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, device_map="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
Update model configuration and terms
Browse files- README.md +2 -0
- config.json +0 -4
README.md
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**Governing Download Terms:** Use of this model is governed by the [OpenMDW License Agreement, version 1.1](https://raw.githubusercontent.com/OpenMDW/OpenMDW/refs/heads/main/1.1/LICENSE.OpenMDW-1.1) (OpenMDW-1.1).
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### Benchmarks
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| **Benchmark** | **Nemotron-Labs-3-Puzzle-75B-A9B-BF16** | **Nemotron-Labs-3-Puzzle-75B-A9B-FP8** | **Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4** |
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**Governing Download Terms:** Use of this model is governed by the [OpenMDW License Agreement, version 1.1](https://raw.githubusercontent.com/OpenMDW/OpenMDW/refs/heads/main/1.1/LICENSE.OpenMDW-1.1) (OpenMDW-1.1).
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This project is currently not accepting contributions.
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### Benchmarks
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| **Benchmark** | **Nemotron-Labs-3-Puzzle-75B-A9B-BF16** | **Nemotron-Labs-3-Puzzle-75B-A9B-FP8** | **Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4** |
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config.json
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"ssm_state_size": 96,
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"tie_word_embeddings": false,
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"time_step_floor": 0.0001,
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"time_step_max": 0.1,
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"time_step_min": 0.001,
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"topk_group": 1,
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"ssm_state_size": 96,
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"tie_word_embeddings": false,
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"time_step_floor": 0.0001,
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"time_step_max": 0.1,
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"time_step_min": 0.001,
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"topk_group": 1,
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