Adapting Multimodal Large Language Models to Domains via Post-Training (EMNLP 2025)

This repos contains the visual-instruction synthesizer in our paper: On Domain-Specific Post-Training for Multimodal Large Language Models.

The main project page is: Adapt-MLLM-to-Domains

1. Basic Usage: Synthesize a task triplet based on a given image-caption pair

To synthesize an "instruction-informative response-precise response" triplet based on the following image-caption pair.

👁 Image

2. Advanced Usage: Convert Image-Caption Pairs into Visual Instructions at Scale

The following steps show how to convert your own data into visual instructions for post-training MLLMs.

We leverage vLLM to accelerate the synthesis process. On a single A100-80GB GPU, it takes about 12.5 hours to convert 100K image-caption pairs.

Citation

If you find our work helpful, please cite us.

Adapt MLLM to Domains (EMNLP 2025 Findings)

@article{adamllm,
 title={On Domain-Adaptive Post-Training for Multimodal Large Language Models},
 author={Cheng, Daixuan and Huang, Shaohan and Zhu, Ziyu and Zhang, Xintong and Zhao, Wayne Xin and Luan, Zhongzhi and Dai, Bo and Zhang, Zhenliang},
 journal={arXiv preprint arXiv:2411.19930},
 year={2024}
}

Adapt LLM to Domains (ICLR 2024)

@inproceedings{
cheng2024adapting,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=y886UXPEZ0}
}

Base model