---
license: apache-2.0
base_model: google/embeddinggemma-2
library_name: coreml
pipeline_tag: feature-extraction
tags:
  - coreml
  - apple-neural-engine
  - embeddings
  - sentence-similarity
  - ios
language:
  - multilingual
---

# EmbeddingGemma 2 · Core ML for the Apple Neural Engine

Core ML conversion of the **text part** of [google/embeddinggemma-2](https://huggingface.co/google/embeddinggemma-2),
used for on-device semantic search in the iOS app *Voce Locale*. It runs on the Apple Neural Engine, so it also
works while the app is in the background (where the GPU is not available on iOS).

This is a converted and quantized derivative of Google's model, redistributed under the same Apache 2.0 license
(see `LICENSE`). It is not an official Google release.

## Files

| File | What it is |
|---|---|
| `EmbeddingGemma2.mlmodelc/` | Compiled Core ML model (8-bit weights, fixed length 512) |
| `tokenizer.json`, `tokenizer_config.json` | The original tokenizer of google/embeddinggemma-2, unchanged |
| `conversion/` | Scripts used for the conversion (`reference.py`, `convert.py`, `quantize.py`, `check.py`) |

## Inputs and outputs

- Input `input_ids`: int32 `[1, 512]`, padded with `0`.
- Output `embedding`: float32 `[1, 768]`, mean-pooled over the tokens and L2-normalized.

The length is fixed at 512 tokens: with flexible shapes Core ML falls back to the CPU (30–40× slower). At 512
tokens the model's sliding window (512) covers the whole input, so the attention mask only excludes padding.

Prompts, as for the original model:
- query: `task: search result | query: …`
- document: `title: <title or none> | text: …`

Tokenize with the original `tokenizer.json` (e.g. swift-transformers or Hugging Face `tokenizers`); the token ids
match the Python reference exactly.

## Conversion

Changes made for Core ML, mathematically identical to the original: attention masks built inside the model,
attention with batch 1, `rotate_half` with `chunk`, `repeat_kv` with `repeat_interleave`. Then 8-bit linear
weight quantization.

```sh
uv venv --python 3.12 .venv
uv pip install --python .venv/bin/python "torch==2.7.0" torchvision==0.22.0 transformers sentence-transformers coremltools numpy pillow librosa
.venv/bin/python conversion/reference.py   # reference vectors (ref.npy)
.venv/bin/python conversion/convert.py     # EG2fixed512.mlpackage (fp16)
.venv/bin/python conversion/quantize.py    # EG2-512-int8.mlpackage
.venv/bin/python conversion/check.py EG2-512-int8.mlpackage 512
xcrun coremlcompiler compile EG2-512-int8.mlpackage out
```

## Measurements (Mac, Apple Silicon, Neural Engine)

| Version | Size | Cosine vs original | Time per 512-token chunk |
|---|---|---|---|
| fp16, flexible shape | 518 MB | 0.999 | 794 ms (CPU) |
| fp16, fixed 512 | 518 MB | 0.999 | 28 ms |
| **8-bit, fixed 512 (this repo)** | **262 MB** | **0.998–1.000** | **27 ms** |

## SHA-256

```
3a4cf264e8322b06dcbfa94f34ff1e5160bbc03d82353db163d2cf623f570e62  EmbeddingGemma2.mlmodelc/analytics/coremldata.bin
b7fc9952cf7e7dcf9b3dcb0b9c7e62ba593dd966c3cf57b2680ea0f1f2272fd0  EmbeddingGemma2.mlmodelc/coremldata.bin
2d1b06ab8f080f03c01c7d898ebf6426d8ffaa642129a3f1c4f9e89769414be8  EmbeddingGemma2.mlmodelc/metadata.json
2fd0524dd86ae24f6b5c767beec120dc59254e18a98e410d7749a3721d03a9a0  EmbeddingGemma2.mlmodelc/model.mil
905dafd9e926250284774fe8561dfe41222f4963a987b8dad04a1ed01f162b9f  EmbeddingGemma2.mlmodelc/weights/weight.bin
4d777ef5bdc1aa36227abdfb77c3e49e7b9c892d16e1b6bda41c393504828be4  tokenizer.json
17bd5d6e9364ca49a534e1502076593317c298d4a663623091ed45388f004874  tokenizer_config.json
```

## License

Apache License 2.0, as the original model. Original model: © Google, [google/embeddinggemma-2](https://huggingface.co/google/embeddinggemma-2).