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Step 1 — catalog hygiene: remove 100 CI/test-stub entries from hf_models.json (tiny-random/*, *-tiny-random, tiny-dummy, ci-random-*, test-*/testing-* repos). These are randomly initialized micro-models whose names shadow real families — e.g. tiny-random/gemma-3 (9M params) captured a local 2.5B gemma-3 GGUF and produced an 880 tok/s estimate for a model that measures 3.8. The scraper now skips them at discovery and at scrape time (is_test_stub), so they cannot return. Step 2 — local calibration: one trustworthy benchmark now corrects the whole estimate column for this machine. Anchors are locally-benched fits on real catalog models (>= 1B params, dense); the median measured/estimated ratio (clamped to [0.05, 3.0]) scales every row's estimate, recorded in estimate_basis.local_calibration and printed in the estimate-basis detail. Application is idempotent, so post-bench refreshes never compound. Stored runs are now also filtered by hardware fingerprint (CPU + GPU name): measurements from a previous machine configuration neither override nor calibrate the current one. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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| .. | ||
| cli.md | ||
| custom-models.md | ||
| development.md | ||
| how-it-works.md | ||
| openclaw.md | ||
| platform-support.md | ||
| providers.md | ||
| tui.md | ||