• [06/11]🔥SliME is coming! We release the paper, code, models, and data for SliME!
• [06/11]🔥SliME-70B will be released soon.

• Install
• Model
• Preparation
• Train
• Evaluation
• Examples
• Citation

Please follow the instructions below to install the required packages.

1. Clone this repository

git clone https://github.com/yfzhang114/SliME.git

2. Install Package

conda create -n slime python=3.10 -y
conda activate slime
cd SliME
pip install --upgrade pip  # enable PEP 660 support
pip install -e .

3. Install additional packages for training cases

pip install -e ".[train]"
pip install ninja
pip install datasets
pip install flash-attn --no-build-isolation

We provide all our fully finetuned models on Stage 1/2 and 3 data for SliME:

Here are the pretrained weights on Stage 1/2 data only:

Please follow LLaVA and SharedGPT4V to prepare the corresponding images and data.

data
├── arxivqa
│   └── images
├── DVQA
│   └── images
├── Geometry3K
│   └── 0-2400 dirs
├── ChartQA
│   └── train_images
└── GeoQA3
│    ├── image
│    └── json
├── mathvision
├── scienceqa
├── tabmwp
└── GeoQA3
│    ├── train
│    └── test
│    └── val
└── ai2d
│    ├── abc_images
│    └── images
└── geoqa+
│   └── images

1. Arxiv QA Download images using this download url

python playground/data/process_arxivqa.py

2. DVQA

Download images using this url.

3. ChartQA

Clone this repo

extract all the training images in ChartQA_Dataset/train/png into ChartQA

4. Geometry3K

Download images using this url.

The image path in our json file will be os.path.join(f'Geometry3K/i', 'img_diagram.png')

5. GeoQA3

Download images using this url

extract all the training images in GeoQA3/image

6. MathVision

Download images using this url

Our data will not include the images from test-mini split automatically

7. ScienceQA

wget https://scienceqa.s3.us-west-1.amazonaws.com/images/train.zip
wget https://scienceqa.s3.us-west-1.amazonaws.com/images/val.zip
wget https://scienceqa.s3.us-west-1.amazonaws.com/images/test.zip

unzip -q train.zip
unzip -q val.zip
unzip -q test.zip

rm train.zip
rm val.zip
rm test.zip

8. Tabmwp

Download images using this url

9. TextbookQA

Download images using this url

10. AI2D:

Download images using this url

11. GeoQA+

Download images using this url

SliME training consists of three stages: (1) training the global projector and attention adapter specifically; (2) training the local compression layer; and (3) training the full model.

SliME is trained on 8 A100 GPUs with 80GB memory. To train on fewer GPUs, you can reduce the per_device_train_batch_size and increase the gradient_accumulation_steps accordingly. Always keep the global batch size the same: per_device_train_batch_size x gradient_accumulation_steps x num_gpus.

Please make sure you download and organize the data following Preparation before training.

If you want to train and finetune SliME, please run the following command for SliME-7B with image size 336:

bash scripts/vicuna/vicuna_7b_pt.sh
bash scripts/vicuna/vicuna_7b_sft.sh

or for SliME-8B with image size 336:

bash scripts/llama/llama3_8b_pt.sh
bash scripts/llama/llama3_8b_sft.sh

Because we reuse the pre-trained projecter weights from the SliME-7B, you can directly use the sft commands stage-3 instruction tuning by changing the PROJECTOR_DIR:

bash scripts/llama/llama3_8b_sft.sh

Please find more training scripts of in scripts/.

We perform evaluation on several image-based benchmarks. Please see Evaluation for the detailes.

If you want to evaluate the model on image-based benchmarks, please use the scripts in scripts/MODEL_PATH/eval. For example, run the following command for TextVQA evaluation with SliME-7B:

bash scripts/llama/eval/textvqa.sh

Please find more evaluation scripts in scripts/MODEL_PATH.

We provide some examples in this section. More examples can be found in our project page.

If you find this repo useful for your research, please consider citing the paper

@misc{zhang2024llavahd,
      title={Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models}, 
      author={Yi-Fan Zhang and Qingsong Wen and Chaoyou Fu and Xue Wang and Zhang Zhang and Liang Wang and Rong Jin},
      year={2024},
      eprint={2406.08487},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

We would like to thank the following repos for their great work:

• This work is built upon the LLaVA.
• This work utilizes LLMs from , Vicuna, and Llama3.

The data and checkpoint is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of LLaVA, LLaMA, Vicuna and GPT-4. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.