Instructions to use nvidia/NVIDIA-Nemotron-Nano-9B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/NVIDIA-Nemotron-Nano-9B-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nvidia/NVIDIA-Nemotron-Nano-9B-v2", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-Nano-9B-v2", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-Nano-9B-v2", trust_remote_code=True, 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nvidia/NVIDIA-Nemotron-Nano-9B-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nvidia/NVIDIA-Nemotron-Nano-9B-v2" # 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-Nano-9B-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2
- SGLang
How to use nvidia/NVIDIA-Nemotron-Nano-9B-v2 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-Nano-9B-v2" \ --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-Nano-9B-v2", "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-Nano-9B-v2" \ --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-Nano-9B-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use nvidia/NVIDIA-Nemotron-Nano-9B-v2 with Docker Model Runner:
docker model run hf.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2
how to run on vllm with tool call parsing -- encountered an error
#9
by radek - opened
Unfortunately, when I add the commands for tool parsing and attempt to start vllm with vllm serve, I get:
from vllm.entrypoints.cli.main import main
File "/vllm/vllm/entrypoints/cli/__init__.py", line 4, in <module>
from vllm.entrypoints.cli.benchmark.serve import BenchmarkServingSubcommand
File "/vllm/vllm/entrypoints/cli/benchmark/serve.py", line 5, in <module>
from vllm.benchmarks.serve import add_cli_args, main
File "/vllm/vllm/benchmarks/serve.py", line 36, in <module>
from vllm.benchmarks.datasets import (SampleRequest, add_dataset_parser,
File "/vllm/vllm/benchmarks/datasets.py", line 32, in <module>
from vllm.lora.utils import get_adapter_absolute_path
File "/vllm/vllm/lora/utils.py", line 16, in <module>
from vllm.lora.fully_sharded_layers import (
File "/vllm/vllm/lora/fully_sharded_layers.py", line 15, in <module>
from vllm.lora.layers import (ColumnParallelLinearWithLoRA,
File "/vllm/vllm/lora/layers.py", line 23, in <module>
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
File "/vllm/vllm/model_executor/layers/linear.py", line 22, in <module>
from vllm.model_executor.layers.utils import dispatch_unquantized_gemm
File "/vllm/vllm/model_executor/layers/utils.py", line 8, in <module>
from vllm import _custom_ops as ops
File "/vllm/vllm/_custom_ops.py", line 440, in <module>
@register_fake("_C::marlin_qqq_gemm")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/torch/library.py", line 1023, in register
use_lib._register_fake(op_name, func, _stacklevel=stacklevel + 1)
File "/usr/local/lib/python3.12/site-packages/torch/library.py", line 214, in _register_fake
handle = entry.fake_impl.register(func_to_register, source)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/torch/_library/fake_impl.py", line 31, in register
if torch._C._dispatch_has_kernel_for_dispatch_key(self.qualname, "Meta"):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: operator _C::marlin_qqq_gemm does not exist
Is there a prebuilt docker image I could use? If not, could you please share how you built vllm to include marlin_qqq_gemm?
Thank you so much for your help!
radek changed discussion status to closed