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
```

Finetune GPT-OSS models 🔥

#33

by hiyouga - opened Aug 5, 2025

Discussion

hiyouga

Aug 5, 2025

•

edited Aug 5, 2025

We use LlamaFactory to perform LoRA fine-tuning on the GPT-OSS model. You can reproduce our experiment using the following steps.

1. Install LlamaFactory and latest Transformers

git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e ".[torch,metrics]" --no-build-isolation
pip install "transformers==4.55.0"

2. Train GPT-OSS on a single GPU (> 44GB) (multi-GPU is also supported)

llamafactory-cli train examples/train_lora/gpt_lora_sft.yaml

3. Merge the LoRA weight into the base model

llamafactory-cli export --model_name_or_path openai/gpt-oss-20b --adapter_name_or_path saves/gpt-20b/lora/sft --export_dir gpt_merged

(Optional) Chat with the fine-tuned model

llamafactory-cli chat --model_name_or_path gpt_merged --template gpt --skip_special_tokens False

You can fine the loss trajectory in the checkpoint folder:

The no-code web UI also supports GPT finetuning: https://huggingface.co/spaces/hiyouga/LLaMA-Board

hiyouga changed discussion title from Fine-tuning GPT-OSS to Finetune GPT-OSS models Aug 5, 2025

hiyouga changed discussion title from Finetune GPT-OSS models to Finetune GPT-OSS models 🔥 Aug 5, 2025

Koitenshin

Aug 6, 2025

I don't think fine tuning can save this model. It's up there with Gemma3 (before abliteration), LFM2 1.2B, and Phi-3.

shaohuay

Aug 7, 2025

@hiyouga after the lora merge, what's the format of the weight file, fp4 or bf16?

hiyouga

Aug 7, 2025

@shaohuay bf16

meixu

Aug 12, 2025

@shaohuay bf16

I want to deploy using fp4. Does lf support exporting fp4 oss-20b models?

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