smollm2-360m-aac-watch

**On-device AAC language model optimized for Apple Watch.** Fine-tuned from SmolLM2-360M-Instruct using LoRA (rank=32) on a curated AAC clinical corpus. Runs entirely on-device via llama.cpp at ~570ms per response — no network required. Evaluated on a 300-case novel AAC evaluation suite covering 10 clinical categories. **Score: 100% (300/300)** at temperature=0.1. - **Symbol expansion**: Convert AAC symbol sequences to natural speech (70 test cases) - **Emergency communication**: Detect and express medical/safety emergencies (30 cases) - **Yes/No clarification**: Answer questions from symbol selections with appropriate polarity (40 cases) - **Intent classification**: SCERTS pragmatic function labeling — request, comment, protest, social, repair (25 cases) - **Multi-turn dialogue**: Contextual follow-up across conversation turns (30 cases) - **Clinical language rephrasing**: Person-first, trauma-informed terminology conversion (20 cases) - **Caregiver bridging**: Communication support between AAC user and care partners (20 cases) - **Vocabulary age-matching**: Developmentally appropriate word selection (25 cases) - **Predictive completion**: Symbol sequence prediction (30 cases) - **Emotion expression**: Nuanced emotional state communication (10 cases)