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, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b")
model = AutoModelForCausalLM.from_pretrained("openai/gpt-oss-20b", device_map="auto")
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
AMD Developer Cloud
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
```

AIME 25 Accuracy Discrepancy for GPT-OSS-20B (Reasoning Effort=High)

#58

by jiayi37u - opened Aug 6, 2025

Discussion

jiayi37u

Aug 6, 2025

Thank you very much for open-sourcing such a powerful large language model.
I’ve noticed that the community has run into difficulties reproducing the results reported in your paper. On the AIME 25, the paper states that GPT-OSS-20B (without tools, with reasoning effort mode set to “high”) achieved an accuracy of 91.7%, whereas our reproduction using vLLM only reached 85.8%. Do you have any suggestions to help us replicate your evaluation results?

Reference link: https://docs.vllm.ai/projects/recipes/en/latest/OpenAI/GPT-OSS.html#tool-use

dkundel-openai

OpenAI org Aug 6, 2025

Hey :) We published all of our eval code here https://github.com/openai/gpt-oss/tree/main/gpt_oss/evals

baihuajun24

Aug 22, 2025

Hi @dkundel-openai
Thank you for providing the eval scripts. I noticed on the published paper (https://arxiv.org/pdf/2508.10925v1): the generation length of aime result for gpt-oss-20b-high is 20k tokens.

But when I followed your eval scripts to run the aime inference and did some statistics on the generation results, I get mean 782 tokens for the 30*8=240 samples.
I don't know what caused the big discrepancy. Is there anything I missed?

zkdzz

Sep 28, 2025

@jiayi37u Did you successfully reproduce the results? I ran it with vllm, dequantize set to bfp16, and in high mode, the result was only 0.566.
What is your run command？

jzhao1122

Jan 8

For me, set the max_token to 65536 achieves approximately 90 accuracy on aime25. I use vllm 0.10.2

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