modelport 0.1.1

Run AI models in Dart and Flutter apps from a modelport.json manifest: download, verify, preprocess, run, and postprocess with one API.

modelport

Run AI models in Flutter and Dart apps from a modelport.json bundle: image classification, object detection, and chat with local language models, through ONNX Runtime, PyTorch ExecuTorch, or llama.cpp, with one API.

final classifier = await ImageClassifier.load(
  'https://github.com/ayanparvaiz/modelport/releases/download/zoo-v1/mobilenet_v3_small.json',
);
print((await classifier.classify(jpegBytes)).first); // Samoyed (0.76)

Engine packages run models well. Everything around them is usually left to you: converting the model, guessing input shapes, writing image preprocessing in Dart, downloading and caching big files, and hoping the phone gives the same answer as Python. This package does that part:

• No model-specific Dart code. A modelport.json manifest describes inputs, preprocessing, outputs, labels, every file, and its checksum. The Python CLI writes it.
• Preprocessing matches Python byte for byte. Resizing follows Pillow's algorithm, including its fixed-point rounding. Twelve cross-language fixtures produce identical tensors in Python and Dart.
• The phone is checked against Python. Bundles carry a saved input and PyTorch's output. model.checkGolden() runs it on the device. On a 2019 mid-range Android phone, MobileNetV3 differed from PyTorch by 3.3e-5.
• Downloads resume and are verified. Interrupted downloads continue with HTTP Range requests, every file is checked against its sha256 before use, and models load offline after the first download.
• Variants fit the device. A bundle can hold fp32, fp16, int8, and ExecuTorch variants, or several GGUF quantizations. The first one a registered engine can run, and that fits in the device's RAM, is used.
• Errors say how to fix them. A missing engine names the package to add; a llama.cpp backend failure on Android names the Gradle setting.

Packages

Package	Use it for
modelport	This package: manifests, downloads, preprocessing, task APIs. Pure Dart.
modelport_flutter	Flutter setup: cache folder, assets, native image decoding, device RAM
modelport_onnx	ONNX Runtime engine
modelport_executorch	PyTorch ExecuTorch engine, the fastest on the test phone
modelport_llamacpp	llama.cpp engine for GGUF language models

In a Flutter app, add modelport_flutter and the engines you need. It re-exports this package.

Usage

await ModelPortFlutter.init(adapters: [OnnxAdapter(), LlamaCppAdapter()]);

// Image classification
final classifier = await ImageClassifier.load(location);
final top = await classifier.classify(jpegBytes, topK: 3);

// Object detection: boxes are in pixels of your image
final detector = await ObjectDetector.load(location);
for (final d in await detector.detect(jpegBytes)) {
  print('${d.label} ${d.score} ${d.box}');
}

// Chat
final llm = await TextGenerator.load(location);
await for (final piece in llm.chat([ChatMessage.user('What is Flutter?')])) {
  stdout.write(piece);
}

// Any tensor model
final model = await ModelPort.load(location);
final outputs = await model.run({'input': Tensor.float32([1, 4], data)});

// Prove this device matches Python
print(await model.checkGolden());

location can be a GitHub release or any HTTPS URL, hf://org/name, asset://assets/models/name, or a local folder.

Models

Ready models are in the model zoo: MobileNetV3, DeiT Tiny, YOLOS Tiny, SmolLM2 135M, and Qwen2.5 0.5B. Make your own bundle from a PyTorch, torchvision, Hugging Face, or GGUF model with the modelport Python CLI:

pip install "modelport-cli[onnx,executorch,torchvision]"
modelport export torchvision:mobilenet_v3_small --target onnx,executorch
modelport verify dist/mobilenet_v3_small

Platform support

Platform	Status
Android	Tested on an OPPO CPH1937 (Android 11, Snapdragon 665) with all three engines
macOS	Tested with all three engines
iOS	Expected to work through the engines' iOS support, not verified yet
Windows, Linux	Core package works; engines depend on their own support
Web	Not supported yet (dart:io)

Documentation

ayanparvaiz.github.io/modelport: guides, the manifest spec, measured performance, and troubleshooting.

License

Apache-2.0