README.md

---
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- medical
- clinical-reasoning
- diagnosis
- grpo
- rlhf
- lora
- qwen
datasets:
- gretelai/symptom_to_diagnosis
language:
- en
pipeline_tag: text-generation
library_name: peft
---

# dx-reasoning-qwen2.5-grpo

A LoRA adapter for clinical diagnostic reasoning, fine-tuned from Qwen2.5-7B-Instruct using Group Relative Policy Optimisation (GRPO).

## Model description

This model was trained to improve clinical diagnostic reasoning by learning to generate step-by-step reasoning before providing a diagnosis. It uses a structured format with `<reasoning>` and `<diagnosis>` tags.

### Training details

- **Base model:** Qwen/Qwen2.5-7B-Instruct
- **Training method:** GRPO (Group Relative Policy Optimisation)
- **LoRA configuration:**
  - Rank (r): 64
  - Alpha: 128
  - Dropout: 0.05
  - Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **Trainable parameters:** 161M / 7.7B total (2.08%)
- **Training steps:** 700 (2+ epochs)
- **Hardware:** 2x NVIDIA H100 80GB
- **Training time:** ~20 hours

### Reward function

The model was trained with a composite reward function:
- **Embedding similarity:** Cosine similarity between generated diagnosis and ground truth using PubMedBERT embeddings
- **Reasoning quality:** Bonus for including structured reasoning steps

### Dataset

Trained on [gretelai/symptom_to_diagnosis](https://huggingface.co/datasets/gretelai/symptom_to_diagnosis):
- 853 training samples
- 200 evaluation samples

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "chrisvoncsefalvay/dx-reasoning-qwen2.5-grpo")

# Example inference
prompt = """You are a medical expert. Given the patient's symptoms, provide a diagnosis.

Patient symptoms: The patient presents with severe headache, sensitivity to light, neck stiffness, and fever.

First, provide your reasoning in <reasoning> tags, then give your diagnosis in <diagnosis> tags."""

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Rollouts

Training rollouts are available in the `rollouts/` directory, containing generation samples at each evaluation step (100, 200, 300, 400, 500, 600, 700). These can be used for per-diagnosis analysis of training progression.

## Limitations

- Trained on a relatively small dataset (853 samples)
- Focused on symptom-to-diagnosis task; may not generalise to other medical reasoning tasks
- Should not be used for actual medical diagnosis - for research purposes only

## Citation

If you use this model, please cite:

```bibtex
@misc{dx-reasoning-qwen2.5-grpo,
  author = {Chris von Csefalvay},
  title = {dx-reasoning-qwen2.5-grpo: Clinical Diagnostic Reasoning with GRPO},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/chrisvoncsefalvay/dx-reasoning-qwen2.5-grpo}
}
```

## Training logs

Training was monitored via Weights & Biases. See the project for detailed metrics and training curves.