Pin the resident model to Qwen3.5-0.8B and name Qwen3-1.7B as the target

The most load-bearing decision in the project was stated four incompatible
ways: the docs said Qwen3-1.7B, deploy/mavend.json said Qwen3.5-2B, the repo's
models/llm/ held an LFM2.5-1.2B gguf, and five code comments still said LFM.
Answering "which model is deployed" meant re-deriving it from scratch every
time.

Two facts the review missed, found while resolving it:

- /mnt/hdd1/llms is bind-mounted over /opt/maven/models/llm, which shadows the
  repo's models/llm/. The LFM2.5 gguf sitting there was never loaded by
  anything, so it was not evidence of the deployed model at all.
- That library holds Qwen3.5-0.8B, -2B and -4B, and no Qwen3-1.7B. The config
  pointed at a file that does exist; the docs' Qwen3-1.7B was the stale claim,
  the reverse of the assumed direction. Qwen3-1.7B is the CPT target, and that
  training is still in flight (Vikunja #122), so no such gguf exists yet.

phraser.model_path moves to Qwen3.5-0.8B (Q4_K_M) — the smallest checkpoint on
disk, chosen for latency, and relevant to whether the LLM router is affordable
on this box. Docs and comments now say the same thing in one voice: 0.8B
resident now, CPT'd Qwen3-1.7B as the target, and the bind-mount shadowing
written down so the next reader does not mistake models/llm/ for ground truth.
Comments name the model, never a filename, so a swap stays a one-line config
change.

n_gpu_layers: 99 is correct and stays — compose passes /dev/dri and the render
gid for Vulkan offload to the Vega iGPU. CLAUDE.md's "CPU-only" was the stale
half of that contradiction and is corrected.

phraser.go also dropped a wrong "sub-1b, prompted not trained" size claim: the
target is trained end-to-end (RU CPT + joint persona/router SFT).

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01X5JApcrCRVGmqrxnhynSik

AGENTS.md

@@ -78,8 +78,10 @@ Russian recall — you may see many "clarify" responses).
 
 The target daemon uses the locally trained Qwen3-1.7B checkpoint for both
 routing and phrasing. Training is Qwen3 Base → RU CPT → joint persona/router
-SFT → merged GGUF. Without a configured model, `StubPhraser` plus the classifier
-remain the deterministic floor.
+SFT → merged GGUF, and is still in flight (#122) — until it lands, the deployed
+resident model is stock **Qwen3.5-0.8B** (`Q4_K_M`), see `deploy/mavend.json`.
+Without a configured model, `StubPhraser` plus the classifier remain the
+deterministic floor.
 
 During training, use the runbook in
 `docs/plans/2026-07-18-qwen3-resident-training-eval.md`. After the decision gate