docs(llm): document qwen-classifier reality, add --reasoning off safety net

- docker-compose.yml: add --reasoning off to qwen-classifier per
  fast-model-choice.md's own recommendation (ggml-org/llama.cpp#20809
  safety net) — missed in the original rollout, caught while writing
  this up.
- docs/coding-cli-setup/qwen-code.md: rewritten to match what's actually
  deployed (qwen-classifier, partial GPU offload, 65536 ctx, Q4_K_XL) —
  previously described an unimplemented llama-server-fast/8192-ctx plan.
  Documents the non-interactive MCP tool allow-list gap found live-testing.
- docs/coding-cli-setup/opencode.md: fix stale 65536 example that didn't
  match its own documented LLAMA_CTX_SIZE/LLAMA_PARALLEL formula (131072).
- docs/research/fast-model-choice.md: implementation note recording where
  the actual rollout diverged from this doc's original recommendations
  (service name, quant, context size, CPU-first-then-GPU path).
- docs/research/omniroute-account-semaphore-timeout.md: new — the
  hardcoded 30s per-connection semaphore timeout found during the
  pr-agent investigation, root-caused against OmniRoute's own source,
  and the maxConcurrent:null + providerSpecificData.timeoutMs fix.

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

docs/coding-cli-setup/qwen-code.md

@@ -2,7 +2,18 @@
 
 [← back to overview](index.md)
 
-Qwen Code speaks plain **OpenAI Chat Completions**, and — unlike the other CLIs — needs *two* models: the main chat model, and a `fastModel` for Auto Mode's action classifier (a separate, always-resident, always-fast instance so classification doesn't queue behind chat prefill; see `docker-compose.yml`'s `llama-server-fast` service and `docs/research/fast-model-choice.md`). Both are registered as separate providers in OmniRoute but reachable through the same gateway URL. Config lives in `~/.qwen/settings.json`:
+Qwen Code speaks plain **OpenAI Chat Completions**, and — unlike the other CLIs — needs *two* models: the main chat model, and a `fastModel` for Auto Mode's action classifier. Both are registered as separate providers in OmniRoute but reachable through the same gateway URL.
+
+## Why a second model exists
+
+Auto Mode's action classifier (`permissions.autoMode`) is qwen-code's per-tool-call safety gate — it decides whether to auto-approve or block a shell command / tool call before it runs. It was originally aliased onto the main 27B model's own OmniRoute connection. That broke two ways in practice (see `docs/research/fast-model-choice.md` for the model research, and the issue-tracker history for the full incident):
+
+- **Queued behind heavy work.** Every classification call competed for the main model's 2 GPU slots with whatever real generation was already running, so a classifier check could sit blocked for minutes.
+- **CPU-only was tried first and was too slow.** Isolating the classifier onto its own CPU-only llama.cpp instance avoided the GPU queue entirely, but real classification calls (which can carry a non-trivial conversation transcript, not just the bare tool call) blew past OmniRoute's request timeout and retry-looped.
+
+The fix: a dedicated, GPU-resident `qwen-classifier` service (`docker-compose.yml`) running a small model (`Qwen3-4B-Instruct-2507`) on its own **partial** GPU offload — enough layers on the R9700 to be fast, sized to leave real VRAM headroom next to the 27B model rather than trusting a naive weights+KV estimate (see that service's comment block in `docker-compose.yml` for the actual measured numbers and the two wrong turns — batch-size tuning, then flash-attn — before partial offload turned out to be the real lever).
+
+## `~/.qwen/settings.json`
 
 ```json
 {
@@ -16,12 +27,12 @@ Qwen Code speaks plain **OpenAI Chat Completions**, and — unlike the other CLI
         "generationConfig": { "contextWindowSize": 131072 }
       },
       {
-        "id": "<fast-model-provider-id-in-omniroute>",
-        "name": "qwen3.8-27b-classifier",
+        "id": "<classifier-provider-id-in-omniroute>",
+        "name": "qwen3-4b-classifier",
         "envKey": "OMNIROUTE_API_KEY",
         "baseUrl": "http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1",
         "generationConfig": {
-          "contextWindowSize": 8192,
+          "contextWindowSize": 65536,
           "extra_body": { "chat_template_kwargs": { "enable_thinking": false } }
         }
       }
@@ -32,15 +43,14 @@ Qwen Code speaks plain **OpenAI Chat Completions**, and — unlike the other CLI
     "name": "<main-model-provider-id-in-omniroute>",
     "baseUrl": "http://<ai-box>:${OMNIROUTE_PORT:-4000}/v1"
   },
-  "fastModel": "<fast-model-provider-id-in-omniroute>"
+  "fastModel": "<classifier-provider-id-in-omniroute>"
 }
 ```
 
 - `envKey` names the environment variable Qwen Code reads the virtual key from — set `OMNIROUTE_API_KEY=<qwen-code-cli virtual key>` before launching. Both providers can share one virtual key (as above); split it into two if you want separate usage tracking for chat vs. classifier calls.
-- **`contextWindowSize` is per-slot, not `LLAMA_CTX_SIZE` itself** — llama.cpp divides `--ctx-size` across `LLAMA_PARALLEL` concurrent slots, and each request only gets one slot's share (same correction applies to OpenCode's `limit.context`). Compute it per model from `.env`:
-  - Main model: `LLAMA_CTX_SIZE / LLAMA_PARALLEL` = `262144 / 2` = **131072**.
-  - Fast model: `LLAMA_FAST_CTX_SIZE / LLAMA_FAST_PARALLEL` = `8192 / 1` = **8192**. Undersizing this one specifically breaks Auto Mode ("Classifier stage 1 unavailable") once `hints.allow`/`softDeny`/`hardDeny` entries and recent-action history push a classifier call past it — see the `LLAMA_FAST_CTX_SIZE` comment in `.env.example` before raising it instead of `LLAMA_FAST_PARALLEL`.
-- `enable_thinking: false` on the fast model matters: the fast model file (`Qwen3-4B-Instruct-2507`) is already non-thinking, but this also suppresses `<think>` output on any fast-model swap that isn't, keeping classifier responses parseable.
+- **`contextWindowSize` for the main model is per-slot, not `LLAMA_CTX_SIZE` itself** — llama.cpp divides `--ctx-size` across `LLAMA_PARALLEL` concurrent slots, and each request only gets one slot's share (same correction applies to OpenCode's `limit.context`). Compute it from `.env`: `LLAMA_CTX_SIZE / LLAMA_PARALLEL` = `262144 / 2` = **131072**.
+- **The classifier's `contextWindowSize` (65536) is not per-slot math** — `qwen-classifier` runs `--parallel 1`, so its whole `--ctx-size` belongs to the one slot. 65536 isn't a guess either: qwen-code's own source hard-caps the classifier transcript (`MAX_TRANSCRIPT_MESSAGES=40`, `MAX_HISTORICAL_ACTION_CHARS=4000`/message in `packages/core/src/permissions/classifier-transcript.ts`) — worst case is ~40-50K tokens, so 65536 gives real margin without wasting VRAM the way the original 131072 (copied from the main model's entry, not an actual qwen-code requirement) would have.
+- `enable_thinking: false` on the classifier matters for parseability, though `Qwen3-4B-Instruct-2507` is already architecturally non-thinking (see `fast-model-choice.md` §3) — this is belt-and-suspenders for any future fast-model swap that isn't.
 - Qwen Code also recognizes `advisorModel`, `visionModel`, `compactionModel`, `imageModel` for other model roles — none are wired up in this stack; only `fastModel` is required.
 
 ## Web search via OmniRoute
@@ -96,9 +106,11 @@ Register it in `~/.qwen/settings.json`:
 
 It reuses the same `OMNIROUTE_API_KEY` env var as the model providers above — the virtual key needs search permission in OmniRoute, not just chat-completions.
 
+**Non-interactive mode (`qwen -p ...`) needs this tool explicitly allow-listed.** MCP tools require interactive confirmation by default; `--approval-mode auto` alone doesn't bypass that for a non-interactive run — pass `--allowed-tools mcp__omniroute-search__search` (or `-y` for full YOLO) alongside `-p`, or the search call never reaches the classifier at all and silently no-ops. Confirmed live: without the allow-list, only the tool calls the CLI's non-interactive gate lets through end up as classifier requests.
+
 ## Auto Mode tuning
 
-Auto Mode's action classifier calls the fast model above — its own request can queue behind other stack traffic before the fast llama-server instance is warm, so the default classifier timeout is worth raising. And since this stack is a single trusted local proxy, it's reasonable to pre-approve requests to it rather than confirm every call:
+Auto Mode's action classifier calls the fast model above. Even on the dedicated GPU-resident instance, give it real timeout headroom rather than trusting OmniRoute's default — and since this stack is a single trusted local proxy, it's reasonable to pre-approve requests to it rather than confirm every call:
 
 ```json
 {
@@ -111,6 +123,8 @@ Auto Mode's action classifier calls the fast model above — its own request can
 }
 ```
 
-`hints.allow` entries are free-text descriptions the classifier matches against, not exact strings — capped at 150 entries/200 chars each (see the `LLAMA_FAST_CTX_SIZE` note above for why that ceiling matters).
+`hints.allow` entries are free-text descriptions the classifier matches against, not exact strings — capped at 150 entries/200 chars each.
+
+Also set a generous per-connection timeout on the classifier's own OmniRoute provider connection (`providerSpecificData.timeoutMs`, dashboard or `PATCH /api/providers/{id}` — not a `.env` value, see `docs/network-access.md` for reaching the dashboard API). 120000ms is comfortable for the current GPU-resident setup (real measured latency: well under a second for a short check, low seconds for the largest realistic transcript) — this isn't the 20-minute figure the main 27B connection needs, since the classifier isn't competing for a contended GPU slot the way the main model can.
 
 Everything else in `~/.qwen/settings.json` (`hooks`, `security.auth`'s underlying tooling, editor prefs) is per-machine, not part of pointing at this stack — don't copy it wholesale between machines.