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.
Adds library_name to the model card, and pipeline_tag where it was missing.
Brings the config and card licenses into agreement, and repairs the variant
declarations that did not match the files actually published here.
Core ML: drop the fp32 variant, fp16 is equivalent in task terms
fp32 is both slower and larger than fp16 on device, and the ANE will not accept fp32 at all, so an fp32 Core ML build is structurally excluded from the accelerator that makes Core ML worth using.
Equivalence was gated with a task metric rather than raw tensor distance: top-1 agreement 100% across 8 real images. Raw relative L2 on logits reads ~1% for the segmentation models, but that is logit noise which does not change the predicted class per pixel.
Models where fp16 and fp32 genuinely diverge (ssdlite320 86% box agreement, fast-sam 93%) keep both variants.
v0.9.0 and below are untouched.
