---
license: apache-2.0
base_model: google/embeddinggemma-2
pipeline_tag: feature-extraction
library_name: coreml
tags:
- embedding
- coreml
- apple-neural-engine
---

# EmbeddingGemma 2 (text) for Core ML and the Apple Neural Engine

A Core ML conversion of the text model of
[google/embeddinggemma-2](https://huggingface.co/google/embeddinggemma-2)
(revision `914f7f89142e33e77833254d9c9b90c3cef7303b`), made by Nemosine for on-device search on iOS 18+ and
macOS 15+. Converted with `nemos/tool/coreml_embedding/convert_gemma2.py`.

- Text only (no vision or audio encoders), 768-dimensional output with
  Matryoshka truncation to 512/256/128 as in the original.
- Float16 weights and compute, no quantization or pruning.
- One multifunction package: `s128`, `s256`, `s512`, `s1024`, `s2048`
  (fixed sequence lengths sharing one copy of the weights).
- Inputs: `input_ids` (int32, BOS … EOS, zero-padded) and `attention_mask`
  (float32, 1 for tokens, 0 for padding). Output: `embedding`, a mean-pooled,
  L2-normalized 768-d vector.
- Prompts are the original model's: `task: search result | query: {query}`
  and `title: {title | none} | text: {text}`.
- Agreement with the original float32 model: see `conversion_report.json`.

## License and modifications

EmbeddingGemma 2 is developed by Google DeepMind and released under the
Apache License 2.0 (see LICENSE). This repository contains a modified version;
NOTICE describes the modifications. Use is subject to the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
Google DeepMind and Gemma are named only to identify the origin of the work;
this conversion is not endorsed by them.