openvino GenAI ```python import openvino_genai import numpy as np import os import huggingface_hub as hf_hub from typing import List model_path = "santhosh/multilingual-e5-base-int8-ov" sentences = ["Sample Data-1", "Sample Data-2"] embedding_pipeline = openvino_genai.TextEmbeddingPipeline(model_path, "CPU") embeddings = embedding_pipeline.embed_documents(sentences) return np.array(embeddings) ```

StyTJU/mor-kv-sharerobot-visionmor-adapter

StyTJU

2025-08-19T05:30:45Z

0

null

[ "safetensors", "region:us" ]

null

2025-08-19T02:56:31Z

# MoR Router Adapter for BAAI/RoboBrain2.0-3B 本仓库权重为全部权重 (safetensors 分片)。(初步Vision MoR训练，Loss较高) 使用前请先加载基座模型RoboBrain-3B然后覆写这些参数。

cucucu666/qiqiu-8.19-female

cucucu666

2025-08-19T05:29:05Z

0

diffusers

[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-Fill-dev", "base_model:adapter:black-forest-labs/FLUX.1-Fill-dev", "license:other", "region:us" ]

text-to-image

2025-08-19T03:44:23Z

--- base_model: black-forest-labs/FLUX.1-Fill-dev library_name: diffusers license: other instance_prompt: labi female face, Crayon Shin-chan style, eyelash, pleading expression, both hands together in a prayer pose, plain white background widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora ---  # Flux-Fill DreamBooth LoRA - cucucu666/qiqiu-8.19-female <Gallery /> ## Model description These are cucucu666/qiqiu-8.19-female DreamBooth LoRA weights for black-forest-labs/FLUX.1-Fill-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with a custom [Flux diffusers trainer](https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `labi female face, Crayon Shin-chan style, eyelash, pleading expression, both hands together in a prayer pose, plain white background` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](cucucu666/qiqiu-8.19-female/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('cucucu666/qiqiu-8.19-female', weight_name='pytorch_lora_weights.safetensors') image = pipeline('labi female face, Crayon Shin-chan style, eyelash, pleading expression, both hands together in a prayer pose, plain white background').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755579680

katanyasekolah

2025-08-19T05:28:06Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T05:28:03Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

lejelly/shannon_entropy-ep1-lr0001-sampling-t07-gen2

lejelly

2025-08-19T05:24:08Z

0

null

[ "safetensors", "mistral", "merge", "parameter_wise", "llm-adamerge", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "region:us" ]

null

2025-08-19T05:21:29Z

--- tags: - merge - parameter_wise - llm-adamerge base_model: mistralai/Mistral-7B-v0.1 --- # Merged Model using LLM-AdaMerge (parameter_wise) This model was created by merging multiple fine-tuned models using the LLM-AdaMerge approach with parameter_wise merging. ## Merge Details - **Merge Type**: parameter_wise - **Base Model**: mistralai/Mistral-7B-v0.1 - **Number of Models Merged**: 3 - **Models Merged**: instruct, math, code - **Final Training Loss**: N/A - **Training Epochs**: 0 ## Lambda Coefficients The following lambda coefficients were learned during training: ### Parameter-wise Lambdas This model uses parameter-wise lambda coefficients. Total parameters with individual lambdas: 291 See the uploaded `learned_lambdas.json` file for detailed parameter-wise coefficients. ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("your-username/model-name") tokenizer = AutoTokenizer.from_pretrained("your-username/model-name") # Use the model inputs = tokenizer("Hello, how are you?", return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0])) ``` ## Training Configuration See the uploaded `training_config.json` file for detailed training configuration. ## Citation If you use this model, please cite the LLM-AdaMerge paper: ```bibtex @article{llmadamerge2024, title={LLM-AdaMerge: Adaptive Model Merging for Large Language Models}, author={...}, year={2024} } ```