---
language: en
license: apache-2.0
base_model: openai/gpt-oss-20b
tags:
- medical
- radiology
- eurorad
- differential-diagnosis
- chain-of-thought
- lora
- gpt-oss
- clinical-reasoning
library_name: transformers
pipeline_tag: text-generation
datasets:
- eurorad
widget:
  - text: >
      A 45-year-old woman presents with acute chest pain radiating to the left
      arm. CT angiography shows a filling defect in the LAD. Provide a structured
      differential diagnosis and the most likely diagnosis.
    example_title: Radiology differential diagnosis
model-index:
  - name: gpt-oss-20b-ddx
    results:
      - task:
          type: text-generation
          name: Radiology differential diagnosis
        dataset:
          type: eurorad
          name: Eurorad radiology cases
        metrics:
          - type: accuracy
            value: 0.862
            name: exact-match accuracy
---

# **GPT-OSS-20B – Differential Diagnosis Radiology Reasoning**

This repository provides a **LoRA adapter** fine-tuned on radiology cases from the **Eurorad** dataset to enhance differential diagnosis and structured medical reasoning. The adapter attaches to the base model **`openai/gpt-oss-20b`**, enabling stronger radiology-focused performance while remaining lightweight and deployable on a single GPU.

**Highlights**
- Improved **differential diagnosis accuracy** on Eurorad cases (exact match boost from 78.6% → **86.2%**)
- Trained with **structured chain-of-thought** derived from gpt-oss-120b
- Works with **Unsloth**, **PEFT**, and **Transformers**

---

## **Quick Start**

### 🔹 Load with PEFT + Transformers

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

BASE = "openai/gpt-oss-20b"
ADAPTER = "alhusains/gpt-oss-20b-eurorad-lora"

tokenizer = AutoTokenizer.from_pretrained(BASE)
base = AutoModelForCausalLM.from_pretrained(BASE, device_map="auto")

model = PeftModel.from_pretrained(base, ADAPTER)
model.eval()

prompt = "Provide a differential diagnosis for multiple bilateral lung nodules."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
---

## **Training Summary**

- **Dataset:** Eurorad radiology case reports (clinical history + imaging findings)
- **Supervision:** Structured chain-of-thought reasoning generated by gpt-oss-120b
- **Objective:** Enhance differential diagnosis and structured medical reasoning
- **Method:** LoRA fine-tuning  
  - Rank: 32  
  - Alpha: 64  
  - Applied to attention, MLP layers, and MoE experts  
- **Sequence length:** 4096 tokens  
- **Framework:** Unsloth + PEFT (4-bit training)  
- **Precision:** bfloat16 mixed precision  
- **Training schedule:** 3 epochs, AdamW, LR = 1e-4 with cosine decay and warmup  
- **Result:** Improved exact-match diagnostic accuracy on Eurorad cases (base 78.6% → fine-tuned **86.2%**)