llamadart #

A Dart/Flutter plugin for llama.cpp. Run LLM inference directly in Dart and Flutter applications using GGUF models with hardware acceleration.

⚠️ Status #

Actively Under Development. The core features are implemented and running. Many more features are in the pipeline, including:

• High-level APIs for easier integration.
• Multi-modality support (Vision/LLaVA).

We welcome contributors to help us test on more platforms (especially Windows)!

🚀 Supported Platforms #

1. Add Dependency #

Add llamadart to your pubspec.yaml:

dependencies:
  llamadart: ^0.1.0

2. Platform Setup #

📱 iOS

No manual setup required. The plugin automatically builds llama.cpp for iOS (Device/Simulator) when you run flutter build ios. Note: The first build will take a few minutes to compile the C++ libraries.

💻 macOS / Linux / Windows

The package handles native builds automatically via CMake.

• macOS: Metal acceleration is enabled by default.
• Linux/Windows: CPU inference is supported.

📱 Android

No manual setup required. The plugin uses CMake to compile the native library automatically.

• Ensure you have the Android NDK installed via Android Studio.
• The first build will take a few minutes to compile the llama.cpp libraries for your target device's architecture.

🌐 Web

Zero-config by default (uses jsDelivr CDN for wllama).

1. Import and use LlamaService.
2. Enable WASM support in Flutter web:

   flutter run -d chrome --wasm
   # OR build with wasm
   flutter build web --wasm
   

Offline / Bundled Usage (Optional):

1. Download assets to your assets/ directory:

   dart run llamadart:download_wllama
   

2. Add the folder to your pubspec.yaml:

   flutter:
     assets:
       - assets/wllama/single-thread/
   

3. Initialize with local asset paths:

   final service = LlamaService(
     wllamaPath: 'assets/wllama/single-thread/wllama.js',
     wasmPath: 'assets/wllama/single-thread/wllama.wasm',
   );
   

📱 Platform Specifics #

iOS #

• Metal: Acceleration enabled by default on physical devices.
• Simulator: Runs on CPU (x86_64 or arm64).

macOS #

• Sandboxing: Add these entitlements to macos/Runner/DebugProfile.entitlements and Release.entitlements for network access (model downloading):

  <key>com.apple.security.network.client</key>
  <true/>
  

Android #

• Architectures: arm64-v8a (most devices) and x86_64 (emulators).
• Vulkan: GPU acceleration is enabled by default on devices with Vulkan support.
• NDK: Requires Android NDK 26+ installed (usually handled by Android Studio).

🎮 GPU Configuration #

GPU backends are enabled by default where available. Use the options below to customize.

Runtime Control (Recommended) #

Control GPU usage at runtime via ModelParams:

// Use GPU with automatic backend selection (default)
await service.init('model.gguf', modelParams: ModelParams(
  gpuLayers: 99,  // Offload all layers to GPU
  preferredBackend: GpuBackend.auto,
));

// Force CPU-only inference
await service.init('model.gguf', modelParams: ModelParams(
  gpuLayers: 0,  // No GPU offloading
  preferredBackend: GpuBackend.cpu,
));

// Request specific backend (if compiled in)
await service.init('model.gguf', modelParams: ModelParams(
  preferredBackend: GpuBackend.vulkan,
));

Available backends: auto, cpu, cuda, vulkan, metal

Compile-Time Options (Advanced) #

To disable GPU backends at build time:

Android (in android/gradle.properties):

LLAMA_DART_NO_VULKAN=true

Desktop (CMake flags):

# Disable CUDA
cmake -DLLAMA_DART_NO_CUDA=ON ...

# Disable Vulkan
cmake -DLLAMA_DART_NO_VULKAN=ON ...

🚀 Usage #

import 'package:llamadart/llamadart.dart';

void main() async {
  final service = LlamaService();

  try {
    // 1. Initialize with model path (GGUF)
    // On iOS/macOS, ensures Metal is used if available.
    await service.init('models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf');
    
    // 2. Generate text (streaming)
    final prompt = "<start_of_turn>user\nTell me a story about a llama.<end_of_turn>\n<start_of_turn>model\n";
    
    await for (final token in service.generate(prompt)) {
      stdout.write(token);
    }
  } finally {
    // 3. Always dispose to free native memory
    service.dispose();
  }
}

📱 Examples #

• Flutter Chat App: example/chat_app
  • A full-featured chat interface with real-time streaming, GPU acceleration support, and model management.
• Basic Console App: example/basic_app
  • Minimal example demonstrating model download and basic inference.

🤝 Contributing #

See CONTRIBUTING.md for detailed instructions on:

• Setting up the development environment.
• Building the native libraries.
• Running tests and examples.

License #

MIT