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

This repos contains the food MLLM developed from Qwen2.5-VL-3B-Instruct in our paper: On Domain-Adaptive Post-Training for Multimodal Large Language Models. The correspoding training dataset is in food-visual-instructions.

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

1. To Chat with AdaMLLM

Our model architecture aligns with the base model: Qwen2.5-VL-3B-Instruct. We provide a usage example below, and you may refer to the official Qwen2.5-VL-3B-Instruct for more advanced usage instructions.

Note: For AdaMLLM, always place the image at the beginning of the input instruction in the messages.

2. To Evaluate Any MLLM on Domain-Specific Benchmarks

Please refer to the food-VQA-benchmark to reproduce our results and evaluate many other MLLMs on domain-specific benchmarks.

3. To Reproduce this Domain-Adapted MLLM

Using our training data, food-visual-instructions, you can easily reproduce our models based on the LlamaFactory repository.

For reference, we train from Qwen2.5-VL-3B-Instruct for 1 epoch with a learning rate of 1e-5, and a global batch size of 128.

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