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-ddx / README.md

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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
	- Adapter-only; the base model remains unchanged
	- 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%)