Instructions to use majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS",
	filename="Nemotron-3-Nano-30B-A3B-RotorQuant-IQ4_XS.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS 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 majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

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 majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

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 majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

LM Studio
Jan

vLLM

How to use majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Ollama
How to use majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS with Ollama:
```
ollama run hf.co/majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS
```

Unsloth Studio

How to use majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS 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 majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS 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 majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS to start chatting

How to use majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

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": "majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

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 majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS with Docker Model Runner:
```
docker model run hf.co/majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS
```

Lemonade

How to use majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull majentik/Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Run and chat with the model

lemonade run user.Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS-IQ4_XS

List all available models

lemonade list

Nemotron-3-Nano-30B-A3B-RotorQuant-GGUF-IQ4_XS

Commit History

chore(card): add hardware compatibility section

f68cddc
verified

majentik commited on Apr 21

chore(card): enrich YAML frontmatter (pipeline_tag, language, library_name, inference)

fd2854a
verified

majentik commited on Apr 17

Update model card with accurate fork requirements and ecosystem status

4a73da2
verified

majentik commited on Apr 15

Add model card

f494299
verified

majentik commited on Apr 14

Add GGUF IQ4_XS

fb13e91
verified

majentik commited on Apr 13

initial commit

72e36a3
verified

majentik commited on Apr 13

Commit History

chore(card): add hardware compatibility section f68cddc verified

chore(card): enrich YAML frontmatter (pipeline_tag, language, library_name, inference) fd2854a verified

Update model card with accurate fork requirements and ecosystem status 4a73da2 verified

Add model card f494299 verified

Add GGUF IQ4_XS fb13e91 verified