Instructions to use medagent/MedExAgent-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use medagent/MedExAgent-8B with Transformers:

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

pipe = pipeline("text-generation", model="medagent/MedExAgent-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("medagent/MedExAgent-8B")
model = AutoModelForCausalLM.from_pretrained("medagent/MedExAgent-8B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use medagent/MedExAgent-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "medagent/MedExAgent-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "medagent/MedExAgent-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/medagent/MedExAgent-8B

SGLang

How to use medagent/MedExAgent-8B 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 "medagent/MedExAgent-8B" \
    --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": "medagent/MedExAgent-8B",
		"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 "medagent/MedExAgent-8B" \
        --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": "medagent/MedExAgent-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use medagent/MedExAgent-8B with Docker Model Runner:
```
docker model run hf.co/medagent/MedExAgent-8B
```

Research-use acknowledgement required

Access requires agreement to the research-only and no-clinical-use terms below.

MedExAgent-8B is a research model for studying medical-agent behavior in simulated diagnostic conversations. It may ask follow-up questions, request simulated examination or test information, and produce diagnostic reasoning or differential-diagnosis style outputs. It is not a medical device, has not been validated for clinical deployment, and may generate incomplete, incorrect, or unsafe medical information. By requesting access, you agree that you will not use this model for clinical care, diagnosis, treatment decisions, emergency triage, patient management, or any other real-world medical decision-making. You are solely responsible for any research use and for complying with all applicable laws, institutional policies, and safety review requirements.

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