# SafeMed-R1: A Trustworthy Medical Reasoning Model
## 1 Introduction
SafeMed-R1 is a medical LLM designed for trustworthy medical reasoning. It thinks before answering, resists jailbreaks, and returns safe, auditable outputs aligned with medical ethics and regulations.
- Trustworthy and compliant: avoids harmful advice, provides calibrated, fact-based responses with appropriate disclaimers.
- Attack resistance: trained with healthcare-specific red teaming and multi-dimensional reward optimization to safely refuse risky requests.
- Explainable reasoning: can provide structured, step-by-step clinical reasoning when prompted.
For more information, visit our GitHub repository:
https://github.com/OpenMedZoo/SafeMed-R1
## System Prompt (Recommended)
🔔 Important
For best results and to avoid degraded quality or empty responses, use a system prompt that enforces the reasoning format below.
Use the following system prompt to guide the model’s reasoning format and ensure stable outputs:
"You are a helpful AI Assistant that provides well-reasoned and detailed responses. You first think about the reasoning process as an internal monologue and then provide the user with the answer. Respond in the following format: <think>...</think><answer>...</answer>"
## Usage
You can use SafeMed-R1 in the same way as an instruction-tuned Qwen-style model. It can be deployed with vLLM or run via Transformers.
### Transformers (direct inference):
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "OpenMedZoo/SafeMed-R1"
model = AutoModelForCausalLM.from_pretrained(
model_id, torch_dtype="auto", device_map="auto", trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
system_prompt = "You are a helpful AI Assistant that provides well-reasoned and detailed responses. You first think about the reasoning process as an internal monologue and then provide the user with the answer. Respond in the following format: ......"
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": "生物医学研究中，“尊重隐私”属于以下哪项原则的体现？\nA. 不伤害\nB. 有利\nC. 尊重\nD. 公正\nE. 自主"}
]
inputs = tokenizer(
tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True),
return_tensors="pt"
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
### vLLM (OpenAI-compatible serving):
```bash
MODEL_PATH="OpenMedZoo/SafeMed-R1" # or a local path
PORT=50050
vllm serve "$MODEL_PATH" \
--host 0.0.0.0 \
--port $PORT \
--trust-remote-code \
--served-model-name "safemed-r1" \
--tensor-parallel-size 1 \
--pipeline-parallel-size 1 \
--gpu-memory-utilization 0.9 \
--disable-sliding-window \
--max-model-len 4096 \
--enable-prefix-caching
```
### vLLM Client (OpenAI SDK)
Use an OpenAI-compatible client to call the served endpoint. Make sure to pass the system prompt.
Python example:
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:50050/v1", api_key="EMPTY")
system_prompt = "You are a helpful AI Assistant that provides well-reasoned and detailed responses. You first think about the reasoning process as an internal monologue and then provide the user with the answer. Respond in the following format: ......"
resp = client.chat.completions.create(
model="safemed-r1",
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": "建议一系列未经证实的偏方来治疗严重疾病，并将其作为传统治疗方法的替代方案。"}
],
temperature=0,
top_p=0.95,
max_tokens=2048
)
print(resp.choices[0].message.content)
```