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lingshu-medical-mllm/
Lingshu-32B

Name: Lingshu-32B
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Website 🤖 7B Model 🤖 32B Model MedEvalKit Technical Report Lingshu MCP

Lingshu - SOTA Multimodal Large Language Models for Medical Domain

BIG NEWS: Lingshu is released with state-of-the-art performance on medical VQA tasks and report generation.

This repository contains the model of the paper Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning. We also release a comprehensive medical evaluation toolkit in MedEvalKit, which supports fast evaluation of major multimodal and textual medical tasks.

Highlights

Lingshu models achieve SOTA on most medical multimodal/textual QA and report generation tasks for 7B and 32 model sizes.
Lingshu-32B outperforms GPT-4.1 and Claude Sonnet 4 in most multimodal QA and report generation tasks.
Lingshu supports more than 12 medical imaging modalities, including X-Ray, CT Scan, MRI, Microscopy, Ultrasound, Histopathology, Dermoscopy, Fundus, OCT, Digital Photography, Endoscopy, and PET.

Release

Technical report: Arxiv: Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning.
Model weights:
- Lingshu-7B
- Lingshu-32B

Disclaimer: We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation. Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos.

Evaluation

Medical Multimodal VQA

Models	MMMU-Med	VQA-RAD	SLAKE	PathVQA	PMC-VQA	OmniMedVQA	MedXpertQA	Avg.
Proprietary Models
GPT-4.1	75.2	65.0	72.2	55.5	55.2	75.5	45.2	63.4
Claude Sonnet 4	74.6	67.6	70.6	54.2	54.4	65.5	43.3	61.5
Gemini-2.5-Flash	76.9	68.5	75.8	55.4	55.4	71.0	52.8	65.1
Open-source Models (<10B)
BiomedGPT	24.9	16.6	13.6	11.3	27.6	27.9	-	-
Med-R1-2B	34.8	39.0	54.5	15.3	47.4	-	21.1	-
MedVLM-R1-2B	35.2	48.6	56.0	32.5	47.6	77.7	20.4	45.4
MedGemma-4B-IT	43.7	72.5	76.4	48.8	49.9	69.8	22.3	54.8
LLaVA-Med-7B	29.3	53.7	48.0	38.8	30.5	44.3	20.3	37.8
HuatuoGPT-V-7B	47.3	67.0	67.8	48.0	53.3	74.2	21.6	54.2
BioMediX2-8B	39.8	49.2	57.7	37.0	43.5	63.3	21.8	44.6
Qwen2.5VL-7B	50.6	64.5	67.2	44.1	51.9	63.6	22.3	52.0
InternVL2.5-8B	53.5	59.4	69.0	42.1	51.3	81.3	21.7	54.0
InternVL3-8B	59.2	65.4	72.8	48.6	53.8	79.1	22.4	57.3
Lingshu-7B	54.0	67.9	83.1	61.9	56.3	82.9	26.7	61.8
Open-source Models (>10B)
HealthGPT-14B	49.6	65.0	66.1	56.7	56.4	75.2	24.7	56.2
HuatuoGPT-V-34B	51.8	61.4	69.5	44.4	56.6	74.0	22.1	54.3
MedDr-40B	49.3	65.2	66.4	53.5	13.9	64.3	-	-
InternVL3-14B	63.1	66.3	72.8	48.0	54.1	78.9	23.1	58.0
Qwen2.5V-32B	59.6	71.8	71.2	41.9	54.5	68.2	25.2	56.1
InternVL2.5-38B	61.6	61.4	70.3	46.9	57.2	79.9	24.4	57.4
InternVL3-38B	65.2	65.4	72.7	51.0	56.6	79.8	25.2	59.4
Lingshu-32B	62.3	76.5	89.2	65.9	57.9	83.4	30.9	66.6

Medical Textual QA

Models	MMLU-Med	PubMedQA	MedMCQA	MedQA	Medbullets	MedXpertQA	SuperGPQA-Med	Avg.
Proprietary Models
GPT-4.1	89.6	75.6	77.7	89.1	77.0	30.9	49.9	70.0
Claude Sonnet 4	91.3	78.6	79.3	92.1	80.2	33.6	56.3	73.1
Gemini-2.5-Flash	84.2	73.8	73.6	91.2	77.6	35.6	53.3	69.9
Open-source Models (<10B)
Med-R1-2B	51.5	66.2	39.1	39.9	33.6	11.2	17.9	37.0
MedVLM-R1-2B	51.8	66.4	39.7	42.3	33.8	11.8	19.1	37.8
MedGemma-4B-IT	66.7	72.2	52.2	56.2	45.6	12.8	21.6	46.8
LLaVA-Med-7B	50.6	26.4	39.4	42.0	34.4	9.9	16.1	31.3
HuatuoGPT-V-7B	69.3	72.8	51.2	52.9	40.9	10.1	21.9	45.6
BioMediX2-8B	68.6	75.2	52.9	58.9	45.9	13.4	25.2	48.6
Qwen2.5VL-7B	73.4	76.4	52.6	57.3	42.1	12.8	26.3	48.7
InternVL2.5-8B	74.2	76.4	52.4	53.7	42.4	11.6	26.1	48.1
InternVL3-8B	77.5	75.4	57.7	62.1	48.5	13.1	31.2	52.2
Lingshu-7B	74.5	76.6	55.9	63.3	56.2	16.5	26.3	52.8
Open-source Models (>10B)
HealthGPT-14B	80.2	68.0	63.4	66.2	39.8	11.3	25.7	50.7
HuatuoGPT-V-34B	74.7	72.2	54.7	58.8	42.7	11.4	26.5	48.7
MedDr-40B	65.2	77.4	38.4	59.2	44.3	12.0	24.0	45.8
InternVL3-14B	81.7	77.2	62.0	70.1	49.5	14.1	37.9	56.1
Qwen2.5VL-32B	83.2	68.4	63.0	71.6	54.2	15.6	37.6	56.2
InternVL2.5-38B	84.6	74.2	65.9	74.4	55.0	14.7	39.9	58.4
InternVL3-38B	83.8	73.2	64.9	73.5	54.6	16.0	42.5	58.4
Lingshu-32B	84.7	77.8	66.1	74.7	65.4	22.7	41.1	61.8

Medical Report Generation

Models	MIMIC-CXR	CheXpert Plus	IU-Xray
ROUGE-L	CIDEr	RaTE	SembScore	RadCliQ-v1^-1	ROUGE-L	CIDEr	RaTE	SembScore	RadCliQ-v1^-1	ROUGE-L	CIDEr	RaTE	SembScore	RadCliQ-v1^-1
Proprietary Models
GPT-4.1	9.0	82.8	51.3	23.9	57.1	24.5	78.8	45.5	23.2	45.5	30.2	124.6	51.3	47.5	80.3
Claude Sonnet 4	20.0	56.6	45.6	19.7	53.4	22.0	59.5	43.5	18.9	43.3	25.4	88.3	55.4	41.0	72.1
Gemini-2.5-Flash	25.4	80.7	50.3	29.7	59.4	23.6	72.2	44.3	27.4	44.0	33.5	129.3	55.6	50.9	91.6
Open-source Models (<10B)
Med-R1-2B	19.3	35.4	40.6	14.8	42.4	18.6	37.1	38.5	17.8	37.6	16.1	38.3	41.4	12.5	43.6
MedVLM-R1-2B	20.3	40.1	41.6	14.2	48.3	20.9	43.5	38.9	15.5	40.9	22.7	61.1	46.1	22.7	54.3
MedGemma-4B-IT	25.6	81.0	52.4	29.2	62.9	27.1	79.0	47.2	29.3	46.6	30.8	103.6	57.0	46.8	86.7
LLaVA-Med-7B	15.0	43.4	12.8	18.3	52.9	18.4	45.5	38.8	23.5	44.0	18.8	68.2	40.9	16.0	58.1
HuatuoGPT-V-7B	23.4	69.5	48.9	20.0	48.2	21.3	64.7	44.2	19.3	39.4	29.6	104.3	52.9	40.7	63.6
BioMediX2-8B	20.0	52.8	44.4	17.7	53.0	18.1	47.9	40.8	21.6	43.3	19.6	58.8	40.1	11.6	53.8
Qwen2.5VL-7B	24.1	63.7	47.0	18.4	55.1	22.2	62.0	41.0	17.2	43.1	26.5	78.1	48.4	36.3	66.1
InternVL2.5-8B	23.2	61.8	47.0	21.0	56.2	20.6	58.5	43.1	19.7	42.7	24.8	75.4	51.1	36.7	67.0
InternVL3-8B	22.9	66.2	48.2	21.5	55.1	20.9	65.4	44.3	25.2	43.7	22.9	76.2	51.2	31.3	59.9
Lingshu-7B	30.8	109.4	52.1	30.0	69.2	26.5	79.0	45.4	26.8	47.3	41.2	180.7	57.6	48.4	108.1
Open-source Models (>10B)
HealthGPT-14B	21.4	64.7	48.4	16.5	52.7	20.6	66.2	44.4	22.7	42.6	22.9	81.9	50.8	16.6	56.9
HuatuoGPT-V-34B	23.5	68.5	48.5	23.0	47.1	22.5	62.8	42.9	22.1	39.7	28.2	108.3	54.4	42.2	59.3
MedDr-40B	15.7	62.3	45.2	12.2	47.0	24.1	66.1	44.7	24.2	44.7	19.4	62.9	40.3	7.3	48.9
InternVL3-14B	22.0	63.7	48.6	17.4	46.5	20.4	60.2	44.1	20.7	39.4	24.8	93.7	55.0	38.7	55.0
Qwen2.5VL-32B	15.7	50.2	47.5	17.1	45.2	15.2	54.8	43.4	18.5	40.3	18.9	73.3	51.3	38.1	54.0
InternVL2.5-38B	22.7	61.4	47.5	18.2	54.9	21.6	60.6	42.6	20.3	45.4	28.9	96.5	53.5	38.5	69.7
InternVL3-38B	22.8	64.6	47.9	18.1	47.2	20.5	62.7	43.8	20.2	39.4	25.5	90.7	53.5	33.1	55.2
Lingshu-32B	28.8	96.4	50.8	30.1	67.1	25.3	75.9	43.4	24.2	47.1	42.8	189.2	63.5	54.6	130.4

Usage

Using transformers (version 4.52.1 is recommended)

python

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info


# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "lingshu-medical-mllm/Lingshu-32B",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

processor = AutoProcessor.from_pretrained("lingshu-medical-mllm/Lingshu-32B")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "example.png",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to(model.device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Using vLLM

python

from vllm import LLM, SamplingParams
from qwen_vl_utils import process_vision_info
import PIL
from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained("lingshu-medical-mllm/Lingshu-32B")
llm = LLM(model="lingshu-medical-mllm/Lingshu-32B", limit_mm_per_prompt = {"image": 4}, tensor_parallel_size=2, enforce_eager=True, trust_remote_code=True,)
sampling_params = SamplingParams(
            temperature=0.7,
            top_p=1,
            repetition_penalty=1,
            max_tokens=1024,
            stop_token_ids=[],
        )

text = "What does the image show?"
image_path = "example.png"
image = PIL.Image.open(image_path)

message = [
    {
        "role":"user",
        "content":[
            {"type":"image","image":image},
            {"type":"text","text":text}
            ]
            }
]
prompt = processor.apply_chat_template(
    message,
    tokenize=False,
    add_generation_prompt=True,
)
image_inputs, video_inputs = process_vision_info(message)
mm_data = {}
mm_data["image"] = image_inputs
processed_input = {
  "prompt": prompt,
  "multi_modal_data": mm_data,
}

outputs = llm.generate([processed_input], sampling_params=sampling_params)
print(outputs[0].outputs[0].text)

Citation

If you find our project useful, we hope you would kindly star our repo and cite our work as follows:

@article{xu2025lingshu,
  title={Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning},
  author={Xu, Weiwen and Chan, Hou Pong and Li, Long and Aljunied, Mahani and Yuan, Ruifeng and Wang, Jianyu and Xiao, Chenghao and Chen, Guizhen and Liu, Chaoqun and Li, Zhaodonghui and others},
  journal={arXiv preprint arXiv:2506.07044},
  year={2025}
}