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

Libraries

How to use kingabzpro/gpt-oss-20b-medical-qa with Transformers:

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kingabzpro/gpt-oss-20b-medical-qa with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/kingabzpro/gpt-oss-20b-medical-qa

SGLang

How to use kingabzpro/gpt-oss-20b-medical-qa 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 "kingabzpro/gpt-oss-20b-medical-qa" \
    --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": "kingabzpro/gpt-oss-20b-medical-qa",
		"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 "kingabzpro/gpt-oss-20b-medical-qa" \
        --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": "kingabzpro/gpt-oss-20b-medical-qa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Model Card for gpt-oss-20b-medical-qa

This model is a fine-tuned version of openai/gpt-oss-20b on the FreedomIntelligence/medical-o1-verifiable-problem dataset. It has been trained using TRL.

Quick start

from transformers import pipeline

prompt = """<|start|>developer<|message|># Instructions

You are a medical expert with advanced knowledge in clinical reasoning and diagnostics. Respond with ONLY the final diagnosis/cause in ≤5 words.<|end|><|start|>user<|message|>An 88-year-old woman with osteoarthritis is experiencing mild epigastric discomfort and has vomited material resembling coffee grounds multiple times. Considering her use of naproxen, what is the most likely cause of her gastrointestinal blood loss?<|end|><|start|>assistant<|message|>"""

# Load pipeline
generator = pipeline(
    "text-generation",
    model="kingabzpro/gpt-oss-20b-medical-qa",
    device="cuda"  # or device=0
)


# Run inference (passing in chat-style format)
output = generator(
    prompt,
    max_new_tokens=20,
    return_full_text=False
)[0]

print(output["generated_text"])

Output:

NSAID use

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.21.0
Transformers: 4.55.2
Pytorch: 2.8.0.dev20250319+cu128
Datasets: 4.0.0
Tokenizers: 0.21.4

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