Instructions to use lingshu-medical-mllm/Lingshu-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lingshu-medical-mllm/Lingshu-32B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="lingshu-medical-mllm/Lingshu-32B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("lingshu-medical-mllm/Lingshu-32B")
model = AutoModelForMultimodalLM.from_pretrained("lingshu-medical-mllm/Lingshu-32B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lingshu-medical-mllm/Lingshu-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lingshu-medical-mllm/Lingshu-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lingshu-medical-mllm/Lingshu-32B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/lingshu-medical-mllm/Lingshu-32B

SGLang

How to use lingshu-medical-mllm/Lingshu-32B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lingshu-medical-mllm/Lingshu-32B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lingshu-medical-mllm/Lingshu-32B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lingshu-medical-mllm/Lingshu-32B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lingshu-medical-mllm/Lingshu-32B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use lingshu-medical-mllm/Lingshu-32B with Docker Model Runner:
```
docker model run hf.co/lingshu-medical-mllm/Lingshu-32B
```

xww033 commited on Jun 12, 2025

Commit

cbe6963

verified ·

1 Parent(s): ab590bc

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -6

README.md CHANGED Viewed

@@ -18,12 +18,6 @@ tags:
 <p align="center">
   <img src="lingshu_logo.png" width="200" />
 </p>
-# *LingShu* - SOTA Multimodal Large Language Models for Medical Domain
-This repository contains the model of the paper [Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning](https://huggingface.co/papers/2506.07044).
 <p align="center">
 <a href="https://huggingface.co/lingshu-medical-mllm/Lingshu-32B" target="_blank" rel="noopener">Website</a>
 &nbsp;&nbsp;
@@ -36,6 +30,9 @@ This repository contains the model of the paper [Lingshu: A Generalist Foundatio
 <a href="https://arxiv.org/pdf/2506.07044" target="_blank" rel="noopener">Technical Report</a>
 </p>
 # <strong style="color: red">BIG NEWS: <a href="https://huggingface.co/lingshu-medical-mllm/Lingshu-32B">LingShu</a> is released with state-of-the-art performance on medical VQA tasks and report generation.</strong>
 <p align="center">

 <p align="center">
   <img src="lingshu_logo.png" width="200" />
 </p>
 <p align="center">
 <a href="https://huggingface.co/lingshu-medical-mllm/Lingshu-32B" target="_blank" rel="noopener">Website</a>
 &nbsp;&nbsp;
 <a href="https://arxiv.org/pdf/2506.07044" target="_blank" rel="noopener">Technical Report</a>
 </p>
+# *LingShu* - SOTA Multimodal Large Language Models for Medical Domain
+This repository contains the model of the paper [Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning](https://huggingface.co/papers/2506.07044).
 # <strong style="color: red">BIG NEWS: <a href="https://huggingface.co/lingshu-medical-mllm/Lingshu-32B">LingShu</a> is released with state-of-the-art performance on medical VQA tasks and report generation.</strong>
 <p align="center">