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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 **
[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 DemoClick the image to chat with MiniGPT-v2 around your images
Click the image to chat with MiniGPT-4 around your images
![]() ![]() ![]() ![]() More examples can be found in the project page. Getting Started Installation1. 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/Vision-CAIR/MiniGPT-4.git cd MiniGPT-4 conda env create -f environment.yml conda activate minigptv2. 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 Download Downlad DownloadThen, set the variable llama_model in the model config file to the LLM weight path. For MiniGPT-v2, set the LLM path here at Line 14. For MiniGPT-4 (Llama2), set the LLM path here at Line 15. For MiniGPT-4 (Vicuna), set the LLM path here at Line 18 3. Prepare the pretrained model checkpoints Download the pretrained model checkpoints MiniGPT-v2 (after stage-2) MiniGPT-v2 (after stage-3) MiniGPT-v2 (online developing demo) Download Download DownloadFor MiniGPT-v2, set the path to the pretrained checkpoint in the evaluation config file in eval_configs/minigptv2_eval.yaml at Line 8. MiniGPT-4 (Vicuna 13B) MiniGPT-4 (Vicuna 7B) MiniGPT-4 (LLaMA-2 Chat 7B) Download Download DownloadFor MiniGPT-4, set the path to the pretrained checkpoint in the evaluation config file in eval_configs/minigpt4_eval.yaml at Line 8 for Vicuna version or eval_configs/minigpt4_llama2_eval.yaml for LLama2 version. Launching Demo LocallyFor MiniGPT-v2, run python demo_v2.py --cfg-path eval_configs/minigptv2_eval.yaml --gpu-id 0For MiniGPT-4 (Vicuna version), run python demo.py --cfg-path eval_configs/minigpt4_eval.yaml --gpu-id 0For MiniGPT-4 (Llama2 version), run python demo.py --cfg-path eval_configs/minigpt4_llama2_eval.yaml --gpu-id 0To save GPU memory, LLMs loads as 8 bit by default, with a beam search width of 1. This configuration requires about 23G GPU memory for 13B LLM and 11.5G GPU memory for 7B LLM. For more powerful GPUs, you can run the model in 16 bit by setting low_resource to False in the relevant config file: MiniGPT-v2: minigptv2_eval.yaml MiniGPT-4 (Llama2): minigpt4_llama2_eval.yaml MiniGPT-4 (Vicuna): minigpt4_eval.yamlThanks @WangRongsheng, you can also run MiniGPT-4 on Colab TrainingFor training details of MiniGPT-4, check here. For finetuning details of MiniGPT-v2, check here EvaluationFor finetuning details of MiniGPT-v2, check here Acknowledgement BLIP2 The model architecture of MiniGPT-4 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! LLaMA The strong open-sourced LLaMA 2 language model.If you're using MiniGPT-4/MiniGPT-v2 in your research or applications, please cite using this BibTeX: @article{chen2023minigptv2, title={MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning}, author={Chen, Jun and Zhu, Deyao and Shen, Xiaoqian and Li, Xiang and Liu, Zechu and Zhang, Pengchuan and Krishnamoorthi, Raghuraman and Chandra, Vikas and Xiong, Yunyang and Elhoseiny, Mohamed}, year={2023}, journal={arXiv preprint arXiv:2310.09478}, } @article{zhu2023minigpt, title={MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models}, author={Zhu, Deyao and Chen, Jun and Shen, Xiaoqian and Li, Xiang and Elhoseiny, Mohamed}, journal={arXiv preprint arXiv:2304.10592}, year={2023} } LicenseThis repository is under BSD 3-Clause License. Many codes are based on Lavis with BSD 3-Clause License here. |
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