Instructions to use ZeZZm/aero-deuce-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZeZZm/aero-deuce-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ZeZZm/aero-deuce-GGUF",
	filename="aero-deuce-q4km.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 ZeZZm/aero-deuce-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ZeZZm/aero-deuce-GGUF
# Run inference directly in the terminal:
llama-cli -hf ZeZZm/aero-deuce-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ZeZZm/aero-deuce-GGUF
# Run inference directly in the terminal:
llama-cli -hf ZeZZm/aero-deuce-GGUF

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 ZeZZm/aero-deuce-GGUF
# Run inference directly in the terminal:
./llama-cli -hf ZeZZm/aero-deuce-GGUF

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 ZeZZm/aero-deuce-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZeZZm/aero-deuce-GGUF

Use Docker

docker model run hf.co/ZeZZm/aero-deuce-GGUF

LM Studio
Jan

vLLM

How to use ZeZZm/aero-deuce-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ZeZZm/aero-deuce-GGUF

Ollama
How to use ZeZZm/aero-deuce-GGUF with Ollama:
```
ollama run hf.co/ZeZZm/aero-deuce-GGUF
```

Unsloth Studio

How to use ZeZZm/aero-deuce-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 ZeZZm/aero-deuce-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 ZeZZm/aero-deuce-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ZeZZm/aero-deuce-GGUF to start chatting

How to use ZeZZm/aero-deuce-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ZeZZm/aero-deuce-GGUF

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": "ZeZZm/aero-deuce-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ZeZZm/aero-deuce-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 ZeZZm/aero-deuce-GGUF

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 ZeZZm/aero-deuce-GGUF

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use ZeZZm/aero-deuce-GGUF with Docker Model Runner:
```
docker model run hf.co/ZeZZm/aero-deuce-GGUF
```

Lemonade

How to use ZeZZm/aero-deuce-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZeZZm/aero-deuce-GGUF

Run and chat with the model

lemonade run user.aero-deuce-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Aero-Deuce — GGUF Q4_K_M

A fine-tuned Gemma 4 12B instruction-following model. This is the GGUF quantized version (~7 GB) that runs locally on CPU or GPU with no Python required.

Download

Click the Files and versions tab above and download aero-deuce-q4km.gguf. That's the only file you need.

Which format should I use?

Format	Best for	Link
GGUF ← you are here	Local inference, llama.cpp, LM Studio, GPT4All	This repo
MLX 4-bit	Apple Silicon (Mac)	ZeZZm/aero-deuce-MLX
LoRA Adapter	Merging with base model, further fine-tuning	ZeZZm/aero-deuce

Quick Start

LM Studio (easiest — GUI app):

Download LM Studio
Search for ZeZZm/aero-deuce-GGUF
Click download, then chat

llama.cpp:

# Download
wget https://huggingface.co/ZeZZm/aero-deuce-GGUF/resolve/main/aero-deuce-q4km.gguf

# Run
llama-cli -m aero-deuce-q4km.gguf -c 4096 --conversation

Ollama:

# After downloading the GGUF file:
echo 'FROM ./aero-deuce-q4km.gguf
SYSTEM "You are Aero-Deuce, developed by the Aero-Deuce team."
PARAMETER stop "<|end_of_turn>"
PARAMETER stop "<|start_of_turn>"' > Modelfile

ollama create aero-deuce -f Modelfile
ollama run aero-deuce

GPT4All:

Download GPT4All
File → Open → select aero-deuce-q4km.gguf
Start chatting

Model Details

Property	Value
Base Model	google/gemma-4-12b-it (12B params)
Training Method	QLoRA + Muon optimizer
Training Data	30K instruction-following samples
Training Steps	2,000
Quantization	Q4_K_M (~4.95 bits per weight)
File Size	~7 GB
Context Length	4,096 tokens

System Prompt

A system prompt identifying the model as Aero-Deuce is embedded in the GGUF chat template. It works automatically in most frontends. For llama-cli, pass -sys "You are Aero-Deuce." for best results.

License

Apache 2.0

Downloads last month: 651

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

ZeZZm
/

aero-deuce-GGUF