Make Qwen3-1.7B the resident model

Stock Qwen3-1.7B, not the CPT'd one — that training is still running. It won
on both fixtures we have, measured tonight on an otherwise idle box:

  routing, 77 RU cases, intent-only:  67.5%  vs  59.7%  for Qwen3.5-0.8B
  talk fixture, 27 cases:             20/27  vs  11-17/27

It also beat Qwen3.5-2B, which is 20% larger, on every routing column.

Two other things came with it:

n_ctx goes 2048 -> 4096. This is a Thinking variant, so reasoning tokens need
the room, and 4096 is the context every score above was measured at. Shipping
2048 would ship something nobody measured.

The doc now says not to bother with sub-500M models, because I checked and they
are not close. LFM2.5-350M routes at 5.2% — worse than guessing among 7 intents
— and answers "столица Франции?" with "Сторзит", which is not a word. The 230M
replies to Russian in Spanish. Their published IFEval and BFCL numbers are good
and they are all English.

Note the routing gain needs the LLM router actually wired on to show up. It is
still nil, so this commit buys the phrasing improvement today and the routing
improvement when that lands.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CGeSZxh1DCtRxmFVSYVGvJ

CLAUDE.md

@@ -7,9 +7,20 @@ talking over unix sockets; one resident small model for routing + phrasing; whis
 Deploy target is a Ryzen laptop (homesrv) with Vulkan offload to the Vega iGPU (`n_gpu_layers: 99`,
 compose passes `/dev/dri` + the render gid) — the resident model stays ≤1.7B either way.
 
-**Resident model:** currently **Qwen3.5-0.8B** (`Q4_K_M`), the smallest checkpoint in the gguf
-library, picked for CPU/iGPU latency. The **target** is the locally CPT'd **Qwen3-1.7B**; that
-training is still in flight (Vikunja #122), so no such gguf exists yet. Model files live in
+**Resident model:** currently **Qwen3-1.7B** (`UD-Q4_K_XL`), stock — not yet the CPT'd one.
+It replaced Qwen3.5-0.8B on 2026-07-31 because it measured better on both fixtures we have:
+67.5% vs 59.7% intent-only on the 77-case RU routing fixture, and 20/27 vs 11-17/27 on the
+talk fixture. See `MODEL-BAKEOFF-31-07-2026.md`. It is a Thinking variant, so `n_ctx` is 4096
+— reasoning tokens need the room, and 4096 is what the scores above were measured at.
+
+The **target** is still the locally CPT'd **Qwen3-1.7B** (Vikunja #122, training in flight).
+Stock already speaks good Russian; what it gets wrong is the persona — it writes `я рад`,
+masculine, where Maven needs `рада`. That is what the CPT is for.
+
+**Do not bother with sub-500M models.** LFM2.5-230M and 350M were measured on 2026-07-31 and
+both are unusable in Russian: the 350M routes at 5.2% (worse than guessing) and answers
+"столица Франции?" with the invented non-word "Сторзит"; the 230M replies to Russian in
+Spanish. Their strong published IFEval/BFCL numbers are English-only. Model files live in
 `/mnt/hdd1/llms`, bind-mounted to `/opt/maven/models/llm` — which **shadows** the repo's
 `models/llm/`, so the LFM2.5 gguf sitting there is not loaded by anything. Swapping the resident
 model is a one-line change to `phraser.model_path` in `deploy/mavend.json`.

deploy/mavend.json

@@ -6,10 +6,10 @@
   "state_dir": "/var/lib/maven",
 
   "phraser": {
-    "model_path": "/opt/maven/models/llm/qwen3.5/Qwen3.5-0.8B.Q4_K_M.gguf",
+    "model_path": "/opt/maven/models/llm/qwen3/Qwen3-1.7B-UD-Q4_K_XL.gguf",
     "bin_path": "llama-server",
     "n_gpu_layers": 99,
-    "n_ctx": 2048,
+    "n_ctx": 4096,
     "timeout": "60s",
     "llm_nudges": false
   },