GMAI-VL & GMAI-VL-5.5M: A Large Vision-Language Model and A Comprehensive Multimodal Dataset Towards General Medical AI

Welcome to the official code repository for GMAI-VL & GMAI-VL-5.5M (AAAI 2026). This repository provides the training code needed for reproducing the results and furthering research in medical AI through vision-language models.

• GMAI-VL: A state-of-the-art general medical vision-language model that achieves 88.48% on OmniMedVQA with only 7B parameters, surpassing models with 34B+ parameters.
• GMAI-VL-5.5M: The largest open-source medical multimodal dataset with 5.5 million QA pairs from 219 professional medical data sources, covering 13 imaging modalities and 18 clinical departments. Available on Hugging Face.

📊 GMAI-VL-5.5M Dataset

GMAI-VL-5.5M is systematically constructed from 219 professional medical data sources using an Annotation-Guided Data Generation pipeline — all text is grounded in expert annotations, not hallucinated.

Dataset Subsets

Comparison with Existing Datasets

👉 Download: 🤗 Hugging Face

🏥 GMAI-VL Model

GMAI-VL is built on the LLaVA architecture with InternLM2.5-7B (LLM) + CLIP Vision Encoder + MLP Projector, trained using a three-stage progressive strategy:

Benchmark Results

With only 7B parameters, GMAI-VL outperforms models with 34B+ parameters on multiple benchmarks, demonstrating the value of high-quality data + progressive training.

🖥️ Visualization Demo

A Gradio-based visualization demo is available in demo/gmai_vl_demo. It supports medical image upload, preset multimodal examples, task/body-system controls, and screenshot-friendly model responses.

cd demo/gmai_vl_demo
pip install -r requirements-gmai-vl-demo.txt
export GMAI_VL_MODEL_PATH=./model_weight
PORT=10083 bash run_demo.sh

🛠️ Model Training Instructions

1. Installation

To set up the environment for training, please follow the instructions in the xtuner repository. Ensure that all dependencies are correctly installed.

git clone https://github.com/uni-medical/GMAI-VL
cd GMAI-VL
pip install -e .

2. Data Preparation

Prepare your training data in the format shown in examples/example_data.json. To support multiple JSON datasets with different sampling ratios, use the format defined in examples/example_list.json.

Example structure for example_list.json:

{
    "FILE1": {
        "image_dir": "",
        "annotations": "examples/example_data.json",
        "sample_ratio": 1.0,
        "length": 38276,
        "data_augment": true
    },
    ...
}

3. Training

Here is a reference script to start training:

SRUN_ARGS=${SRUN_ARGS:-""}

export PYTHONPATH="$(pwd):$(pwd)/../"
export MASTER_PORT=34220


export NPROC_PER_NODE=${KUBERNETES_CONTAINER_RESOURCE_GPU}
export PORT=${MASTER_PORT}
export NNODES=${WORLD_SIZE}
export NODE_RANK=${RANK}
export ADDR=${MASTER_ADDR}
export HF_HOME=~/.cache
export USE_TRITON_KERNEL=1

torchrun --nnodes=${WORLD_SIZE} --node_rank=${RANK} --nproc_per_node=${NPROC_PER_NODE} --master_addr=${MASTER_ADDR} --master_port=${MASTER_PORT} \
    tools/llava/llava_sft.py \
    --freeze-llm \
    --freeze-vit \
    --llava $base_model_path/ \
    --chat-template internlm2 \
    --datasets examples/examples_list.json \
    --num-workers 6 \
    --mirco-batch-size $MIRCO_BATCH_SIZE \
    --global-batch-size $GLOBAL_BATCH_SIZE \
    --lr 1e-5 \
    --wd 0 \
    --dset-pack-level soft \
    --shard-strategy 'zero2' \
    --group-by-length \
    --resume \
    --max-length 2048 \
    --checkpoint-interval 500 \
    --log-interval 10 \
    --work-dir $work_dir/ \
    --dset-cache-dir $work_dir/cache/ \
    --dset-from-cache

💡 Note: Our training follows a multi-stage strategy. At each stage, different components (e.g., LLM or ViT) may be frozen or fine-tuned. Please adjust flags such as --freeze-llm, --freeze-vit, and learning rates accordingly, as described in the paper.

📈 Evaluation

For evaluation, we use VLMEvalKit. GMAI-VL has been evaluated on 7 mainstream medical multimodal benchmarks: OmniMedVQA, GMAI-MMBench, MMMU (Health & Medicine), VQA-RAD, SLAKE, PMC-VQA, and PathVQA. See the paper for full results.

📦 Open-Source Resources

🔗 Stay Connected

For inquiries, collaboration opportunities, or access requests, feel free to reach out via email or open a GitHub issue.

Thank you for your interest and support!

🙏 Acknowledgements

We would like to express our sincere gratitude to the open-source community. Our work is built upon the excellent contributions of the following toolkits:

• XTuner: An efficient, flexible, and full-featured toolkit for fine-tuning large language and vision-language models. It served as the core training framework for GMAI-VL.
• VLMEvalKit: A comprehensive evaluation toolkit for large vision-language models (LVLMs). It enabled us to conduct rigorous and standardized evaluations across multiple benchmarks.

We deeply appreciate the efforts of the developers and contributors behind these projects.

📄 Citation

If you find our work helpful in your research, please consider citing us:

@inproceedings{li2026gmai,
  title={Gmai-vl \& gmai-vl-5.5 m: A large vision-language model and a comprehensive multimodal dataset towards general medical ai},
  author={Li, Tianbin and Su, Yanzhou and Li, Wei and Fu, Bin and Chen, Zhe and Huang, Ziyan and Wang, Guoan and Ma, Chenglong and Chen, Ying and Hu, Ming and others},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={40},
  number={28},
  pages={23177--23185},
  year={2026}
}