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)

# 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
```

gpt-oss-puzzle-88B / config.json

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	{
	"architectures": [
	"GptOssPuzzleForCausalLM"
	],
	"attention_bias": true,
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_gpt_oss_puzzle.GptOssPuzzleConfig",
	"AutoModelForCausalLM": "modeling_gpt_oss_puzzle.GptOssPuzzleForCausalLM"
	},
	"block_configs": [
	{
	"num_local_experts": 128,
	"sliding_window": 128
	},
	{
	"num_local_experts": 128,
	"sliding_window": null
	},
	{
	"num_local_experts": 128,
	"sliding_window": 128
	},
	{
	"num_local_experts": 128,
	"sliding_window": 8192
	},
	{
	"num_local_experts": 128,
	"sliding_window": 128
	},
	{
	"num_local_experts": 128,
	"sliding_window": 8192
	},
	{
	"num_local_experts": 128,
	"sliding_window": 128
	},
	{
	"num_local_experts": 128,
	"sliding_window": null
	},
	{
	"num_local_experts": 128,
	"sliding_window": 128
	},
	{
	"num_local_experts": 128,
	"sliding_window": null
	},
	{
	"num_local_experts": 128,
	"sliding_window": 128
	},
	{
	"num_local_experts": 128,
	"sliding_window": null
	},
	{
	"num_local_experts": 128,
	"sliding_window": 128
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	"sliding_window": null
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	{
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	"sliding_window": 128
	},
	{
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	"sliding_window": null
	},
	{
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	"sliding_window": 128
	},
	{
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	"sliding_window": null
	},
	{
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	"sliding_window": 128
	},
	{
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	"sliding_window": null
	},
	{
	"num_local_experts": 128,
	"sliding_window": 128
	},
	{
	"num_local_experts": 64,
	"sliding_window": 8192
	},
	{
	"num_local_experts": 64,
	"sliding_window": 128
	},
	{
	"num_local_experts": 64,
	"sliding_window": 8192
	},
	{
	"num_local_experts": 64,
	"sliding_window": 128
	},
	{
	"num_local_experts": 64,
	"sliding_window": null
	},
	{
	"num_local_experts": 64,
	"sliding_window": 128
	},
	{
	"num_local_experts": 64,
	"sliding_window": 8192
	},
	{
	"num_local_experts": 64,
	"sliding_window": 128
	},
	{
	"num_local_experts": 64,
	"sliding_window": null
	},
	{
	"num_local_experts": 64,
	"sliding_window": 128
	},
	{
	"num_local_experts": 64,
	"sliding_window": 8192
	},
	{
	"num_local_experts": 64,
	"sliding_window": 128
	},
	{
	"num_local_experts": 64,
	"sliding_window": 8192
	},
	{
	"num_local_experts": 64,
	"sliding_window": 128
	},
	{
	"num_local_experts": 64,
	"sliding_window": 8192
	}
	],
	"dtype": "bfloat16",
	"eos_token_id": 200002,
	"head_dim": 64,
	"hidden_act": "silu",
	"hidden_size": 2880,
	"initializer_range": 0.02,
	"intermediate_size": 2880,
	"layer_types": [
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention"
	],
	"max_position_embeddings": 229376,
	"model_type": "gpt_oss_puzzle",
	"num_attention_heads": 64,
	"num_experts_per_tok": 4,
	"num_hidden_layers": 36,
	"num_key_value_heads": 8,
	"output_router_logits": false,
	"pad_token_id": 199999,
	"quantization_config": {
	"modules_to_not_convert": [
	"model.layers.*.self_attn",
	"model.layers.*.mlp.router",
	"model.embed_tokens",
	"lm_head"
	],
	"quant_method": "mxfp4"
	},
	"rms_norm_eps": 1e-05,
	"rope_parameters": {
	"beta_fast": 32.0,
	"beta_slow": 1.0,
	"factor": 56.0,
	"original_max_position_embeddings": 4096,
	"rope_type": "yarn",
	"truncate": false
	},
	"rope_scaling": {
	"beta_fast": 32.0,
	"beta_slow": 1.0,
	"factor": 56.0,
	"original_max_position_embeddings": 4096,
	"rope_type": "yarn",
	"truncate": false
	},
	"rope_theta": 150000,
	"router_aux_loss_coef": 0.9,
	"tie_word_embeddings": false,
	"transformers_version": "4.57.6",
	"use_cache": true,
	"vocab_size": 201088
	}