ChartLite captures any clinical encounter by voice or photo and runs the entire loop on the phone itself — speech-to-text, a structured medical note, and a safety check that cross-references every drug, diagnosis, and symptom against a real medical database — using Gemma 4 on Android, fully offline. We benchmarked it against 12 models on 6 independent datasets before we shipped.
Debug-signed APK runs on any Android 8+ device (330 MB). First launch downloads the Gemma 4 weights from Hugging Face (~2.8 GB for E4B, ~1.5 GB for E2B). Demo recorded on Galaxy Fold 7 running Gemma 4 E2B — both E2B and E4B ship; tier routing picks based on RAM.
Download ChartLite.apk →One-page engineering companion to the 3-minute video: hardware-aware Gemma 4 routing, MediaPipe LiteRT integration, function calling against BODHI, and the honest audit of BODHI's safety lift.
Open the technical brief →12 models × 6 datasets · ~54,000 model-question evaluations · every number traces to raw JSON. Methodology, headline findings, and the honest BODHI audit on one readable page.
Open the benchmark →Full submission writeup: architecture, why Gemma 4, the multi-model benchmark, the honest BODHI audit, multimodal capture, and how to verify every number.
Open the writeup →Apache 2.0. App + 8,000-line reproducible benchmark suite. Disprove any number on the dashboard with the raw data.
github.com/prismindanalytics/chartlite →ChartLite picks the right Gemma 4 size automatically: the larger E4B on 6 GB+ phones, the smaller E2B on 4 GB phones, and a tiny fallback on 3 GB phones. Same app, every device.
View the routing code →BODHI lifts gross safety detection by 26–63 percentage points across the 12 models we tested. We publish the decomposition so you can verify it:
Net of artifact and noise, the lift is most pronounced where deployment is real: Qwen 3.5 0.8B goes 3% → 66%; Gemma 4 e4b 30% → 57%; least pronounced on saturated frontier. Full audit in the technical brief.