Instructions to use WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4

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 WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4

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 WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-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": "WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4

Ollama
How to use WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF with Ollama:
```
ollama run hf.co/WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4
```

Unsloth Studio

How to use WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-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 WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-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 WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF to start chatting

How to use WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4

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": "WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4
```

Lemonade

How to use WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.Nemotron-3-Nano-30B-A3B-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4

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 WhiskyAKM/Nemotron-3-Nano-30B-A3B-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

Description

Nemotron 3 Nano 30B A3B NVFP4 GGUF created from https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4

Model Overview

Nemotron-3-Nano-30B-A3B is a large language model (LLM) trained from scratch by NVIDIA, designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be configured through a flag in the chat template. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks.

The model employs a hybrid Mamba2-Transformer Mixture-of-Experts (MoE) architecture, consisting of 23 Mamba-2 and 23 MoE layers, along with 6 Attention layers. Each MoE layer includes 128 routed experts plus 1 shared expert, with 6 experts activated per token. The model has 3.5B active parameters and 30B parameters in total. Supported languages include English, German, Spanish, French, Italian, and Japanese.

This repository contains a GGUF conversion of the NVIDIA NVFP4-quantized checkpoint, making it usable with llama.cpp and other GGUF-compatible inference engines.

Model Architecture

Property	Value
Architecture Type	Mamba2-Transformer Hybrid MoE
Total Parameters	30B
Active Parameters	3.5B
Layers	52 (23 MoE + 23 Mamba-2 + 6 Attention)
Context Length	256K tokens (up to 1M supported)
Vocabulary Size	131,072
MoE Configuration	6 active / 128 routed experts + 1 shared
Attention Heads	32 (2 KV heads, Grouped Query Attention)
Hidden Size	2688
Quantization	NVFP4 (KV cache: FP8)
Supported Languages	English, Spanish, French, German, Japanese, Italian

GGUF File

File	Description
`nemotron-3-nano-30b-a3b-nvfp4.gguf`	Single-file GGUF model (NVFP4 quantization)

A chat_template.jinja file is also provided for use with chat-based inference.

Usage

llama.cpp

# Build llama.cpp with CUDA support (recommended for NVFP4)
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release

# Run inference
./build/bin/llama-cli \
  -m nemotron-3-nano-30b-a3b-nvfp4.gguf \
  -p "Explain quantum computing in simple terms." \
  --temp 1.0 --top-p 1.0

llama-server (OpenAI-compatible API)

./build/bin/llama-server \
  -m nemotron-3-nano-30b-a3b-nvfp4.gguf \
  --host 0.0.0.0 --port 8080

Generation Parameters

The recommended generation parameters (from the original model's generation_config.json):

Parameter	Value
Temperature	1.0
Top-P	1.0
BOS Token ID	1
EOS Token IDs	2, 11
Pad Token ID	0

For reasoning tasks: temperature=1.0, top_p=1.0. For tool calling: temperature=0.6, top_p=0.95.

Evaluation Results (copied from: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4)

Benchmark	BF16	FP8	NVFP4
MMLU-Pro	78.3	78.1	77.4
AIME25 (no tools)	89.1	87.7	86.7
GPQA (no tools)	73.0	72.5	71.9
LiveCodeBench (v6)	68.3	67.6	65.4
SciCode (subtask)	33.0	31.9	30.7
HLE (no tools)	10.2	10.3	9.4
TauBench V2 (Average)	49.0	47.0	45.6
IFBench (prompt)	71.5	72.2	70.7
AA-LCR	35.9	36.1	33.3
MMLU-ProX (avg over langs)	59.50	59.6	57.8