inference 1.0.0

Zero-setup ML inference for Flutter using Rust engines (Candle, Linfa). Load PyTorch, train on-device with unified API. Cross-platform support.

1.0.0

• 🎉 First stable release of the Inference Flutter package
• Core API Implementation: Complete implementation of the unified inference API
  • ✅ InferenceSession.load() with automatic engine detection
  • ✅ InferenceSession.loadFromUrl() with smart caching
  • ✅ InferenceSession.loadFromHuggingFace() for direct Hub integration
  • ✅ InferenceSession.trainLinfa() for on-device training
• Model Architecture Support:
  • ✅ BERT Models: Complete wrapper for text classification and NLP
  • ✅ ResNet Models: Complete wrapper for image classification
  • ✅ Generic SafeTensors: Support for any SafeTensors model format
• Engine Integration:
  • ✅ Candle Engine: Full PyTorch model support with GPU acceleration
  • ✅ Linfa Engine: On-device training with K-means clustering
  • ✅ Automatic Detection: Smart engine selection based on model format
• Input/Output System:
  • ✅ ImageInput, NLPInput, TensorInput, AudioInput classes
  • ✅ InferenceResult with convenience accessors (scalar, vector, matrix)
  • ✅ Classification helpers (argmax, topK())
• HuggingFace Integration:
  • ✅ Direct model loading from HuggingFace Hub
  • ✅ Automatic URL construction and caching
  • ✅ Support for specific revisions and filenames
• Performance Features:
  • ✅ Automatic GPU acceleration when available
  • ✅ Batch processing with predictBatch()
  • ✅ Smart model caching with size management
  • ✅ Resource management with dispose() methods
• Cross-Platform Support: Verified on Android, iOS, Windows, macOS, Linux
• Documentation: Comprehensive README with real-world examples
• Future Roadmap: Model Wrappers Roadmap with 20+ additional architectures planned

0.1.0-beta.4

• Enhanced examples and updated dependencies for better pub.dev experience
• Added comprehensive standalone example.dart showcasing core functionality
• Created detailed example README.md following Dart package layout conventions
• Updated dependencies to latest versions with proper version constraints
• Improved pub.dev score with comprehensive documentation and code quality fixes
• Added detailed library documentation with examples and feature overview
• Documented all missing constructors and classes for 100% API coverage
• Fixed static analysis issues including unused imports and style improvements
• Added package topics for better discoverability
• Enhanced code formatting and consistency

0.1.0-beta.2

• Bug fixes and improvements for the second beta release
• Enhanced stability and performance optimizations
• Improved error handling and documentation
• Updated dependencies and build configurations

0.1.0-beta.1

• Initial beta release of the Inference Flutter package
• Zero-setup ML inference with unified API for Candle and Linfa engines
• Support for automatic model format detection (.safetensors, .pt, .pth)
• Cross-platform support (Android, iOS, Windows, macOS, Linux)
• Multiple input types: ImageInput, NLPInput, TensorInput, AudioInput
• Comprehensive example app with image classification, text sentiment, and on-device training
• Built with Flutter Rust Bridge 2.0 for optimal performance
• GPU acceleration support where available

0.0.1

• TODO: Describe initial release.