Instructions to use OpenMedZoo/MedGo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenMedZoo/MedGo with Transformers:

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

pipe = pipeline("text-generation", model="OpenMedZoo/MedGo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenMedZoo/MedGo")
model = AutoModelForCausalLM.from_pretrained("OpenMedZoo/MedGo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Local Apps Settings

vLLM

How to use OpenMedZoo/MedGo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenMedZoo/MedGo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenMedZoo/MedGo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenMedZoo/MedGo

SGLang

How to use OpenMedZoo/MedGo 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 "OpenMedZoo/MedGo" \
    --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": "OpenMedZoo/MedGo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "OpenMedZoo/MedGo" \
        --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": "OpenMedZoo/MedGo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenMedZoo/MedGo with Docker Model Runner:
```
docker model run hf.co/OpenMedZoo/MedGo
```

TiramisuQiao commited on Dec 3, 2025

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@@ -315,11 +315,13 @@ MedGo 采用**两阶段微调策略**，兼顾通用医学知识与临床任务
 - **HuggingFace**: [模型主页](https://huggingface.co/OpenMedZoo/MedGo)
 ## 版权声明
-著作权：同济大学附属东方医院
-技术支持：上海烁乐信息科技有限公司
 ---
 <div align="center">

 - **HuggingFace**: [模型主页](https://huggingface.co/OpenMedZoo/MedGo)
 ## 版权声明
+- 发布单位：同济大学附属东方医院｜唯一通信作者
+- 联合研发单位：上海烁乐科技有限公司（提供技术支持）
+- 联系方式：dongfyy@pudong.gov.cn
+- 版本：v1.0
+- 署名/引用：使用或再发布时请注明来源与版本号
+“Powered by Med-Go32B, released by Tongji University Affiliated East Hospital (v1.0).”
 ---
 <div align="center">