tinytrainer-mobile
dormantprototypeTinyTrainer Mobile
Proves the full loop: import kit -> classify locally -> correct predictions -> personalize on device -> measure improvement.
iOS/iPadOS reference app for on-device ML personalization.
Version
0.1.0
License
MIT
Kind
desktop
Languages
SwiftUI, Core ML, NaturalLanguage, ZIPFoundation
Lines of code
0
Last commit
2026-04-20 (61d ago)
Health signals
Tests: noREADME: yesLICENSE: yesBuildable: unknown
Patterns proven here
onDeviceMLPersonalizationdata-model
End-to-end pipeline for personalizing an ML model on-device using user corrections.
Why it matters
Demonstrates a complete end-to-end ML pipeline running entirely on-device, from importing a pre-trained model to personalizing it with user corrections and measuring accuracy improvements.