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
```

Function call token ordering mismatch with Harmony format and chat template

#218

by bharathi1604 - opened Jan 20

Discussion

bharathi1604

Jan 20

I noticed a mismatch between the function-call format emitted by the current
chat_template.jinja and the expected Harmony-style serialization.

Template-generated example:

<|start|>assistant to=functions.GET_ORDER_STATUS<|channel|>commentary json<|message|>"{"order_id": "ORD_12345"}"<|call|>

However, the Harmony reference format expects the following structure:

<|start|>assistant<|channel|>commentary to=functions.GET_ORDER_STATUS <|constrain|>json<|message|>{"order_id":"ORD_12345"}<|call|>|

Key differences observed:

to=functions.* appears after <|channel|>commentary in Harmony,
but before it in the current template.
The content type (json) is wrapped inside <|constrain|> in Harmony,
whereas the template emits it as a raw token.
The arguments payload is not double-JSON-encoded in Harmony.
Token ordering is strict in Harmony and differs from the template output.

This mismatch likely explains discrepancies when validating or consuming
function-call outputs against Harmony-compliant parsers.

This observation is based directly on the official template at:
https://huggingface.co/openai/gpt-oss-20b/blob/main/chat_template.jinja

bharathi1604

Jan 20

please solve this quickly. Because while finetuning this may affect tool calling

lingjie23

Jan 23

I have meet the same problem!

architectyou

Jan 29

Is this the reason why function calling doesn't work properly when serving the GPT-OSS model with vLLM, even when using the 'openai' tool-calling parser?

lingjie23

Jan 29

Is this the reason why function calling doesn't work properly when serving the GPT-OSS model with vLLM, even when using the 'openai' tool-calling parser?

If the tool response format is wrong, then vllm cann't parse the tool correctly. However I can use the vllm with current template to call function correctly althought the template is different with the official template which I think it is the generability of the model

Salafite

Jun 21

I have same issue

linboliu

11 days ago

+1 same issue

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment