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.
The previous removal commit said earlier tags still contain the MLX artifacts. That is wrong: MLX for this model was only ever published on main / v0.10.0, and no tag at v0.9.0 or below ever carried it. The files remain recoverable from git history. v0.9.0 and below are, and have been, untouched.
Remove MLX artifacts: 7.5-19.6x slower than Core ML on device
MLX measured substantially slower than Core ML on an iPhone 16 for every convolutional vision model tested (efficientnet-v2-s 19.6x, yolo26-s 16.0x, yolo26-seg-s 7.5x, yolo26-pose-n 10.0x). MLX weight quantization also cannot shrink a conv model, since it only rewrites nn.Linear/embedding modules.
The .pte and config.json are removed; each mlx/ dir keeps a NOTES.md with the measurements. The exporter is retained in export-scripts so this can be re-tested against a future ExecuTorch/MLX release.
Earlier tags (v0.9.0 and below) are untouched and still contain the MLX artifacts.
