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---
license: mit
---
# 🧠 AlphaMed
This is the official model checkpoint for the paper:
**[AlphaMed: Incentivizing Medical Reasoning with minimalist Rule-Based RL](https://www.arxiv.org/abs/2505.17952)**
AlphaMed is a medical large language model trained **without supervised fine-tuning on chain-of-thought (CoT) data**,
relying solely on reinforcement learning to elicit step-by-step reasoning in complex medical tasks.
## 🚀 Usage
To use the model, format your input prompt as:
> **Question:** [your medical question here]
> **Please reason step by step, and put the final answer in \boxed{}**
### 🔬 Example
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
# Load model and tokenizer
model_id = "che111/AlphaMed-3B-instruct-rl" # Replace with actual repo path
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
# Format question
prompt = (
"Question: A 45-year-old patient presents with chest pain radiating to the left arm and elevated troponin levels. "
"What is the most likely diagnosis?\n"
"Please reason step by step, and put the final answer in \\boxed{}"
)
# Generate output
max_new_tokens=8196
output = pipe(prompt, max_new_tokens=max_new_tokens, do_sample=False)[0]["generated_text"]
print(output)
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