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