ShuhuaiRen
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README.md
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---
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license: mit
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datasets:
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- ShuhuaiRen/TimeIT
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language:
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- en
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---
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# TimeChat Model Card
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## Model details
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**Model type:**
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TimeChat is an open-source chatbot trained by fine-tuning LLaMA-2 on time-sensitive video-centric instruction-following data (See [TimeIT](https://huggingface.co/datasets/ShuhuaiRen/TimeIT)).
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It is an auto-regressive language model, based on the transformer architecture.
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**Model date:**
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TimeChat-7B was trained in November 2023.
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**Paper or resources for more information:**
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[Paper](https://arxiv.org/abs/2312.02051), [Code](https://github.com/RenShuhuai-Andy/TimeChat)
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## License
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Llama 2 is licensed under the LLAMA 2 Community License,
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Copyright (c) Meta Platforms, Inc. All Rights Reserved.
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**Where to send questions or comments about the model:**
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https://github.com/RenShuhuai-Andy/TimeChat/issues
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## Intended use
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**Primary intended uses:**
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The primary use of TimeChat is research on large multimodal models and chatbots.
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**Primary intended users:**
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The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
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## Training dataset
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- 104K time-sensitive video-centric instruction-tuning data from [TimeIT](https://huggingface.co/datasets/ShuhuaiRen/TimeIT).
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- 73K video instruction-tuning data from [Valley-Instruct-73k](https://huggingface.co/datasets/luoruipu1/Valley-Instruct-73k).
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## Evaluation dataset
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Three tasks of long video understanding, i.e., dense video captioning (YouCook2), temporal grounding (Charades-STA), and highlight detection (QVHighlights).
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## Citation
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If you find our project useful, hope you can star our repo and cite our paper as follows:
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```
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@article{Ren2023TimeChat,
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title={TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding},
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author={Shuhuai Ren and Linli Yao and Shicheng Li and Xu Sun and Lu Hou},
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journal={ArXiv},
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year={2023},
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volume={abs/2312.02051},
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}
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```
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