Instructions to use openai/gpt-oss-20b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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") 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
Update chat_template.jinja to correctly parse types nested in arrays
By forcing recursive calls in render_typescript_type for "array" types, it now correctly renders any array types that contains items of enum strings or have a "description" field. Before the fix, neither enums nor the "description" field are rendered due to prematurely stopping parsing.
Additionally updated the "any[]" truncation length from 50 to 200, as Enum types typically need longer descriptions.
E.g., these tool schemas are now correctly rendered:
sample['tools'] = \
[{'function': {'description': 'tool1 does XYZ',
'name': 'tool1',
'parameters': {'properties': {'enum_array': {'description': 'Array of enums',
'items': {'description': 'One of these enums',
'enum': ['TypeA', 'TypeB'],
'type': 'string'},
'type': 'array'}},
'required': ['enum_array'],
'type': 'object'}},
'type': 'function'}]
When running templated_output = tokenizer.apply_chat_template(sample['messages'], tools=sample['tools']) and decoding with tokenizer.decode(templated_output['input_ids'], skip_special_tokens=False),
Before this change, it omits enum definitions:
# Tools
## functions
namespace functions {
// tool1 does XYZ
type tool1 = (_: {
// Array of enums
enum_array: string[],
}) => any;
} // namespace functions
After this change, it prints:
# Tools
## functions
namespace functions {
// tool1 does XYZ
type tool1 = (_: {
// Array of enums
enum_array: "TypeA" | "TypeB"[],
}) => any;
} // namespace functions
Here, the line enum_array: "TypeA" | "TypeB"[], is the expected behavior.