# MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models
[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

**King Abdullah University of Science and Technology**

## Online Demo

Click the image to chat with MiniGPT-4 around your images
[![demo](figs/online_demo.png)](https://minigpt-4.github.io)


## Examples
  |   |   |
:-------------------------:|:-------------------------:
![find wild](figs/examples/wop_2.png) |  ![write story](figs/examples/ad_2.png)
![solve problem](figs/examples/fix_1.png)  |  ![write Poem](figs/examples/rhyme_1.png)

More examples can be found in the [project page](https://minigpt-4.github.io).



## Introduction
- MiniGPT-4 aligns a frozen visual encoder from BLIP-2 with a frozen LLM, Vicuna, using just one projection layer. 
- 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.
- 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.
- 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.
- MiniGPT-4 yields many emerging vision-language capabilities similar to those demonstrated in GPT-4. 


![overview](figs/overview.png)


## Getting Started
### Installation

**1. Prepare the code and the environment**

Git clone our repository, creating a python environment and ativate 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 minigpt4
```


**2. Prepare the pretrained Vicuna weights**

The current version of MiniGPT-4 is built on the v0 versoin of Vicuna-13B.
Please refer to our instruction [here](PrepareVicuna.md) 
to prepare the Vicuna weights.
The final weights would be in a single folder with the following structure:

```
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 
[here](minigpt4/configs/models/minigpt4.yaml#L16) at Line 16.

**3. Prepare the pretrained MiniGPT-4 checkpoint**

To play with our pretrained model, download the pretrained checkpoint 
[here](https://drive.google.com/file/d/1a4zLvaiDBr-36pasffmgpvH5P7CKmpze/view?usp=share_link).
Then, set the path to the pretrained checkpoint in the evaluation config file 
in [eval_configs/minigpt4_eval.yaml](eval_configs/minigpt4_eval.yaml#L10) at Line 11. 



### Launching Demo Locally

Try out our demo [demo.py](demo.py) on your local machine by running

```
python demo.py --cfg-path eval_configs/minigpt4_eval.yaml  --gpu-id 0
```

Here, we load Vicuna as 8 bit by default to save some GPU memory usage. 
Besides, the default beam search width is 1. 
Under this setting, the demo cost about 23G GPU memory. 
If you have a more powerful GPU with larger GPU memory, you can run the model 
in 16 bit by setting low_resource to False in the config file 
[minigpt4_eval.yaml](eval_configs/minigpt4_eval.yaml) and use a larger beam search width.


### Training
The training of MiniGPT-4 contains two alignment stages.

**1. First pretraining stage**

In the first pretrained stage, the model is trained using image-text pairs from Laion and CC datasets
to align the vision and language model. To download and prepare the datasets, please check 
our [first stage dataset preparation instruction](dataset/README_1_STAGE.md). 
After the first stage, the visual 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 4 A100. 
You can change the save path in the config file 
[train_configs/minigpt4_stage1_pretrain.yaml](train_configs/minigpt4_stage1_pretrain.yaml)

```bash
torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/minigpt4_stage1_pretrain.yaml
```

A MiniGPT-4 checkpoint with only stage one training can be downloaded 
[here](https://drive.google.com/file/d/1u9FRRBB3VovP1HxCAlpD9Lw4t4P6-Yq8/view?usp=share_link).
Compared to the model after stage two, this checkpoint generate incomplete and repeated sentences frequently.


**2. Second finetuning stage**

In the second stage, we use a small high quality image-text pair dataset created by ourselves
and convert it to a conversation format to further align MiniGPT-4.
To download and prepare our second stage dataset, please check our 
[second stage dataset preparation instruction](dataset/README_2_STAGE.md).
To launch the second stage alignment, 
first specify the path to the checkpoint file trained in stage 1 in 
[train_configs/minigpt4_stage1_pretrain.yaml](train_configs/minigpt4_stage2_finetune.yaml).
You can also specify the output path there. 
Then, run the following command. In our experiments, we use 1 A100.

```bash
torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/minigpt4_stage2_finetune.yaml
```

After the second stage alignment, MiniGPT-4 is able to talk about the image coherently and user-friendly. 




## Acknowledgement

+ [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!
+ [Lavis](https://github.com/salesforce/LAVIS) This repository is built upon Lavis!
+ [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!


If you're using MiniGPT-4 in your research or applications, please cite using this BibTeX:
```bibtex
@misc{zhu2022minigpt4,
      title={MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models}, 
      author={Deyao Zhu and Jun Chen and Xiaoqian Shen and xiang Li and Mohamed Elhoseiny},
      year={2023},
}
```


## License
This repository is under [BSD 3-Clause License](LICENSE.md).
Many codes are based on [Lavis](https://github.com/salesforce/LAVIS) with 
BSD 3-Clause License [here](LICENSE_Lavis.md).

region:us
{}
TAGS
#region-us 

MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models
======================================================================================


Deyao Zhu\* (On Job Market!), Jun Chen\* (On Job Market!), Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. \*Equal Contribution


King Abdullah University of Science and Technology


Online Demo
-----------


Click the image to chat with MiniGPT-4 around your images
![demo](URL)


Examples
--------



More examples can be found in the project page.


Introduction
------------


* MiniGPT-4 aligns a frozen visual encoder from BLIP-2 with a frozen LLM, Vicuna, using just one projection layer.
* 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.
* 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.
* 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.
* MiniGPT-4 yields many emerging vision-language capabilities similar to those demonstrated in GPT-4.


!overview


Getting Started
---------------


### Installation


1. Prepare the code and the environment


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


2. Prepare the pretrained Vicuna weights


The current version of MiniGPT-4 is built on the v0 versoin of Vicuna-13B.
Please refer to our instruction here
to prepare the Vicuna weights.
The final weights would be in a single folder with the following structure:


Then, set the path to the vicuna weight in the model config file
here at Line 16.


3. Prepare the pretrained MiniGPT-4 checkpoint


To play with our pretrained model, download the pretrained checkpoint
here.
Then, set the path to the pretrained checkpoint in the evaluation config file
in eval\_configs/minigpt4\_eval.yaml at Line 11.


### Launching Demo Locally


Try out our demo URL on your local machine by running


Here, we load Vicuna as 8 bit by default to save some GPU memory usage.
Besides, the default beam search width is 1.
Under this setting, the demo cost about 23G GPU memory.
If you have a more powerful GPU with larger GPU memory, you can run the model
in 16 bit by setting low\_resource to False in the config file
minigpt4\_eval.yaml and use a larger beam search width.


### Training


The training of MiniGPT-4 contains two alignment stages.


1. First pretraining stage


In the first pretrained stage, the model is trained using image-text pairs from Laion and CC datasets
to align the vision and language model. To download and prepare the datasets, please check
our first stage dataset preparation instruction.
After the first stage, the visual 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 4 A100.
You can change the save path in the config file
train\_configs/minigpt4\_stage1\_pretrain.yaml


A MiniGPT-4 checkpoint with only stage one training can be downloaded
here.
Compared to the model after stage two, this checkpoint generate incomplete and repeated sentences frequently.


2. Second finetuning stage


In the second stage, we use a small high quality image-text pair dataset created by ourselves
and convert it to a conversation format to further align MiniGPT-4.
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
train\_configs/minigpt4\_stage1\_pretrain.yaml.
You can also specify the output path there.
Then, run the following command. In our experiments, we use 1 A100.


After the second stage alignment, MiniGPT-4 is able to talk about the image coherently and user-friendly.


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!


If you're using MiniGPT-4 in your research or applications, please cite using this BibTeX:


License
-------


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



### Installation


1. Prepare the code and the environment


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


2. Prepare the pretrained Vicuna weights


The current version of MiniGPT-4 is built on the v0 versoin of Vicuna-13B.
Please refer to our instruction here
to prepare the Vicuna weights.
The final weights would be in a single folder with the following structure:


Then, set the path to the vicuna weight in the model config file
here at Line 16.


3. Prepare the pretrained MiniGPT-4 checkpoint


To play with our pretrained model, download the pretrained checkpoint
here.
Then, set the path to the pretrained checkpoint in the evaluation config file
in eval\_configs/minigpt4\_eval.yaml at Line 11. ### Launching Demo Locally


Try out our demo URL on your local machine by running


Here, we load Vicuna as 8 bit by default to save some GPU memory usage.
Besides, the default beam search width is 1.
Under this setting, the demo cost about 23G GPU memory.
If you have a more powerful GPU with larger GPU memory, you can run the model
in 16 bit by setting low\_resource to False in the config file
minigpt4\_eval.yaml and use a larger beam search width. ### Training


The training of MiniGPT-4 contains two alignment stages.


1. First pretraining stage


In the first pretrained stage, the model is trained using image-text pairs from Laion and CC datasets
to align the vision and language model. To download and prepare the datasets, please check
our first stage dataset preparation instruction.
After the first stage, the visual 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 4 A100.
You can change the save path in the config file
train\_configs/minigpt4\_stage1\_pretrain.yaml


A MiniGPT-4 checkpoint with only stage one training can be downloaded
here.
Compared to the model after stage two, this checkpoint generate incomplete and repeated sentences frequently.


2. Second finetuning stage


In the second stage, we use a small high quality image-text pair dataset created by ourselves
and convert it to a conversation format to further align MiniGPT-4.
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
train\_configs/minigpt4\_stage1\_pretrain.yaml.
You can also specify the output path there.
Then, run the following command. In our experiments, we use 1 A100.


After the second stage alignment, MiniGPT-4 is able to talk about the image coherently and user-friendly.


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!


If you're using MiniGPT-4 in your research or applications, please cite using this BibTeX:


License
-------


This repository is under BSD 3-Clause License.
Many codes are based on Lavis with
BSD 3-Clause License here.
TAGS
#region-us 
 ### Installation


1. Prepare the code and the environment


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


2. Prepare the pretrained Vicuna weights


The current version of MiniGPT-4 is built on the v0 versoin of Vicuna-13B.
Please refer to our instruction here
to prepare the Vicuna weights.
The final weights would be in a single folder with the following structure:


Then, set the path to the vicuna weight in the model config file
here at Line 16.


3. Prepare the pretrained MiniGPT-4 checkpoint


To play with our pretrained model, download the pretrained checkpoint
here.
Then, set the path to the pretrained checkpoint in the evaluation config file
in eval\_configs/minigpt4\_eval.yaml at Line 11. ### Launching Demo Locally


Try out our demo URL on your local machine by running


Here, we load Vicuna as 8 bit by default to save some GPU memory usage.
Besides, the default beam search width is 1.
Under this setting, the demo cost about 23G GPU memory.
If you have a more powerful GPU with larger GPU memory, you can run the model
in 16 bit by setting low\_resource to False in the config file
minigpt4\_eval.yaml and use a larger beam search width. ### Training


The training of MiniGPT-4 contains two alignment stages.


1. First pretraining stage


In the first pretrained stage, the model is trained using image-text pairs from Laion and CC datasets
to align the vision and language model. To download and prepare the datasets, please check
our first stage dataset preparation instruction.
After the first stage, the visual 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 4 A100.
You can change the save path in the config file
train\_configs/minigpt4\_stage1\_pretrain.yaml


A MiniGPT-4 checkpoint with only stage one training can be downloaded
here.
Compared to the model after stage two, this checkpoint generate incomplete and repeated sentences frequently.


2. Second finetuning stage


In the second stage, we use a small high quality image-text pair dataset created by ourselves
and convert it to a conversation format to further align MiniGPT-4.
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
train\_configs/minigpt4\_stage1\_pretrain.yaml.
You can also specify the output path there.
Then, run the following command. In our experiments, we use 1 A100.


After the second stage alignment, MiniGPT-4 is able to talk about the image coherently and user-friendly.


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!


If you're using MiniGPT-4 in your research or applications, please cite using this BibTeX:


License
-------


This repository is under BSD 3-Clause License.
Many codes are based on Lavis with
BSD 3-Clause License here.
passage: TAGS
#region-us 
### Installation


1. Prepare the code and the environment


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


2. Prepare the pretrained Vicuna weights


The current version of MiniGPT-4 is built on the v0 versoin of Vicuna-13B.
Please refer to our instruction here
to prepare the Vicuna weights.
The final weights would be in a single folder with the following structure:


Then, set the path to the vicuna weight in the model config file
here at Line 16.


3. Prepare the pretrained MiniGPT-4 checkpoint


To play with our pretrained model, download the pretrained checkpoint
here.
Then, set the path to the pretrained checkpoint in the evaluation config file
in eval\_configs/minigpt4\_eval.yaml at Line 11.### Launching Demo Locally


Try out our demo URL on your local machine by running


Here, we load Vicuna as 8 bit by default to save some GPU memory usage.
Besides, the default beam search width is 1.
Under this setting, the demo cost about 23G GPU memory.
If you have a more powerful GPU with larger GPU memory, you can run the model
in 16 bit by setting low\_resource to False in the config file
minigpt4\_eval.yaml and use a larger beam search width.