license: gemma
base_model: google/embeddinggemma-300m
pipeline_tag: feature-extraction
tags:
- coreml
- text-embeddings
- sentence-transformers
EmbeddingGemma → Core ML
This repository contains Core ML conversions of Google's official
embeddinggemma-300m checkpoint. Each package uses a fixed 512-token input
and returns one normalized 768-dimensional embedding.
Conversion scripts, validation details, and the evidence trail live in the source repository.
Packages
| Package | Minimum platform | Saved size |
|---|---|---|
f32-ios18/EmbeddingGemmaF32.mlpackage |
iOS/iPadOS 18, macOS 15 | 1,235.3 MB |
f16-ios15/EmbeddingGemmaF16.mlpackage |
iOS/iPadOS 15, macOS 12 | 620.4 MB |
int4-attention-int8-ios18/EmbeddingGemmaInt4AttentionInt8.mlpackage |
iOS/iPadOS 18, macOS 15 | 216.5 MB |
The F32 iOS 18 package is the source for the int4 candidate. The mixed-FP16
package uses FP16 only for gather and linear operations; attention,
normalization, pooling, and outputs remain Float32. The int4 candidate uses
per-block int4 linear weights, except attention Q/K/V/output projections,
which remain int8.
Tokenizer and Inputs
Tokenizer assets are in tokenizer/.
| Name | Type and shape |
|---|---|
input_ids |
Int32 [1, 512] |
attention_mask |
Int32 [1, 512] |
embedding |
Float32 [1, 768] |
Use right padding. Add BOS and EOS tokens; <pad> is ID 0, <eos> is ID 1,
<bos> is ID 2, and <unk> is ID 3. The 512-token limit includes the prompt
and special tokens.
Use these prompts before tokenization as adviced by The Gemma team:
- Query:
task: search result | query: - Document:
title: none | text:
Validation
The original checkpoint is pinned to
57c266a740f537b4dc058e1b0cda161fd15afa75.
The saved F32 iOS 18 package passed macOS assessment on ten fixtures,
including exact 511- and 512-token inputs; its largest recorded elementwise
error against the original-model baseline was 4.34e-7.
The mixed-FP16 package passed its saved-artifact assessment with cosine
similarity from 0.999911 to 0.999960 against that baseline. The int4/int8
package is intentionally lossy; its exact 511- and 512-token fixtures had
cosine near 0.901 against F32.
BEIR SciFact Retrieval
The F32 and int4/int8 packages were evaluated across 252 eligible SciFact claims and 4,799 abstracts. Records over the fixed 512-token limit were excluded.
| Model | Saved size | Recall@1 | Recall@10 | nDCG@10 | MRR@10 |
|---|---|---|---|---|---|
| Core ML F32 | 1,235.3 MB | 0.5742 | 0.8878 | 0.7488 | 0.7102 |
| Core ML int4 / attention int8 | 216.5 MB | 0.5671 | 0.8882 | 0.7414 | 0.7037 |
This is a quick, single-dataset retrieval check added to catch large quality regressions from conversion or quantization. It is not a broad benchmark, an app-quality claim, or a device-performance result.
Source and License
Based on google/embeddinggemma-300m. These packages and tokenizer assets are Gemma model derivatives and are subject to the Gemma Terms of Use.