Text Generation
Transformers
Safetensors
PyTorch
nemotron_h
nvidia
conversational
custom_code
Eval Results
Instructions to use nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", trust_remote_code=True) 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-Nano-30B-A3B-BF16", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", trust_remote_code=True) 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
- Local Apps Settings
- vLLM
How to use nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 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-Nano-30B-A3B-BF16" # 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-Nano-30B-A3B-BF16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
- SGLang
How to use nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 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-Nano-30B-A3B-BF16" \ --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-Nano-30B-A3B-BF16", "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-Nano-30B-A3B-BF16" \ --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-Nano-30B-A3B-BF16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 with Docker Model Runner:
docker model run hf.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
| from vllm.reasoning.abs_reasoning_parsers import ReasoningParserManager | |
| from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser | |
| from vllm.entrypoints.openai.protocol import DeltaMessage | |
| class NanoV3ReasoningParser(DeepSeekR1ReasoningParser): | |
| """ | |
| Reasoning parser for Nemotron-3-Nano that handles the enable_thinking flag. | |
| When enable_thinking is False, the model doesn't emit <think> tags, so all | |
| content comes through as "reasoning" in the parent parser. This class swaps | |
| the fields so content appears in the correct field for both streaming and | |
| non-streaming modes. | |
| """ | |
| def __init__(self, tokenizer, **kwargs): | |
| super().__init__(tokenizer, **kwargs) | |
| # Capture chat_template_kwargs for use in streaming | |
| self.chat_template_kwargs = kwargs.get("chat_template_kwargs", {}) | |
| self._thinking_disabled = ( | |
| self.chat_template_kwargs | |
| and self.chat_template_kwargs.get("enable_thinking") is False | |
| ) | |
| def extract_reasoning(self, model_output, request): | |
| reasoning_content, final_content = super().extract_reasoning( | |
| model_output, request | |
| ) | |
| if ( | |
| hasattr(request, "chat_template_kwargs") | |
| and request.chat_template_kwargs | |
| and request.chat_template_kwargs.get("enable_thinking") is False | |
| and final_content is None | |
| ): | |
| reasoning_content, final_content = final_content, reasoning_content | |
| return reasoning_content, final_content | |
| def extract_reasoning_streaming( | |
| self, | |
| previous_text: str, | |
| current_text: str, | |
| delta_text: str, | |
| previous_token_ids: tuple, | |
| current_token_ids: tuple, | |
| delta_token_ids: tuple, | |
| ) -> DeltaMessage | None: | |
| # Get result from parent class | |
| result = super().extract_reasoning_streaming( | |
| previous_text, | |
| current_text, | |
| delta_text, | |
| previous_token_ids, | |
| current_token_ids, | |
| delta_token_ids, | |
| ) | |
| # When thinking is disabled and we get reasoning content, swap to content | |
| if result is not None and self._thinking_disabled: | |
| if result.reasoning_content is not None and result.content is None: | |
| return DeltaMessage( | |
| content=result.reasoning_content, | |
| reasoning_content=None, | |
| ) | |
| return result |