Lugia

Overview

Lugia is a high-performance local model inference system designed for macOS, built on top of MLX (Machine Learning X). It aims to provide a seamless experience for deploying and running machine learning models locally with minimal resource consumption.

Features

Fast Inference: Optimized for local inference with MLX, ensuring low latency and high throughput.
Customizable Sampling: Advanced sampling techniques including adaptive sampling to generate diverse outputs from language models.
Modular Tooling: Built in a modular way to allow easy extension and integration of additional functionalities.
User Interface: Text-based user interface (TUI) for easy interaction.
Text-to-Speech (TTS): Integration with TTS systems for voice-based interactions.
Speech-to-Text (STT): Integration with STT systems for voice recognition.
Sentiment Analysis (SST): Basic sentiment analysis capabilities.
Conversational assistant: Mimics a conversational assistant using language models.

Goals

Jarvis: Emulate a personal assistant akin to Siri or Alexa but running entirely locally.

Anti-Goals

Avoid Bloat: Minimize dependencies and keep the implementation lightweight.
Efficiency: Avoid excessive computational overhead.
API-based: Focus on command-line and TUI interactions rather than serving APIs.

Installation

Install dependencies:
```
pip install -r requirements.txt
```

Clone the repository:

git clone https://github.com/your-repo/lugia.git
cd lugia

Usage

Running the Talk Interface

python talk.py

This script records audio input, transcribes it, sends the text to a local language model, and then plays the response as audio.

Running Custom Tools

You can use tool.py to perform various tasks such as transcribing audio, sampling from models, etc.

python tool.py --task transcribe --audio_path path_to_audio.wav

vovw/lugia