Instructions to use mdouglas/gpt-oss-20b-bnb-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mdouglas/gpt-oss-20b-bnb-nf4 with Transformers:

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

pipe = pipeline("text-generation", model="mdouglas/gpt-oss-20b-bnb-nf4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mdouglas/gpt-oss-20b-bnb-nf4")
model = AutoModelForCausalLM.from_pretrained("mdouglas/gpt-oss-20b-bnb-nf4", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mdouglas/gpt-oss-20b-bnb-nf4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mdouglas/gpt-oss-20b-bnb-nf4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mdouglas/gpt-oss-20b-bnb-nf4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mdouglas/gpt-oss-20b-bnb-nf4

SGLang

How to use mdouglas/gpt-oss-20b-bnb-nf4 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 "mdouglas/gpt-oss-20b-bnb-nf4" \
    --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": "mdouglas/gpt-oss-20b-bnb-nf4",
		"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 "mdouglas/gpt-oss-20b-bnb-nf4" \
        --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": "mdouglas/gpt-oss-20b-bnb-nf4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mdouglas/gpt-oss-20b-bnb-nf4 with Docker Model Runner:
```
docker model run hf.co/mdouglas/gpt-oss-20b-bnb-nf4
```

This repository is an experimental re-quantized version of the original model openai/gpt-oss-20b.

It requires development versions of transformers and bitsandbytes.

Quantization

The MLP expert parameters have been dequantized from MXFP4 to BF16, and then requantized in the NF4 double-quantization format using an experimental bnb_4bit_target_parameters configuration option. The self-attention, routing, and embedding parameters are kept in BF16.

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Safetensors

Model size

11B params

Tensor type

F32

BF16

Model tree for mdouglas/gpt-oss-20b-bnb-nf4

Base model

openai/gpt-oss-20b

Quantized

(254)

this model