perf(brain): pin chat model per-request, unload embeddings; default qwen2.5:3b

Replace the blunt global OLLAMA_KEEP_ALIVE=-1 (which kept every model,
including nomic-embed, resident in VRAM forever) with per-request residency:

- llm.py: all three /api/chat payloads send keep_alive=30m so the actively
  used chat model stays resident and voice turns never pay a cold reload.
- embeddings.py: /api/embeddings sends keep_alive=0 so nomic-embed unloads
  right after each call instead of squatting in VRAM next to the chat model.
- docker-compose.yml: drop the global OLLAMA_KEEP_ALIVE=-1; document the
  per-request scheme on the ollama service.

Switch the default chat model qwen3:8b -> qwen2.5:3b. Verified live on the
RTX 5050 (8GB):
- ollama ps: qwen2.5:3b 2.4GB, 100% GPU (8B was 92% GPU / 8% CPU), UNTIL ~30m
  (the 30m pin, not "Forever"); nomic-embed absent after several enriched turns.
- nvidia-smi: ~3.2GB VRAM used total (qwen 2.4GB + whisper 0.7GB) vs ~6.6GB.
- Korean /text turns: warm 1.7-4s (cold first load ~52s), vs ~5-7s on 8B;
  time/weather/places tool calls fire and reply in Korean.

Known limitation: qwen2.5:3b can occasionally leak a trailing CJK phrase on
free-form chit-chat (factual/tool replies stay clean).

.env.example

@@ -47,8 +47,11 @@ MELO_SPEED=1.5
 # ---------------------------------------------------------------------------
 # In docker-compose this is overridden to http://ollama:11434 automatically.
 OLLAMA_BASE_URL=http://127.0.0.1:11434
-# qwen3:8b — best 8GB-VRAM pick: strongest tool-calling, ~5GB Q4, fits the RTX 5050.
-OLLAMA_CHAT_MODEL=qwen3:8b
+# qwen2.5:3b — small non-reasoning instruct model. ~2.4GB, runs 100% on the GPU
+# (the 8B offloads ~8% to CPU), warm voice turns ~2-4s vs ~5-7s on 8B. Clean
+# Korean on factual/tool replies; can occasionally leak a trailing CJK phrase on
+# free-form chit-chat. Swap back to qwen3:8b for the strongest tool-calling.
+OLLAMA_CHAT_MODEL=qwen2.5:3b
 OLLAMA_EMBED_MODEL=nomic-embed-text
 WHISPER_MODEL=small