React Native ExecuTorch

React Native ExecuTorch provides a declarative way to run AI models on-device using React Native, powered by ExecuTorch 🚀. It offers out-of-the-box support for a wide range of LLMs, computer vision models, and more. Visit our HuggingFace page to explore these models.

ExecuTorch, developed by Meta, is a novel framework allowing AI model execution on devices like mobile phones or microcontrollers.

React Native ExecuTorch bridges the gap between React Native and native platform capabilities, enabling developers to efficiently run local AI models on mobile devices. This can be achieved without the need for extensive expertise in native programming or machine learning.

Table of contents:

☯️ Supported versions
🤖 Ready-made models
📚 Documentation
🦙 Quickstart - Running Llama
📲 Examples
⚖️ License
🔜 What's next?

☯️ Supported versions

The minimal supported version are:

iOS 17.0
Android 13
React Native 0.76

Important

React Native Executorch supports only the New React Native architecture.

🤖 Ready-made models

Our library has a number of ready-to-use AI models; a complete list is available in the documentation. If you're interested in running your own AI model, you need to first export it to the .pte format. Instructions on how to do this are available in the Python API.

📚 Documentation

Check out how our library can help you build your React Native AI features by visiting our docs:
https://docs.swmansion.com/react-native-executorch

🦙 Quickstart - Running Llama

Get started with AI-powered text generation in 3 easy steps!

1️⃣ Installation

# Install the package
yarn add react-native-executorch
# Depending on the platform, choose either iOS or Android
yarn expo run:< ios | android >

2️⃣ Setup & Initialization

Add this to your component file:

import {
  useLLM,
  LLAMA3_2_1B,
} from 'react-native-executorch';

function MyComponent() {
  // Initialize the model 🚀
  const llm = useLLM({ model: LLAMA3_2_1B });
  // ... rest of your component
}

3️⃣ Run the model!

const handleGenerate = async () => {
  const chat = [
    { role: 'system', content: 'You are a helpful assistant' },
    { role: 'user', content: 'What is the meaning of life?' }
  ];

  // Chat completion
  await llm.generate(chat);
  console.log('Llama says:', llm.response);
};

📲 Examples

We currently host a few example apps demonstrating use cases of our library:

llm - Chat application showcasing use of LLMs
speech-to-text - Whisper and Moonshine models ready for transcription tasks
computer-vision - Computer vision related tasks
text-embeddings - Computing text representations for semantic search

If you would like to run demo app, navigate to its project directory and install dependencies with:

yarn

Then, depending on the platform, choose either iOS or Android:

yarn expo run:< ios | android >

Warning

Running LLMs requires a significant amount of RAM. If you are encountering unexpected app crashes, try to increase the amount of RAM allocated to the emulator.

⚖️ License

This library is licensed under The MIT License.

🔜 What's next?

To learn about our upcoming plans and developments, please visit our milestones.