runanywhere_llamacpp 0.19.13

LlamaCpp backend for RunAnywhere Flutter SDK. High-performance on-device LLM text generation with GGUF model support.

RunAnywhere LlamaCpp Backend

High-performance LLM text generation backend for the RunAnywhere Flutter SDK, powered by llama.cpp.

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Features

Feature	Description
GGUF Model Support	Run any GGUF-quantized model (Q4, Q5, Q8, etc.)
Streaming Generation	Token-by-token streaming for real-time UI updates
Metal Acceleration	Hardware acceleration on iOS devices
NEON Acceleration	ARM NEON optimizations on Android
Privacy-First	All processing happens locally on device
Memory Efficient	Quantized models reduce memory footprint

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Installation

Add both the core SDK and this backend to your pubspec.yaml:

dependencies:
  runanywhere: ^0.19.13
  runanywhere_llamacpp: ^0.19.13

Then run:

flutter pub get

Note: This package requires the core runanywhere package. It won't work standalone.

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Platform Support

Platform	Minimum Version	Acceleration
iOS	17.0+	Metal GPU
Android	API 24+	NEON SIMD

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Quick Start

1. Initialize & Register

import 'package:runanywhere/runanywhere.dart';
import 'package:runanywhere_llamacpp/runanywhere_llamacpp.dart';

void main() async {
  WidgetsFlutterBinding.ensureInitialized();

  // Initialize SDK
  await RunAnywhere.initialize();

  // Register LlamaCpp backend
  await LlamaCpp.register();

  runApp(MyApp());
}

2. Register a Model

Use the core SDK registry — backends do not own model catalogs.

RunAnywhere.models.register(
  id: 'smollm2-360m-q8_0',
  name: 'SmolLM2 360M Q8_0',
  url: Uri.parse('https://huggingface.co/prithivMLmods/SmolLM2-360M-GGUF/resolve/main/SmolLM2-360M.Q8_0.gguf'),
  framework: InferenceFramework.INFERENCE_FRAMEWORK_LLAMA_CPP,
  memoryRequirement: 500000000,  // ~500 MB
);

3. Download & Load

// Download the model
final progress = RunAnywhere.downloads.start('smollm2-360m-q8_0');
await for (final p in progress) {
  print('${p.stage}: ${(p.stageProgress * 100).toStringAsFixed(1)}%');
  if (p.stage == DownloadStage.DOWNLOAD_STAGE_COMPLETED) break;
}

// Load the model
await RunAnywhere.llm.load('smollm2-360m-q8_0');
print('Model loaded: ${RunAnywhere.isLLMModelLoaded}');

4. Generate Text

// Streaming generation
final stream = RunAnywhere.llm.generateStream(
  'Write a short poem about Flutter',
  LLMGenerationOptions(maxTokens: 100, temperature: 0.7),
);

await for (final event in stream) {
  if (event.isFinal) break;
  if (event.token.isNotEmpty) stdout.write(event.token);
}

// Non-streaming with metrics
final result = await RunAnywhere.llm.generate(
  'Tell me a fact.',
  LLMGenerationOptions(maxTokens: 64),
);
print('Tokens/sec: ${result.tokensPerSecond.toStringAsFixed(1)}');

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API Reference

LlamaCpp Class

register()

Register the LlamaCpp backend with the SDK.

static Future<void> register({int priority = 100})

Parameters:

• priority – Backend priority (higher = preferred). Default: 100.

Registering models

The LlamaCpp module does not own a model catalog. Register your GGUF models through the core SDK after calling LlamaCpp.register():

RunAnywhere.models.register(
  id: 'my-model',
  name: 'My Model',
  url: Uri.parse('https://.../my-model.gguf'),
  framework: InferenceFramework.INFERENCE_FRAMEWORK_LLAMA_CPP,
  memoryRequirement: 500000000,
  supportsThinking: false,
);

Multi-file VLM models (main GGUF + mmproj) use RunAnywhere.models.registerMultiFile(...).

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Supported Models

Any GGUF model compatible with llama.cpp:

Recommended Models

Model	Size	Memory	Use Case
SmolLM2 360M Q8_0	~400MB	~500MB	Fast responses, mobile
Qwen2.5 0.5B Q8_0	~600MB	~700MB	Good quality, small
Qwen2.5 1.5B Q4_K_M	~1GB	~1.2GB	Better quality
Phi-3.5-mini Q4_K_M	~2GB	~2.5GB	High quality
Llama 3.2 1B Q4_K_M	~800MB	~1GB	Balanced
DeepSeek R1 1.5B Q4_K_M	~1.2GB	~1.5GB	Reasoning, thinking

Quantization Guide

Format	Quality	Size	Speed
Q8_0	Highest	Largest	Slower
Q6_K	Very High	Large	Medium
Q5_K_M	High	Medium	Medium
Q4_K_M	Good	Small	Fast
Q4_0	Lower	Smallest	Fastest

Tip: For mobile, Q4_K_M or Q5_K_M offer the best quality/size balance.

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Memory Management

Checking Memory

// Get available models with their memory requirements
final models = await RunAnywhere.models.available();
for (final model in models) {
  if (model.downloadSize != null) {
    print('${model.name}: ${(model.downloadSize! / 1e9).toStringAsFixed(1)} GB');
  }
}

Unloading Models

// Unload to free memory
await RunAnywhere.llm.unload();

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Generation Options

final result = await RunAnywhere.llm.generate(
  'Your prompt here',
  LLMGenerationOptions(
    maxTokens: 200,           // Maximum tokens to generate
    temperature: 0.7,         // Randomness (0.0 = deterministic, 1.0 = creative)
    topP: 0.9,                // Nucleus sampling
    systemPrompt: 'You are a helpful assistant.',
  ),
);

Option	Default	Range	Description
maxTokens	100	1-4096	Maximum tokens to generate
temperature	0.8	0.0-2.0	Response randomness
topP	1.0	0.0-1.0	Nucleus sampling threshold
systemPrompt	null	-	System prompt prepended to input

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Troubleshooting

Model Loading Fails

Symptom: SDKError.modelLoadFailed

Solutions:

1. Verify model is fully downloaded (check model.isDownloaded)
2. Ensure sufficient memory available
3. Check model format is GGUF (not GGML or safetensors)

Slow Generation

Solutions:

1. Use smaller quantization (Q4_K_M instead of Q8_0)
2. Use a smaller model
3. Reduce maxTokens
4. On iOS, ensure Metal is available (device not in low power mode)

Out of Memory

Solutions:

1. Unload current model before loading new one
2. Use smaller quantization
3. Use a smaller model

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Related Packages

• runanywhere — Core SDK (required)
• runanywhere_llamacpp — LLM backend (this package)
• runanywhere_onnx — STT/TTS/VAD backend

Resources

• Flutter Starter Example
• Documentation
• GitHub Issues

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License

This software is licensed under the RunAnywhere License, which is based on Apache 2.0 with additional terms for commercial use. See LICENSE for details.

For commercial licensing inquiries, contact: [e-mail]