This repository contains the implementation of the following paper:
Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models [Project Page] [Paper]
Gen Luo1, Yiyi Zhou12, Tianhe Ren1, Shengxin Chen1, Xiaoshuai Sun12, Rongrong Ji12
1Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University
2Institute of Artificial Intelligence, Xiamen University
In this work, we propose a novel and affordable solution for vision-language instruction tuning, namely Mixture-of-Modality Adaptation (MMA). Particularly, MMA is an end-to-end optimization regime, which connects the image encoder and LLM via lightweight adapters. Meanwhile, we also propose a novel routing algorithm in MMA, which can help the model automatically shifts the reasoning paths for single- and multi-modal instructions. Based on MMA, we develop a large vision-language instructed model called LaVIN, which demonstrates superior training efficiency and better reasoning ability than existing multimodal LLMs in various instruction-following tasks.
2023/06/30: 🔥🔥🔥 With very limited training data and cost, LaVIN achieves 5-th place of Perception and Cognition on MME benchmark， outperforming seven existing multimodal LLMs. Evaluation codes are available.2023/06/27: 🔥4-bit trainings are available now ! LaVIN-lite can be trained on one 3090 GPU, taking around 9G and 15G GPU memory for the scales of 7B and 13B , respectively. Technical details are available in 知乎.2023/05/29: 🔥We released the demo and the pre-trained checkpoint (LLaMA-13B) for multimodal chatbot.2023/05/25: 🔥We released the code of LaVIN: Large Vision-Language Instructed model, which achieves 89.4 (LaVIN-7B) and 90.8 (LaVIN-13B) accuracy on ScienceQA! 🔥With the proposed mixture-of-modality adaptation, the training time and trainable parameters can be reduced to 1.4 hours and 3.8M, respectively! Checkout the paper.
- Release training codes.
- Release checkpoints and demo.
- 4-bit training.
- Support more modalities, e.g., audio and video.
conda create -n lavin python=3.8 -y
conda activate lavin
# install pytorch
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 -c pytorch
# install dependency and lavin
pip install -r requirements.txt
pip install -e .- For ScienceQA, please prepare the dataset from the official repo.
- For Multimodal Chatbot, download the images in train2014 split from MSCOCO, and obtain the prepared 52k text-only and 158k text-image instruction-following data from here.
- Obtain the weights of LLaMA from this form (official) or Download LLaMA-7B and LLaMA-13B from HuggingFace (unofficial).
- If you want to use Vicuna weights to initialize the model, please download from here. After that, the file structure should look like:
LaVIN/
|-- lavin
|-- scripts
|-- train.py
|-- eval.py
......
data/
|-- problem.json
|-- pid_splits.json
|-- captions.json
|-- all_data.json
|-- images
|-- train2014 # MSCOCO 2014
|-- val2014 # MSCOCO 2014
|-- train # ScienceQA train image
|-- val # ScienceQA val image
|-- test # ScienceQA test image
|-- weights
|-- tokenizer.model
|--7B
|-- params.json
|-- consolidated.00.pth
|--13B
|-- params.json
|-- consolidated.00.pth
|-- consolidated.01.pth
|--vicuna_7B
|--vicuna_13B
|-- config.json
|-- generation_config.json
|-- pytorch_model.bin.index.json
|-- special_tokens_map.json
|-- tokenizer_config.json
|-- tokenizer.model
|-- pytorch_model-00001-of-00003.bin
|-- pytorch_model-00002-of-00003.bin
|-- pytorch_model-00003-of-00003.bin
......Reproduce the performance of LaVIN-7B on ScienceQA. For 7B model, we fine-tune it on 2x A100 (we find that the performance will be affected by the number of GPUs. We are working to address this problem).
LLaMA weights:
bash ./scripts/finetuning_sqa_7b.shVicuna weights:
bash ./scripts/finetuning_sqa_vicuna_7b.shLaVIN-lite with LLaMA weights (single GPU):
bash ./scripts/finetuning_sqa_vicuna_7b_lite.shReproduce the performance of LaVIN-13B on ScienceQA (~2 hours on 8x A100 (80G)). For 13B model, we fine-tune it on 8x A100.
LLaMA weights:
bash ./scripts/finetuning_sqa_13b.shVicuna weights:
bash ./scripts/finetuning_sqa_vicuna_13b.shLaVIN-lite with LLaMA weights (single GPU):
bash ./scripts/finetuning_sqa_vicuna_13b_lite.shFine-tune LaVIN-13B on 210k instruction-following data (~ 75 hours with 15 epochs and ~25 hours with 5 epochs on 8x A100 (80G) )
LLaMA weights:
bash ./scripts/vl_instruction_tuning_13b.shVicuna weights:
bash ./scripts/vl_instruction_tuning_vicuna_13b.shTo train on fewer GPUs, you can reduce the number of gpus in the scripts and increase gradient accumulation via --accum_iter to guarantee the total batch size of 32. Setting --gradient_checkpointing and --bits 4bit in the scripts will greatly reduce the requirements of GPU memory.
LaVIN supports both single- and multi-modal instruction inputs. Try your custom instructions in our demo:
- Launch a gradio web server on your machine, then you can interact with LaVIN as you like.
torchrun --nproc_per_node 1 demo.py --server_name 127.0.0.1
| Model | Weights | Time | Memory | #Params | Acc | Weights |
|---|---|---|---|---|---|---|
| LaVIN-7B-lite | LLaMA | 29 hours (single GPU) | 9G | 3.8M | 88.35 | google drive |
| LaVIN-13B-lite | LLaMA | 42 hours (single GPU) | 14G | 5.4M | 89.44 | google drive |
| LaVIN-7B | LLaMA | 1.4 hours | 33.9G | 3.8M | 89.37 | google drive |
| LaVIN-7B | Vicuna | 1.4 hours | 33.9G | 3.8M | 89.41 | google drive |
| LaVIN-13B | LLaMA | 2 hours | 55.9G | 5.4M | 90.54 | google drive |
| LaVIN-13B | LLaMA | 4 hours | 55.9G | 5.4M | 90.8 | - |
| Model | Weights | Time | Memory | #Params | Acc | Weights |
|---|---|---|---|---|---|---|
| LaVIN-13B | LLaMA | 25 hours | 55.9G | 5.4M | - | - |
| LaVIN-13B | LLaMA | 75 hours | 55.9G | 5.4M | - | google drive |
If you think our code and paper helpful, please kindly cite LaVIN and RepAdapter:
@article{luo2023towards,
title={Towards Efficient Visual Adaption via Structural Re-parameterization},
author={Luo, Gen and Huang, Minglang and Zhou, Yiyi and Sun, Xiaoshuai and Jiang, Guangnan and Wang, Zhiyu and Ji, Rongrong},
journal={arXiv preprint arXiv:2302.08106},
year={2023}
}
@article{luo2023cheap,
title={Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models},
author={Luo, Gen and Zhou, Yiyi and Ren, Tianhe and Chen, Shengxin and Sun, Xiaoshuai and Ji, Rongrong},
journal={arXiv preprint arXiv:2305.15023},
year={2023}
}This repo borrows some data and codes from LLaMA, Stanford Alpaca, LLaVA, MiniGPT-4 and LLaMA-Adapter. Thanks for their great works.