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>

.env.example

@@ -59,11 +59,12 @@ OLLAMA_BASE_URL=http://127.0.0.1:11434
 # free-form chit-chat. Swap back to qwen3:8b for the strongest tool-calling.
 OLLAMA_CHAT_MODEL=qwen2.5:3b
 # Model for the auxiliary small-model calls: intent judge, tool router, weather
-# place extraction, query decomposition. BLANK (default) reuses OLLAMA_CHAT_MODEL
-# so the stack runs on one already-warm model. The code's built-in default
-# (gemma4:e2b) is NOT pulled by this stack, so leaving this unset previously made
-# every router/extractor call silently fail. Only set this if you also pull the
-# model into Ollama.
+# place extraction, query decomposition. These are classification/JSON calls,
+# NOT the spoken answer, so a small fast model here cuts 2-3 big-model round
+# trips per command without touching answer quality. BLANK uses the stack
+# default qwen2.5:3b, which ollama-init pulls automatically. Set it equal to
+# OLLAMA_CHAT_MODEL to run everything on one resident model instead (saves VRAM
+# at the cost of slower routing when the chat model is large).
 OLLAMA_INTENT_MODEL=
 OLLAMA_EMBED_MODEL=nomic-embed-text
 WHISPER_MODEL=small