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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%**) |