MLX Omni Server

MLX Omni Server is a local inference server powered by Apple's MLX framework, specifically designed for Apple Silicon (M-series) chips. It implements OpenAI-compatible API endpoints, enabling seamless integration with existing OpenAI SDK clients while leveraging the power of local ML inference.

Features

• 🚀 Apple Silicon Optimized: Built on MLX framework, optimized for M1/M2/M3/M4 series chips
• 🔌 OpenAI API Compatible: Drop-in replacement for OpenAI API endpoints
• 🎯 Multiple AI Capabilities:
  • Audio Processing (TTS & STT)
  • Chat Completion
  • Image Generation
• ⚡ High Performance: Local inference with hardware acceleration
• 🔐 Privacy-First: All processing happens locally on your machine
• 🛠 SDK Support: Works with official OpenAI SDK and other compatible clients

Supported API Endpoints

The server implements OpenAI-compatible endpoints:

• Chat completions: /v1/chat/completions
  • ✅ Chat
  • ✅ Tools, Function Calling
  • ✅ Structured Output
  • ✅ LogProbs
  • 🚧 Vision
• Audio
  • ✅ /v1/audio/speech - Text-to-Speech
  • ✅ /v1/audio/transcriptions - Speech-to-Text
• Models
  • ✅ /v1/models - List models
  • ✅ /v1/models/{model} - Retrieve or Delete model
• Images
  • ✅ /v1/images/generations - Image generation

Installation

# Install using pip
pip install mlx-omni-server

Quick Start

1. Start the server:

# If installed via pip as a package
mlx-omni-server

you can use --port to specify a different port,such as: mlx-omni-server --port 10240, default port is 10240.

You can view more startup parameters by using mlx-omni-server --help.

2. Use with OpenAI SDK:

from openai import OpenAI

# Configure client to use local server
client = OpenAI(
    base_url="http://localhost:10240/v1",  # Point to local server
    api_key="not-needed"  # API key is not required for local server
)

# Text-to-Speech Example
response = client.audio.speech.create(
    model="lucasnewman/f5-tts-mlx",
    input="Hello, welcome to MLX Omni Server!"
)

# Speech-to-Text Example
audio_file = open("speech.mp3", "rb")
transcript = client.audio.transcriptions.create(
    model="mlx-community/whisper-large-v3-turbo",
    file=audio_file
)

# Chat Completion Example
chat_completion = client.chat.completions.create(
    model="meta-llama/Llama-3.2-3B-Instruct",
    messages=[
        {"role": "user", "content": "What can you do?"}
    ]
)

# Image Generation Example
image_response = client.images.generate(
    model="argmaxinc/mlx-FLUX.1-schnell",
    prompt="A serene landscape with mountains and a lake",
    n=1,
    size="512x512"
)

You can view more examples in examples.

Contributing

We welcome contributions! If you're interested in contributing to MLX Omni Server, please check out our Development Guide for detailed information about:

• Setting up the development environment
• Running the server in development mode
• Contributing guidelines
• Testing and documentation

For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with MLX by Apple
• API design inspired by OpenAI
• Uses FastAPI for the server implementation
• Chat(text generation) by mlx-lm
• Image generation by diffusionkit
• Text-to-Speech by lucasnewman/f5-tts-mlx
• Speech-to-Text by mlx-whisper

Disclaimer

This project is not affiliated with or endorsed by OpenAI or Apple. It's an independent implementation that provides OpenAI-compatible APIs using Apple's MLX framework.

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