Instructions to use Xx-Vexento-xX/openhome-nova with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xx-Vexento-xX/openhome-nova with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Xx-Vexento-xX/openhome-nova",
	filename="nova.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Xx-Vexento-xX/openhome-nova with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Xx-Vexento-xX/openhome-nova
# Run inference directly in the terminal:
llama-cli -hf Xx-Vexento-xX/openhome-nova

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Xx-Vexento-xX/openhome-nova
# Run inference directly in the terminal:
llama-cli -hf Xx-Vexento-xX/openhome-nova

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 Xx-Vexento-xX/openhome-nova
# Run inference directly in the terminal:
./llama-cli -hf Xx-Vexento-xX/openhome-nova

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 Xx-Vexento-xX/openhome-nova
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Xx-Vexento-xX/openhome-nova

Use Docker

docker model run hf.co/Xx-Vexento-xX/openhome-nova

LM Studio
Jan
Ollama
How to use Xx-Vexento-xX/openhome-nova with Ollama:
```
ollama run hf.co/Xx-Vexento-xX/openhome-nova
```

Unsloth Studio

How to use Xx-Vexento-xX/openhome-nova 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 Xx-Vexento-xX/openhome-nova 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 Xx-Vexento-xX/openhome-nova to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Xx-Vexento-xX/openhome-nova to start chatting

How to use Xx-Vexento-xX/openhome-nova with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Xx-Vexento-xX/openhome-nova

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": "Xx-Vexento-xX/openhome-nova"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Xx-Vexento-xX/openhome-nova with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Xx-Vexento-xX/openhome-nova

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 Xx-Vexento-xX/openhome-nova

Run Hermes

hermes

Docker Model Runner
How to use Xx-Vexento-xX/openhome-nova with Docker Model Runner:
```
docker model run hf.co/Xx-Vexento-xX/openhome-nova
```

Lemonade

How to use Xx-Vexento-xX/openhome-nova with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Xx-Vexento-xX/openhome-nova

Run and chat with the model

lemonade run user.openhome-nova-{{QUANT_TAG}}

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

I'm So Sorry however this version of Nova is broken, and spits out garbage. Use the updated v2 model please

openHome 🏠

A local, privacy-first smart home security system with an optional AI reasoning layer. No cloud, no subscriptions, no data leaving your network.

What It Does

openHome connects ESP32 sensors around your home to a Raspberry Pi hub that monitors events in real time. A web dashboard lets you view device states and control everything manually. Optionally, NOVA — a fine-tuned Qwen 0.5B model — reasons over sensor events and natural language commands to automate responses.

Without AI: sensors report → hub stores state → dashboard updates → you control devices manually.

With AI: sensors report → NOVA reasons → actions execute automatically (lights, thermostat, notifications, etc.)

Hardware

Device	Role
Raspberry Pi	Central hub running FastAPI server
ESP32 (×13)	Distributed sensors and actuators
RGB lights	Controllable via hub
Smart plugs	Controllable via hub
Thermostat	Controllable via hub

Supported Sensor Types

Door sensors
Motion sensors
Flood/water sensors
Smoke + CO sensors
RFID locks
Panic buttons
Doorbells
Garage door controller
Climate monitors
Air quality sensors
Smart plugs
RGB lights
Thermostat

Software Stack

ESP32 Sensors
     │  (HMAC-signed HTTPS payloads)
     ▼
Raspberry Pi Hub  ──  FastAPI server
     │                    │
     │              SQLite state store
     │              RAG memory (ChromaDB)
     │              NOVA via Ollama
     ▼
Web Dashboard (vanilla JS)
     │
     └── Manual controls, live device state, notifications

Project Structure

openHome/
├── pi/                         # Raspberry Pi hub
│   ├── server.py               # FastAPI main server
│   ├── ai_brain.py             # NOVA inference + action dispatch
│   ├── action_executor.py      # Sends commands back to ESP32s
│   ├── device_schemas.py       # Device type definitions
│   ├── rag_memory.py           # ChromaDB RAG layer
│   └── secrets_config.py       # API keys + HMAC secrets (gitignored)
│
├── esp32/                      # ESP32 firmware (Arduino IDE)
│   ├── door_sensor/
│   ├── motion_sensor/
│   ├── flood_sensor/
│   ├── smoke_co_sensor/
│   ├── rfid_lock/
│   ├── panic_button/
│   ├── doorbell/
│   ├── garage_door/
│   ├── climate_monitor/
│   ├── air_quality/
│   ├── smart_plug/
│   ├── rgb_lights/
│   └── thermostat/
│
├── dashboard/                  # Web UI
│   ├── index.html
│   ├── style.css
│   └── app.js
│
├── dataset/                    # NOVA training pipeline
│   ├── generate_training_data.py
│   ├── train_cpu.py
│   ├── openhome_train.jsonl
│   └── openhome_val.jsonl
│
└── README.md

NOVA — The AI Layer

NOVA is a Qwen2.5-0.5B model fine-tuned on 1,909 openHome-specific examples covering:

Sensor event → action reasoning (doors, motion, smoke, flood, panic, RFID, etc.)
Natural language commands ("goodnight", "movie mode", "I'm leaving")
Time-of-day context (night vs. day behavior)
Constraint enforcement (read-only sensors, garage with car present, thermostat limits)
Multi-action routines

Training stats:

Base model: Qwen/Qwen2.5-0.5B-Instruct
Dataset: 1,718 train / 191 val examples
Final loss: ~0.039
Format: GGUF Q8_0 via llama.cpp
Inference: Ollama

NOVA always replies with a valid JSON array of action objects or [] for no action. It never explains itself, never fabricates device IDs, and respects all device constraints.

Setup

Prerequisites

Raspberry Pi (3B+ or newer)
Python 3.11+
Ollama installed on the Pi
Arduino IDE for ESP32 firmware

1. Clone the repo

git clone https://github.com/DaEpickid540/openHome.git
cd openHome

2. Generate secrets

cd pi
python gen_keys.py

This creates secrets_config.py with your API key and HMAC secret. Keep this file out of version control.

3. Install Pi dependencies

pip install fastapi uvicorn chromadb sentence-transformers requests

4. Load NOVA into Ollama

# Create Modelfile
cat > Modelfile << 'EOF'
FROM /path/to/nova.gguf
PARAMETER temperature 0.1
PARAMETER stop "<|im_end|>"
SYSTEM "You are NOVA, the AI brain of openHome. Reply ONLY with valid JSON arrays of action objects. No explanation, no markdown."
EOF

ollama create openhome-nova -f Modelfile

5. Start the hub server

uvicorn server:app --host 0.0.0.0 --port 8765

6. Flash ESP32 firmware

Open each sketch in esp32/ with Arduino IDE. Update the following in each sketch before flashing:

const char* WIFI_SSID = "your_wifi";
const char* WIFI_PASS = "your_password";
const char* HUB_URL   = "http://<pi_ip>:8765/sensor";
const char* API_KEY   = "your_api_key_from_secrets_config";
const char* HMAC_KEY  = "your_hmac_key_from_secrets_config";

7. Open the dashboard

Open dashboard/index.html in a browser pointed at your Pi's IP. Done.

Security

All ESP32 payloads are HMAC-SHA256 signed
API key required on every request
NOVA runs 100% locally — no data leaves your network
Secrets are never committed to the repo

Roadmap

MIFARE DESFire challenge-response RFID (replacing UID-based auth)
DPO fine-tuning pass on NOVA with Qwen-as-critic
Voice command input
Tailscale remote access guide
Mobile push notifications

Author

Sarvin — @DaEpickid540

License

MIT

Downloads last month: 139

Safetensors

Model size

0.5B params

Tensor type

F32

BF16

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