mplug_owl_2

Train@mPLUG-Owl-2

This document provides instruction on how to train with Q-Instruct dataset on mPLUG-Owl-2 (LLaMA2-7B), under the proposed two strategies (mix and after), shown as follows.

Due to copyright issues, the pre-trained weights of mPLUG-Owl-2 is not available at present. Therefore, the open-source mix strategy at present is slightly different, that we need to use a mix of high-level datasets and Q-Instruct on the SFT checkpoint of mPLUG-Owl-2.

Step 0: Pre-requisites

Install mPLUG-Owl under the main repository, with flash attention for more efficient training.

git clone https://github.com/X-PLUG/mPLUG-Owl.git
cd mPLUG-Owl/mPLUG-Owl2
pip install -e ".[train]"
pip install flash_attn --no-build-isolation
cd ..

After that, you can conduct low-level visual instruction tuning as follows, under either mix or after strategy.

Step 1: Download Training Datasets

Download Q-Instruct

Note: If you have already downloaded for LLaVA, you may directly copy them here.

For the Q-Instruct dataset, download them directly via the following script:

cd mPLUG-Owl/mPLUG-Owl2/playground/data
wget https://huggingface.co/datasets/teowu/Q-Instruct/resolve/main/cleaned_labels.json
wget https://huggingface.co/datasets/teowu/Q-Instruct/resolve/main/q-instruct-images.tar
tar -xf q-instruct-images.tar
rm -f q-instruct-images.tar
cd ../../../..

Make sure you have the file structures as follows under LLaVA/playground/data.

├── spaq_koniq
├── livefb_liveitw_aigc
├── cleaned_labels.json

Download Public High-level Instruction Tuning Datasets

Note: If you have already downloaded for LLaVA, you may directly copy them here.

If you choose the mix strategy, the high-level datasets also need to be downloaded via the following steps:

1. Download the annotation of the final mixture our instruction tuning data llava_v1_5_mix665k.json:

wget -P mPLUG-Owl/mPLUG-Owl2/playground/data https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json

2. Download the images from constituting datasets:

• COCO: train2017
• GQA: images
• OCR-VQA: download script, we save all files as .jpg
• TextVQA: train_val_images
• VisualGenome: part1, part2

After downloading all of them, organize the high-level data as follows in LLaVA/playground/data,

├── coco
│   └── train2017
├── gqa
│   └── images
├── ocr_vqa
│   └── images
├── textvqa
│   └── train_images
└── vg
    ├── VG_100K
    └── VG_100K_2

3. Merge the Q-Instruct labels with labels from high-level datasets.

jq -s 'add' mPLUG-Owl/mPLUG-Owl2/playground/data/cleaned_labels.json mPLUG-Owl/mPLUG-Owl2/playground/data/llava_v1_5_mix665k.json > mPLUG-Owl/mPLUG-Owl2/playground/data/mix_cleaned_labels.json

Step 2: Start Training

Please make sure you have enough computational resources before training.

• [Must Do!] Replace all the <image> token in the json into <|image|>, Otherwise the image will not be loaded into training.

sed -i 's/<image>/<|image|>/g' mPLUG-Owl/mPLUG-Owl2/playground/data/mix_cleaned_labels.json

Strategy (a): Mix with High-level Datasets

• Training (requires 8x A100 80G), 11h

sh scripts/mplug_owl_2/mix_qinstruct.sh

Strategy (b): After High-level Datasets

• Training (requires 8x A100 80G), 1.5h

sh scripts/mplug_owl_2/after_qinstruct.sh