Commit History
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feat(tflite): integrate medgemma_micro_cardio_350m model and M2 testing dashboard
- Track all .tflite and *knowledge_base_350m.json artifacts with Git LFS (.gitattributes) - Add android_export deployment suite with 301.93 MB unified model, Kotlin runtime manager, and integration guides - Add LiteRT XNNPACK delegate CPU acceleration for Apple Silicon M2 in app.py - Add dual-engine model switcher between TFLite 350M and PyTorch Edge in dashboard UI - Add M2 automated benchmark suite modal and /api/tflite/benchmark endpoint - Implement hybrid dense 768-D semantic and lexical retrieval across 1,552 clinical guidelines - Update test_interface.py and test_unified_350m_model.py test suites63139fb
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feat: upgrade to sub-512MB mobile architecture for iOS (Core ML) and Android (LiteRT)
- Upgrade deployment target to iOS and Android mobile devices (>= 8GB RAM) under strict < 512MB ceiling - Implement 1D-Conformer Biosignal Encoder (Depthwise Separable Conv + Multi-Head Self-Attention) - Implement Temporal Cross-Attention Projector Bridge (K=8 query tokens, d=896) - Add zero-cloud On-Device Clinical RAG engine (< 25MB) with ACC/AHA and ESC guideline retrieval - Distillation pipeline for MedGemma-to-Qwen2.5-0.5B with 4-bit block-wise quantization - Add Apple Core ML (ANE/Metal) and Android LiteRT (Qualcomm Hexagon/GGUF) mobile export pipelines - Overhaul README.md, DOCUMENTATION.md, Colab notebook, and interactive dashboard - Pass all 7/7 architecture/budget tests and 8/8 API/safety waiver tests201095b -
Initial commit: MedGemma-Micro multimodal cardiology edge model with lifestyle management & legal waiver guard
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