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# MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models
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[Deyao Zhu](https://tsutikgiau.github.io/)* (On Job Market!), [Jun Chen](https://junchen14.github.io/)* (On Job Market!), [Xiaoqian Shen](https://xiaoqian-shen.github.io), [Xiang Li](https://xiangli.ac.cn), and [Mohamed Elhoseiny](https://www.mohamed-elhoseiny.com/). *Equal Contribution
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**King Abdullah University of Science and Technology**
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<a href='https://minigpt-4.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://arxiv.org/abs/2304.10592'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> <a href='https://huggingface.co/spaces/Vision-CAIR/minigpt4'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> <a href='https://huggingface.co/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a> [](https://colab.research.google.com/drive/1OK4kYsZphwt5DXchKkzMBjYF6jnkqh4R?usp=sharing) [](https://www.youtube.com/watch?v=__tftoxpBAw&feature=youtu.be)
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## News
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We now provide a pretrained MiniGPT-4 aligned with Vicuna-7B! The demo GPU memory consumption now can be as low as 12GB.
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## Online Demo
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Click the image to chat with MiniGPT-4 around your images
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[](https://minigpt-4.github.io)
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## Examples
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:-------------------------:|:-------------------------:
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More examples can be found in the [project page](https://minigpt-4.github.io).
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## Introduction
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- MiniGPT-4 aligns a frozen visual encoder from BLIP-2 with a frozen LLM, Vicuna, using just one projection layer.
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- We train MiniGPT-4 with two stages. The first traditional pretraining stage is trained using roughly 5 million aligned image-text pairs in 10 hours using 4 A100s. After the first stage, Vicuna is able to understand the image. But the generation ability of Vicuna is heavilly impacted.
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- To address this issue and improve usability, we propose a novel way to create high-quality image-text pairs by the model itself and ChatGPT together. Based on this, we then create a small (3500 pairs in total) yet high-quality dataset.
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- The second finetuning stage is trained on this dataset in a conversation template to significantly improve its generation reliability and overall usability. To our surprise, this stage is computationally efficient and takes only around 7 minutes with a single A100.
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- MiniGPT-4 yields many emerging vision-language capabilities similar to those demonstrated in GPT-4.
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## Getting Started
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### Installation
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**1. Prepare the code and the environment**
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Git clone our repository, creating a python environment and ativate it via the following command
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```bash
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git clone https://github.com/Vision-CAIR/MiniGPT-4.git
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cd MiniGPT-4
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conda env create -f environment.yml
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conda activate minigpt4
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```
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**2. Prepare the pretrained Vicuna weights**
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The current version of MiniGPT-4 is built on the v0 versoin of Vicuna-13B.
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Please refer to our instruction [here](PrepareVicuna.md)
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to prepare the Vicuna weights.
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The final weights would be in a single folder in a structure similar to the following:
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```
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vicuna_weights
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├── config.json
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├── generation_config.json
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├── pytorch_model.bin.index.json
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├── pytorch_model-00001-of-00003.bin
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...
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```
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Then, set the path to the vicuna weight in the model config file
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[here](minigpt4/configs/models/minigpt4.yaml#L16) at Line 16.
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**3. Prepare the pretrained MiniGPT-4 checkpoint**
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Download the pretrained checkpoints according to the Vicuna model you prepare.
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| Checkpoint Aligned with Vicuna 13B | Checkpoint Aligned with Vicuna 7B |
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:------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:
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[Downlad](https://drive.google.com/file/d/1a4zLvaiDBr-36pasffmgpvH5P7CKmpze/view?usp=share_link) | [Download](https://drive.google.com/file/d/1RY9jV0dyqLX-o38LrumkKRh6Jtaop58R/view?usp=sharing)
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Then, set the path to the pretrained checkpoint in the evaluation config file
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in [eval_configs/minigpt4_eval.yaml](eval_configs/minigpt4_eval.yaml#L10) at Line 11.
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### Launching Demo Locally
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Try out our demo [demo.py](demo.py) on your local machine by running
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```
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python demo.py --cfg-path eval_configs/minigpt4_eval.yaml --gpu-id 0
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```
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To save GPU memory, Vicuna loads as 8 bit by default, with a beam search width of 1.
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This configuration requires about 23G GPU memory for Vicuna 13B and 11.5G GPU memory for Vicuna 7B.
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For more powerful GPUs, you can run the model
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in 16 bit by setting low_resource to False in the config file
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[minigpt4_eval.yaml](eval_configs/minigpt4_eval.yaml) and use a larger beam search width.
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Thanks [@WangRongsheng](https://github.com/WangRongsheng), you can also run our code on [Colab](https://colab.research.google.com/drive/1OK4kYsZphwt5DXchKkzMBjYF6jnkqh4R?usp=sharing)
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### Training
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The training of MiniGPT-4 contains two alignment stages.
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**1. First pretraining stage**
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In the first pretrained stage, the model is trained using image-text pairs from Laion and CC datasets
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to align the vision and language model. To download and prepare the datasets, please check
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our [first stage dataset preparation instruction](dataset/README_1_STAGE.md).
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After the first stage, the visual features are mapped and can be understood by the language
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model.
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To launch the first stage training, run the following command. In our experiments, we use 4 A100.
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You can change the save path in the config file
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[train_configs/minigpt4_stage1_pretrain.yaml](train_configs/minigpt4_stage1_pretrain.yaml)
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```bash
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torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/minigpt4_stage1_pretrain.yaml
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```
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A MiniGPT-4 checkpoint with only stage one training can be downloaded
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[here (13B)](https://drive.google.com/file/d/1u9FRRBB3VovP1HxCAlpD9Lw4t4P6-Yq8/view?usp=share_link) or [here (7B)](https://drive.google.com/file/d/1HihQtCEXUyBM1i9DQbaK934wW3TZi-h5/view?usp=share_link).
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Compared to the model after stage two, this checkpoint generate incomplete and repeated sentences frequently.
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**2. Second finetuning stage**
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In the second stage, we use a small high quality image-text pair dataset created by ourselves
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and convert it to a conversation format to further align MiniGPT-4.
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To download and prepare our second stage dataset, please check our
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[second stage dataset preparation instruction](dataset/README_2_STAGE.md).
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To launch the second stage alignment,
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first specify the path to the checkpoint file trained in stage 1 in
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[train_configs/minigpt4_stage1_pretrain.yaml](train_configs/minigpt4_stage2_finetune.yaml).
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You can also specify the output path there.
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Then, run the following command. In our experiments, we use 1 A100.
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```bash
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torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/minigpt4_stage2_finetune.yaml
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```
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After the second stage alignment, MiniGPT-4 is able to talk about the image coherently and user-friendly.
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## Acknowledgement
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+ [BLIP2](https://huggingface.co/docs/transformers/main/model_doc/blip-2) 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!
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+ [Lavis](https://github.com/salesforce/LAVIS) This repository is built upon Lavis!
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+ [Vicuna](https://github.com/lm-sys/FastChat) The fantastic language ability of Vicuna with only 13B parameters is just amazing. And it is open-source!
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If you're using MiniGPT-4 in your research or applications, please cite using this BibTeX:
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```bibtex
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@article{zhu2023minigpt,
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title={MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models},
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author={Zhu, Deyao and Chen, Jun and Shen, Xiaoqian and Li, Xiang and Elhoseiny, Mohamed},
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journal={arXiv preprint arXiv:2304.10592},
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year={2023}
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}
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```
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## License
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This repository is under [BSD 3-Clause License](LICENSE.md).
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Many codes are based on [Lavis](https://github.com/salesforce/LAVIS) with
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BSD 3-Clause License [here](LICENSE_Lavis.md).
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