gallerywise/coreml-embeddings

Core ML (.mlpackage, fp16, cpu_and_gpu) conversions of the embedding backbones used by the on-device pipeline in gallerywise.ai. Converted with scripts/coreml/convert_embeddings.py (AIB-72 / AIB-119); loaded at runtime through pyobjc Core ML with no torch in the shipped app.

Contents

File	Source model	Notes
siglip_vision.mlpackage.zip	google/siglip-base-patch16-384	vision tower, 768-D
siglip_text.mlpackage.zip	google/siglip-base-patch16-384	text tower, 768-D
text_embed.mlpackage.zip	sentence-transformers/all-MiniLM-L6-v2	384-D; mean-pool + L2-normalize baked into the graph (fixed seq-len 64)
siglip_logit_params.json	—	SigLIP logit scale/bias

The .mlpackage bundles are directories, so they are hosted zipped (each archive contains exactly one top-level *.mlpackage/ entry). The client fetches, sha256-verifies, and unzips them on first launch.

License & attribution

Apache-2.0, inherited from both source models (SigLIP, all-MiniLM-L6-v2), which are themselves Apache-2.0. These are format conversions (Core ML) of those weights — no retraining or fine-tuning.

Intended use

The gallerywise.ai macOS app's embedding stages (semantic search, zero-shot tags, near-duplicate / moment grouping). Not a general-purpose endpoint.