Instructions to use hungrynovalabs/nova-pup-4b-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 hungrynovalabs/nova-pup-4b-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 hungrynovalabs/nova-pup-4b-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf hungrynovalabs/nova-pup-4b-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hungrynovalabs/nova-pup-4b-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf hungrynovalabs/nova-pup-4b-GGUF:Q8_0

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 hungrynovalabs/nova-pup-4b-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf hungrynovalabs/nova-pup-4b-GGUF:Q8_0

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 hungrynovalabs/nova-pup-4b-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hungrynovalabs/nova-pup-4b-GGUF:Q8_0

Use Docker

docker model run hf.co/hungrynovalabs/nova-pup-4b-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use hungrynovalabs/nova-pup-4b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hungrynovalabs/nova-pup-4b-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": "hungrynovalabs/nova-pup-4b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hungrynovalabs/nova-pup-4b-GGUF:Q8_0

Ollama
How to use hungrynovalabs/nova-pup-4b-GGUF with Ollama:
```
ollama run hf.co/hungrynovalabs/nova-pup-4b-GGUF:Q8_0
```

Unsloth Studio

How to use hungrynovalabs/nova-pup-4b-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 hungrynovalabs/nova-pup-4b-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 hungrynovalabs/nova-pup-4b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hungrynovalabs/nova-pup-4b-GGUF to start chatting

How to use hungrynovalabs/nova-pup-4b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hungrynovalabs/nova-pup-4b-GGUF:Q8_0

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": "hungrynovalabs/nova-pup-4b-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use hungrynovalabs/nova-pup-4b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hungrynovalabs/nova-pup-4b-GGUF:Q8_0

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 "hungrynovalabs/nova-pup-4b-GGUF:Q8_0" \
  --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 hungrynovalabs/nova-pup-4b-GGUF with Docker Model Runner:
```
docker model run hf.co/hungrynovalabs/nova-pup-4b-GGUF:Q8_0
```

Lemonade

How to use hungrynovalabs/nova-pup-4b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hungrynovalabs/nova-pup-4b-GGUF:Q8_0

Run and chat with the model

lemonade run user.nova-pup-4b-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use hungrynovalabs/nova-pup-4b-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 hungrynovalabs/nova-pup-4b-GGUF:Q8_0

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 hungrynovalabs/nova-pup-4b-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

Nova Pup 4B — GGUF (Q8_0)

By Matthew Salinas Hernandez — Hungry Nova Labs LLC

Quantized GGUF build of hungrynovalabs/nova-pup-4b, a 4B-parameter Linux systems specialist and multi-agent problem solver. See the main repo for the full model card, benchmarks, and limitations.

Run it

With Ollama (or use the curated build: ollama run hungrynovalabs/nova-pup:4b):

ollama run hf.co/hungrynovalabs/nova-pup-4b-GGUF

With llama.cpp:

llama-cli -m nova-pup-4b.Q8_0.gguf -cnv

Training data & licensing

Nova Pup was trained using lawfully acquired technical materials and publicly available Linux documentation. Openly licensed materials retain their respective licenses. Copyrighted materials were used only for intermediate model training and are not distributed with the model.

Base model: InternScience Agents-A1-4B (Apache-2.0). Quantization: Q8_0. Based on Qwen3.5-4B (Apache 2.0) by the Qwen Team.

Downloads last month: 106

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

8-bit

Model tree for hungrynovalabs/nova-pup-4b-GGUF

Base model

InternScience/Agents-A1-4B

Adapter

hungrynovalabs/nova-pup-4b

Quantized

(1)

this model