Text Generation
Transformers
Safetensors
PyTorch
nemotron_h_puzzle
nvidia
nemotron-3
latent-moe
mtp
conversational
custom_code
8-bit precision
modelopt
Instructions to use nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4", 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/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4" # 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/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4
- SGLang
How to use nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 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/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4" \ --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/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4", "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/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4" \ --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/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 with Docker Model Runner:
docker model run hf.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4
| # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # SPDX-License-Identifier: OpenMDW-1.1 | |
| from . import modeling_nemotron_h | |
| from .configuration_nemotron_h_puzzle import NemotronHPuzzleConfig | |
| from .modeling_nemotron_h import NemotronHForCausalLM, NemotronHBlock | |
| class NemotronHPuzzleBlock(NemotronHBlock): | |
| def __init__(self, config: NemotronHPuzzleConfig, layer_idx: int): | |
| layer_config = config.get_nemotron_h_config_for_layer(layer_idx) | |
| super().__init__(config=layer_config, layer_idx=layer_idx) | |
| class NemotronHPuzzleForCausalLM(NemotronHForCausalLM): | |
| """ | |
| A child class of NemotronHForCausalLM to support heterogeneous layer configurations. | |
| This class uses monkey-patching to inject custom behavior into the parent class while maximizing | |
| code reuse and minimizing duplication. During `__init__`, it temporarily replaces the block layer | |
| class to use `NemotronHPuzzleBlock`, so that each layer is built from its own per-layer | |
| configuration (derived from the matching entry in `config.block_configs`). | |
| """ | |
| config_class = NemotronHPuzzleConfig | |
| _no_split_modules = ["NemotronHPuzzleBlock"] | |
| def __init__(self, config): | |
| original_block_class = modeling_nemotron_h.NemotronHBlock | |
| try: | |
| modeling_nemotron_h.NemotronHBlock = NemotronHPuzzleBlock | |
| super().__init__(config) | |
| finally: | |
| modeling_nemotron_h.NemotronHBlock = original_block_class | |