visible-cx/embeddinggemma-300m-CoreML

embeddinggemma-300m — Core ML (int8, seq 128, ANE)

google/embeddinggemma-300m as a compiled Core ML encoder for Apple silicon, mirrored and rebuild-verified by visible-cx. One call in, one 768-d L2-normalised embedding out.

Weights reproduce bit-exactly from the published recipe: an independent rebuild on a different OS and CPU architecture produced a weight.bin identical to the published one, SHA-256 f81f60eb…, 0 of 308,616,576 bytes differing.

Provenance

Base checkpoint	google/embeddinggemma-300m
Upstream bundle	mlboydaisuke/embeddinggemma-300m-coreml @ 3fa12f0b97b8afe23264f76800afe14af4615ca5
Recipe	john-rocky/CoreML-LLM → conversion/build_embeddinggemma_bundle.py --max-seq-len 128 --quantize int8
Format	Core ML .mlmodelc (compiled), int8 weights
Sequence length	128 (static)
Output	768-d embedding, mean pooling → dense stack → L2 normalise
ANE residency	~99.80%, 1950/1954 ops (upstream published claim, not re-measured here)

The mirror was verified byte-exact against upstream on all six files of the published artifact.

Contents

3fa12f0b97b8afe23264f76800afe14af4615ca5/ — the pinned upstream artifact, byte for byte, under its upstream revision as the directory name. 309,358,096 B total.

File	Bytes
encoder.mlmodelc/weights/weight.bin	308,616,576
encoder.mlmodelc/model.mil	735,948
encoder.mlmodelc/metadata.json	2,570
encoder.mlmodelc/coremldata.bin	408
encoder.mlmodelc/analytics/coremldata.bin	243
model_config.json	2,351

rebuild-verification/2026-08-17/ — an independent rebuild of the same recipe on a different operating system and CPU architecture, published so the reproducibility claim can be checked. 309,346,242 B total.

File	Bytes
encoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin	308,616,576
encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel	723,393
encoder.mlpackage/Manifest.json	617
model_config.json	2,351
SHA256SUMS	427
VERIFICATION.md	2,878

The shapes differ deliberately: the pinned artifact is a compiled .mlmodelc, the rebuild is the uncompiled .mlpackage the recipe emits. Compilation (xcrun coremlcompiler) is a macOS-only step and was not performed on the rebuild host.

Requirements

• Apple silicon, Core ML runtime. The encoder is shaped for the Neural Engine.
• Static sequence length 128. Inputs must be padded or truncated to 128 tokens; this is a compile-time property of the artifact, not a runtime option.
• Weights ≈ 0.31 GB resident. Minimum practical machine memory: 8 GB.
• model_config.json beside the encoder carries the pooling/dense/normalisation contract the host must honour: mean pooling → dense stack → L2 normalise, 768-d out.

Note the sequence-length difference from the Core AI artifact in this org, which is seq 256. The two are not drop-in substitutes for each other.

Performance

No throughput or latency figure is published here. The ANE residency figure above is the upstream published claim, not a measurement made here.

A cross-runtime quality datapoint on record is a cosine of ~0.966 on short text between this Core ML encoder and the LiteRT .tflite of the same base model, measured against previously installed copies rather than against the files in this repo.

Status

Artifact	Status
3fa12f0b…/encoder.mlmodelc + model_config.json	SHIP — byte-exact mirror of the pinned upstream revision, with the weight.bin SHA-256 matching and independently reproduced.
rebuild-verification/2026-08-17/encoder.mlpackage	VERIFICATION EVIDENCE, not a runtime artifact — uncompiled and never executed.

Verification

Weights and config reproduce bit-exactly from the recipe, across operating systems and CPU architectures:

File class	Verdict
weights/weight.bin (308,616,576 B)	IDENTICAL — SHA-256 f81f60eb…, 0 differing bytes
model_config.json (2,351 B)	IDENTICAL — SHA-256 0b949875…
tokenizer / config JSON emitted by the recipe	IDENTICAL — all files
encoder.mlmodelc/model.mil, coremldata.bin ×2, metadata.json	not produced on the rebuild host — products of the macOS-only xcrun coremlcompiler step

So the claim is scoped precisely: everything the recipe produces reproduces exactly; the remaining four files are a macOS compile step that was not run. Closing that gap means compiling rebuild-verification/2026-08-17/encoder.mlpackage on a Mac and diffing the resulting encoder.mlmodelc against the pinned artifact.

This is the exception in this org. Core AI .aimodel bundles are not byte-reproducible — the exporter is not deterministic even against itself — so their integrity rests on per-file SHA-256 of the exact published bytes. For this artifact, "verify by rebuilding" is genuinely available for the weights.

License

EmbeddingGemma is Gemma-family. These files are a derivative of google/embeddinggemma-300m and use is subject to the Gemma Terms of Use and the Gemma Prohibited Use Policy. Those terms travel with the artifact and with any redistribution of it. The contribution here is the mirror and the rebuild verification, not the weights.