v0.6.0 — dense weight warm-up

Dense weight warm-up

Models larger than device RAM no longer pay a slow-start: the non-expert weights (embeddings,
attention, norms, lm_head), which stay mmap-resident while experts stream, are now page-cached with
one sequential sweep at load instead of faulting in lazily during the first tokens.

Measured (OnePlus 15R, 11.4 GB RAM): on gpt-oss-120b (~5.2× RAM) the first token drops from
~18 s to ~1 s and the first-five-token wall average improves ~20×. Smaller-dense models (Qwen, Gemma)
are neutral — hit rate and cache budget unchanged, streaming path byte-identical.

On by default; opt out via the Dense weight warm-up toggle in Settings, or --no-warm-dense
on the CLI. Details and per-token data in docs/warmup-analysis.md and
docs/bench-data/2026-07-14-warmup/.

Install

app-dev-debug.apk is the sideload (dev) build for arm64-v8a. It reads models adb-pushed to
/sdcard/Download or /data/local/tmp; installing it does not touch already-downloaded models.