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.
+# qwen2.5:3b — small non-reasoning instruct model. ~2.4GB, runs 100% on the GPU
-OLLAMA_CHAT_MODEL=qwen3:8b
+# (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
 

docker-compose.yml

@@ -17,11 +17,12 @@ services:
   ollama:
     image: ollama/ollama:latest
     restart: unless-stopped
-    environment:
+    # Model residency is controlled per-request, not globally. The brain pins
-      # Keep the chat + embed models resident so voice turns never pay a cold
+    # the chat model with keep_alive=30m (src/jarvis/llm.py) so voice turns
-      # reload. Default keep_alive is 5 min, so every post-idle turn took
+    # never pay a cold reload, while embeddings pass keep_alive=0
-      # ~30-60s while Qwen3 8B reloaded into the GPU. -1 = never unload.
+    # (src/jarvis/memory/embeddings.py) so nomic-embed unloads right after use.
-      OLLAMA_KEEP_ALIVE: "-1"
+    # A global OLLAMA_KEEP_ALIVE=-1 was removed because it also kept the embed
+    # model resident forever, wasting VRAM next to the chat model.
     volumes:
       - ollama_models:/root/.ollama
     # GPU: needs nvidia-container-toolkit on the host (CDI). Verified on the
@@ -37,7 +38,7 @@ services:
     restart: "no"
     environment:
       OLLAMA_HOST: http://ollama:11434
-      CHAT_MODEL: ${OLLAMA_CHAT_MODEL:-qwen3:8b}
+      CHAT_MODEL: ${OLLAMA_CHAT_MODEL:-qwen2.5:3b}
       EMBED_MODEL: ${OLLAMA_EMBED_MODEL:-nomic-embed-text}
     entrypoint: ["/bin/sh", "-c"]
     command:
@@ -59,7 +60,7 @@ services:
     environment:
       # Point the brain at the ollama service and the bot at the in-container bridge.
       OLLAMA_BASE_URL: http://ollama:11434
-      OLLAMA_CHAT_MODEL: ${OLLAMA_CHAT_MODEL:-qwen3:8b}
+      OLLAMA_CHAT_MODEL: ${OLLAMA_CHAT_MODEL:-qwen2.5:3b}
       OLLAMA_EMBED_MODEL: ${OLLAMA_EMBED_MODEL:-nomic-embed-text}
       WHISPER_MODEL: ${WHISPER_MODEL:-small}
       WHISPER_DEVICE: ${WHISPER_DEVICE:-cuda}

src/jarvis/llm.py

@@ -41,6 +41,12 @@ def call_llm_direct(base_url: str, chat_model: str, system_prompt: str, user_con
         "stream": False,
         "options": options,
         "think": thinking,
+        # Keep the chat model resident between calls. Without an explicit
+        # keep_alive Ollama evicts it after its default idle window and the
+        # next turn pays a cold reload. We pin the chat model only (embeddings
+        # pass keep_alive=0 so they unload after use) instead of a global
+        # OLLAMA_KEEP_ALIVE=-1, which would keep every model resident forever.
+        "keep_alive": "30m",
     }
 
     try:
@@ -98,6 +104,8 @@ def call_llm_streaming(
         "stream": True,
         "options": {"num_ctx": 4096},
         "think": thinking,
+        # Keep the chat model resident between calls (see call_llm_direct).
+        "keep_alive": "30m",
     }
 
     # Use ``with`` so the streaming response (and the underlying TCP
@@ -201,6 +209,8 @@ def chat_with_messages(
         "stream": False,
         "options": {"num_ctx": 8192},
         "think": thinking,
+        # Keep the chat model resident between turns (see call_llm_direct).
+        "keep_alive": "30m",
     }
     if extra_options and isinstance(extra_options, dict):
         # Merge shallowly into options

src/jarvis/memory/embeddings.py

@@ -6,7 +6,11 @@ def get_embedding(text: str, base_url: str, model: str, timeout_sec: float = 15.
     try:
         resp = requests.post(
             f"{base_url.rstrip('/')}/api/embeddings",
-            json={"model": model, "prompt": text},
+            # keep_alive=0 unloads the embedding model right after the call so
+            # it does not sit resident in VRAM alongside the chat model. The
+            # chat model is pinned separately (llm.py keep_alive=30m); only the
+            # actively-used chat model should stay loaded.
+            json={"model": model, "prompt": text, "keep_alive": 0},
             timeout=timeout_sec,
         )
         resp.raise_for_status()