kami
533f0acda8
The file ran two sweeps and the second one changed the resident model, but the lede still opened with "Recommendation: keep Qwen3.5-0.8B". Anyone landing on the file read the wrong conclusion and had to scroll 100 lines to find that it had been replaced — and it contradicted CLAUDE.md, which already says the resident model is Qwen3-1.7B. Both sweeps are accurate, so nothing is rewritten. The lede now states the outcome and the first sweep's verdict is scoped to what it actually tested: it rejects LFM2.5-1.2B, which still holds. It never was a case for keeping 0.8B as the resident model. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CGeSZxh1DCtRxmFVSYVGvJ
217 lines
11 KiB
Markdown
217 lines
11 KiB
Markdown
# Resident model bake-off — 31-07-2026
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**Outcome: the resident model is stock Qwen3-1.7B** (`UD-Q4_K_XL`). Two sweeps ran this
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evening and the second one changed the answer — read to the end before acting on any table
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here. [Second sweep](#second-sweep-same-evening--five-models-and-a-resident-model-change)
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is the one that holds.
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## First sweep — LFM2.5-1.2B vs Qwen3.5-0.8B
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**Verdict, scoped to this pair: keep Qwen3.5-0.8B over LFM2.5-1.2B.** LFM2.5-1.2B is worse
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at routing (52.6% vs 60.5% intent accuracy), and the loss is almost entirely Russian
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(18/61 vs 22/61 RU, while EN is a wash). It is also 2.4× slower. The Thinking variant is
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far worse again. This verdict still stands as written — it rejects LFM2.5-1.2B. It is
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**not** a recommendation to keep 0.8B as the resident model; the second sweep replaced it
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with Qwen3-1.7B.
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Settles Vikunja **#278 / #250**.
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- Same fixture and scorer as `ROUTING-EVAL-31-07-2026.md`: `internal/router/eval/`
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(`ru_routing_v1.json`, 76 held-out cases).
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- Reproduce: `MAVEN_LLM_URL=http://127.0.0.1:<port> make eval-router`
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(`TestLLMRouterBaseline`). Note: there is no `make eval-models` target.
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- All three models served by the same `llama-server` flags — `-c 2048 -ngl 99 -t 6`, only
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`-m` and `--port` differ. One server at a time on an otherwise idle box, so latencies are
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real and not contention.
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- Measured on top of the router prompt fix (`origin/overnight/router-prompt` merged in), so
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the Qwen column is directly comparable to the numbers already recorded.
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## Results
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`llm-only` — the model alone. This is the column that measures the model.
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| | Qwen3.5-0.8B | LFM2.5-1.2B Instruct | LFM2.5-1.2B Thinking |
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|---|---|---|---|
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| **intent-only accuracy** | **60.5%** | 52.6% | 36.8% |
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| full accuracy (intent+slots+gate) | **36.8%** | 32.9% | 21.1% |
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| **RU** | **22/61** | 18/61 | 10/61 |
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| EN | 6/15 | **7/15** | 6/15 |
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| route errors | 0 | 0 | 0 |
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| **p50 / p95 latency** | **1.05s / 1.71s** | 2.47s / 3.62s | 2.42s / 3.24s |
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| missed clarify | 6 / 6 | 6 / 6 | 6 / 6 |
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`cascade+llm` — stage-0 → model → classifier floor, what #320 would actually ship. Same
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ordering.
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| | Qwen3.5-0.8B | LFM2.5-1.2B Instruct | LFM2.5-1.2B Thinking |
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|---|---|---|---|
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| intent-only accuracy | **61.8%** | 55.3% | 38.2% |
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| full accuracy | **46.1%** | 42.1% | 30.3% |
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| RU / EN | **27/61** / 8/15 | 23/61 / **9/15** | 15/61 / 8/15 |
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| route errors | 0 | 0 | 0 |
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| p50 / p95 latency | **1.28s / 1.94s** | 2.18s / 2.72s | 2.27s / 3.19s |
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Full logs: the three runs are archived in the session scratchpad
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(`qwen08.txt`, `lfm-instruct.txt`, `lfm-thinking.txt`).
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## Russian-specific failures — the owner's worry is confirmed
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LFM2.5's Russian loss is not spread out. It has one large, specific failure: **it hears
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almost any Russian imperative or short phrase as `reminder`.**
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- `перезапусти докер` → reminder (want act)
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- `включи вытяжку` → reminder (want act)
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- `закрой жалюзи` → reminder (want act)
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- `заметка: продлить домен в августе` → reminder (want note)
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- `запиши что кран на кухне снова капает` → reminder (want note)
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- `доброе утро` → reminder (want chat)
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- `спасибо тебе` → reminder (want note/chat)
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- `переходи в тихий режим` → reminder (want system)
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That is `note→reminder ×4`, `act→reminder ×4`, `chat→reminder ×2` in one run. Qwen's
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equivalent failure axis is `query→fact ×8`, which is a narrower and already-understood bug.
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Two more Russian-side problems worth naming:
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1. **Fact keys come back empty or wrong in Russian.** `воды попил наконец`, `поужинал`,
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`поспал часов пять` and `отметь что я позавтракал овсянкой` all returned an empty key.
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`сходил в душ` and `отдохнул минут двадцать` both returned `water`. Qwen does not do this.
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2. **It leaked German.** `slept about seven hours` produced the fact key
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`"7 Stunden geschlafen"`. Grammar-valid, semantically garbage — a sign the multilingual
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mix is not anchored where Maven needs it.
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The claimed tool-calling advantage did not show up here. `act` is the closest thing this
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fixture has to a tool call, and LFM2.5 got it wrong more often than Qwen, mostly by calling
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it a reminder. It also produced no `fn` slot on any act, same as Qwen.
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## The Thinking variant
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Not viable. 36.8% intent accuracy, 10/61 Russian, and no latency saving over Instruct — the
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thinking trace costs time without buying accuracy on a short enum classification. With the
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`enable_thinking=false` diagnostic it collapsed further to 28.9% with 2 route errors
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(`query→reminder ×12`). Do not pursue.
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## Notes
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- Nothing crashed, nothing ignored the GBNF grammar, and no model produced unparseable JSON
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in the shippable configurations. Zero route errors for both Instruct and Thinking in
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`llm-only` and `cascade+llm`. The problem with LFM2.5 is what it decides, not whether it
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can emit the contract.
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- The `6 / 6` missed clarify is unchanged across all three models. No model fixes the missing
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refusal lane — that is `Confidence: 1.0` hardcoded in `llmrouter.go` (Vikunja #359), not a
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model property.
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- The report labels every configuration `(0.8B)`; that string is hardcoded in the test, not a
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reflection of which gguf was loaded. Model identity was confirmed per run via `/v1/models`.
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- No Go code was changed for this measurement, and no bug was found that needed one.
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## What this does not settle
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Routing only. LFM2.5 might still phrase better, and phrasing is the resident model's other
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job — that needs its own fixture. But routing is the load-bearing path and Maven is
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Russian-first, so on the evidence here the switch is not worth making.
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---
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# Second sweep, same evening — five models, and a resident-model change
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The sections above compared LFM2.5-1.2B against Qwen3.5-0.8B on routing and concluded
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"the switch is not worth making". That still holds. This sweep asked a different
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question — whether a *smaller* model could work, since LFM2.5's published
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instruction-following scores beat Qwen3.5-0.8B badly — and answered it, plus found a
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better resident model by accident.
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**Outcome: the resident model is now stock Qwen3-1.7B.** Sub-500M is a dead end.
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## Routing — 77 Russian cases, one run each
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| model | on disk | llm-only (full) | llm-only (intent) | cascade + fallback |
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| LFM2.5-230M-Q8_0 | 246 MB | 23.4% | 33.8% | 36.4% |
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| LFM2.5-350M-Q8_0 | 379 MB | 2.6% | **5.2%** | 20.8% |
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| Qwen3.5-0.8B-Q4_K_M | 527 MB | 36.4% | 59.7% | 61.0% |
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| Qwen3.5-2B-UD-Q4_K_XL | 1.34 GB | 42.9% | 62.3% | 63.6% |
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| **Qwen3-1.7B-UD-Q4_K_XL (stock)** | 1.13 GB | **44.2%** | **67.5%** | **72.7%** |
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Qwen3-1.7B wins every column, including against a model 20% larger than it.
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## Talk fixture — 27 cases, three runs each, idle box
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| | Qwen3.5-0.8B | Qwen3-1.7B stock |
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| composite | 13, 11, 8 | **20, 21, 18** |
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| address | 21, 18, 18 | **26, 25, 23** |
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| feminine | 27, 25, 26 | 26, 27, 26 |
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| lang | 27, 27, 26 | 26, 27, 27 |
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| ontopic | 16, 19, 19 | **22, 23, 23** |
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| canned fallbacks | 8, 5, 6 | **0, 2, 0** |
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This also fills the row `TALK-EVAL-31-07-2026.md` had to void for contamination:
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**600ch/1024tok on Qwen3.5-0.8B scores 13, 11, 8.**
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`address` is the headline. It sat at 18-22 of 27 on the 0.8B no matter how the prompt
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was worded — the prompt explicitly forbids "вы" and the model writes `вашей`,
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`подождите`, `делаете` anyway. That was read as "prompting is out of levers", and it
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was really "0.8B is out of capacity". The 1.7B mostly holds the constraint.
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The fallback column matters too: 5-8 of 27 turns on the 0.8B end in a hardcoded
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`"не знаю."`, meaning it failed to emit parseable JSON about a quarter of the time.
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The 1.7B does that 0-2 times.
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## Latency — the long tail is not the Thinking block
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| | p50 | p95 |
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| Qwen3.5-0.8B | 2.4s, 2.9s, 2.0s | 17.4s, 17.6s, 17.4s |
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| Qwen3-1.7B stock | 2.7s, 2.6s, 2.8s | 16.4s, 6.6s, 3.9s |
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p50 is flat across a 2× size difference. The first instinct on seeing the 1.7B's
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16s p95 was "that is the reasoning trace, cap it" — wrong. The 0.8B's p95 is a
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consistent 17s and the 1.7B beat it in two of three runs. The tail is shared and
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lives somewhere else. Do not spend time on `/no_think` on this evidence.
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## Sub-500M: not close, and the benchmarks say otherwise for a reason
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LFM2.5-350M publishes IFEval 76.96 against Qwen3.5-0.8B's 59.94, and BFCLv3 44.11
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against 35.08 — better at instruction-following and structured output, at 2/3 the
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size. Those numbers are real and they are **English**. Every benchmark in that
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table except Multi-IF is English-only.
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In Russian, with a 300-token budget and temperature 0:
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- **350M**, «Столица Франции? Ответь кратко.» → *«Сторзит в Париже.»* — `Сторзит` is
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not a word; it is invented morphology.
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- **350M**, asked to read back a reminder → a fortune cookie about being attentive
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and confident. No reminder in it.
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- **230M**, «Привет, как дела?» → answered **in Spanish**.
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The 230M beating the 350M six-fold on routing (33.8% vs 5.2%) is the other tell:
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when the larger sibling collapses like that it is format compliance failing, not
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reasoning.
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This is a pretraining gap, not a fine-tuning gap. Teaching Russian to a 350M from
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near-zero is not an afternoon on a Colab, which was the premise worth checking.
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## Why this vindicates the 1.7B CPT
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Stock Qwen3-1.7B, untrained and unprompted, answers all three probes in fluent
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correct Russian. What it gets wrong is the persona: *«Привет! Я рад, что ты здесь»*
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— `рад` is masculine and Maven needs `рада`. That is the right kind of remaining
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problem, and it is exactly what the CPT (Vikunja #122) is for.
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The 1.7B was the correct model choice. What was wrong was treating it as a
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**blocker**: stock already beats what was deployed, so it ships now and gets
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swapped again when the CPT lands.
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## Caveats
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- Routing is one run per model, not three. The gaps between families are far larger
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than the run-to-run spread seen on the talk fixture, but the 2B-vs-1.7B gap (62.3
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vs 67.5) is not safe to call on one run.
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- The routing numbers only reach production once the LLM router is wired on. It is
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still `nil`.
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- `/mnt/hdd1/llms/LFM2.5/Qwen3-1.7B-UD-Q4_K_XL.gguf` is a 293 MB truncated download
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in the wrong directory. The good 1.13 GB copy is in `qwen3/`. Delete the stray one.
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- Harness: `scratchpad/bakeoff.sh`, one server at a time, health-checked before each
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run, `/v1/models` recorded per run. Never run two LLM consumers at once — see the
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contamination note in `TALK-EVAL-31-07-2026.md`.
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