perf: run auxiliary LLM calls on a small model, big model only for the answer

Intent judging, tool routing and arg extraction are classification/JSON calls,
not the spoken answer, yet the stack aliased OLLAMA_INTENT_MODEL back to the big
chat model — so each command paid the big model's cost 2-3 times for routing
before the reply even ran. With the GPU on, that round-trip stacking is the main
remaining per-turn latency. Default OLLAMA_INTENT_MODEL to qwen2.5:3b (the
project's reference small model, clean Korean on classification) and have
ollama-init pull it. The reply engine already routes these calls through
intent_judge_model, so answer quality is untouched; set OLLAMA_INTENT_MODEL =
OLLAMA_CHAT_MODEL to fold back onto one resident model.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

docker/entrypoint.sh

@@ -10,12 +10,12 @@ set -euo pipefail
 : "${OLLAMA_BASE_URL:=http://ollama:11434}"
 : "${OLLAMA_CHAT_MODEL:=qwen3:8b}"
 # Auxiliary small-model calls (intent judge, tool router, weather place
-# extraction, query decomposition). The code default is gemma4:e2b, which this
-# stack does not pull, so those calls would silently fail and fall open —
-# crippling tool routing and arg extraction. Reuse the (already warm) chat model
-# by default so everything runs on one resident model; override if you pull a
-# dedicated small model.
-: "${OLLAMA_INTENT_MODEL:=${OLLAMA_CHAT_MODEL}}"
+# extraction, query decomposition). Default to a small fast model so the big
+# chat model only runs for the actual spoken answer — the main per-turn latency
+# win once the GPU is in use, since the 2-3 routing/extraction calls per command
+# no longer pay the big model's cost. ollama-init pulls this model. Set it equal
+# to OLLAMA_CHAT_MODEL to fold everything back onto one resident model.
+: "${OLLAMA_INTENT_MODEL:=qwen2.5:3b}"
 # Cap chat-model output tokens per turn (worst-case latency guard). Spoken
 # answers are 1-2 sentences; 512 is safe headroom above tool-call JSON. 0 = off.
 : "${OLLAMA_NUM_PREDICT:=512}"