🤖 Multi-modal GPT
Train a multi-modal chatbot with visual and language instructions!
Based on the open-source multi-modal model OpenFlamingo, we create various visual instruction data with open datasets, including VQA, Image Captioning, Visual Reasoning, Text OCR, and Visual Dialogue. Additionally, we also train the language model component of OpenFlamingo using only language-only instruction data.
The joint training of visual and language instructions effectively improves the performance of the model! For more details please refer to our technical report.
Welcome to join us!
English | 简体中文
Features
- Support various vision and language instruction data
- Parameter efficient fine-tuning with LoRA
- Tuning vision and language at the same time, complement each other
Installation
To install the package in an existing environment, run
git clone https://github.com/open-mmlab/Multimodal-GPT.git
cd Multimodal-GPT
pip install -r requirements.txt
pip install -v -e .or create a new conda environment
conda env create -f environment.ymlLaunch Demo Locally
-
Download the pre-trained weights.
Use this script for converting LLaMA weights to Hugging Face format.
Download the OpenFlamingo pre-trained model from openflamingo/OpenFlamingo-9B.
Download our LoRA Weight from here.
Then place these models in
checkpointsfolders like this:checkpoints ├── llama-7b_hf │ ├── config.json │ ├── pytorch_model-00001-of-00002.bin │ ├── ...... │ └── tokenizer.model ├── OpenFlamingo-9B │ └──checkpoint.pt ├──mmgpt-lora-v0-release.pt -
launch the gradio demo
python app.py
Examples
Recipe:
Travel plan:
Movie:
Famous person:
Fine-tuning
Prepare datasets
-
Download annotation from this link and unzip to
data/aokvqa/annotations.It also requires images from coco dataset which can be downloaded from here.
-
Download from this link and unzip to
data/coco.It also requires images from coco dataset which can be downloaded from here.
-
Download from this link and place in
data/OCR_VQA/. -
Download from liuhaotian/LLaVA-Instruct-150K and place in
data/llava/.It also requires images from coco dataset which can be downloaded from here.
-
Download from Vision-CAIR/cc_sbu_align and place in
data/cc_sbu_align/. -
Download from databricks/databricks-dolly-15k and place it in
data/dolly/databricks-dolly-15k.jsonl. -
Download it from this link and place it in
data/alpaca_gpt4/alpaca_gpt4_data.json.
You can also customize the data path in the configs/dataset_config.py.
Start training
torchrun --nproc_per_node=8 mmgpt/train/instruction_finetune.py \
--lm_path checkpoints/llama-7b_hf \
--tokenizer_path checkpoints/llama-7b_hf \
--pretrained_path checkpoints/OpenFlamingo-9B/checkpoint.pt \
--run_name train-my-gpt4 \
--learning_rate 1e-5 \
--lr_scheduler cosine \
--batch_size 1 \
--tuning_config configs/lora_config.py \
--dataset_config configs/dataset_config.py \
--report_to_wandbAcknowledgements
If you find our project useful for your research and applications, please cite using this BibTeX:
@misc{gong2023multimodalgpt,
title={MultiModal-GPT: A Vision and Language Model for Dialogue with Humans},
author={Tao Gong and Chengqi Lyu and Shilong Zhang and Yudong Wang and Miao Zheng and Qian Zhao and Kuikun Liu and Wenwei Zhang and Ping Luo and Kai Chen},
year={2023},
eprint={2305.04790},
archivePrefix={arXiv},
primaryClass={cs.CV}
}