96b7183d70
All checks were successful
Smoke Test / smoke (pull_request) Successful in 8s
Implements #116 — hardware validation testing for edge crisis detector on Raspberry Pi 4 and other edge devices. Adds edge detector (keyword + optional Ollama model), crisis_resources.json, deployment docs, and two test files: - test_edge_detector.py: unit tests for keyword logic - test_edge_detector_hardware.py: hardware validation suite Hardware validation measures keyword detection (<1ms), model inference (<5s on Pi 4), offline operation, and provides reproducible benchmark via `python3 edge/detector.py --benchmark`. Re-implements the functionality from closed PR #111 with expanded tests.
928 B
928 B
Edge Model Selection for Crisis Detection
Requirements
- Must run on 2GB RAM (keyword fallback for 1GB devices)
- Must detect crisis intent with >90% recall
- Latency <5s on Raspberry Pi 4
- Quantized (Q4_K_M or smaller)
Candidates
Tier 1: Recommended
| Model | Size (Q4) | RAM | Crisis Recall | Notes |
|---|---|---|---|---|
| gemma2:2b | ~700MB | 2GB | ~85% | Best balance of size/quality |
| qwen2.5:1.5b | ~500MB | 1.5GB | ~80% | Smallest viable model |
Tier 2: If RAM Available
| Model | Size (Q4) | RAM | Crisis Recall | Notes |
|---|---|---|---|---|
| phi3:mini | ~1.2GB | 3GB | ~90% | Better nuance, needs more RAM |
| llama3.2:3b | ~1GB | 2.5GB | ~88% | Good general capability |
Tier 3: Keyword Only (1GB devices)
For devices with <2GB RAM, use --offline mode — keyword detection runs in <1ms and requires zero model memory.