feat: add llama-server-fast, a small non-thinking classifier model

Second, always-resident llama.cpp instance (Qwen3-4B-Instruct-2507,
Q8_0 GGUF, ~5GB VRAM) alongside the existing Qwen3.8-27B instance, for
use as qwen-code CLI's Auto Mode classifier fastModel. Model choice
researched in docs/research/fast-model-choice.md: architecturally
non-thinking (unlike Qwen3-1.7B/0.6B), --reasoning off added
defensively per a known (closed) llama.cpp misdetection bug.

- docker-compose.yml: llama-server-fast + downloader-fast services,
  omniroute depends_on updated
- .env.example: LLAMA_FAST_* vars
- scripts/update.sh: runs the new downloader profile

Refs #44

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MrnMEdzeQzqZE5soVEXPCx

.env.example

@@ -96,3 +96,20 @@ COMFYUI_PUID=
 COMFYUI_PGID=
 COMFYUI_VIDEO_GID=
 COMFYUI_RENDER_GID=
+
+# --- llama.cpp / fast model (second, always-resident instance — see
+# docs/research/fast-model-choice.md and issue #44) ---
+# Qwen3-4B-Instruct-2507: architecturally non-thinking (never emits
+# <think> blocks, unlike Qwen3-1.7B/0.6B which need a per-call toggle) —
+# picked specifically so it stays fast enough for qwen-code's Auto Mode
+# classifier (Stage 1 wants ~300ms). Same publisher (unsloth) as the main
+# model for consistency.
+LLAMA_FAST_MODEL_FILE=Qwen3-4B-Instruct-2507-UD-Q8_K_XL.gguf
+# Same reasoning as LLAMA_GPU_LAYERS above — full GPU offload, this model
+# is dense too.
+LLAMA_FAST_GPU_LAYERS=999
+# Classifier transcripts are truncated/bounded by qwen-code itself (see its
+# own Auto Mode docs) — no need for anywhere near the 27B's huge context.
+# 8192 keeps this instance's KV cache negligible.
+LLAMA_FAST_CTX_SIZE=8192
+LLAMA_FAST_PARALLEL=2