Instructions to use nvidia/gpt-oss-puzzle-88B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/gpt-oss-puzzle-88B with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/gpt-oss-puzzle-88B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("nvidia/gpt-oss-puzzle-88B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/gpt-oss-puzzle-88B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/gpt-oss-puzzle-88B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/gpt-oss-puzzle-88B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/gpt-oss-puzzle-88B

SGLang

How to use nvidia/gpt-oss-puzzle-88B 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 "nvidia/gpt-oss-puzzle-88B" \
    --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": "nvidia/gpt-oss-puzzle-88B",
		"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 "nvidia/gpt-oss-puzzle-88B" \
        --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": "nvidia/gpt-oss-puzzle-88B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/gpt-oss-puzzle-88B with Docker Model Runner:
```
docker model run hf.co/nvidia/gpt-oss-puzzle-88B
```

Update README.md

by eladsegal - opened Mar 26

base: refs/heads/main

←

from: refs/pr/3

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@@ -267,8 +267,8 @@ docker run --gpus all -p 8000:8000 \
   vllm/vllm-openai:v0.17.1 \
   -c "
     apt-get update && apt-get install -y git &&
-    VLLM_PRECOMPILED_WHEEL_LOCATION=https://wheels.vllm.ai/1ac2ef2e5335ca0af99aee438998c9305461f563/vllm-0.18.1rc1.dev127%2Bg1ac2ef2e5-cp38-abi3-manylinux_2_31_$(uname -m).whl VLLM_USE_PRECOMPILED=1 pip install --no-build-isolation 'git+https://github.com/vllm-project/vllm.git@88f6aaad7cc2fe8904d5fcf31d06eb57f15a6d25' &&
-    pip install flashinfer-cubin==0.6.6 flashinfer-jit-cache==0.6.6 --extra-index-url https://flashinfer.ai/whl/cu129 &&
     export PYTORCH_ALLOC_CONF=expandable_segments:True &&
     vllm serve nvidia/gpt-oss-puzzle-88B \
       -tp 1 \

   vllm/vllm-openai:v0.17.1 \
   -c "
     apt-get update && apt-get install -y git &&
+    VLLM_USE_PRECOMPILED=1 pip install --no-build-isolation 'git+https://github.com/vllm-project/vllm.git@refs/pull/38135/head' &&
+    pip install flashinfer-cubin==0.6.6 flashinfer-jit-cache==0.6.6 --extra-index-url https://flashinfer.ai/whl/cu\$(echo \$CUDA_VERSION | cut -d. -f1,2 | tr -d '.') &&
     export PYTORCH_ALLOC_CONF=expandable_segments:True &&
     vllm serve nvidia/gpt-oss-puzzle-88B \
       -tp 1 \