2 Commits

Include renames

Author	SHA1	Message	Date
aleleba	c65d309719	Phase 6.4: make the gates fail when they cannot verify something ... A code review found seven ways these gates could pass green with something actually wrong. All are the same family: a missing value was treated as OK. The rule now written into all three files is that absent is not OK, absent is "could not verify", and that either fails or is reported as an explicit SKIP - it never slips through as green. 30_eval_suite.py: - A bucket with no baseline of its own fell back to the global 0.2750 and printed it in a column headed "baseline", as if it were that bucket's number. Measured against the real eval.jsonl buckets: negativos going from 0.12 to 0.33 is a real +0.21 regression, but the computed delta was +0.055 and it PASSED; manejo_errores sitting unchanged at 0.42 produced a fabricated +0.145 FAIL that would have discarded a healthy candidate mid-downtime. Now such buckets print SKIP and the verdict reports how many went unverified. - "VEREDICTO: FAIL" exited 0, so a runbook chaining the gate into quantization would have carried on to write 24 GB. Now exits 1. - A typo in BASELINE_BUCKET_LOSSES silently matched nothing; now aborts. - The penpot exemption is labelled honestly: those 11 rows are pre-existing LoRA #1 tool-calling, not new capability, so gate 1 has no regression coverage there and the log says so. 20_merge_lora.py dry-run (merge path untouched, verified by AST diff): - adapter_config.get("use_rslora", False) meant a missing key passed AND the log printed use_rslora=False, asserting it had checked something that was never there. A different PEFT version omitting a key was enough. - lora_bias was not checked at all, only bias. They are different fields: lora_bias puts a bias inside lora_B, which W + scaling * (B @ A) ignores. - The 620 keys were printed but never asserted, so an adapter with extra tensors printed "310 + 310 = 930" and passed. - rank_pattern/alpha_pattern were not checked. They set r per module, so scaling is not uniformly alpha/r while both the dry-run and the merge apply a single 2.0 to all 310 tensors. - A missing family was invisible: swap linear_attn for 150 mlp.gate targets and the total is still 310, no norm is zero because the family is simply gone, and it passed. Now presence and per-family counts are asserted, derived from the real adapter: linear_attn 150, shared_expert 120, attention_qkvo 40, otros 0. Verified against seven synthetic adapters plus the real phase 3 one; only the correct adapter passes. 21_quantize_nvfp4.py (recipe and oneshot untouched): the calibration cache now carries a provenance.json recording the training file's sha256, the recipe numbers and the bucket distribution, and loading aborts on mismatch. This is the phase's number one risk and it had no mechanical defence: the phase 5 cache on disk has exactly 512 rows, the same as the v2 recipe, so the only existing check could not tell them apart and reusing it would have calibrated with zero design data and washed out the new capability silently. Verified: that cache now aborts. gate 5: retry transport failures against the Penpot MCP, which drops connections mid-call intermittently (seen before in phase 4's gate 4). Without it a blip on prompt 6 of 8 kills a whole run and reads like a model failure. PluginNotConnected is deliberately not retried - that is a real state of the world. Also unwrap the {"result":..., "log":...} envelope the server wraps execute_code returns in; the gate was reading keys off the outer object and rejecting a valid page setup.	2026-07-30 17:37:46 +00:00
aleleba	8ea4572edd	Phase 6.3: add gate 5, design quality in Penpot ... Unlike gates 2-4 this one needs a real agent loop - model, tool call, live MCP, result, up to 14 turns - because design quality only exists after the code executes. It talks to vLLM over the OpenAI API and to the Penpot MCP over HTTP (initialize, notifications/initialized, tools/list, tools/call), handling both application/json and text/event-stream responses. Endpoints and credentials come from env with no defaults and are never printed or stored; requests errors are reduced to the exception type because the requests message embeds the URL. Eight graded prompts, a fresh page per prompt named gate5/<tag>/<id>/<ts>, and the gate never deletes anything. The 17 metrics are computed by an audit payload the gate injects, not the model. placeholderGreys and forbidden behaviour are veto metrics: any hit scores that prompt 0. Two things worth calling out. The forbidden-pattern regexes are imported from 07_lint_penpot_code.py rather than duplicated, and the gate runs those same regexes over its own setup and audit payloads at startup - a gate that violated the API it is grading would be measuring its own bug. The holdout mode had a silent failure that is exactly the kind this phase exists to catch: with the endpoint down, all 60 generations failed, each entered the denominator with zero forbidden patterns found, and the gate reported 0% forbidden API and APPROVED. Since that number is the fallback trigger, a false pass there would have launched the quantization run. Request errors are now counted separately, never enter the denominator, and block approval outright. Known issue, resolved separately: gate prompt 6 is the exact production failure ("hazme una landing page de una pizzería con colores vibrantes"), and the flagship B6 seed was written to the same wording. Shingle overlap measures 20%, under the 34% threshold, but the seed prompt is a literal substring of the gate prompt - the threshold is too loose for prompts this short. The seed's domain gets changed rather than the gate's, so gate 5 measures transfer instead of memorisation; the real pizzeria prompt still runs in the human acceptance test, which is the criterion that decides.	2026-07-30 17:18:01 +00:00