feat(llm): dedicate a CPU-only backend for the qwen-code tool-call classifier

fastModel in ~/.qwen/settings.json (permissions.autoMode.classifier) was
aliased onto llama-server's own 27B connection, so every tool-call safety
check queued behind whatever heavy generation was already running on that
model's 2 GPU slots.

Add qwen-classifier: a separate llama.cpp instance, CPU-only, running
Qwen3-4B-Instruct-2507 (the smallest Qwen3 with native >=131072 context,
qwen-code's requirement, without lossy RoPE scaling). Structurally isolated
from llama-server's queue instead of sharing it. Sized for gameserver's
~17GiB free system RAM: q8_0/q8_0 KV at full 131072 ctx (~9.8GiB) + Q4_K_M-
class weights (~2.3GiB) fits comfortably, with better KV quality than the
q4_0 that would've been needed to fit this on the GPU's ~6GiB free VRAM.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

.env.example

@@ -29,18 +29,27 @@ LLAMA_GPU_LAYERS=999
 # this size) — total ~25.6GB, ~6GB headroom, the same footprint the old
 # 131072 fp16 setting used. See docs/research/qwen3.8-27b-quant.md.
 LLAMA_CTX_SIZE=262144
-# Concurrent request slots. Was implicitly 4 (llama.cpp's compiled-in
-# default) with no flag set — under concurrent subagent fan-out, 4 requests
-# split the same GPU compute, so a large-context prefill can queue behind
-# others long enough to blow past OmniRoute's stream-idle timeout, which then
-# cancels the request (see issue-tracker notes on the timeout/cancel loop).
-# Dropped to 2 so each slot gets more compute and finishes prefill sooner;
-# raise back toward 4 if throughput (not latency) becomes the bottleneck
-# instead. Each slot gets LLAMA_CTX_SIZE / LLAMA_PARALLEL tokens of context —
-# real sessions have hit ~66K tokens, so don't drop LLAMA_CTX_SIZE without
-# checking that per-slot number stays comfortably above observed usage.
+# Concurrent request slots — the real hardware ceiling for this GPU, not a
+# tunable to raise for throughput (was implicitly 4, llama.cpp's compiled-in
+# default; dropped to 2 because more contended prefill was blowing requests
+# past OmniRoute's idle timeout — see OMNIROUTE_STREAM_IDLE_TIMEOUT_MS below).
+# The 3rd+ request now queues on llama.cpp itself instead — its own queue has
+# no timeout (tools/server/server-queue.cpp), it just waits for a slot — so
+# the timeout that matters moved to OmniRoute's per-connection
+# providerSpecificData.timeoutMs (dashboard/API only, not in this file; see
+# handoff notes in the issue tracker). Each slot gets LLAMA_CTX_SIZE /
+# LLAMA_PARALLEL tokens of context — real sessions have hit ~66K tokens, so
+# don't drop LLAMA_CTX_SIZE without checking that per-slot number stays
+# comfortably above observed usage.
 LLAMA_PARALLEL=2
 
+# Dedicated CPU-only backend for qwen-code's tool-call harmfulness classifier
+# (fastModel in ~/.qwen/settings.json) — see docker-compose.yml's
+# qwen-classifier service comment for the why. 131072 ctx / q8_0 KV / Q4_K_M-
+# class weights ≈ 12GiB, fits gameserver's ~17GiB free system RAM with room
+# to spare.
+LLAMA_CLASSIFIER_MODEL_FILE=Qwen3-4B-Instruct-2507-UD-Q4_K_XL.gguf
+
 # --- Lazytainer ---
 # Seconds of inactivity before llama-server is stopped. 900 = 15 min.
 LAZYTAINER_INACTIVE_TIMEOUT=900