quantized duplicated precisions.json, which is authoritative for the
quantized/non-quantized partition. default expressed a per-repository
recommendation nothing consumed: the library resolves its own per-platform
default. Both are gone from the schema.
Core ML: pin compute_unit=CPU_ONLY, drop the fp32 variant
These graphs fail the Apple Neural Engine path (coremltools#2825 masked mean-pool over a dynamic sequence dim; clip-text additionally ANECCompile FAILED). Core ML still returned correct output because it silently fell back to CPU, but the failed ANE compile was paid on every call. Pinning compute_unit=CPU_ONLY skips the attempt.
Measured on an iPhone 16, fp16, 3 warmup + 15 timed runs, median:
all-MiniLM 13.7 ms -> 2.5 ms 5.5x
multi-qa 14.1 ms -> 2.5 ms 5.6x
paraphrase-L12 26.9 ms -> 4.2 ms 6.4x
distiluse 33.0 ms -> 8.0 ms 4.1x
clip-text 18.1 ms -> 4.9 ms 3.7x
The fp32 variant is removed. Once fp16 is pinned it is dominated on both axes: slower (all-MiniLM 4.5 vs 2.5 ms, clip-text 9.5 vs 4.9 ms) and twice the size. Output fidelity against fp32 was gated first: relative L2 4e-6 (all-MiniLM, multi-qa), 0.0010 (paraphrase), 0.0046 (distiluse).
v0.9.0 and below are untouched.
