---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
pipeline_tag: image-text-to-text
library_name: transformers
base_model:
- OpenGVLab/InternVL3-1B
language:
- multilingual
tags:
- internvl
- multimodal
- biology
- medical
datasets:
- AdaptLLM/biomed-visual-instructions
---

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

This repos contains the **biomedicine MLLM developed from InternVL3-1B** in our paper: [On Domain-Adaptive Post-Training for Multimodal Large Language Models](https://huggingface.co/papers/2411.19930). The correspoding training dataset is in [biomed-visual-instructions](https://huggingface.co/datasets/AdaptLLM/biomed-visual-instructions).

The main project page is: [Adapt-MLLM-to-Domains](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains)

## 1. To Chat with AdaMLLM  
Our model architecture aligns with the base model: InternVL3-1B, so you can refer to the official [OpenGVLab/InternVL3-1B](https://huggingface.co/OpenGVLab/InternVL3-1B) for the usage instructions.

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

## 2. Domain-Specific Benchmarks  

We provide [biomed-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/biomed-VQA-benchmark) for evaluating MLLMs on domain-specific tasks.

## 3. To Reproduce this Domain-Adapted MLLM  

Using our training data, [biomed-visual-instructions](https://huggingface.co/datasets/AdaptLLM/biomed-visual-instructions), you can easily reproduce our models based on the [LlamaFactory](https://github.com/hiyouga/LLaMA-Factory) repository.

For reference, we train from [OpenGVLab/InternVL3-1B-hf](https://huggingface.co/OpenGVLab/InternVL3-1B-hf) (note that we train from the `-hf` version) for 1 epoch with a learning rate of 1e-5 and a global batch size of 128.
After training, follow [this instruction](https://github.com/hiyouga/LLaMA-Factory/issues/8086#issuecomment-2898640569) to convert the `hf` version back to the official [OpenGVLab/InternVL3-1B](https://huggingface.co/OpenGVLab/InternVL3-1B) version.


## Citation
If you find our work helpful, please cite us.

[Adapt MLLM to Domains](https://huggingface.co/papers/2411.19930) (EMNLP 2025 Findings)
```bibtex
@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](https://huggingface.co/papers/2309.09530) (ICLR 2024)
```bibtex
@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}
}
```