ChatBridge

ChatBridge, an approach to learning a unified multimodal model to interpret, correlate, and reason about various modalities without relying on all combinations of paired data.

Introduction

ChatBridge is a multimodal language model capable of perceiving real-world multimodal information, as well as following instructions, thinking, and interacting with humans in natural language. Inspired by Flamingo and BLIP-2, we introduce perceiver modules to bridge the encoders and the LLM. we choose open-sourced Vicuna-13B as the LLM, which is built upon LLaMA, and reported to achieve 90% of ChatGPT's quality as per GPT-4's evaluation. As for the modal-specific encoders, we choose EVA-ViT-G as the vision encoder to encode images and videos, and BEAT as the audio encoder to encoder audios.

• Stage 1: Bridge each modality with language, leverage large-scale language-paired two-modality data for multimodal alignment training, including image-text, video-text, and audio-text pairs.
• Stage 2: Multimodal Instruction Tuning, instruction-finetune ChatBridge to align the model with user intent on a multimodal instruction dataset MULTIS, enabling more effective zero-shot generalization on multimodal tasks.

Examples

More examples can be found in the project page.

Getting Started

Installation

1. Prepare the code and the environment

Git clone our repository, creating a python environment and activate it via the following command

git clone https://github.com/joez17/ChatBridge.git
cd ChatBridge
conda env create -f environment.yml
conda activate chatbridge

2. Prepare the pretrained Vicuna weights

Please refer to MiniGPT-4's instruction here to prepare the Vicuna-13B weights. The final weights would be in a single folder in a structure similar to the following:

vicuna_weights
├── config.json
├── generation_config.json
├── pytorch_model.bin.index.json
├── pytorch_model-00001-of-00003.bin
...   

Then, set the path to the vicuna weight in the model config file eval_configs/chatbridge_eval.yaml.

3. Prepare the pretrained checkpoint

Download the pretrained checkpoints Baidu key:o4v9.

Then, set the path to the pretrained checkpoint in the evaluation config file in eval_configs/chatbridge_eval.yaml.

Training

The training of ChatBridge contains two alignment stages.

1. First pretraining stage

We use three kind of datasets to train ChatBridge in the first stage, including image-text datasets(CC, LAION), video-text datasets(Webvid-2.5M) and audio-text datasets(Wavcaps). To download and prepare the datasets, please check our first stage dataset preparation instruction. After the first stage, the multi-modal features are mapped and can be understood by the language model.

To launch the first stage training, run the following command. In our experiments, we use 8 A100. You can change the save path in the config file train_configs/chatbridge_pretrain.yaml

torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/chatbridge_pretrain.yaml

A ChatBridge checkpoint with only stage one training can be downloaded here.

2. Second finetuning stage

In the second stage, we use a small high quality instruction dataset MULTIS created by ourselves and convert it to a conversation format. To download and prepare our second stage dataset, please check our second stage dataset preparation instruction. To launch the second stage alignment, first specify the path to the checkpoint file trained in stage 1 in instructiontuning_configs/instructtuning_ivaavchat_ckpt13.yaml. You can also specify the output path there. Then, run the following command.

torchrun --nproc-per-node NUM_GPU train.py --cfg-path instructiontuning_configs/instructtuning_ivaavchat_ckpt13.yaml

Launching Demo Locally

Try out our demo demo.py on your local machine by running

python demo.py --cfg-path eval_configs/chatbridge_eval.yaml  --gpu-id 0

MULTIS Data

We provide re-organized text annotation files in MULTIS datasets in this googledrive_link or baiduyun_link key:2gt3.

please check our second stage dataset preparation instruction for more details.

Acknowledgement

• BLIP2 The model architecture of ChatBridge follows BLIP-2. Don't forget to check this great open-source work if you don't know it before!
• Lavis This repository is built upon Lavis!
• Vicuna The fantastic language ability of Vicuna with only 13B parameters is just amazing. And it is open-source!
• MiniGPT4 and LLaVA. We utilize their instruction data and drew inspiration from their approach to design a more comprehensive multimodal instruction dataset. They are all open-source!

If you're using ChatBridge in your research or applications, please cite using this BibTeX:

@article{zhao2023chatbridge,
  title={ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst},
  author={Zhao, Zijia and Guo, Longteng and Yue, Tongtian and Chen, Sihan and Shao, Shuai and Zhu, Xinxin and Yuan, Zehuan and Liu, Jing},
  journal={arXiv preprint arXiv:2305.16103},
  year={2023}
}

License

This repository is under BSD 3-Clause License. Many codes are based on Lavis with BSD 3-Clause License here.