Quickstart

This quickstart uses the core LlamaEngine API.

Minimal generation example#

import 'package:llamadart/llamadart.dart';

Future<void> main() async {
  final LlamaEngine engine = LlamaEngine(LlamaBackend());

  try {
    await engine.loadModel('path/to/model.gguf');

    await for (final String token in engine.generate(
      'Write one short sentence about local inference.',
    )) {
      print(token);
    }
  } finally {
    await engine.dispose();
  }
}

LiteRT-LM .litertlm bundles load through the same engine. Native targets load local bundle paths, including paths resolved by loadModelSource(...); web targets load web-compatible .litertlm URLs through the @litert-lm/core JavaScript runtime.

await engine.loadModel(
  'path/to/gemma-4-E2B-it.litertlm',
  modelParams: const ModelParams(
    liteRtLmBackend: LiteRtLmBackendPreference.gpu,
  ),
);

LiteRtLmBackendPreference.auto is the default. It chooses GPU on Android, macOS, and web, and CPU on other current LiteRT-LM targets. Android native callers can request LiteRtLmBackendPreference.npu for devices and model bundles that support the LiteRT-LM NPU delegate. Web rejects NPU selection explicitly.

Stateless chat completions#

For OpenAI-style message arrays, use engine.create(...):

final messages = [
  LlamaChatMessage.fromText(
    role: LlamaChatRole.user,
    text: 'Give me three bullet points about Dart.',
  ),
];

await for (final chunk in engine.create(messages)) {
  final text = chunk.choices.first.delta.content;
  if (text != null) {
    print(text);
  }
}

Embeddings (single and batch)#

final single = await engine.embed('hello world');
final batch = await engine.embedBatch([
  'semantic search',
  'document retrieval',
]);

print('single dims=${single.length}');
print('batch size=${batch.length}');

Embeddings are a llama.cpp/GGUF capability in the current package. Check engine.supportsEmbeddings before calling these APIs when your app can switch between GGUF and LiteRT-LM models.

Next steps#

• Use First Chat Session for automatic history.
• Choose a runtime with Choosing llama.cpp or LiteRT-LM.
• Build retrieval flows with Embeddings.
• Tune Runtime Parameters.
• Add tools with Tool Calling.