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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b")
model = AutoModelForMultimodalLM.from_pretrained("openai/gpt-oss-20b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Quick Results: Testing Multilingual Capabilities via Code Generation from Ukrainian Competitive Programming Tasks

#87

by anon-researcher-ua - opened Aug 8, 2025

Discussion

anon-researcher-ua

Aug 8, 2025

Just ran gpt-oss-20b and gpt-oss-120b (medium reasoning effort) on UA-Code-Bench benchmark with 500 competitive-programming tasks in Ukrainian across five difficulty tiers. For a Python solution to count, it must compile and pass every hidden test on the Eolymp platform, exactly as in a real contest.

gpt-oss-20b: 203 / 500
gpt-oss-120b: 229 / 500

That puts the 20 B model just ahead of DeepSeek-R1 (196) and the 120 B model a bit below o4-mini (234).

The code itself looks solid, but both models never “think” in Ukrainian, so a small UA reasoning tuning is next on my list. Still, getting >200 accepted answers straight out of the box from open-weight model is awesome - big thanks to the OpenAI team for making these models available!

volsgd

OpenAI org Aug 8, 2025

Amazing result, thanks for sharing! Curious if you have a chance to run low and high?

anon-researcher-ua

Aug 9, 2025

Yep, I’ll give it a try, though it may take some time to generate and run the tests.
Low reasoning shouldn’t be an issue at all, but with high reasoning I’ve noticed the generation sometimes becomes unstable past a certain point (mostly repeating thoughts or reasoning without producing an answer). I’ve seen this with other reasoning models as well, so it probably just needs some sampling parameter tweaks.

anon-researcher-ua

Aug 19, 2025

Evaluated oss models with reasoning_effort=low and also ran GPT-5. For reasoning_effort=medium, I reran some tasks with corrected input parameters, so the 20B and 120B scores have shifted slightly.

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