ZhangYuanhan
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Update README.md
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README.md
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tags:
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- multimodal
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model-index:
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- name: LLaVA-
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results:
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- task:
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type: multimodal
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- lmms-lab/llava-onevision-qwen2-7b-si
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---
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# LLaVA-
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## Table of Contents
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## Model Summary
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The LLaVA-
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This model support at most 64 frames.
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### Intended use
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The model was trained on [LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-
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spare_frames = vr.get_batch(frame_idx).asnumpy()
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# import pdb;pdb.set_trace()
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return spare_frames,frame_time,video_time
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pretrained = "lmms-lab/LLaVA-
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model_name = "llava_qwen"
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device = "cuda"
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device_map = "auto"
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tags:
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- multimodal
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model-index:
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- name: LLaVA-Video-7B-Qwen2
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results:
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- task:
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type: multimodal
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- lmms-lab/llava-onevision-qwen2-7b-si
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---
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# LLaVA-Video-7B-Qwen2
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## Table of Contents
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## Model Summary
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The LLaVA-Video models are 7/72B parameter models trained on [LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K) and [LLaVA-OneVision Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), based on Qwen2 language model with a context window of 32K tokens.
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This model support at most 64 frames.
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### Intended use
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The model was trained on [LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K) and [LLaVA-OneVision Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), having the ability to interact with images, multi-image and videos, but specific to videos.
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spare_frames = vr.get_batch(frame_idx).asnumpy()
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# import pdb;pdb.set_trace()
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return spare_frames,frame_time,video_time
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pretrained = "lmms-lab/LLaVA-Video-7B-Qwen2"
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model_name = "llava_qwen"
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device = "cuda"
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device_map = "auto"
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