feat: add calibrate() to OutlierTurboQuant for data-driven channel split#64
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brosequist wants to merge 1 commit intoTheTom:mainfrom
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feat: add calibrate() to OutlierTurboQuant for data-driven channel split#64brosequist wants to merge 1 commit intoTheTom:mainfrom
brosequist wants to merge 1 commit intoTheTom:mainfrom
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Previously the outlier/inlier channel split was set at construction time and never adjusted. calibrate(calibration_vectors) now computes per-channel RMS, flags channels whose RMS exceeds 3× the median as outliers, and updates the split on the compressor — matching the dynamic-threshold approach described in the LLM.int8() and SmoothQuant literature. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Summary
OutlierTurboQuant.calibrate(calibration_vectors)for data-driven outlier channel detection.Test plan
pytest tests/test_outlier.py— includes tests for calibrate() updating the channel mask and handling all-inlier / all-outlier edge cases.🤖 Generated with Claude Code