Vision-CAIR /MiniGPT-4
Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
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README
MiniGPT-V
MiniGPT-v2: Large Language Model as a Unified Interface for Vision-Language Multi-task Learning
Jun Chen, Deyao Zhu, Xiaoqian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong☨, Mohamed Elhoseiny☨
☨equal last author
MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, Mohamed Elhoseiny
equal contribution
King Abdullah University of Science and Technology
💡 Get help - Q&A or Discord 💬
Example Community Efforts Built on Top of MiniGPT-4
InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4 Lai Wei, Zihao Jiang, Weiran Huang, Lichao Sun, Arxiv, 2023
PatFig: Generating Short and Long Captions for Patent Figures.", Aubakirova, Dana, Kim Gerdes, and Lufei Liu, ICCVW, 2023
SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model, Juexiao Zhou and Xiaonan He and Liyuan Sun and Jiannan Xu and Xiuying Chen and Yuetan Chu and Longxi Zhou and Xingyu Liao and Bin Zhang and Xin Gao, Arxiv, 2023
ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4.", Yuan, Zhengqing, Huiwen Xue, Xinyi Wang, Yongming Liu, Zhuanzhe Zhao, and Kun Wang, Arxiv, 2023
News
[Oct.31 2023] We release the evaluation code of our MiniGPT-v2.
[Oct.24 2023] We release the finetuning code of our MiniGPT-v2.
[Oct.13 2023] Breaking! We release the first major update with our MiniGPT-v2
[Aug.28 2023] We now provide a llama 2 version of MiniGPT-4
Online Demo
Click the image to chat with MiniGPT-v2 around your images
Click the image to chat with MiniGPT-4 around your images
MiniGPT-v2 Examples
!MiniGPT-v2 demos
MiniGPT-4 Examples
| | |
:-------------------------:|:-------------------------:
!find wild | !write story
!solve problem | !write Poem
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
bash
git clone https://github.com/Vision-CAIR/MiniGPT-4.git
cd MiniGPT-4
conda env create -f environment.yml
conda activate minigptv
2. Prepare the pretrained LLM weights
MiniGPT-v2 is based on Llama2 Chat 7B. For MiniGPT-4, we have both Vicuna V0 and Llama 2 version.
Download the corresponding LLM weights from the following huggingface space via clone the repository using git-lfs.
| Llama 2 Chat 7B | Vicuna V0 13B | Vicuna V0 7B |
:------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------