Instructions to use xiaohan666/MedSearch-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xiaohan666/MedSearch-R1 with Transformers:

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

pipe = pipeline("text-generation", model="xiaohan666/MedSearch-R1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xiaohan666/MedSearch-R1")
model = AutoModelForCausalLM.from_pretrained("xiaohan666/MedSearch-R1", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xiaohan666/MedSearch-R1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xiaohan666/MedSearch-R1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xiaohan666/MedSearch-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xiaohan666/MedSearch-R1

SGLang

How to use xiaohan666/MedSearch-R1 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 "xiaohan666/MedSearch-R1" \
    --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": "xiaohan666/MedSearch-R1",
		"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 "xiaohan666/MedSearch-R1" \
        --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": "xiaohan666/MedSearch-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xiaohan666/MedSearch-R1 with Docker Model Runner:
```
docker model run hf.co/xiaohan666/MedSearch-R1
```

MedSearch-R1

MedSearch-R1 is a locally deployable medical search agent policy initialized from Qwen3.5-4B. The model is trained through cold-start knowledge distillation, step-level on-policy distillation, trajectory-level on-policy distillation, and accuracy-based reinforcement learning.

This repository contains the policy-model weights and tokenizer only. The Search--Visit tools, source-policy filters, helper-model configuration, and evaluation pipeline are not embedded in the checkpoint. Exact agent-loop code and reproducibility configurations will be provided in the associated GitHub repository.

Loading

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "xiaohan666/MedSearch-R1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

The checkpoint uses the Qwen3.5 architecture and requires a Transformers release with Qwen3_5ForCausalLM support.

Intended Use

The model is intended for research on medical reasoning agents and tool-augmented language models. Reproducing the paper's agent results requires the accompanying Search--Visit loop and source-filtering configuration.

Limitations

MedSearch-R1 is not a medical device and must not be used as a substitute for professional medical judgment. Generated answers and retrieved evidence can be incomplete or incorrect. Local policy inference reduces full-context exposure to external model providers, but generated search queries may still reveal medical concepts and do not constitute a formal privacy guarantee.

License

The model is released under the Apache License 2.0, following the license of the Qwen3.5-4B base model.

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Safetensors

Model size

4B params

Tensor type

BF16

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