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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
htdung167/ViLegalBERT-v0	htdung167	2025-08-22T08:34:10Z	88	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-08-20T07:30:00Z	--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: ViLegalBERT-v0 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. --> # ViLegalBERT-v0 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4575 - Accuracy: 0.8898 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:------:\|:---------------:\|:--------:\| \| 0.7791 \| 0.0483 \| 1000 \| 0.7469 \| 0.8340 \| \| 0.7006 \| 0.0965 \| 2000 \| 0.7024 \| 0.8423 \| \| 0.6624 \| 0.1448 \| 3000 \| 0.6724 \| 0.8477 \| \| 0.6255 \| 0.1930 \| 4000 \| 0.6500 \| 0.8523 \| \| 0.6027 \| 0.2413 \| 5000 \| 0.6289 \| 0.8560 \| \| 0.5836 \| 0.2895 \| 6000 \| 0.6169 \| 0.8583 \| \| 0.5743 \| 0.3378 \| 7000 \| 0.6033 \| 0.8609 \| \| 0.5613 \| 0.3860 \| 8000 \| 0.5947 \| 0.8626 \| \| 0.5486 \| 0.4343 \| 9000 \| 0.5874 \| 0.8642 \| \| 0.5447 \| 0.4825 \| 10000 \| 0.5778 \| 0.8657 \| \| 0.5389 \| 0.5308 \| 11000 \| 0.5710 \| 0.8668 \| \| 0.5295 \| 0.5791 \| 12000 \| 0.5650 \| 0.8688 \| \| 0.5188 \| 0.6273 \| 13000 \| 0.5551 \| 0.8704 \| \| 0.5102 \| 0.6756 \| 14000 \| 0.5491 \| 0.8715 \| \| 0.5096 \| 0.7238 \| 15000 \| 0.5462 \| 0.8723 \| \| 0.5052 \| 0.7721 \| 16000 \| 0.5386 \| 0.8736 \| \| 0.4981 \| 0.8203 \| 17000 \| 0.5339 \| 0.8747 \| \| 0.491 \| 0.8686 \| 18000 \| 0.5272 \| 0.8757 \| \| 0.4894 \| 0.9168 \| 19000 \| 0.5243 \| 0.8764 \| \| 0.4853 \| 0.9651 \| 20000 \| 0.5232 \| 0.8768 \| \| 0.4812 \| 1.0133 \| 21000 \| 0.5152 \| 0.8779 \| \| 0.4732 \| 1.0616 \| 22000 \| 0.5143 \| 0.8789 \| \| 0.474 \| 1.1098 \| 23000 \| 0.5101 \| 0.8791 \| \| 0.4701 \| 1.1581 \| 24000 \| 0.5060 \| 0.8803 \| \| 0.4678 \| 1.2063 \| 25000 \| 0.5025 \| 0.8806 \| \| 0.4661 \| 1.2546 \| 26000 \| 0.5003 \| 0.8811 \| \| 0.464 \| 1.3028 \| 27000 \| 0.4949 \| 0.8822 \| \| 0.461 \| 1.3511 \| 28000 \| 0.4929 \| 0.8825 \| \| 0.4574 \| 1.3994 \| 29000 \| 0.4916 \| 0.8829 \| \| 0.4598 \| 1.4476 \| 30000 \| 0.4896 \| 0.8834 \| \| 0.4549 \| 1.4959 \| 31000 \| 0.4878 \| 0.8839 \| \| 0.4523 \| 1.5441 \| 32000 \| 0.4849 \| 0.8846 \| \| 0.4482 \| 1.5924 \| 33000 \| 0.4820 \| 0.8850 \| \| 0.4477 \| 1.6406 \| 34000 \| 0.4802 \| 0.8854 \| \| 0.4467 \| 1.6889 \| 35000 \| 0.4789 \| 0.8853 \| \| 0.4434 \| 1.7371 \| 36000 \| 0.4765 \| 0.8862 \| \| 0.443 \| 1.7854 \| 37000 \| 0.4752 \| 0.8865 \| \| 0.4417 \| 1.8336 \| 38000 \| 0.4741 \| 0.8865 \| \| 0.4393 \| 1.8819 \| 39000 \| 0.4713 \| 0.8870 \| \| 0.4362 \| 1.9302 \| 40000 \| 0.4708 \| 0.8874 \| \| 0.4356 \| 1.9784 \| 41000 \| 0.4687 \| 0.8877 \| \| 0.4343 \| 2.0266 \| 42000 \| 0.4677 \| 0.8880 \| \| 0.4333 \| 2.0749 \| 43000 \| 0.4638 \| 0.8888 \| \| 0.4319 \| 2.1231 \| 44000 \| 0.4645 \| 0.8889 \| \| 0.4363 \| 2.1714 \| 45000 \| 0.4633 \| 0.8886 \| \| 0.4281 \| 2.2197 \| 46000 \| 0.4602 \| 0.8892 \| \| 0.4242 \| 2.2679 \| 47000 \| 0.4609 \| 0.8895 \| \| 0.4262 \| 2.3162 \| 48000 \| 0.4576 \| 0.8898 \| \| 0.4231 \| 2.3644 \| 49000 \| 0.4554 \| 0.8904 \| \| 0.4197 \| 2.4127 \| 50000 \| 0.4562 \| 0.8903 \| \| 0.4231 \| 2.4609 \| 51000 \| 0.4556 \| 0.8902 \| \| 0.422 \| 2.5092 \| 52000 \| 0.4522 \| 0.8909 \| \| 0.4222 \| 2.5574 \| 53000 \| 0.4526 \| 0.8906 \| \| 0.4208 \| 2.6057 \| 54000 \| 0.4497 \| 0.8916 \| \| 0.42 \| 2.6539 \| 55000 \| 0.4510 \| 0.8914 \| \| 0.4218 \| 2.7022 \| 56000 \| 0.4492 \| 0.8920 \| \| 0.4162 \| 2.7505 \| 57000 \| 0.4479 \| 0.8922 \| \| 0.4168 \| 2.7987 \| 58000 \| 0.4466 \| 0.8922 \| \| 0.418 \| 2.8470 \| 59000 \| 0.4466 \| 0.8921 \| \| 0.4164 \| 2.8952 \| 60000 \| 0.4447 \| 0.8928 \| \| 0.4133 \| 2.9435 \| 61000 \| 0.4437 \| 0.8929 \| \| 0.4103 \| 2.9917 \| 62000 \| 0.4418 \| 0.8932 \| \| 0.4106 \| 3.0400 \| 63000 \| 0.4397 \| 0.8939 \| \| 0.4122 \| 3.0882 \| 64000 \| 0.4392 \| 0.8938 \| \| 0.4082 \| 3.1365 \| 65000 \| 0.4380 \| 0.8942 \| \| 0.4069 \| 3.1847 \| 66000 \| 0.4379 \| 0.8942 \| \| 0.4076 \| 3.2330 \| 67000 \| 0.4369 \| 0.8944 \| \| 0.4079 \| 3.2812 \| 68000 \| 0.4355 \| 0.8946 \| \| 0.4045 \| 3.3295 \| 69000 \| 0.4351 \| 0.8946 \| \| 0.4032 \| 3.3777 \| 70000 \| 0.4350 \| 0.8950 \| \| 0.4043 \| 3.4260 \| 71000 \| 0.4329 \| 0.8952 \| \| 0.4018 \| 3.4742 \| 72000 \| 0.4319 \| 0.8952 \| \| 0.4028 \| 3.5225 \| 73000 \| 0.4324 \| 0.8954 \| \| 0.4017 \| 3.5708 \| 74000 \| 0.4303 \| 0.8956 \| \| 0.4013 \| 3.6190 \| 75000 \| 0.4312 \| 0.8957 \| \| 0.4003 \| 3.6673 \| 76000 \| 0.4290 \| 0.8959 \| \| 0.397 \| 3.7155 \| 77000 \| 0.4289 \| 0.8958 \| \| 0.3978 \| 3.7638 \| 78000 \| 0.4280 \| 0.8962 \| \| 0.3985 \| 3.8120 \| 79000 \| 0.4266 \| 0.8967 \| \| 0.3972 \| 3.8603 \| 80000 \| 0.4257 \| 0.8966 \| \| 0.3926 \| 3.9085 \| 81000 \| 0.4241 \| 0.8969 \| \| 0.397 \| 3.9568 \| 82000 \| 0.4244 \| 0.8968 \| \| 0.3963 \| 4.0050 \| 83000 \| 0.4239 \| 0.8971 \| \| 0.3952 \| 4.0533 \| 84000 \| 0.4234 \| 0.8970 \| \| 0.3916 \| 4.1015 \| 85000 \| 0.4217 \| 0.8973 \| \| 0.3938 \| 4.1498 \| 86000 \| 0.4190 \| 0.8979 \| \| 0.3926 \| 4.1980 \| 87000 \| 0.4188 \| 0.8981 \| \| 0.3924 \| 4.2463 \| 88000 \| 0.4198 \| 0.8978 \| \| 0.3895 \| 4.2945 \| 89000 \| 0.4195 \| 0.8978 \| \| 0.3918 \| 4.3428 \| 90000 \| 0.4186 \| 0.8982 \| \| 0.3906 \| 4.3911 \| 91000 \| 0.4182 \| 0.8983 \| \| 0.3927 \| 4.4393 \| 92000 \| 0.4178 \| 0.8983 \| \| 0.3902 \| 4.4876 \| 93000 \| 0.4170 \| 0.8986 \| \| 0.3924 \| 4.5358 \| 94000 \| 0.4179 \| 0.8982 \| \| 0.3879 \| 4.5841 \| 95000 \| 0.4144 \| 0.8989 \| \| 0.3897 \| 4.6323 \| 96000 \| 0.4150 \| 0.8989 \| \| 0.3903 \| 4.6806 \| 97000 \| 0.4158 \| 0.8989 \| \| 0.3893 \| 4.7288 \| 98000 \| 0.4190 \| 0.8983 \| \| 0.5424 \| 4.7771 \| 99000 \| 0.6255 \| 0.8573 \| \| 0.4768 \| 4.8253 \| 100000 \| 0.4770 \| 0.8850 \| \| 0.4418 \| 4.8736 \| 101000 \| 0.4506 \| 0.8913 \| \| 0.4562 \| 4.9219 \| 102000 \| 0.4713 \| 0.8871 \| \| 0.4494 \| 4.9701 \| 103000 \| 0.4575 \| 0.8898 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.21.4
VIDEOS-18-brown-girl-viral-video-Clip-XX/New.full.videos.brown.girl.Viral.Video.Official.Tutorial	VIDEOS-18-brown-girl-viral-video-Clip-XX	2025-08-22T08:33:01Z	0	0	null	[ "region:us" ]	null	2025-08-22T08:32:52Z	<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
internlm/Intern-S1-mini-FP8	internlm	2025-08-22T08:31:43Z	46	1	null	[ "safetensors", "interns1", "image-text-to-text", "conversational", "custom_code", "arxiv:2508.15763", "base_model:internlm/Intern-S1-mini", "base_model:quantized:internlm/Intern-S1-mini", "license:apache-2.0", "fp8", "region:us" ]	image-text-to-text	2025-08-18T06:37:20Z	--- license: apache-2.0 pipeline_tag: image-text-to-text base_model: - internlm/Intern-S1-mini --- ## Intern-S1-mini <div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/642695e5274e7ad464c8a5ba/E43cgEXBRWjVJlU_-hdh6.png" /> <div> </div> [💻Github Repo](https://github.com/InternLM/Intern-S1) • [🤗Model Collections](https://huggingface.co/collections/internlm/intern-s1-6882e325e8ac1c58ba108aa5) • [📜Technical Report](https://arxiv.org/abs/2508.15763) • [💬Online Chat](https://chat.intern-ai.org.cn/) </div> <p align="center"> 👋 join us on <a href="https://discord.gg/xa29JuW87d" target="_blank">Discord</a> and <a href="https://cdn.vansin.top/intern-s1.jpg" target="_blank">WeChat</a> </p> ## Introduction We introduce Intern-S1-mini, a lightweight open-source multimodal reasoning model based on the same techniques as [Intern-S1](https://huggingface.co/internlm/Intern-S1). Built upon a 8B dense language model (Qwen3) and a 0.3B Vision encoder (InternViT), Intern-S1-mini has been further pretrained on 5 trillion tokens of multimodal data, including over 2.5 trillion scientific-domain tokens. This enables the model to retain strong general capabilities while excelling in specialized scientific domains such as interpreting chemical structures, understanding protein sequences, and planning compound synthesis routes, making Intern-S1-mini to be a capable research assistant for real-world scientific applications. ## Features - Strong performance across language and vision reasoning benchmarks, especially scientific tasks. - Continuously pretrained on a massive 5T token dataset, with over 50% specialized scientific data, embedding deep domain expertise. - Dynamic tokenizer enables native understanding of molecular formulas and protein sequences. ## Performance We evaluate the Intern-S1-mini on various benchmarks including general datasets and scientific datasets. We report the performance comparison with the recent VLMs and LLMs below. \| \| \| Intern-S1-mini \| Qwen3-8B \| GLM-4.1V \| MiMo-VL-7B-RL-2508 \| \|------------\|----------------\|-------------------\|----------\|----------\|--------------------\| \| General \| MMLU-Pro \| 74.78 \| 73.7 \| 57.1 \| 73.93 \| \| 　 \| MMMU \| 72.33 \| N/A \| 69.9 \| 70.4 \| \| 　 \| MMStar \| 65.2 \| N/A \| 71.5 \| 72.9 \| \| 　 \| GPQA \| 65.15 \| 62 \| 50.32 \| 60.35 \| \| 　 \| AIME2024 \| 84.58 \| 76 \| 36.2 \| 72.6 \| \| 　 \| AIME2025 \| 80 \| 67.3 \| 32 \| 64.4 \| \| 　 \| MathVision \| 51.41 \| N/A \| 53.9 \| 54.5 \| \| 　 \| MathVista \| 70.3 \| N/A \| 80.7 \| 79.4 \| \| 　 \| IFEval \| 81.15 \| 85 \| 71.53 \| 71.4 \| \| \| \| \| \| \| \| \| Scientific \| SFE \| 35.84 \| N/A \| 43.2 \| 43.9 \| \| 　 \| Physics \| 28.76 \| N/A \| 28.3 \| 28.2 \| \| 　 \| SmolInstruct \| 32.2 \| 17.6 \| 18.1 \| 16.11 \| \| 　 \| ChemBench \| 76.47 \| 61.1 \| 56.2 \| 66.78 \| \| 　 \| MatBench \| 61.55 \| 45.24 \| 54.3 \| 46.9 \| \| 　 \| MicroVQA \| 56.62 \| N/A \| 50.2 \| 50.96 \| \| 　 \| ProteinLMBench \| 58.47 \| 59.1 \| 58.3 \| 59.8 \| \| 　 \| MSEarthMCQ \| 58.12 \| N/A \| 50.3 \| 47.3 \| \| 　 \| XLRS-Bench \| 51.63 \| N/A \| 49.8 \| 12.29 \| We use the [OpenCompass](https://github.com/open-compass/OpenCompass/) and [VLMEvalkit](https://github.com/open-compass/vlmevalkit) to evaluate all models. ## Quick Start ### Sampling Parameters We recommend using the following hyperparameters to ensure better results ```python top_p = 1.0 top_k = 50 min_p = 0.0 temperature = 0.8 ``` ### Transformers The following provides demo code illustrating how to generate based on text and multimodal inputs. > Please use transformers>=4.55.2 to ensure the model works normally. #### Text input ```python from transformers import AutoProcessor, AutoModelForCausalLM import torch model_name = "internlm/Intern-S1-mini-FP8" processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "text", "text": "tell me about an interesting physical phenomenon."}, ], } ] inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16) generate_ids = model.generate(inputs, max_new_tokens=32768) decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True) print(decoded_output) ``` #### Image input ```python from transformers import AutoProcessor, AutoModelForCausalLM import torch model_name = "internlm/Intern-S1-mini-FP8" processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"}, {"type": "text", "text": "Please describe the image explicitly."}, ], } ] inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16) generate_ids = model.generate(inputs, max_new_tokens=32768) decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True) print(decoded_output) ``` #### Video input Please ensure that the decord video decoding library is installed via `pip install decord`. To avoid OOM, please install flash_attention and use at least 2 GPUS. ```python from transformers import AutoProcessor, AutoModelForCausalLM import torch model_name = "internlm/Intern-S1-mini-FP8" processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True) messages = [ { "role": "user", "content": [ { "type": "video", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4", }, {"type": "text", "text": "What type of shot is the man performing?"}, ], } ] inputs = processor.apply_chat_template( messages, return_tensors="pt", add_generation_prompt=True, video_load_backend="decord", tokenize=True, return_dict=True, ).to(model.device, dtype=torch.float16) generate_ids = model.generate(inputs, max_new_tokens=32768) decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True) print(decoded_output) ``` ### Serving The minimum hardware requirements for deploying Intern-S1 series models are: \| Model \| A100(GPUs) \| H800(GPUs) \| H100(GPUs) \| H200(GPUs) \| \| :---------------------------------------------------------------------: \| :--------: \| :--------: \| :--------: \| :--------: \| \| [internlm/Intern-S1-mini](https://huggingface.co/internlm/Intern-S1-mini) \| 1 \| 1 \| 1 \| 1 \| \| [internlm/Intern-S1-mini-FP8](https://huggingface.co/internlm/Intern-S1-mini-FP8) \| - \| 1 \| 1 \| 1 \| You can utilize one of the following LLM inference frameworks to create an OpenAI compatible server: #### [lmdeploy (>=0.9.2)](https://github.com/InternLM/lmdeploy) ```bash lmdeploy serve api_server internlm/Intern-S1-mini-FP8 --reasoning-parser intern-s1 --tool-call-parser intern-s1 ``` #### [vllm (>=0.10.1)](https://github.com/vllm-project/vllm) ```bash vllm serve internlm/Intern-S1-mini-FP8 --trust-remote-code ``` #### [sglang](https://github.com/sgl-project/sglang) ```bash python3 -m sglang.launch_server \ --model-path internlm/Intern-S1-mini-FP8 \ --trust-remote-code \ --grammar-backend none ``` #### ollama for local deployment: ```bash # install ollama curl -fsSL https://ollama.com/install.sh \| sh # fetch model ollama pull internlm/interns1-mini # run model ollama run internlm/interns1-mini # then use openai client to call on http://localhost:11434/v1 ``` ## Advanced Usage ### Tool Calling Many Large Language Models (LLMs) now feature Tool Calling, a powerful capability that allows them to extend their functionality by interacting with external tools and APIs. This enables models to perform tasks like fetching up-to-the-minute information, running code, or calling functions within other applications. A key advantage for developers is that a growing number of open-source LLMs are designed to be compatible with the OpenAI API. This means you can leverage the same familiar syntax and structure from the OpenAI library to implement tool calling with these open-source models. As a result, the code demonstrated in this tutorial is versatile—it works not just with OpenAI models, but with any model that follows the same interface standard. To illustrate how this works, let's dive into a practical code example that uses tool calling to get the latest weather forecast (based on lmdeploy api server). ```python from openai import OpenAI import json def get_current_temperature(location: str, unit: str = "celsius"): """Get current temperature at a location. Args: location: The location to get the temperature for, in the format "City, State, Country". unit: The unit to return the temperature in. Defaults to "celsius". (choices: ["celsius", "fahrenheit"]) Returns: the temperature, the location, and the unit in a dict """ return { "temperature": 26.1, "location": location, "unit": unit, } def get_temperature_date(location: str, date: str, unit: str = "celsius"): """Get temperature at a location and date. Args: location: The location to get the temperature for, in the format "City, State, Country". date: The date to get the temperature for, in the format "Year-Month-Day". unit: The unit to return the temperature in. Defaults to "celsius". (choices: ["celsius", "fahrenheit"]) Returns: the temperature, the location, the date and the unit in a dict """ return { "temperature": 25.9, "location": location, "date": date, "unit": unit, } def get_function_by_name(name): if name == "get_current_temperature": return get_current_temperature if name == "get_temperature_date": return get_temperature_date tools = [{ 'type': 'function', 'function': { 'name': 'get_current_temperature', 'description': 'Get current temperature at a location.', 'parameters': { 'type': 'object', 'properties': { 'location': { 'type': 'string', 'description': 'The location to get the temperature for, in the format \'City, State, Country\'.' }, 'unit': { 'type': 'string', 'enum': [ 'celsius', 'fahrenheit' ], 'description': 'The unit to return the temperature in. Defaults to \'celsius\'.' } }, 'required': [ 'location' ] } } }, { 'type': 'function', 'function': { 'name': 'get_temperature_date', 'description': 'Get temperature at a location and date.', 'parameters': { 'type': 'object', 'properties': { 'location': { 'type': 'string', 'description': 'The location to get the temperature for, in the format \'City, State, Country\'.' }, 'date': { 'type': 'string', 'description': 'The date to get the temperature for, in the format \'Year-Month-Day\'.' }, 'unit': { 'type': 'string', 'enum': [ 'celsius', 'fahrenheit' ], 'description': 'The unit to return the temperature in. Defaults to \'celsius\'.' } }, 'required': [ 'location', 'date' ] } } }] messages = [ {'role': 'user', 'content': 'Today is 2024-11-14, What\'s the temperature in San Francisco now? How about tomorrow?'} ] openai_api_key = "EMPTY" openai_api_base = "http://0.0.0.0:23333/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) model_name = client.models.list().data[0].id response = client.chat.completions.create( model=model_name, messages=messages, max_tokens=32768, temperature=0.8, top_p=0.8, stream=False, extra_body=dict(spaces_between_special_tokens=False, enable_thinking=False), tools=tools) print(response.choices[0].message) messages.append(response.choices[0].message) for tool_call in response.choices[0].message.tool_calls: tool_call_args = json.loads(tool_call.function.arguments) tool_call_result = get_function_by_name(tool_call.function.name)(tool_call_args) tool_call_result = json.dumps(tool_call_result, ensure_ascii=False) messages.append({ 'role': 'tool', 'name': tool_call.function.name, 'content': tool_call_result, 'tool_call_id': tool_call.id }) response = client.chat.completions.create( model=model_name, messages=messages, temperature=0.8, top_p=0.8, stream=False, extra_body=dict(spaces_between_special_tokens=False, enable_thinking=False), tools=tools) print(response.choices[0].message.content) ``` ### Switching Between Thinking and Non-Thinking Modes Intern-S1-mini enables thinking mode by default, enhancing the model's reasoning capabilities to generate higher-quality responses. This feature can be disabled by setting `enable_thinking=False` in `tokenizer.apply_chat_template` ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # think mode indicator ) ``` With LMDeploy serving Intern-S1-mini models, you can dynamically control the thinking mode by adjusting the `enable_thinking` parameter in your requests. ```python from openai import OpenAI import json messages = [ { 'role': 'user', 'content': 'who are you' }, { 'role': 'assistant', 'content': 'I am an AI' }, { 'role': 'user', 'content': 'AGI is?' }] openai_api_key = "EMPTY" openai_api_base = "http://0.0.0.0:23333/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) model_name = client.models.list().data[0].id response = client.chat.completions.create( model=model_name, messages=messages, temperature=0.8, top_p=0.8, max_tokens=2048, extra_body={ "enable_thinking": False, } ) print(json.dumps(response.model_dump(), indent=2, ensure_ascii=False)) ``` For vllm and sglang users, configure this through, ```python extra_body={ "chat_template_kwargs": {"enable_thinking": False} } ``` ## Fine-tuning See this [documentation](https://github.com/InternLM/Intern-S1/blob/main/docs/sft.md) for more details. ## Citation If you find this work useful, feel free to give us a cite. ``` @misc{bai2025interns1scientificmultimodalfoundation, title={Intern-S1: A Scientific Multimodal Foundation Model}, author={Lei Bai and Zhongrui Cai and Maosong Cao and Weihan Cao and Chiyu Chen and Haojiong Chen and Kai Chen and Pengcheng Chen and Ying Chen and Yongkang Chen and Yu Cheng and Yu Cheng and Pei Chu and Tao Chu and Erfei Cui and Ganqu Cui and Long Cui and Ziyun Cui and Nianchen Deng and Ning Ding and Nanqin Dong and Peijie Dong and Shihan Dou and Sinan Du and Haodong Duan and Caihua Fan and Ben Gao and Changjiang Gao and Jianfei Gao and Songyang Gao and Yang Gao and Zhangwei Gao and Jiaye Ge and Qiming Ge and Lixin Gu and Yuzhe Gu and Aijia Guo and Qipeng Guo and Xu Guo and Conghui He and Junjun He and Yili Hong and Siyuan Hou and Caiyu Hu and Hanglei Hu and Jucheng Hu and Ming Hu and Zhouqi Hua and Haian Huang and Junhao Huang and Xu Huang and Zixian Huang and Zhe Jiang and Lingkai Kong and Linyang Li and Peiji Li and Pengze Li and Shuaibin Li and Tianbin Li and Wei Li and Yuqiang Li and Dahua Lin and Junyao Lin and Tianyi Lin and Zhishan Lin and Hongwei Liu and Jiangning Liu and Jiyao Liu and Junnan Liu and Kai Liu and Kaiwen Liu and Kuikun Liu and Shichun Liu and Shudong Liu and Wei Liu and Xinyao Liu and Yuhong Liu and Zhan Liu and Yinquan Lu and Haijun Lv and Hongxia Lv and Huijie Lv and Qidang Lv and Ying Lv and Chengqi Lyu and Chenglong Ma and Jianpeng Ma and Ren Ma and Runmin Ma and Runyuan Ma and Xinzhu Ma and Yichuan Ma and Zihan Ma and Sixuan Mi and Junzhi Ning and Wenchang Ning and Xinle Pang and Jiahui Peng and Runyu Peng and Yu Qiao and Jiantao Qiu and Xiaoye Qu and Yuan Qu and Yuchen Ren and Fukai Shang and Wenqi Shao and Junhao Shen and Shuaike Shen and Chunfeng Song and Demin Song and Diping Song and Chenlin Su and Weijie Su and Weigao Sun and Yu Sun and Qian Tan and Cheng Tang and Huanze Tang and Kexian Tang and Shixiang Tang and Jian Tong and Aoran Wang and Bin Wang and Dong Wang and Lintao Wang and Rui Wang and Weiyun Wang and Wenhai Wang and Yi Wang and Ziyi Wang and Ling-I Wu and Wen Wu and Yue Wu and Zijian Wu and Linchen Xiao and Shuhao Xing and Chao Xu and Huihui Xu and Jun Xu and Ruiliang Xu and Wanghan Xu and GanLin Yang and Yuming Yang and Haochen Ye and Jin Ye and Shenglong Ye and Jia Yu and Jiashuo Yu and Jing Yu and Fei Yuan and Bo Zhang and Chao Zhang and Chen Zhang and Hongjie Zhang and Jin Zhang and Qiaosheng Zhang and Qiuyinzhe Zhang and Songyang Zhang and Taolin Zhang and Wenlong Zhang and Wenwei Zhang and Yechen Zhang and Ziyang Zhang and Haiteng Zhao and Qian Zhao and Xiangyu Zhao and Xiangyu Zhao and Bowen Zhou and Dongzhan Zhou and Peiheng Zhou and Yuhao Zhou and Yunhua Zhou and Dongsheng Zhu and Lin Zhu and Yicheng Zou}, year={2025}, eprint={2508.15763}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2508.15763}, } ```
18-VIDEOS-Uppal-Farm-Girl-viral-video-Clip/New.full.videos.Uppal.Farm.Girl.Viral.Video.Official.Tutorial	18-VIDEOS-Uppal-Farm-Girl-viral-video-Clip	2025-08-22T08:30:19Z	0	0	null	[ "region:us" ]	null	2025-08-22T08:30:12Z	<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
finding1/ERNIE-4.5-300B-A47B-MLX-8.5bpw	finding1	2025-08-22T08:29:51Z	2	0	mlx	[ "mlx", "safetensors", "ernie4_5_moe", "ERNIE4.5", "text-generation", "conversational", "en", "zh", "base_model:baidu/ERNIE-4.5-300B-A47B-PT", "base_model:quantized:baidu/ERNIE-4.5-300B-A47B-PT", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-08-21T08:45:25Z	--- license: apache-2.0 language: - en - zh pipeline_tag: text-generation tags: - ERNIE4.5 - mlx library_name: mlx base_model: baidu/ERNIE-4.5-300B-A47B-PT --- This model [finding1/ERNIE-4.5-300B-A47B-MLX-8.5bpw](https://huggingface.co/finding1/ERNIE-4.5-300B-A47B-MLX-8.5bpw) was converted to MLX format from [baidu/ERNIE-4.5-300B-A47B-PT](https://huggingface.co/baidu/ERNIE-4.5-300B-A47B-PT) using mlx-lm version 0.26.3 with `mlx_lm.convert --quantize --q-bits 8 --hf-path baidu/ERNIE-4.5-300B-A47B-PT`.
Wuhall/code-search-net-tokenizer	Wuhall	2025-08-22T08:29:31Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-22T08:29:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
kapalbalap/blockassist-bc-peaceful_wary_owl_1755851289	kapalbalap	2025-08-22T08:29:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "peaceful wary owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:28:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - peaceful wary owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
eddie-c/MoD-MoE-AdamW	eddie-c	2025-08-22T08:28:42Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-22T08:28:42Z	--- license: apache-2.0 ---
ypszn/blockassist-bc-yapping_pawing_worm_1755851170	ypszn	2025-08-22T08:27:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping pawing worm", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:26:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping pawing worm --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
flralex1408/first-model	flralex1408	2025-08-22T08:26:31Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "base_model:HuggingFaceTB/SmolLM2-135M-Instruct", "base_model:finetune:HuggingFaceTB/SmolLM2-135M-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-22T08:02:57Z	--- base_model: HuggingFaceTB/SmolLM2-135M-Instruct library_name: transformers model_name: first-model tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for first-model This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-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="flralex1408/first-model", 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: 4.55.3 - Pytorch: 2.2.2 - 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}} } ```
Dejiat/blockassist-bc-savage_unseen_bobcat_1755851047	Dejiat	2025-08-22T08:24:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:24:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pasithbas159/Gemma3_HII_satellite_v3	pasithbas159	2025-08-22T08:24:14Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-22T08:23:10Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: pasithbas159 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755851012	0xaoyama	2025-08-22T08:23:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:23:52Z	--- 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).
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755849464	vwzyrraz7l	2025-08-22T08:23:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:23:44Z	--- 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).
colabbear/bce-reranker-base_v1-Q4_K_M-GGUF	colabbear	2025-08-22T08:21:34Z	0	0	sentence-transformers	[ "sentence-transformers", "gguf", "transformers", "llama-cpp", "gguf-my-repo", "text-classification", "en", "zh", "ja", "ko", "base_model:maidalun1020/bce-reranker-base_v1", "base_model:quantized:maidalun1020/bce-reranker-base_v1", "license:apache-2.0", "endpoints_compatible", "region:us", "feature-extraction" ]	text-classification	2025-08-22T08:21:30Z	--- license: apache-2.0 pipeline_tag: text-classification tags: - transformers - sentence-transformers - llama-cpp - gguf-my-repo language: - en - zh - ja - ko base_model: maidalun1020/bce-reranker-base_v1 --- # colabbear/bce-reranker-base_v1-Q4_K_M-GGUF This model was converted to GGUF format from [`maidalun1020/bce-reranker-base_v1`](https://huggingface.co/maidalun1020/bce-reranker-base_v1) 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/maidalun1020/bce-reranker-base_v1) 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 colabbear/bce-reranker-base_v1-Q4_K_M-GGUF --hf-file bce-reranker-base_v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo colabbear/bce-reranker-base_v1-Q4_K_M-GGUF --hf-file bce-reranker-base_v1-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 colabbear/bce-reranker-base_v1-Q4_K_M-GGUF --hf-file bce-reranker-base_v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo colabbear/bce-reranker-base_v1-Q4_K_M-GGUF --hf-file bce-reranker-base_v1-q4_k_m.gguf -c 2048 ```
artfulf/test-google-gemma-2-2b-it	artfulf	2025-08-22T08:18:04Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-22T07:41:12Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mang3dd/blockassist-bc-tangled_slithering_alligator_1755849058	mang3dd	2025-08-22T08:16:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:16:29Z	--- 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).
tammycra121/blockassist-bc-marine_rangy_eel_1755849062	tammycra121	2025-08-22T08:15:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "marine rangy eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:15:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - marine rangy eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mynkjd/watergel	mynkjd	2025-08-22T08:15:03Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-22T08:15:01Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Watergel <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/mynkjd/watergel/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('mynkjd/watergel', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/mynkjd/watergel/discussions) to add images that show off what you’ve made with this LoRA.
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755850430	0xaoyama	2025-08-22T08:14:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:14:12Z	--- 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).
artfulf/test-deepseek-ai-DeepSeek-R1-Distill-Llama-8B	artfulf	2025-08-22T08:13:42Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-22T07:41:08Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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artfulf/test-google-gemma-3-1b-it	artfulf	2025-08-22T08:13:30Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-22T07:41:10Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755850337	IvanJAjebu	2025-08-22T08:13:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:13:17Z	--- 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).
caolahuu121/blockassist-bc-solitary_tenacious_gerbil_1755848949	caolahuu121	2025-08-22T08:12:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "solitary tenacious gerbil", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:12:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - solitary tenacious gerbil --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
InfoJelly/blockassist-bc-majestic_prehistoric_capybara_1755850323	InfoJelly	2025-08-22T08:12:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "majestic prehistoric capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:12:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - majestic prehistoric capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
2hpsatt/blockassist-bc-huge_deft_eagle_1755850195	2hpsatt	2025-08-22T08:11:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge deft eagle", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:11:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge deft eagle --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ypszn/blockassist-bc-yapping_pawing_worm_1755850214	ypszn	2025-08-22T08:10:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping pawing worm", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:10:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping pawing worm --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
GeniusJunP/grab_candy_policy	GeniusJunP	2025-08-22T08:10:26Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "act", "dataset:GeniusJunP/grab_candy", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-22T07:51:44Z	--- datasets: GeniusJunP/grab_candy library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - robotics - act - lerobot --- # 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
artfulf/test-deepseek-ai-DeepSeek-R1-Distill-Qwen-7B	artfulf	2025-08-22T08:09:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-22T07:41:07Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
InfoJelly/blockassist-bc-majestic_prehistoric_capybara_1755850096	InfoJelly	2025-08-22T08:08:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "majestic prehistoric capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:08:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - majestic prehistoric capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mynkjd/latest	mynkjd	2025-08-22T08:08:42Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-22T08:08:41Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Latest <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/mynkjd/latest/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('mynkjd/latest', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/mynkjd/latest/discussions) to add images that show off what you’ve made with this LoRA.
nema122/blockassist-bc-robust_fluffy_ram_1755849762	nema122	2025-08-22T08:03:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "robust fluffy ram", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:03:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - robust fluffy ram --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kapalbalap/blockassist-bc-peaceful_wary_owl_1755849668	kapalbalap	2025-08-22T08:02:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "peaceful wary owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T08:01:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - peaceful wary owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
InfoJelly/blockassist-bc-majestic_prehistoric_capybara_1755849563	InfoJelly	2025-08-22T07:59:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "majestic prehistoric capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:59:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - majestic prehistoric capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ypszn/blockassist-bc-yapping_pawing_worm_1755849507	ypszn	2025-08-22T07:59:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping pawing worm", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:59:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping pawing worm --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ranveerphakade/ISL-Sign-Lang-Detection	ranveerphakade	2025-08-22T07:59:24Z	0	0	null	[ "region:us" ]	null	2025-08-22T07:57:50Z	# Indian Sign Language Detection with IoT Integration A real-time Indian Sign Language (ISL) detection system using TensorFlow, MediaPipe, and ESP32-CAM. This project can recognize hand gestures for alphabets (A-Z) and numbers (0-9) in real-time using either a webcam or ESP32-CAM, with results displayed on an OLED screen. ## Features - Real-time hand gesture recognition - Support for 36 different signs (A-Z, 0-9) - Word suggestions based on detected gestures - IoT integration with ESP32-CAM - OLED display output - Support for both single and double-handed gestures - ~98% accuracy on test data ## System Architecture 1. Image Capture: - ESP32-CAM captures images - Sends via HTTP POST to Flask server 2. Processing Server: - Flask server receives images - Processes using MediaPipe for hand landmark detection - Uses TensorFlow model for gesture recognition 3. Display: - Results sent back to ESP32 - Displayed on SSD1306 OLED screen - Shows gesture and confidence score ## Requirements ### Software - Python 3.8 or higher - Arduino IDE for ESP32 - Required Python packages (see requirements.txt) ### Hardware - ESP32-CAM module - SSD1306 OLED Display (128x64) - USB-TTL converter for ESP32 programming - Connecting wires - 5V power supply ## Installation 1. Clone the repository: ```bash git clone https://github.com/ranveerphakade/ISL-sign-lang-detection.git cd ISL-sign-lang-detection ``` 2. Create and activate a virtual environment: ```bash # On Windows python -m venv venv venv\Scripts\activate # On Linux/Mac python3 -m venv venv source venv/bin/activate ``` 3. Install required packages: ```bash pip install -r requirements.txt ``` 4. Configure ESP32: - Open `esp32_code/esp32_cam_oled.ino` in Arduino IDE - Install required libraries: - ESP32 board support - Adafruit SSD1306 - Adafruit GFX - Update WiFi credentials and server IP - Upload to ESP32-CAM ## Project Structure ``` ISL-sign-lang-detection/ ├── flask_server/ │ ├── app.py # Flask server │ ├── static/ # Static files │ ├── templates/ # HTML templates │ └── test_client.py # Test script ├── esp32_code/ │ └── esp32_cam_oled.ino # ESP32 code ├── dummy_input/ # Test images ├── gesture_model.h5 # Trained model ├── train.py # Training script ├── detect.py # Webcam detection script ├── process.py # Data processing └── README.md ``` ## Usage ### Server Setup 1. Start the Flask server: ```bash cd flask_server python app.py ``` ### Testing Without Hardware 1. Add test images to `dummy_input/` directory 2. Run the test client: ```bash cd flask_server python test_client.py ``` ### Hardware Setup 1. Connect OLED display to ESP32-CAM: - VCC → 5V - GND → GND - SCL → GPIO22 - SDA → GPIO21 2. Power up the ESP32-CAM 3. The system will automatically: - Connect to WiFi - Start capturing images - Display results on OLED ## Model Architecture The system uses a deep neural network with: - Input layer: Hand landmark features - Hidden layers with dropout and batch normalization - Output layer: 36 classes (A-Z, 0-9) ## Performance - Training accuracy: ~98% - Real-time detection with confidence scores - Gesture stability checking to prevent false detections ## Contributing Feel free to contribute to this project by: 1. Fork the repository 2. Create your feature branch (`git checkout -b feature/amazing-feature`) 3. Commit your changes (`git commit -m 'Add amazing feature'`) 4. Push to the branch (`git push origin feature/amazing-feature`) 5. Open a Pull Request ## License This project is licensed under the MIT License - see the LICENSE file for details. ## Acknowledgments - TensorFlow team for the deep learning framework - MediaPipe team for the hand landmark detection system - OpenCV team for the computer vision tools - ESP32 community for IoT support
abemi/test	abemi	2025-08-22T07:57:24Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "act", "dataset:abemi/record-test_2", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-22T07:57:07Z	--- datasets: abemi/record-test_2 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 python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
artfulf/test-Qwen-Qwen2.5-7B-Instruct	artfulf	2025-08-22T07:55:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-22T07:41:00Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
hwang2006/qwen2.5-7b-alpaca-1pct-lora	hwang2006	2025-08-22T07:54:18Z	4	0	peft	[ "peft", "safetensors", "lora", "unsloth", "qwen", "instruction-tuning", "text-generation", "conversational", "en", "dataset:yahma/alpaca-cleaned", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "region:us" ]	text-generation	2025-08-22T07:54:12Z	--- license: "apache-2.0" base_model: "Qwen/Qwen2.5-7B-Instruct" tags: ["lora", "unsloth", "peft", "qwen", "instruction-tuning"] language: ["en"] datasets: ["yahma/alpaca-cleaned"] library_name: peft pipeline_tag: text-generation --- # LoRA Adapter for Qwen/Qwen2.5-7B-Instruct This repository hosts a LoRA adapter (and tokenizer files) trained on top of Qwen/Qwen2.5-7B-Instruct. ## ✨ What’s inside - PEFT type: LORA - LoRA r: 16 - LoRA alpha: 16 - LoRA dropout: 0.0 - Target modules: q_proj, gate_proj, o_proj, down_proj, k_proj, v_proj, up_proj ## 📚 Datasets - yahma/alpaca-cleaned ## 🌐 Languages - en ## 📝 Usage ### (A) Use adapter with the official base model ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel import torch base = "Qwen/Qwen2.5-7B-Instruct" adapter_id = "hwang2006/qwen2.5-7b-alpaca-1pct-lora" tok = AutoTokenizer.from_pretrained(base) base_model = AutoModelForCausalLM.from_pretrained( base, torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32, device_map="auto", ) model = PeftModel.from_pretrained(base_model, adapter_id) messages = [ {"role":"system","content":"You are a helpful assistant."}, {"role":"user","content":"Quick test?"}, ] prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tok(prompt, return_tensors="pt").to(model.device) with torch.inference_mode(): out = model.generate(inputs, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.9) print(tok.decode(out[0], skip_special_tokens=True)) ``` ### (B) 4-bit on the fly (if VRAM is tight) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from peft import PeftModel import torch bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16) base = "Qwen/Qwen2.5-7B-Instruct" adapter_id = "hwang2006/qwen2.5-7b-alpaca-1pct-lora" tok = AutoTokenizer.from_pretrained(base) base_model = AutoModelForCausalLM.from_pretrained(base, quantization_config=bnb, device_map="auto") model = PeftModel.from_pretrained(base_model, adapter_id) ``` ## ⚠️ Notes - Use a compatible base (architecture & tokenizer) with this LoRA. - This repo contains only adapters/tokenizer, not full model weights. - License here reflects this adapter’s repository. Ensure the base model’s license** fits your use.
abhinayadutta/flan-t5-large-counter-speech-gen_QLORA_v2	abhinayadutta	2025-08-22T07:54:13Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-08-22T07:49:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Medved444/blockassist-bc-bellowing_finicky_manatee_1755848076	Medved444	2025-08-22T07:54:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bellowing finicky manatee", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:53:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bellowing finicky manatee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pkj1702/crypto-longshort_enter-8b	pkj1702	2025-08-22T07:53:42Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-22T07:43:32Z	--- 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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FlagRelease/Qwen3-235B-A22B-Instruct-2507-hygon-FlagOS	FlagRelease	2025-08-22T07:52:28Z	0	0	null	[ "safetensors", "qwen3_moe", "region:us" ]	null	2025-08-22T01:49:42Z	# Introduction FlagOS is a unified heterogeneous computing software stack for large models, co-developed with leading global chip manufacturers. With core technologies such as the FlagScale distributed training/inference framework, FlagGems universal operator library, FlagCX communication library, and FlagTree unified compiler, the FlagRelease platform leverages the FlagOS stack to automatically produce and release various combinations of <chip + open-source model>. This enables efficient and automated model migration across diverse chips, opening a new chapter for large model deployment and application. Based on this, the Qwen3-235B-A22B-Instruct-2507-hygon-FlagOS model is adapted for the Hygon chip using the FlagOS software stack, enabling: ### Integrated Deployment - Deep integration with the open-source [FlagScale framework](https://github.com/FlagOpen/FlagScale) - Out-of-the-box inference scripts with pre-configured hardware and software parameters - Released FlagOS container image supporting deployment within minutes ### Consistency Validation - Rigorously evaluated through benchmark testing: Performance and results from the FlagOS software stack are compared against native stacks on multiple public. # Technical Overview ## FlagScale Distributed Training and Inference Framework FlagScale is an end-to-end framework for large models across heterogeneous computing resources, maximizing computational efficiency and ensuring model validity through core technologies. Its key advantages include: - Unified Deployment Interface: Standardized command-line tools support one-click service deployment across multiple hardware platforms, significantly reducing adaptation costs in heterogeneous environments. - Intelligent Parallel Optimization: Automatically generates optimal distributed parallel strategies based on chip computing characteristics, achieving dynamic load balancing of computation/communication resources. - Seamless Operator Switching: Deep integration with the FlagGems operator library allows high-performance operators to be invoked via environment variables without modifying model code. ## FlagGems Universal Large-Model Operator Library FlagGems is a Triton-based, cross-architecture operator library collaboratively developed with industry partners. Its core strengths include: - Full-stack Coverage: Over 100 operators, with a broader range of operator types than competing libraries. - Ecosystem Compatibility: Supports 7 accelerator backends. Ongoing optimizations have significantly improved performance. - High Efficiency: Employs unique code generation and runtime optimization techniques for faster secondary development and better runtime performance compared to alternatives. ## FlagEval Evaluation Framework FlagEval (Libra) is a comprehensive evaluation system and open platform for large models launched in 2023. It aims to establish scientific, fair, and open benchmarks, methodologies, and tools to help researchers assess model and training algorithm performance. It features: - Multi-dimensional Evaluation: Supports 800+ model evaluations across NLP, CV, Audio, and Multimodal fields, covering 20+ downstream tasks including language understanding and image-text generation. - Industry-Grade Use Cases: Has completed horizontal evaluations of mainstream large models, providing authoritative benchmarks for chip-model performance validation. # Evaluation Results ## Benchmark Result \| Metrics \| Qwen3-235B-A22B-Instruct-2507-H100-CUDA \| Qwen3-235B-A22B-Instruct-2507-hygon-FlagOS \| \| --------- \| ------------------ \| ---------------------- \| \| liveBench-0shot@avg1 \| 0.753 \| 0.751 \| \| AIME-0shot@avg1 \| 0.833 \| 0.800 \| \| MMLU-5shots@avg1 \| 0.833 \| 0.835 \| \| MUSR-0shot@avg1 \| 0.597 \| 0.612 \| \| GPQA-0shot@avg1 \| - \| 0.579 \| # User Guide Environment Setup \| Item \| Version \| \| ------------- \| ------------------------------------------------------------ \| \| Docker Version \| Docker version 24.0.6, build ed223bc \| \| Operating System \| Ubuntu 22.04.4 LTS \| \| FlagScale \| Version: 0.8.0 \| \| FlagGems \| Version: 3.0 \| ## Operation Steps ### Download Open-source Model Weights ```bash pip install modelscope modelscope download --model Qwen/Qwen3-235B-A22B-Instruct-2507 --local_dir /share/Qwen3-235B-A22B-Instruct-2507 ``` ### Download FlagOS Image BE AWARE!, Hygon's FLAGOS image have not decided public-accesible through internet or not. To obtain this image, you can contact us or hygon through issues. ```bash docker pull harbor.baai.ac.cn/flagrelease-inner/flagrelease_hygon_qwen3_2507 ``` ### Start the inference service ```bash #Container Startup docker run -it \ --name=flagos \ --network=host \ --privileged \ --ipc=host \ --shm-size=16G \ --memory="512g" \ --ulimit stack=-1:-1 \ --ulimit memlock=-1:-1 \ --cap-add=SYS_PTRACE \ --security-opt seccomp=unconfined \ --device=/dev/kfd \ --device=/dev/dri \ --group-add video \ -u root \ -v /opt/hyhal:/opt/hyhal \ -v /share:/share \ harbor.baai.ac.cn/flagrelease-inner/flagrelease_hygon_qwen3_2507 \ /bin/bash ``` ### Serve ```bash flagscale serve qwen3 ``` ## Service Invocation ### API-based Invocation Script ```bash import openai openai.api_key = "EMPTY" openai.base_url = "http://<server_ip>:9010/v1/" model = "Qwen3-235B-A22B-Instruct-2507-hygon-flagos" messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What's the weather like today?"} ] response = openai.chat.completions.create( model=model, messages=messages, temperature=0.7, top_p=0.95, stream=False, ) for item in response: print(item) ``` ### AnythingLLM Integration Guide #### 1. Download & Install - Visit the official site: https://anythingllm.com/ - Choose the appropriate version for your OS (Windows/macOS/Linux) - Follow the installation wizard to complete the setup #### 2. Configuration - Launch AnythingLLM - Open settings (bottom left, fourth tab) - Configure core LLM parameters - Click "Save Settings" to apply changes #### 3. Model Interaction - After model loading is complete: - Click "New Conversation"** - Enter your question (e.g., “Explain the basics of quantum computing”) - Click the send button to get a response # Contributing We warmly welcome global developers to join us: 1. Submit Issues to report problems 2. Create Pull Requests to contribute code 3. Improve technical documentation 4. Expand hardware adaptation support # License 本模型的权重来源于Qwen/Qwen3-235B-A22B-Instruct-2507，以apache2.0协议https://www.apache.org/licenses/LICENSE-2.0.txt开源。
2hpsatt/blockassist-bc-huge_deft_eagle_1755849054	2hpsatt	2025-08-22T07:52:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge deft eagle", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:52:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge deft eagle --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
indoempatnol/blockassist-bc-fishy_wary_swan_1755847438	indoempatnol	2025-08-22T07:51:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:51:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Bocklitz-Lab/lit2vec-tldr-bart-model	Bocklitz-Lab	2025-08-22T07:51:04Z	0	0	transformers	[ "transformers", "pytorch", "onnx", "safetensors", "bart", "text2text-generation", "chemistry", "scientific-summarization", "distilbart", "abstractive", "tldr", "knowledge-graphs", "summarization", "en", "dataset:Bocklitz-Lab/lit2vec-tldr-bart-dataset", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	summarization	2025-08-15T15:24:52Z	--- language: - en library_name: transformers pipeline_tag: summarization license: apache-2.0 tags: - chemistry - scientific-summarization - distilbart - abstractive - tldr - knowledge-graphs datasets: - Bocklitz-Lab/lit2vec-tldr-bart-dataset model-index: - name: lit2vec-tldr-bart results: - task: name: Summarization type: summarization dataset: name: Lit2Vec TL;DR Chemistry Dataset type: Bocklitz-Lab/lit2vec-tldr-bart-dataset split: test size: 1001 metrics: - type: rouge1 value: 56.11 - type: rouge2 value: 30.78 - type: rougeLsum value: 45.43 --- # lit2vec-tldr-bart (DistilBART fine-tuned for chemistry TL;DRs) lit2vec-tldr-bart is a DistilBART model fine-tuned on 19,992 CC-BY licensed chemistry abstracts to produce concise TL;DR-style summaries aligned with methods → results → significance. It’s designed for scientific abstractive summarization, semantic indexing, and knowledge-graph population in chemistry and related fields. - Base model: `sshleifer/distilbart-cnn-12-6` - Training data: [`Bocklitz-Lab/lit2vec-tldr-bart-dataset`](https://huggingface.co/datasets/Bocklitz-Lab/lit2vec-tldr-bart-dataset) - Max input length: 1024 tokens - Target length: ~128 tokens --- ## 🧪 Evaluation (held-out test) \| Split \| ROUGE-1 \| ROUGE-2 \| ROUGE-Lsum \| \|------:\|--------:\|--------:\|-----------:\| \| Test \| 56.11 \| 30.78 \| 45.43 \| > Validation RLsum: 46.05 > Metrics computed with `evaluate`'s `rouge` (NLTK sentence segmentation, `use_stemmer=True`). --- ## 🚀 Quickstart ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, GenerationConfig repo = "Bocklitz-Lab/lit2vec-tldr-bart" tok = AutoTokenizer.from_pretrained(repo) model = AutoModelForSeq2SeqLM.from_pretrained(repo) gen = GenerationConfig.from_pretrained(repo) # loads default decoding params text = "Proton exchange membrane fuel cells convert chemical energy into electricity..." inputs = tok(text, return_tensors="pt", truncation=True, max_length=1024) summary_ids = model.generate(inputs, gen.to_dict()) print(tok.decode(summary_ids[0], skip_special_tokens=True)) ```` ### Batch inference (PyTorch) ```python texts = [ "Abstract 1 ...", "Abstract 2 ...", ] batch = tok(texts, return_tensors="pt", padding=True, truncation=True, max_length=1024) out = model.generate(batch, gen.to_dict()) summaries = tok.batch_decode(out, skip_special_tokens=True) ``` --- ## 🔧 Default decoding (saved in `generation_config.json`) These are the defaults saved with the model (you can override at `generate()` time): ```json { "max_length": 142, "min_length": 56, "early_stopping": true, "num_beams": 4, "length_penalty": 2.0, "no_repeat_ngram_size": 3, "forced_bos_token_id": 0, "forced_eos_token_id": 2 } ``` --- ## 📊 Training details * Base: `sshleifer/distilbart-cnn-12-6` (Distilled BART) * Data: 19,992 CC-BY chemistry abstracts with TL;DR summaries * Splits: train=17,992 / val=999 / test=1,001 * Max lengths: input 1024, target 128 * Optimizer: AdamW, lr=2e-5 * Batching: per-device train/eval batch size 4, gradient\_accumulation\_steps=4 * Epochs: 5 * Precision: fp16 (when CUDA available) * Hardware: single NVIDIA RTX 3090 * Seed: 42 * Libraries: 🤗 Transformers + Datasets, `evaluate` for ROUGE, NLTK for sentence splitting --- ## ✅ Intended use * TL;DR abstractive summaries for chemistry and adjacent domains (materials science, chemical engineering, environmental science). * Semantic indexing, IR reranking, and knowledge graph ingestion where concise method/result statements are helpful. ### Limitations & risks * May hallucinate details not present in the abstract (typical for abstractive models). * Not a substitute for expert judgment; avoid using summaries as sole evidence for scientific claims. * Trained on CC-BY English abstracts; performance may degrade on other domains/languages. --- ## 📦 Files This repo should include: * `config.json`, `pytorch_model.bin` or `model.safetensors` * `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`, merges/vocab as applicable * `generation_config.json` (decoding defaults) --- ## 🔁 Reproducibility * Dataset: [`Bocklitz-Lab/lit2vec-tldr-bart-dataset`](https://huggingface.co/datasets/Bocklitz-Lab/lit2vec-tldr-bart-dataset) * Recommended preprocessing: truncate inputs at 1024 tokens; targets at 128. * ROUGE evaluation: `evaluate.load("rouge")`, NLTK sentence tokenization, `use_stemmer=True`. --- ## 📚 Citation If you use this model or dataset, please cite: ```bibtex @software{lit2vec_tldr_bart_2025, title = {lit2vec-tldr-bart: DistilBART fine-tuned for chemistry TL;DR summarization}, author = {Bocklitz Lab}, year = {2025}, url = {https://huggingface.co/Bocklitz-Lab/lit2vec-tldr-bart}, note = {Model trained on CC-BY chemistry abstracts; dataset at Bocklitz-Lab/lit2vec-tldr-bart-dataset} } ``` Dataset: ```bibtex @dataset{lit2vec_tldr_dataset_2025, title = {Lit2Vec TL;DR Chemistry Dataset}, author = {Bocklitz Lab}, year = {2025}, url = {https://huggingface.co/datasets/Bocklitz-Lab/lit2vec-tldr-bart-dataset} } ``` --- ## 📝 License * Model weights & code: Apache-2.0 * Dataset: CC BY 4.0 (attribution in per-record metadata) --- ## 🙌 Acknowledgements * Base model: DistilBART (`sshleifer/distilbart-cnn-12-6`) * Licensing and OA links curated from publisher/aggregator sources; dataset restricted to CC-BY content.
suraj5556/tokenizer-en-mar	suraj5556	2025-08-22T07:50:55Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-22T07:50:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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suraj5556/transformer-en-mar	suraj5556	2025-08-22T07:50:44Z	0	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-22T07:47:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jethac/MyGemmaNPC2	jethac	2025-08-22T07:50:28Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-22T07:49:10Z	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: MyGemmaNPC2 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MyGemmaNPC2 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="jethac/MyGemmaNPC2", 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+cu126 - 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}} } ```
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755847496	vwzyrraz7l	2025-08-22T07:50:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:50:19Z	--- 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).
rbelanec/train_mmlu_1755681415	rbelanec	2025-08-22T07:50:15Z	15	0	peft	[ "peft", "safetensors", "llama-factory", "ia3", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2025-08-20T09:17:40Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - llama-factory - ia3 - generated_from_trainer model-index: - name: train_mmlu_1755681415 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. --> # train_mmlu_1755681415 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the mmlu dataset. It achieves the following results on the evaluation set: - Loss: 0.1708 - Num Input Tokens Seen: 488118104 ## 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: 4 - eval_batch_size: 4 - seed: 123 - 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: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Input Tokens Seen \| \|:-------------:\|:------:\|:------:\|:---------------:\|:-----------------:\| \| 0.1364 \| 0.5000 \| 11233 \| 0.2498 \| 24389728 \| \| 0.0552 \| 1.0000 \| 22466 \| 0.2182 \| 48789280 \| \| 0.2115 \| 1.5001 \| 33699 \| 0.2011 \| 73201984 \| \| 0.0865 \| 2.0001 \| 44932 \| 0.1919 \| 97620120 \| \| 0.0963 \| 2.5001 \| 56165 \| 0.1872 \| 122127480 \| \| 0.1485 \| 3.0001 \| 67398 \| 0.1822 \| 146471872 \| \| 0.1541 \| 3.5002 \| 78631 \| 0.1787 \| 170850208 \| \| 0.1574 \| 4.0002 \| 89864 \| 0.1760 \| 195267312 \| \| 0.0981 \| 4.5002 \| 101097 \| 0.1755 \| 219639056 \| \| 0.1848 \| 5.0002 \| 112330 \| 0.1736 \| 244095744 \| \| 0.1731 \| 5.5002 \| 123563 \| 0.1730 \| 268478944 \| \| 0.1704 \| 6.0003 \| 134796 \| 0.1713 \| 292933144 \| \| 0.0277 \| 6.5003 \| 146029 \| 0.1724 \| 317335480 \| \| 0.1044 \| 7.0003 \| 157262 \| 0.1709 \| 341742832 \| \| 0.1996 \| 7.5003 \| 168495 \| 0.1710 \| 366182192 \| \| 0.1288 \| 8.0004 \| 179728 \| 0.1710 \| 390549264 \| \| 0.0802 \| 8.5004 \| 190961 \| 0.1711 \| 414924016 \| \| 0.088 \| 9.0004 \| 202194 \| 0.1708 \| 439335448 \| \| 0.1749 \| 9.5004 \| 213427 \| 0.1710 \| 463693944 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755848938	IvanJAjebu	2025-08-22T07:50:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:49:53Z	--- 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).
jethac/MyGemmaNPC	jethac	2025-08-22T07:49:08Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "base_model:google/gemma-3-1b-it", "base_model:finetune:google/gemma-3-1b-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-22T07:18:54Z	--- base_model: google/gemma-3-1b-it library_name: transformers model_name: MyGemmaNPC tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for MyGemmaNPC This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-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="jethac/MyGemmaNPC", 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+cu126 - 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}} } ```
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755847289	quantumxnode	2025-08-22T07:48:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:48:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
nema122/blockassist-bc-robust_fluffy_ram_1755848831	nema122	2025-08-22T07:48:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "robust fluffy ram", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:48:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - robust fluffy ram --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
TharunSivamani/mcprl-7b-doris	TharunSivamani	2025-08-22T07:47:48Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-22T07:47:33Z	--- base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: TharunSivamani - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755847164	helmutsukocok	2025-08-22T07:46:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:46:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ypszn/blockassist-bc-yapping_pawing_worm_1755848686	ypszn	2025-08-22T07:45:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping pawing worm", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:45:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping pawing worm --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
InfoJelly/blockassist-bc-majestic_prehistoric_capybara_1755848698	InfoJelly	2025-08-22T07:45:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "majestic prehistoric capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:45:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - majestic prehistoric capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
llencia/blockassist-bc-wiry_wise_hedgehog_1755848559	llencia	2025-08-22T07:43:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry wise hedgehog", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:43:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry wise hedgehog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
srikar-v05/Qwen2.5-3B-GRPO-LoRA	srikar-v05	2025-08-22T07:42:37Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-22T07:42:23Z	--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: srikar-v05 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
wasabuko/blockassist-bc-noisy_zealous_macaw_1755845875	wasabuko	2025-08-22T07:38:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "noisy zealous macaw", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:35:07Z	--- 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).
eshaaftab900/EN_DeepSeek-R1-Distill-Llama-8B-ft-QRCD-and-Quran-lora-adapters	eshaaftab900	2025-08-22T07:36:59Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "region:us" ]	text-generation	2025-08-22T07:36:28Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit - lora - sft - transformers - trl - 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. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0
mradermacher/Afxumo-toxicity-somaliland-SO-GGUF	mradermacher	2025-08-22T07:36:01Z	173	0	transformers	[ "transformers", "gguf", "RoBERTa", "acfp", "automatic_classifiers_for_peace", "hatespeech", "toxicity", "afxumo", "so", "base_model:datavaluepeople/Afxumo-toxicity-somaliland-SO", "base_model:quantized:datavaluepeople/Afxumo-toxicity-somaliland-SO", "license:agpl-3.0", "endpoints_compatible", "region:us", "feature-extraction" ]	null	2025-08-04T20:48:52Z	--- base_model: datavaluepeople/Afxumo-toxicity-somaliland-SO language: - so library_name: transformers license: agpl-3.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - RoBERTa - acfp - automatic_classifiers_for_peace - hatespeech - toxicity - afxumo --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/datavaluepeople/Afxumo-toxicity-somaliland-SO <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Afxumo-toxicity-somaliland-SO-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Afxumo-toxicity-somaliland-SO-GGUF/resolve/main/Afxumo-toxicity-somaliland-SO.Q2_K.gguf) \| Q2_K \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Afxumo-toxicity-somaliland-SO-GGUF/resolve/main/Afxumo-toxicity-somaliland-SO.Q3_K_S.gguf) \| Q3_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Afxumo-toxicity-somaliland-SO-GGUF/resolve/main/Afxumo-toxicity-somaliland-SO.Q3_K_M.gguf) \| Q3_K_M \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Afxumo-toxicity-somaliland-SO-GGUF/resolve/main/Afxumo-toxicity-somaliland-SO.IQ4_XS.gguf) \| IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Afxumo-toxicity-somaliland-SO-GGUF/resolve/main/Afxumo-toxicity-somaliland-SO.Q3_K_L.gguf) \| Q3_K_L \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Afxumo-toxicity-somaliland-SO-GGUF/resolve/main/Afxumo-toxicity-somaliland-SO.Q4_K_S.gguf) \| Q4_K_S \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Afxumo-toxicity-somaliland-SO-GGUF/resolve/main/Afxumo-toxicity-somaliland-SO.Q4_K_M.gguf) \| Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Afxumo-toxicity-somaliland-SO-GGUF/resolve/main/Afxumo-toxicity-somaliland-SO.Q5_K_S.gguf) \| Q5_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Afxumo-toxicity-somaliland-SO-GGUF/resolve/main/Afxumo-toxicity-somaliland-SO.Q5_K_M.gguf) \| Q5_K_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Afxumo-toxicity-somaliland-SO-GGUF/resolve/main/Afxumo-toxicity-somaliland-SO.Q6_K.gguf) \| Q6_K \| 0.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Afxumo-toxicity-somaliland-SO-GGUF/resolve/main/Afxumo-toxicity-somaliland-SO.Q8_0.gguf) \| Q8_0 \| 0.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Afxumo-toxicity-somaliland-SO-GGUF/resolve/main/Afxumo-toxicity-somaliland-SO.f16.gguf) \| f16 \| 0.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
0xGareeb/blockassist-bc-diving_jumping_llama_1755848040	0xGareeb	2025-08-22T07:35:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving jumping llama", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:34:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving jumping llama --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
InfoJelly/blockassist-bc-majestic_prehistoric_capybara_1755848085	InfoJelly	2025-08-22T07:35:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "majestic prehistoric capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:35:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - majestic prehistoric capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755846863	Sayemahsjn	2025-08-22T07:34:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:33:56Z	--- 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).
thanobidex/blockassist-bc-colorful_shiny_hare_1755846412	thanobidex	2025-08-22T07:32:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:32:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful shiny hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VesileT/xlm-roberta-sentiment	VesileT	2025-08-22T07:31:59Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-22T07:31:59Z	--- license: apache-2.0 ---
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755847825	IvanJAjebu	2025-08-22T07:31:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:31:28Z	--- 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).
sarayusapa/T5_Large_GEC_LoRA	sarayusapa	2025-08-22T07:30:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-22T05:41:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
zxcczx/blockassist-bc-durable_energetic_fly_1755844124	zxcczx	2025-08-22T07:28:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "durable energetic fly", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:28:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - durable energetic fly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755846029	hakimjustbao	2025-08-22T07:27:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:27:27Z	--- 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).
Tank-123/act_so101_test_0822_based_on_robot_vla	Tank-123	2025-08-22T07:27:17Z	0	0	lerobot	[ "lerobot", "safetensors", "act", "robotics", "dataset:Tank-123/act_test_0822_based_on_robot_vla", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-22T07:26:18Z	--- datasets: Tank-123/act_test_0822_based_on_robot_vla library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - act - lerobot - robotics --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`. ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details * License: apache-2.0
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755846080	calegpedia	2025-08-22T07:27:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:26:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggml-org/gpt-oss-120b-GGUF	ggml-org	2025-08-22T07:26:59Z	31,760	21	null	[ "gguf", "base_model:openai/gpt-oss-120b", "base_model:quantized:openai/gpt-oss-120b", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-02T16:09:37Z	--- base_model: - openai/gpt-oss-120b --- # gpt-oss-120b Detailed guide for using this model with `llama.cpp`: https://github.com/ggml-org/llama.cpp/discussions/15396 Quick start: ```sh llama-server -hf ggml-org/gpt-oss-120b-GGUF -c 0 -fa --jinja # Then, access http://localhost:8080 ```
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755847507	IvanJAjebu	2025-08-22T07:26:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:26:14Z	--- 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).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755845873	kojeklollipop	2025-08-22T07:25:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:25:11Z	--- 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).
ypszn/blockassist-bc-yapping_pawing_worm_1755847466	ypszn	2025-08-22T07:25:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping pawing worm", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:25:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping pawing worm --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kapalbalap/blockassist-bc-peaceful_wary_owl_1755847440	kapalbalap	2025-08-22T07:24:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "peaceful wary owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:24:38Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - peaceful wary owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755845763	coelacanthxyz	2025-08-22T07:24:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:24:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755845730	katanyasekolah	2025-08-22T07:24:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:24:12Z	--- 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).
kapalbalap/blockassist-bc-peaceful_wary_owl_1755847258	kapalbalap	2025-08-22T07:22:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "peaceful wary owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:21:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - peaceful wary owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
nema122/blockassist-bc-robust_fluffy_ram_1755847147	nema122	2025-08-22T07:20:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "robust fluffy ram", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:20:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - robust fluffy ram --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yataka112/blockassist-bc-extinct_jumping_lynx_1755847127	yataka112	2025-08-22T07:20:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "extinct jumping lynx", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:20:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - extinct jumping lynx --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mia-project-2025/bert-base-uncased-LoRA-quora-question-pairs	mia-project-2025	2025-08-22T07:20:03Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-08-21T21:02:03Z	--- license: apache-2.0 --- # BERT Base Uncased + LoRA Fine-Tuned For Quora Duplicate Question Detection This model applies LoRA (Low-Rank Adaptation) fine-tuning on [tomaarsen/bert-base-nq-prompts](https://huggingface.co/tomaarsen/bert-base-nq-prompts) for the Quora Question Pairs dataset. It classifies whether two questions are duplicates. --- ## Model Details - Base Model: `tomaarsen/bert-base-nq-prompts` - Fine-tuning Method: LoRA (PEFT) - LoRA Config: - `r=8`, `alpha=64`, `dropout=0.1` - Target modules: `query`, `key`, `value`, `dense` - Dataset: [Quora Question Pairs](https://huggingface.co/datasets/quora) - Training Epochs: 16 - Optimizer: AdamW (torch fused) - Batch Size: 64 (gradient accumulation = 2) - Loss Function: CrossEntropyLoss --- ## Performance (Epoch 16) \| Metric \| Score \| \|-------------\|---------\| \| Train Loss \| 0.447 \| \| Eval Loss \| 0.257 \| \| Accuracy \| 89.98% \| \| Precision \| 83.41% \| \| Recall \| 90.80% \| \| F1 Score \| 86.95% \| --- ## Example Usage ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification from peft import PeftModel # Load base model and tokenizer model_name = "tomaarsen/bert-base-nq-prompts" tokenizer = AutoTokenizer.from_pretrained(model_name) # Load LoRA fine-tuned model base_model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2) model = PeftModel.from_pretrained(base_model, "mia-project-2025/bert-base-uncased-LoRA-quora-question-pairs") model.eval() def predict_duplicate(q1, q2): inputs = tokenizer(q1, q2, return_tensors="pt", truncation=True, padding=True, max_length=128) with torch.no_grad(): logits = model(**inputs).logits pred = torch.argmax(logits, dim=1).item() return "Duplicate" if pred == 1 else "Not Duplicate" # Example print(predict_duplicate("How can I learn Python?", "What are the best ways to learn Python programming?"))
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755847072	IvanJAjebu	2025-08-22T07:19:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:19:00Z	--- 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).
mradermacher/Amaru-VL-3B-i1-GGUF	mradermacher	2025-08-22T07:18:20Z	100	0	transformers	[ "transformers", "gguf", "base_model:adapter:unsloth/Qwen2.5-VL-3B-Instruct", "lora", "sft", "trl", "unsloth", "es", "dataset:NovaIALATAM/CuPer_Text", "dataset:NovaIALATAM/CuPer_Images", "base_model:NovaIALATAM/Amaru-VL-3B", "base_model:adapter:NovaIALATAM/Amaru-VL-3B", "license:cc-by-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-21T16:05:18Z	--- base_model: NovaIALATAM/Amaru-VL-3B datasets: - NovaIALATAM/CuPer_Text - NovaIALATAM/CuPer_Images language: - es library_name: transformers license: cc-by-4.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - base_model:adapter:unsloth/Qwen2.5-VL-3B-Instruct - lora - sft - transformers - trl - unsloth --- ## 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/NovaIALATAM/Amaru-VL-3B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Amaru-VL-3B-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/Amaru-VL-3B-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/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 1.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-Q2_K.gguf) \| i1-Q2_K \| 1.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 1.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 1.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 1.7 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 1.9 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-Q4_0.gguf) \| i1-Q4_0 \| 1.9 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 1.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Amaru-VL-3B-i1-GGUF/resolve/main/Amaru-VL-3B.i1-Q6_K.gguf) \| i1-Q6_K \| 2.6 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
suwesh/llamatron-1B-peft	suwesh	2025-08-22T07:17:40Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "peft", "chat", "LoRA", "RTX3060", "conversational", "en", "dataset:nvidia/Llama-Nemotron-Post-Training-Dataset", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T12:55:10Z	--- license: llama3.2 datasets: - nvidia/Llama-Nemotron-Post-Training-Dataset language: - en base_model: - meta-llama/Llama-3.2-1B-Instruct library_name: transformers tags: - peft - chat - LoRA - RTX3060 --- # Model Information This model is a fine-tuned version of the [meta-llama/Llama-3.2-1B-Instruct](https://www.huggingface.co/meta-llama/Llama-3.2-1B-Instruct) large language model. Fine tuning was performed using PEFT (Parameter Efficient Fine Tuning) with LoRA (Low-Rank Adaptation) on the chat subset of the [nvidia/Llama-Nemotron-Post-Training-Dataset](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset) dataset. LoRA Configuration: <pre>lora_config = LoraConfig( task_type="CAUSAL_LM", r=32, lora_alpha=32, lora_dropout=0.1, target_modules=["q_proj", "k_proj", "v_proj"], modules_to_save=["lm_head", "embed_token"], ) </pre> # Use with Transformers <pre>pip install transformers pip install torch</pre> <pre> from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("suwesh/llamatron-1B-peft").to("cuda") tokenizer = AutoTokenizer.from_pretrained("suwesh/llamatron-1B-peft") input_text = "Hello, how are you?" inputs = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=512) response = tokenizer.decode(outputs[0], skip_special_tokens=True) </pre> Or with pipeline <pre> import torch import transformers from transformers import pipeline pipe = pipeline( "text-generation", model=("suwesh/llamatron-1B-peft"), tokenizer=transformers.AutoTokenizer.from_pretrained("suwesh/llamatron-1B-peft"), torch_dtype=torch.bfloat16, device="cuda", ) def to_model(input_text, system_message): messages = [ {"role": "system", "content": system_message}, {"role": "user", "content": input_text} ] outputs = pipe( messages, max_new_tokens=512, temperature=0.6, top_p=0.95 ) return outputs[0]["generated_text"][-1]['content'] response = to_model("Write a joke about windows.", "detailed thinking on") </pre> # Load adapter checkpoint for further fine tuning <pre> from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct") tokenizer = AutoTokenizer.from_pretrained("suwesh/llamatron-1B-peft") model = PeftModel.from_pretrained(base_model, "suwesh/llamatron-1B-peft", subfolder="checkpoint-11000") </pre> # Training details <pre>Initial Training and Validation losses: 1.69 \| 1.67</pre> <pre>Checkpoint 11000 Training and Validation losses: 1.06 \| 1.09</pre> # Evaluation details We use the [nvidia/Llama-3.1-Nemotron-Nano](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1) LLM as a Judge for evaluating the responses between the base llama 3.2 1b instruct and our PEFT model. The following are the judge's preference for each prompt to the two models, we also provide the ground truth in the prompt to the judge: <pre>base: 122 peft: 388 tie: 29 </pre> system_message = """ You are an expert evaluator comparing two AI responses to a user instruction. Use the following criteria: -Clarity -Factual correctness (compared to the reference answer) -Instruction-following -Depth of reasoning Below is the reference answer, which is the ideal or expected response: """
mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-GGUF	mradermacher	2025-08-22T07:16:34Z	46	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "base_model:Jasaxion/MathSmith-HC-Qwen3-32B-ShortCoT", "base_model:quantized:Jasaxion/MathSmith-HC-Qwen3-32B-ShortCoT", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-21T19:47:49Z	--- base_model: Jasaxion/MathSmith-HC-Qwen3-32B-ShortCoT language: - en library_name: transformers license: other mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - llama-factory - full - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/Jasaxion/MathSmith-HC-Qwen3-32B-ShortCoT <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#MathSmith-HC-Qwen3-32B-ShortCoT-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-GGUF/resolve/main/MathSmith-HC-Qwen3-32B-ShortCoT.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-GGUF/resolve/main/MathSmith-HC-Qwen3-32B-ShortCoT.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-GGUF/resolve/main/MathSmith-HC-Qwen3-32B-ShortCoT.Q3_K_M.gguf) \| Q3_K_M \| 16.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-GGUF/resolve/main/MathSmith-HC-Qwen3-32B-ShortCoT.Q3_K_L.gguf) \| Q3_K_L \| 17.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-GGUF/resolve/main/MathSmith-HC-Qwen3-32B-ShortCoT.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-GGUF/resolve/main/MathSmith-HC-Qwen3-32B-ShortCoT.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-GGUF/resolve/main/MathSmith-HC-Qwen3-32B-ShortCoT.Q4_K_M.gguf) \| Q4_K_M \| 19.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-GGUF/resolve/main/MathSmith-HC-Qwen3-32B-ShortCoT.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-GGUF/resolve/main/MathSmith-HC-Qwen3-32B-ShortCoT.Q5_K_M.gguf) \| Q5_K_M \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-GGUF/resolve/main/MathSmith-HC-Qwen3-32B-ShortCoT.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MathSmith-HC-Qwen3-32B-ShortCoT-GGUF/resolve/main/MathSmith-HC-Qwen3-32B-ShortCoT.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mia-project-2025/bert-base-uncased-LoRA-glue-mnli	mia-project-2025	2025-08-22T07:15:34Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-08-22T07:08:47Z	--- license: apache-2.0 --- # BERT-base + LoRA on GLUE MNLI This repository contains a BERT-base model fine-tuned with LoRA adapters on the [GLUE MNLI dataset](https://huggingface.co/datasets/glue/viewer/mnli). The model is trained for natural language inference (NLI) with three labels: entailment, neutral, and contradiction. --- ## Dataset - Name: GLUE Multi-Genre Natural Language Inference (MNLI) - Task: Natural Language Inference - Size: 392k training examples, with validation splits for matched (in-domain) and mismatched (out-of-domain). - Labels: - `0` = Contradiction - `1` = Entailment - `2` = Neutral --- ## Training Setup - Base Model: `bert-base-uncased` - Fine-tuning Method: Parameter-Efficient Fine-Tuning (PEFT) using LoRA adapters - LoRA Configuration: - Rank `r = 8` - Alpha = `64` - Target modules = `query`, `key`, `value`, `dense` - Dropout = `0.1` - Hyperparameters: - Epochs: `15` - Batch size: `64` - Learning rate: `5e-5` - Optimizer: `AdamW (fused)` - Gradient accumulation: `2` - Weight decay: `0.01` - Warmup ratio: `0.1` - Hardware: Trained on GPUs with mixed precision (`fp16`). --- ## Results ### Final Evaluation (Epoch 15) \| Metric \| Matched (in-domain) \| Mismatched (out-of-domain) \| \|---------------\|----------------------\|-----------------------------\| \| Eval Loss \| 0.4549 \| 0.4476 \| \| Accuracy \| 83.62% \| 83.65% \| \| Precision \| 0.8378 \| 0.8380 \| \| Recall \| 0.8362 \| 0.8365 \| \| F1 Score \| 0.8367 \| 0.8370 \| \| Train Loss \| 0.8461 \| 0.8461 \| \| Train Runtime \| 21540.38s \| 21540.38s \| \| Eval Runtime \| 9.53s \| 9.55s \| --- ## Usage You can load this model and tokenizer using Hugging Face Transformers: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification model_name = "mia-project-2025/bert-base-uncased-LoRA-glue-mnli" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) text = {"premise": "The cat is sleeping on the sofa.", "hypothesis": "The cat is awake."} inputs = tokenizer(text["premise"], text["hypothesis"], return_tensors="pt") outputs = model(**inputs) pred = outputs.logits.argmax(-1).item() print(pred) # 0=Contradiction, 1=Entailment, 2=Neutral
unitova/blockassist-bc-zealous_sneaky_raven_1755845283	unitova	2025-08-22T07:14:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:14:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kapalbalap/blockassist-bc-peaceful_wary_owl_1755846819	kapalbalap	2025-08-22T07:14:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "peaceful wary owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:14:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - peaceful wary owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755845156	helmutsukocok	2025-08-22T07:13:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:13:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755845150	ihsanridzi	2025-08-22T07:11:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:11:35Z	--- 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).
llencia/blockassist-bc-wiry_wise_hedgehog_1755846574	llencia	2025-08-22T07:09:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry wise hedgehog", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:09:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry wise hedgehog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
0xGareeb/blockassist-bc-diving_jumping_llama_1755846500	0xGareeb	2025-08-22T07:09:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving jumping llama", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:09:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving jumping llama --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Bnaad/PARENT_bert	Bnaad	2025-08-22T07:05:44Z	0	0	transformers	[ "transformers", "bert", "text-classification", "privacy-policy", "gdpr", "torchscript", "en", "dataset:MAPP-116", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	text-classification	2025-08-08T16:37:57Z	--- language: en license: apache-2.0 library_name: transformers tags: - bert - text-classification - privacy-policy - gdpr - torchscript datasets: - MAPP-116 metrics: - f1 model-index: - name: PARENT BERT results: - task: type: text-classification dataset: name: MAPP-116 type: text metrics: - name: f1 type: score value: 0.80 # replace with your actual F1 score --- # PARENT BERT Models for Privacy Policy Analysis This repository contains TorchScript versions of 15 fine-tuned BERT models used in the PARENT project to analyse mobile app privacy policies. These models identify what data is collected, why it is collected, and how it is processed, helping assess GDPR compliance. They are part of a hybrid framework designed for non-technical users, particularly parents concerned about children’s privacy. --- ## Model Purpose - Segment privacy policies to detect: - Data collection types (e.g., contact info, location) - Purpose of data collection - How data is processed - Support GDPR compliance evaluation - Detect potential third-party sharing (in combination with a logistic regression model) --- ## References - MAPP Dataset: Arora, S., Hosseini, H., Utz, C., Bannihatti Kumar, V., Dhellemmes, T., Ravichander, A., Story, P., Mangat, J., Chen, R., Degeling, M., Norton, T.B., Hupperich, T., Wilson, S., & Sadeh, N.M. (2022). A tale of two regulatory regimes: Creation and analysis of a bilingual privacy policy corpus. Proceedings of the International Conference on Language Resources and Evaluation (LREC 2022). [PDF link](https://aclanthology.org/2022.lrec-1.585.pdf) [Accessed 12 July 2025]. --- ## Usage ```python import torch from transformers import BertTokenizerFast from huggingface_hub import hf_hub_download device = torch.device("cuda" if torch.cuda.is_available() else "cpu") REPO_ID = "Bnaad/PARENT_bert" # Load tokenizer tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") # Load one TorchScript model from Hugging Face label_name = "Information Type_Contact information" safe_label = label_name.replace(" ", "_").replace("/", "_") filename = f"torchscript_{safe_label}.pt" model_path = hf_hub_download(repo_id=REPO_ID, filename=filename) model = torch.jit.load(model_path, map_location=device) model.to(device) model.eval() # Example inference sample_text = """For any questions about your account or our services, please contact our customer support team by emailing support@example.com, calling +1-800-555-1234, or visiting our office at 123 Main Street, Springfield, IL, 62701 during business hours""" inputs = tokenizer( sample_text, return_tensors="pt", truncation=True, padding="max_length", max_length=512 ).to(device) with torch.no_grad(): outputs = model(inputs["input_ids"], inputs["attention_mask"]) print("Logits:", outputs) prob = torch.sigmoid(outputs.squeeze()) print(prob)
inlee/proactive-agent-reward-model-llama3.1-8b	inlee	2025-08-22T07:04:23Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-22T07:00:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
roeker/blockassist-bc-quick_wiry_owl_1755846181	roeker	2025-08-22T07:03:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T07:03:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

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