Plain svd truncation of hosted deltas is optimal in weight space but not
in output space: activations concentrate energy in a few input channels,
so scaling the delta by per-channel input RMS before the svd spends the
rank budget on output error instead. Statistics stream from the model's
own forwards on sub-8-bit SDNQ checkpoints and cache per checkpoint;
measured on real LoKR files this raises output-delta retention by ~0.05
at rank 256 and ~0.09 at rank 64, most on MLP down projections.
- modules/lora/lora_calib.py: capture hooks, per-checkpoint cache under
data/sdnq-calib, statistics land on layers as sdnq_calib_rms; gated by
lora_sdnq_host_calib, skipped when the model is compiled
- lora_sdnq.apply_hosted: weighted truncation when statistics exist,
calib count in the load summary
- cli/sdnq-calibrate.py: complete calibration now against a live server
- cli/lora-quant-fidelity.py --calib: hosted rho scored in the
activation-weighted norm
- test/test-sdnq-lora-factors.py: calibration category, 5 tests
CalamitousFelicitousness
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