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test(ai): benchmark llama3.2:3b + qwen2.5-coder:3b, optimized-harness baselines

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Add tracked baselines for two additional CPU models under the optimized
harness (split-tag recovery + greedy decode). Both land at 1/12 — they emit
proper structured calls and fail on capability/content, not parse, confirming
the parser lift is concentrated on the weakest model (0.5b). granite3.1-dense:2b
is incompatible with the installed Ollama version.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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leetcrypt
2026-06-10 14:01:49 -07:00
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@@ -69,6 +69,8 @@ baseline explicitly (e.g. `--out bench/results/baseline-<model>.json` then
## Tracked baselines (2026-06-10, CPU-only, shared room)
Original-harness baselines (default sampling, structured `tool_calls` only):
| model | size | PASS | avg/med s |
|---|---|---|---|
| qwen2.5:0.5b | 397 MB | 1/12 | 55 / 49 |
@@ -76,10 +78,24 @@ baseline explicitly (e.g. `--out bench/results/baseline-<model>.json` then
| **qwen2.5:3b** | 1.9 GB | 1/12 | 51 / 46 |
| qwen2.5-coder:7b | 4.7 GB | 2/12 | 141 / 106 |
All cluster at 1–2/12 with high run-to-run variance — treat as a *relative*
regression yardstick, not an absolute grade. The 7B buys no pass-rate gain at
~3× latency; **qwen2.5:3b stays the default**. The biggest score sink across
every model is bare tool-call-as-text leaks (a harness parse gap, not model
capability) — see `docs/findings-native-harness-2026-06-10.md`. Only
qwen2.5(-coder) sizes 0.5b–7b are usable here; `deepseek-r1` and
`westenfelder/NL2SH` reject Ollama's `tools` field.
After the harness work (split-tag recovery + `temperature:0` for the tool loop +
fenced-prose recovery — see `docs/findings-native-harness-2026-06-10.md`):
| model | size | PASS (2 runs) | avg s | note |
|---|---|---|---|---|
| **qwen2.5:0.5b** | 397 MB | **4/12, 3/12** | ~50 | split-tag leak dominates → 3× lift |
| qwen2.5:3b | 1.9 GB | 2/12, 0/12 | ~50 | emits proper calls → unchanged |
| llama3.2:3b | 2.0 GB | 1/12 | 55 | leaks positional prose (`run_shell "x"`) — unparseable |
| qwen2.5-coder:3b | 1.9 GB | 1/12 | 53 | coder gains nothing on these tasks |
Takeaways: the parser fixes lift exactly the **weakest** model (0.5b), which leaks
malformed text instead of structured calls; a 3B-class model emits proper calls and
fails on capability/content, which no parser can fix, so it stays in the 0–2/12 noise
band. **qwen2.5:3b stays the default** (best capability/latency); 0.5b is now a viable
floor option for trivial tasks. Treat absolute scores as a *relative* regression
yardstick, not a grade.
Usable models: qwen2.5(-coder) 0.5b–7b, llama3.2:3b (all tool-capable). Rejected:
`deepseek-r1` and `westenfelder/NL2SH` reject Ollama's `tools` field;
`granite3.1-dense:2b` errors (`model not supported by your version of Ollama` — needs
a newer Ollama).