Instructions to use second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF", dtype="auto")

llama-cpp-python

How to use second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF",
	filename="NVIDIA-Nemotron-Nano-9B-v2-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M

SGLang

How to use second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF 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 "second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF" \
    --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": "second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF",
		"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 "second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF" \
        --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": "second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF with Ollama:
```
ollama run hf.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M
```

Unsloth Studio

How to use second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF to start chatting

How to use second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M
```

Lemonade

How to use second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NVIDIA-Nemotron-Nano-9B-v2-GGUF-Q4_K_M

List all available models

lemonade list

Configuration Parsing Warning:Invalid JSON for config file config.json

NVIDIA-Nemotron-Nano-9B-v2-GGUF

Original Model

nvidia/NVIDIA-Nemotron-Nano-9B-v2

Run with LlamaEdge

LlamaEdge version: coming soon

Prompt template

Prompt type: nemotron-2-chat

Prompt string

Thinking mode

<SPECIAL_10>System
You are a helpful assistant.

<SPECIAL_11>User
Hi, can you explain what quantum computing is? /think

<SPECIAL_11>Assistant
<think>
The user is asking about quantum computing, I should give a simple explanation.
</think>
Quantum computing uses principles of quantum mechanics, such as superposition and entanglement, to perform computations that would be hard for classical computers.
<SPECIAL_12>

<SPECIAL_11>User
That sounds complicated. Can you give me an example? /think

<SPECIAL_11>Assistant
<think>
I should give a concrete example, like Shor’s algorithm or Grover’s algorithm, but in simple terms.
</think>
For example, a quantum computer can factor very large numbers much faster than classical computers, which could break some encryption methods.
<SPECIAL_12>

<SPECIAL_11>User
Okay, explain it in a simpler way for a 10-year-old. /think

<SPECIAL_11>Assistant
<think>

No-thinking mode

<SPECIAL_10>System
You are a helpful assistant.

/no_think

<SPECIAL_11>User
Hi, can you explain what quantum computing is? /no_think

<SPECIAL_11>Assistant
<think></think>
Quantum computing is a type of computing that uses the rules of quantum mechanics to solve certain problems faster than regular computers.
<SPECIAL_12>

<SPECIAL_11>User
That sounds complicated. Can you give me an example? /no_think

<SPECIAL_11>Assistant
<think></think>
For example, quantum computers could quickly factor very large numbers, which is important for cryptography.
<SPECIAL_12>

<SPECIAL_11>User
Okay, explain it even more simply. /no_think

<SPECIAL_11>Assistant
<think></think>

Context size: 128000

Run as LlamaEdge service

wasmedge --dir .:. \
  --nn-preload default:GGML:AUTO:NVIDIA-Nemotron-Nano-9B-v2-Q5_K_M.gguf \
  llama-api-server.wasm \
  --prompt-template nemotron-2-chat \
  --ctx-size 128000 \
  --model-name nemotron-nano-v2

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
NVIDIA-Nemotron-Nano-9B-v2-Q2_K.gguf	Q2_K	2	5.01 GB	smallest, significant quality loss - not recommended for most purposes
NVIDIA-Nemotron-Nano-9B-v2-Q3_K_L.gguf	Q3_K_L	3	5.49 GB	small, substantial quality loss
NVIDIA-Nemotron-Nano-9B-v2-Q3_K_M.gguf	Q3_K_M	3	5.38 GB	very small, high quality loss
NVIDIA-Nemotron-Nano-9B-v2-Q3_K_S.gguf	Q3_K_S	3	5.13 GB	very small, high quality loss
NVIDIA-Nemotron-Nano-9B-v2-Q4_0.gguf	Q4_0	4	5.31 GB	legacy; small, very high quality loss - prefer using Q3_K_M
NVIDIA-Nemotron-Nano-9B-v2-Q4_K_M.gguf	Q4_K_M	4	6.53 GB	medium, balanced quality - recommended
NVIDIA-Nemotron-Nano-9B-v2-Q4_K_S.gguf	Q4_K_S	4	6.21 GB	small, greater quality loss
NVIDIA-Nemotron-Nano-9B-v2-Q5_0.gguf	Q5_0	5	6.35 GB	legacy; medium, balanced quality - prefer using Q4_K_M
NVIDIA-Nemotron-Nano-9B-v2-Q5_K_M.gguf	Q5_K_M	5	7.07 GB	large, very low quality loss - recommended
NVIDIA-Nemotron-Nano-9B-v2-Q5_K_S.gguf	Q5_K_S	5	6.78 GB	large, low quality loss - recommended
NVIDIA-Nemotron-Nano-9B-v2-Q6_K.gguf	Q6_K	6	9.14 GB	very large, extremely low quality loss
NVIDIA-Nemotron-Nano-9B-v2-Q8_0.gguf	Q8_0	8	17.8 GB	very large, extremely low quality loss - not recommended
NVIDIA-Nemotron-Nano-9B-v2-f16.gguf	f16	16	30.0 GB

Quantized with llama.cpp b6315.

Downloads last month: 239

GGUF

Model size

9B params

Architecture

nemotron_h

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF

Base model

nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base

Finetuned

nvidia/NVIDIA-Nemotron-Nano-12B-v2

Finetuned

nvidia/NVIDIA-Nemotron-Nano-9B-v2

Quantized

(16)

this model