A more efficient multimodal large language model series.
AuroraCap Efficient, Performant Video Detailed Captioning and a New Benchmark
- [2024/10/07] Release technical report at arXiv.
- [2024/10/01] Release AuroraCap model and VDC benchmark, as well as the training and evaluation code.
- PR to HuggingFace transformers, but you can also use our dev branch for now.
- PR to EvolvingLMMs-Lab/lmms-eval with model and benchmark.
- Support SGLang deployment.
- Support training with Xtuner-lite for faster training and easier configuration.
We recommend installing aurora in a virtual environment from Conda (Python>=3.10).
conda create -n aurora python=3.10
conda activate aurora
Install PyTorch following instruction.
pip install torch torchvision
Clone this repository and install from source.
git clone https://github.com/rese1f/aurora.git && cd aurora
For training, install additional dependencies.
cd src/xtuner && pip install -e '.[all]'
For evaluation, install additional dependencies.
cd src/lmms-eval && pip install -e .
python inference.py \
--model_path wchai/AuroraCap-7B-VID-xtuner \
--prompt "Describe the video in detail." \
--visual_input assets/auroracap/test.mp4 \
--num_frm 8 \
--token_kept_ratio 0.8 \
--temperature 0.0 \
--top_p 1.0 \
--num_beams 1 \
--max_new_tokens 2048
with Gradio GUI
python gradio_gui.py
beta version with transformers see here.
Q: Can I only use token merging during inference?
A: No, our experiments show that token merging is also a way to accelerate training while maintaining similar performance. Additionally, besides auroracap, you can also use token merging on other llava-like models.
Q: How should I set the token_kept_ratio parameter?
A: AuroraCap uses token merging technique to reduce the number of visual tokens before fed into the llm decoder. We using token_kept_ratio range from 0 to 1 to control the number of visual tokens kept. For example, if token_kept_ratio is 0.5, then 50% of the visual tokens will be kept. We recommend to use token_kept_ratio in the range of 0.2 to 0.4 for better performance-cost trade-off for captioning tasks, above 0.5 for visual question answering tasks, and above 0.8 for OCR-related tasks.
Q: Why do we provide both Huggingface format and Xtuner format weights for AuroraCap?
A: While Xtuner supports saving checkpoints in multiple formats, it currently only allows continued training with the Xtuner format. Therefore, we currently provide the model in the Xtuner format for both continued training and inference. In the future, we will provide the model in the Huggingface format for both training and inference, enabling quicker SGLang deployment and integration with the transformers.
@article{chai2024auroracap,
title={AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark },
author={Wenhao Chai, Enxin Song, Yilun Du, Chenlin Meng, Vashisht Madhavan, Omer Bar-Tal, Jeng-Neng Hwang, Saining Xie, Christopher D. Manning},
journal={arXiv preprint arXiv:2410.03051},
year={2024}
}This project is released under the Apache License 2.0. Please also adhere to the Licenses of models and datasets being used.