Text Generation
Transformers
Safetensors
English
nemotron-nas
nvidia
llama3.3
conversational
custom_code
Instructions to use nvidia/Llama-3_3-Nemotron-Super-49B-GenRM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/Llama-3_3-Nemotron-Super-49B-GenRM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nvidia/Llama-3_3-Nemotron-Super-49B-GenRM", 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/Llama-3_3-Nemotron-Super-49B-GenRM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nvidia/Llama-3_3-Nemotron-Super-49B-GenRM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nvidia/Llama-3_3-Nemotron-Super-49B-GenRM" # 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/Llama-3_3-Nemotron-Super-49B-GenRM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nvidia/Llama-3_3-Nemotron-Super-49B-GenRM
- SGLang
How to use nvidia/Llama-3_3-Nemotron-Super-49B-GenRM 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/Llama-3_3-Nemotron-Super-49B-GenRM" \ --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/Llama-3_3-Nemotron-Super-49B-GenRM", "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/Llama-3_3-Nemotron-Super-49B-GenRM" \ --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/Llama-3_3-Nemotron-Super-49B-GenRM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use nvidia/Llama-3_3-Nemotron-Super-49B-GenRM with Docker Model Runner:
docker model run hf.co/nvidia/Llama-3_3-Nemotron-Super-49B-GenRM
| Field | Response |
|---|---|
| Generatable or reverse engineerable personal data? | No |
| Personal data used to create this model? | None Known. For data included in the base Llama 3.3 model, reference the Llama 3.3 model card. |
| How often is the dataset reviewed (if applicable)? | Before Release |
| Is there provenance for all datasets used in training? | Yes |
| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |
| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |
| Applicable Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/ |