Google's EmbeddingGemma 300m (QAT q4_0 weights) exported to a Core ML package producing 768-dimensional sentence embeddings on-device (Apple Neural Engine / GPU / CPU).

Consumed by ES-Memory as its universal summary embedder, with the model card's prompt conventions: queries as task: search result | query: …, documents as title: <title|none> | text: ….

Yields EmbeddingGemmaEncoder.mlpackage, loadable with MLModel directly or compilable to .mlmodelc.

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