📃 Paper • 🤗 Model (SOLO-7B)

We present SOLO, a single Transformer architecture for unified vision-language modeling. SOLO accepts both raw image patches (in pixels) and texts as inputs, without using a separate pre-trained vision encoder.

• Release the instruction tuning data mixture

  ✅ Release the code for instruction tuning

  ✅ Release the pre-training code

  ✅ Release the SOLO model 🤗 Model (SOLO-7B)

  ✅ Paper on arxiv 📃 Paper

git clone https://github.com/Yangyi-Chen/SOLO
git submodule update --init --recursive

conda env create -f environment.yml
conda activate solo

OR simply

pip install -r requirements.txt

Check scripts/notebook/demo.ipynb for an example of performing inference on the model.

Please refer to PRETRAIN_GUIDE.md for more details about how to perform pre-training. The following table documents the data statistics in pre-training:

Please refer to SFT_GUIDE.md for more details about how to perform instruction fine-tuning. The following table documents the data statistics in instruction fine-tuning:

If you use or extend our work, please consider citing our paper.

@article{chen2024single,
  title={A Single Transformer for Scalable Vision-Language Modeling},
  author={Chen, Yangyi and Wang, Xingyao and Peng, Hao and Ji, Heng},
  journal={arXiv preprint arXiv:2407.06438},
  year={2024}
}