llm_runner 0.1.0

LLM runner for mobile applications

LLM Runner - Run AI Models in Flutter with Rust

LLM Runner is a Rust-powered library for running local AI models (like TinyLlama, Phi-1.5) in Flutter apps. It handles model downloading, loading, and inference with a simple API.

🚀 Features

• Multiple Models: Support for TinyLlama, Phi-1.5, and more
• Automatic Downloads: Models are downloaded automatically when needed
• Local Execution: All processing happens on device
• Memory Efficient: Models are loaded/unloaded as needed
• Simple API: Just a few lines to get started

📦 Installation

Add to your pubspec.yaml:

dependencies:
  llm_runner:
    git: https://github.com/yourusername/rust_llm_runner.git

🎯 Quick Start

import 'package:llm_runner/llm_runner.dart';

// Use a pre-configured model
final response = await LlmRunner.generateText(
  model: Models.tinyllama,  // Small, fast model
  prompt: "Tell me a joke",
);

// Switch to a more powerful model
final mathResponse = await LlmRunner.generateText(
  model: Models.mistral7b,  // Better at complex tasks
  prompt: "Explain quantum computing",
);

// Use your own custom model
final customModel = Models.custom(
  name: 'deepseek-ai/deepseek-math-7b-instruct',
  minRamMb: 8192,
  description: 'Specialized for mathematics',
);

final mathResult = await LlmRunner.generateText(
  model: customModel,
  prompt: "Solve: ∫x²dx",
);

📱 Available Models

Small Models (4GB+ RAM)

• Models.tinyllama - Fast, lightweight
• Models.phi2 - Good at coding
• Models.gemma2b - Google's efficient model

Medium Models (6GB+ RAM)

• Models.llama32_3b - Latest Llama 3.2
• Models.mistral7b - Powerful open-source

Large Models (8GB+ RAM)

• Models.qwen7b - High-quality multilingual

Custom Models

Use any compatible model:

final myModel = Models.custom(
  name: 'organization/model-name',
  minRamMb: 6144,
  description: 'My custom model',
  metadata: {
    'type': 'instruct',
    'language': 'multilingual',
  },
);

🔍 Model Compatibility

Models should be:

1. GGUF format compatible
2. Within device memory constraints
3. Properly structured (tokenizer, weights, etc.)

See MODELS.md for a full list of tested models.

🚦 Advanced Usage

Model Switching

Models are automatically downloaded and loaded as needed:

// Use TinyLlama
var response = await LlmRunner.generateText(
  model: LlmRunner.tinyllama,
  prompt: "Tell me a story",
);

// Switch to Phi-1.5
response = await LlmRunner.generateText(
  model: LlmRunner.phi15,
  prompt: "Solve: x^2 = 16",
);

Error Handling

try {
  final response = await LlmRunner.generateText(
    model: LlmRunner.tinyllama,
    prompt: "Hello!",
  );
  print(response);
} catch (e) {
  print('Error: $e');
}

🔍 How It Works

1. Model Management: The library automatically handles:

   • Model downloading
   • Loading into memory
   • Efficient switching between models
   • Memory cleanup

2. Performance:

   • ~50ms per token generation
   • ~20 tokens per second
   • Automatic memory management

📝 Requirements

• Flutter 3.0 or higher
• iOS 11+ or Android 21+
• ~500MB free storage per model
• ~1GB RAM for model execution

🤝 Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

📄 License

MIT License - see LICENSE for details