Text Generation
Transformers
Safetensors
PyTorch
nemotron_h
nvidia
nemotron-3
latent-moe
mtp
conversational
Eval Results
8-bit precision
modelopt
Instructions to use nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4") model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-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-3-Ultra-550B-A55B-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-3-Ultra-550B-A55B-NVFP4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4
- SGLang
How to use nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-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-3-Ultra-550B-A55B-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-3-Ultra-550B-A55B-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-3-Ultra-550B-A55B-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-3-Ultra-550B-A55B-NVFP4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 with Docker Model Runner:
docker model run hf.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4
Add files using upload-large-folder tool
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"weight_map": {
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"weight_map": {
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| 11 |
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| 12 |
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"backbone.layers.47.norm.weight": "model-00048-of-00113.safetensors",
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"weight_map": {
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model-00077-of-00113.json
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model-00078-of-00113.json
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model-00082-of-00113.json
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model-00084-of-00113.safetensors
ADDED
|
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size 6291991440
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