Instructions to use alhusains/gpt-oss-20b-ddx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alhusains/gpt-oss-20b-ddx with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="alhusains/gpt-oss-20b-ddx")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("alhusains/gpt-oss-20b-ddx", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alhusains/gpt-oss-20b-ddx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alhusains/gpt-oss-20b-ddx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alhusains/gpt-oss-20b-ddx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alhusains/gpt-oss-20b-ddx

SGLang

How to use alhusains/gpt-oss-20b-ddx with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "alhusains/gpt-oss-20b-ddx" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alhusains/gpt-oss-20b-ddx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "alhusains/gpt-oss-20b-ddx" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alhusains/gpt-oss-20b-ddx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alhusains/gpt-oss-20b-ddx with Docker Model Runner:
```
docker model run hf.co/alhusains/gpt-oss-20b-ddx
```

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

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%)

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for alhusains/gpt-oss-20b-ddx

Base model

openai/gpt-oss-20b

Adapter

(238)

this model

Evaluation results

exact-match accuracy on Eurorad radiology cases
self-reported

0.862