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Reason: e \"/usr/local/lib/python3.10/site-packages/datasets/load.py\", line 1063, in get_module\n data_files = DataFilesDict.from_patterns(\n File \"/usr/local/lib/python3.10/site-packages/datasets/data_files.py\", line 721, in from_patterns\n else DataFilesList.from_patterns(\n File \"/usr/local/lib/python3.10/site-packages/datasets/data_files.py\", line 624, in from_patterns\n resolve_pattern(\n File \"/usr/local/lib/python3.10/site-packages/datasets/data_files.py\", line 388, in resolve_pattern\n for filepath, info in fs.glob(pattern, detail=True, **glob_kwargs).items()\n File \"/usr/local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py\", line 409, in glob\n return super().glob(path, **kwargs)\n File \"/usr/local/lib/python3.10/site-packages/fsspec/spec.py\", line 611, in glob\n allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs)\n File \"/usr/local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py\", line 422, in find\n return super().find(\n File \"/usr/local/lib/python3.10/site-packages/fsspec/spec.py\", line 502, in find\n out[path] = self.info(path)\n File \"/usr/local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py\", line 532, in info\n paths_info = self._api.get_paths_info(\n File \"/usr/local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 114, in _inner_fn\n return fn(*args, **kwargs)\n File \"/usr/local/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 3237, in get_paths_info\n hf_raise_for_status(response)\n File \"/usr/local/lib/python3.10/site-packages/huggingface_hub/utils/_http.py\", line 477, in hf_raise_for_status\n raise _format(HfHubHTTPError, str(e), response) from e\nhuggingface_hub.errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/lmms-lab/LiveBenchResults/paths-info/ec0ecf261e39ee25d7b2734680a0c63b7d295e76 (Request ID: Root=1-67c72d8f-3e38f933782cd5454f28b206;ad1d61a2-27ce-4457-a931-b836146f8c44)\n\nprocess spawn timed out after 10s\n","replicas":{"requested":1},"devMode":false,"domains":[{"domain":"lmms-lab-livebench.hf.space","stage":"READY"}]},"title":"LiveBench","isLikedByUser":false,"originRepo":{"name":"demo-leaderboard-backend/leaderboard","author":{"_id":"655dbd8360009b03e4451217","avatarUrl":"https://www.gravatar.com/avatar/48236a8e5b71950f0708b3f2e3e7925f?d=retro&size=100","fullname":"Demo leaderboard with an integrated backend","name":"demo-leaderboard-backend","type":"org","isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":22,"isUserFollowing":false}},"trendingScore":0,"tags":["gradio","region:us"],"featured":false},{"author":"lmms-lab","authorData":{"_id":"6583eb89bed3689928f5d845","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d3f7d84b0933c48f3cdd9c/0sliNO9xGhOjVWw20A1Ge.png","fullname":"LMMs-Lab","name":"lmms-lab","type":"org","isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":774,"isUserFollowing":false},"colorFrom":"indigo","colorTo":"red","createdAt":"2025-09-24T05:57:21.000Z","emoji":"📉","id":"lmms-lab/LLaVA-OneVision-1.5","lastModified":"2025-09-30T02:12:55.000Z","likes":12,"pinned":false,"private":false,"sdk":"gradio","repoType":"space","runtime":{"stage":"RUNNING","hardware":{"current":"cpu-basic","requested":"cpu-basic"},"storage":null,"gcTimeout":172800,"replicas":{"current":1,"requested":1},"devMode":false,"domains":[{"domain":"lmms-lab-llava-onevision-1-5.hf.space","stage":"READY"}],"sha":"5168dc4afd0e760e1a11b3930cc759bd9e4caa21"},"title":"LLaVA OneVision 1.5","isLikedByUser":false,"ai_short_description":"Interact with a multimodal chatbot using text and images","ai_category":"Chatbots","trendingScore":0,"tags":["gradio","region:us"],"featured":false},{"author":"lmms-lab","authorData":{"_id":"6583eb89bed3689928f5d845","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d3f7d84b0933c48f3cdd9c/0sliNO9xGhOjVWw20A1Ge.png","fullname":"LMMs-Lab","name":"lmms-lab","type":"org","isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":774,"isUserFollowing":false},"colorFrom":"yellow","colorTo":"purple","createdAt":"2025-04-29T13:53:02.000Z","emoji":"💬","id":"lmms-lab/Aero-1-Audio-Demo","lastModified":"2025-05-03T17:43:01.000Z","likes":43,"pinned":false,"private":false,"sdk":"gradio","repoType":"space","runtime":{"stage":"RUNTIME_ERROR","hardware":{"current":null,"requested":"zero-a10g"},"storage":null,"gcTimeout":172800,"errorMessage":"Exit code: 1. Reason: s]\u001b[A\rprocessor_config.json: 100%|██████████| 145/145 [00:00<00:00, 976kB/s]\n\n\rchat_template.json: 0%| | 0.00/1.17k [00:00\n processor = AutoProcessor.from_pretrained(\"lmms-lab/Aero-1-Audio-1.5B\", trust_remote_code=True)\n File \"/usr/local/lib/python3.10/site-packages/transformers/models/auto/processing_auto.py\", line 339, in from_pretrained\n processor_class = get_class_from_dynamic_module(\n File \"/usr/local/lib/python3.10/site-packages/transformers/dynamic_module_utils.py\", line 570, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/usr/local/lib/python3.10/site-packages/transformers/dynamic_module_utils.py\", line 267, in get_class_in_module\n module_spec.loader.exec_module(module)\n File \"\", line 883, in exec_module\n File \"\", line 241, in _call_with_frames_removed\n File \"/home/user/.cache/huggingface/modules/transformers_modules/lmms-lab/Aero-1-Audio-1.5B/2efba6e9de291fa30e11075849631ae410b8485a/processing_aero.py\", line 26, in \n from transformers.video_utils import VideoInput\nModuleNotFoundError: No module named 'transformers.video_utils'\n","replicas":{"requested":1},"devMode":false,"domains":[{"domain":"lmms-lab-aero-1-audio-demo.hf.space","stage":"READY"}]},"shortDescription":"Demo for Aero-1-Audio","title":"Aero 1 Audio Demo","isLikedByUser":false,"ai_short_description":"Generate text from audio and prompt","ai_category":"Text Generation","trendingScore":0,"tags":["gradio","region:us"],"featured":false},{"author":"lmms-lab","authorData":{"_id":"6583eb89bed3689928f5d845","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d3f7d84b0933c48f3cdd9c/0sliNO9xGhOjVWw20A1Ge.png","fullname":"LMMs-Lab","name":"lmms-lab","type":"org","isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":774,"isUserFollowing":false},"colorFrom":"yellow","colorTo":"purple","createdAt":"2025-03-03T02:36:17.000Z","emoji":"💬","id":"lmms-lab/Multimodal-SAE","lastModified":"2025-03-14T11:18:44.000Z","likes":9,"pinned":false,"private":false,"sdk":"gradio","repoType":"space","runtime":{"stage":"SLEEPING","hardware":{"current":null,"requested":"zero-a10g"},"storage":null,"gcTimeout":172800,"replicas":{"requested":1},"devMode":false,"domains":[{"domain":"kcz358-multimodal-sae.hf.space","stage":"READY"},{"domain":"lmms-lab-multimodal-sae.hf.space","stage":"READY"}]},"shortDescription":"Demo for Multimodal-SAE","title":"Multimodal SAE","isLikedByUser":false,"ai_short_description":"Explore and manipulate image features to visualize and influence model outputs","ai_category":"Image Analysis","trendingScore":0,"tags":["gradio","region:us"],"featured":false},{"author":"lmms-lab","authorData":{"_id":"6583eb89bed3689928f5d845","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d3f7d84b0933c48f3cdd9c/0sliNO9xGhOjVWw20A1Ge.png","fullname":"LMMs-Lab","name":"lmms-lab","type":"org","isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":774,"isUserFollowing":false},"colorFrom":"indigo","colorTo":"red","createdAt":"2024-07-11T02:46:04.000Z","emoji":"😻","id":"lmms-lab/LLaVA-NeXT-Interleave-Demo","lastModified":"2024-07-24T23:54:16.000Z","likes":51,"pinned":false,"private":false,"sdk":"gradio","repoType":"space","runtime":{"stage":"RUNTIME_ERROR","hardware":{"current":null,"requested":"zero-a10g"},"storage":null,"gcTimeout":172800,"errorMessage":"Exit code: 1. Reason: SchemaHandler = current_handler,\n File \"/usr/local/lib/python3.10/site-packages/pydantic/json_schema.py\", line 1823, in definitions_schema\n The name of the argument.\n File \"/usr/local/lib/python3.10/site-packages/pydantic/json_schema.py\", line 553, in generate_inner\n def new_handler_func(\n File \"/usr/local/lib/python3.10/site-packages/pydantic/_internal/_schema_generation_shared.py\", line 37, in __call__\n def __call__(self, core_schema: CoreSchemaOrField, /) -> JsonSchemaValue:\n File \"/usr/local/lib/python3.10/site-packages/pydantic/json_schema.py\", line 527, in new_handler_func\n # similar to typing issue in _update_class_schema when we're working with callable js extra\n File \"/usr/local/lib/python3.10/site-packages/pydantic/main.py\", line 669, in __get_pydantic_json_schema__\n obj: The object containing string data to validate.\n File \"/usr/local/lib/python3.10/site-packages/pydantic/_internal/_schema_generation_shared.py\", line 37, in __call__\n def __call__(self, core_schema: CoreSchemaOrField, /) -> JsonSchemaValue:\n File \"/usr/local/lib/python3.10/site-packages/pydantic/json_schema.py\", line 527, in new_handler_func\n # similar to typing issue in _update_class_schema when we're working with callable js extra\n File \"/usr/local/lib/python3.10/site-packages/pydantic/_internal/_generate_schema.py\", line 223, in modify_model_json_schema\n File \"/usr/local/lib/python3.10/site-packages/pydantic/dataclasses.py\", line 14, in \n from ._internal import _dataclasses as _pydantic_dataclasses\n File \"/usr/local/lib/python3.10/site-packages/pydantic/_internal/_dataclasses.py\", line 30, in \n from ._utils import LazyClassAttribute\nImportError: cannot import name 'LazyClassAttribute' from 'pydantic._internal._utils' (/usr/local/lib/python3.10/site-packages/pydantic/_internal/_utils.py)\n/usr/local/lib/python3.10/site-packages/gradio/analytics.py:106: UserWarning: IMPORTANT: You are using gradio version 4.35.0, however version 4.44.1 is available, please upgrade. \n--------\n print(\n","replicas":{"requested":1},"devMode":false,"domains":[{"domain":"lmms-lab-llava-next-interleave-demo.hf.space","stage":"READY"}]},"title":"LLaVA-NeXT-Interleave-Demo","isLikedByUser":false,"trendingScore":0,"tags":["gradio","region:us"],"featured":false}],"buckets":[],"numBuckets":0,"numDatasets":165,"numModels":61,"numSpaces":7,"lastOrgActivities":[{"time":"2026-02-18T13:39:39.057Z","user":"Chunyuan24","userAvatarUrl":"/avatars/430560ec2c2547f819225769ab432f30.svg","type":"paper","paper":{"id":"2602.12279","title":"UniT: Unified Multimodal Chain-of-Thought Test-time Scaling","publishedAt":"2026-02-12T18:59:49.000Z","upvotes":19,"isUpvotedByUser":true}},{"time":"2026-02-17T15:50:44.095Z","user":"xiangan","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6478679d7b370854241b2ad8/dBczWYYdfEt9tQcnVGhQk.jpeg","type":"paper","paper":{"id":"2602.08683","title":"OneVision-Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence","publishedAt":"2026-02-09T14:06:17.000Z","upvotes":45,"isUpvotedByUser":true}},{"time":"2026-02-16T03:40:27.485Z","user":"yiyexy","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/655c70d331c4978366d4b2e6/X-KjTNkxtzeYu9ngBOh_C.jpeg","type":"paper-daily","paper":{"id":"2602.08683","title":"OneVision-Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence","thumbnailUrl":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.08683.png","upvotes":45,"publishedAt":"2026-02-09T14:06:17.000Z","isUpvotedByUser":true}}],"acceptLanguages":["*"],"canReadRepos":false,"canReadSpaces":false,"blogPosts":[],"currentRepoPage":0,"filters":{},"paperView":false}">
View all Papers
models
61
datasets
165
- \n
[2025-12] 🤞🤞 See the real world in motion | Presenting OneVision Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence.
\n\n \n[2025-11] 🔭🔭 Introducing LongVT: an end-to-end agentic framework for \"Thinking with Long Videos\" via native tool calling
\n💻 GitHub | 🤗 Model and Dataset | 📖 Paper | 📚 Blog | 💻 Demo
\n \n[2025-11] 🔥🔥 Introducing
\n\nOpenMMReasoner: a fully transparent two-stage recipe for multimodal reasoning spanning supervised fine-tuning (SFT) and reinforcement learning (RL). \n[2025-9] 🔥🔥 Introducing
\n\nLLaVA-OneVision-1.5: a novel family of fully open-source Large Multimodal Models (LMMs) that achieves state-of-the-art performance with substantially lower cost through training on native resolution images. \n[2025-9] 🔥🔥 Introducing
\n\nLLaVA-Critic-R1: A family of generative critic VLM trained through GRPO using pairwise critic data. LLaVA-Critic-R1 not only demonstrates strong critic capability, but also achieves SoTA policy performance at the 7B scale. \n[2025-4] 🔈🔈 Introducing
\nAero-1-Audio: It is a compact audio model adept at various audio tasks, including speech recognition, audio understanding, and following audio instructions.📚 Blog | 🤗 Model Checkpoints | 📖 Evaluation Results | 📚 Cookbook
\n \n[2025-3] 👓👓 Introducing
\n\nEgoLife: Towards Egocentric Life Assistant. For one week, six individuals lived together, capturing every moment through AI glasses, and creating the EgoLife dataset. Based on this we build models and benchmarks to drive the future of AI life assistants that capable of recalling past events, tracking habits, and providing personalized, long-context assistance to enhance daily life. \n[2025-1] 🎬🎬 Introducing
\n\nVideoMMMU: Evaluating Knowledge Acquisition from Professional Videos. Spanning 6 professional disciplines (Art, Business, Science, Medicine, Humanities, Engineering) and 30 diverse subjects, Video-MMMU challenges models to learn and apply college-level knowledge from videos. \n[2024-11] 🔔🔔 We are excited to introduce LMMs-Eval/v0.3.0, focusing on audio understanding. Building upon LMMs-Eval/v0.2.0, we have added audio models and tasks. Now, LMMs-Eval provides a consistent evaluation toolkit across image, video, and audio modalities.
\n\n \n[2024-11] 🤯🤯 We introduce Multimodal SAE, the first framework designed to interpret learned features in large-scale multimodal models using Sparse Autoencoders. Through our approach, we leverage LLaVA-OneVision-72B to analyze and explain the SAE-derived features of LLaVA-NeXT-LLaMA3-8B. Furthermore, we demonstrate the ability to steer model behavior by clamping specific features to alleviate hallucinations and avoid safety-related issues.
\n\n \n[2024-10] 🔥🔥 We present
\n\nLLaVA-Critic, the first open-source large multimodal model as a generalist evaluator for assessing LMM-generated responses across diverse multimodal tasks and scenarios. \n[2024-10] 🎬🎬 Introducing
\n\nLLaVA-Video, a family of open large multimodal models designed specifically for advanced video understanding. We're open-sourcing LLaVA-Video-178K, a high-quality, synthetic dataset for video instruction tuning. \n[2024-08] 🤞🤞 We present
\n\nLLaVA-OneVision, a family of LMMs developed by consolidating insights into data, models, and visual representations. \n[2024-06] 🧑🎨🧑🎨 We release
\n\nLLaVA-NeXT-Interleave, an LMM extending capabilities to real-world settings: Multi-image, Multi-frame (videos), Multi-view (3D), and Multi-patch (single-image). \n[2024-06] 🚀🚀 We release
\n\nLongVA, a long language model with state-of-the-art video understanding performance. \n
\n
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code: 1. Reason: s]\u001b[A\rprocessor_config.json: 100%|██████████| 145/145 [00:00<00:00, 976kB/s]\n\n\rchat_template.json: 0%| | 0.00/1.17k [00:00\n processor = AutoProcessor.from_pretrained(\"lmms-lab/Aero-1-Audio-1.5B\", trust_remote_code=True)\n File \"/usr/local/lib/python3.10/site-packages/transformers/models/auto/processing_auto.py\", line 339, in from_pretrained\n processor_class = get_class_from_dynamic_module(\n File \"/usr/local/lib/python3.10/site-packages/transformers/dynamic_module_utils.py\", line 570, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/usr/local/lib/python3.10/site-packages/transformers/dynamic_module_utils.py\", line 267, in get_class_in_module\n module_spec.loader.exec_module(module)\n File \"Older Updates (2024-06 and earlier)
\n\n- \n
[2024-06] 🎬🎬 The
\n\nlmms-eval/v0.2toolkit now supports video evaluations for models like LLaVA-NeXT Video and Gemini 1.5 Pro. \n[2024-05] 🚀🚀 We release
\n\nLLaVA-NeXT Video, a model performing at Google's Gemini level on video understanding tasks. \n[2024-05] 🚀🚀 The
\n\nLLaVA-NeXTmodel family reaches near GPT-4V performance on multimodal benchmarks, with models up to 110B parameters. \n[2024-03] We release
\n\nlmms-eval, a toolkit for holistic evaluations with 50+ multimodal datasets and 10+ models. \n
AI & ML interests
Feeling and building the multimodal intelligence.
Recent Activity
View all activity
Papers
OneVision-Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence
LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling
Organization Card
We are looking for collaborators and compute sponsors! Email to drluodian@gmail.com if you want to build with us.
[2025-12] 🤞🤞 See the real world in motion | Presenting OneVision Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence.
[2025-11] 🔭🔭 Introducing LongVT: an end-to-end agentic framework for "Thinking with Long Videos" via native tool calling
💻 GitHub | 🤗 Model and Dataset | 📖 Paper | 📚 Blog | 💻 Demo
[2025-11] 🔥🔥 Introducing
OpenMMReasoner: a fully transparent two-stage recipe for multimodal reasoning spanning supervised fine-tuning (SFT) and reinforcement learning (RL).[2025-9] 🔥🔥 Introducing
LLaVA-OneVision-1.5: a novel family of fully open-source Large Multimodal Models (LMMs) that achieves state-of-the-art performance with substantially lower cost through training on native resolution images.[2025-9] 🔥🔥 Introducing
LLaVA-Critic-R1: A family of generative critic VLM trained through GRPO using pairwise critic data. LLaVA-Critic-R1 not only demonstrates strong critic capability, but also achieves SoTA policy performance at the 7B scale.[2025-4] 🔈🔈 Introducing
Aero-1-Audio: It is a compact audio model adept at various audio tasks, including speech recognition, audio understanding, and following audio instructions.📚 Blog | 🤗 Model Checkpoints | 📖 Evaluation Results | 📚 Cookbook
[2025-3] 👓👓 Introducing
EgoLife: Towards Egocentric Life Assistant. For one week, six individuals lived together, capturing every moment through AI glasses, and creating the EgoLife dataset. Based on this we build models and benchmarks to drive the future of AI life assistants that capable of recalling past events, tracking habits, and providing personalized, long-context assistance to enhance daily life.[2025-1] 🎬🎬 Introducing
VideoMMMU: Evaluating Knowledge Acquisition from Professional Videos. Spanning 6 professional disciplines (Art, Business, Science, Medicine, Humanities, Engineering) and 30 diverse subjects, Video-MMMU challenges models to learn and apply college-level knowledge from videos.[2024-11] 🔔🔔 We are excited to introduce LMMs-Eval/v0.3.0, focusing on audio understanding. Building upon LMMs-Eval/v0.2.0, we have added audio models and tasks. Now, LMMs-Eval provides a consistent evaluation toolkit across image, video, and audio modalities.
[2024-11] 🤯🤯 We introduce Multimodal SAE, the first framework designed to interpret learned features in large-scale multimodal models using Sparse Autoencoders. Through our approach, we leverage LLaVA-OneVision-72B to analyze and explain the SAE-derived features of LLaVA-NeXT-LLaMA3-8B. Furthermore, we demonstrate the ability to steer model behavior by clamping specific features to alleviate hallucinations and avoid safety-related issues.
[2024-10] 🔥🔥 We present
LLaVA-Critic, the first open-source large multimodal model as a generalist evaluator for assessing LMM-generated responses across diverse multimodal tasks and scenarios.[2024-10] 🎬🎬 Introducing
LLaVA-Video, a family of open large multimodal models designed specifically for advanced video understanding. We're open-sourcing LLaVA-Video-178K, a high-quality, synthetic dataset for video instruction tuning.[2024-08] 🤞🤞 We present
LLaVA-OneVision, a family of LMMs developed by consolidating insights into data, models, and visual representations.[2024-06] 🧑🎨🧑🎨 We release
LLaVA-NeXT-Interleave, an LMM extending capabilities to real-world settings: Multi-image, Multi-frame (videos), Multi-view (3D), and Multi-patch (single-image).[2024-06] 🚀🚀 We release
LongVA, a long language model with state-of-the-art video understanding performance.
Older Updates (2024-06 and earlier)
[2024-06] 🎬🎬 The
lmms-eval/v0.2toolkit now supports video evaluations for models like LLaVA-NeXT Video and Gemini 1.5 Pro.[2024-05] 🚀🚀 We release
LLaVA-NeXT Video, a model performing at Google's Gemini level on video understanding tasks.[2024-05] 🚀🚀 The
LLaVA-NeXTmodel family reaches near GPT-4V performance on multimodal benchmarks, with models up to 110B parameters.[2024-03] We release
lmms-eval, a toolkit for holistic evaluations with 50+ multimodal datasets and 10+ models.
models
61
lmms-lab/LLaVA-OneVision-1.5-4B-Instruct
Image-Text-to-Text
•
5B
•
Updated
•
3.46k
•
17
lmms-lab/BAGEL-7B-MoT-ver.LE
Text Generation
•
15B
•
Updated
•
3.88k
•
1
lmms-lab/LLaVA-OneVision-1.5-8B-Instruct
Image-Text-to-Text
•
9B
•
Updated
•
37.8k
•
62
lmms-lab/LLaVA-OneVision-1.5-4B-Base
Image-Text-to-Text
•
5B
•
Updated
•
2.06k
•
1
lmms-lab/LLaVA-OneVision-1.5-8B-Base
Image-Text-to-Text
•
9B
•
Updated
•
47
•
1
lmms-lab/LLaVA-OneVision-1.5-4B-stage0
4B
•
Updated
•
13
•
1
lmms-lab/LLaVA-OneVision-1.5-8B-stage0
9B
•
Updated
•
5
•
2
lmms-lab/LLaVA-Critic-R1-7B-LLaMA32v
11B
•
Updated
lmms-lab/LLaVA-Critic-R1-7B-Plus-Mimo
8B
•
Updated
•
2
•
1
lmms-lab/MMSearch-R1-7B-0807
8B
•
Updated
•
1
datasets
165
lmms-lab/Spatial457
Updated
•
12
lmms-lab/SciBench_Physics
Viewer
•
Updated
•
227
•
18
lmms-lab/SciBench_Chemistry
Viewer
•
Updated
•
266
•
37
•
1
lmms-lab/WenetSpeech
Updated
•
88
lmms-lab/CSBench_Assertion
Viewer
•
Updated
•
442
•
90
lmms-lab/CSBench_MCQ
Viewer
•
Updated
•
1.34k
•
90
lmms-lab/SuperGPQA
Viewer
•
Updated
•
26.5k
•
5
lmms-lab/MEDQA
Viewer
•
Updated
•
1.27k
•
43
lmms-lab/PUBMEDQA
Viewer
•
Updated
•
500
•
3
lmms-lab/MEDMCQA
Viewer
•
Updated
•
4.18k
•
10