| --- |
| 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](https://github.com/Mannyking/embeddinggemma-coreml). |
|
|
| ## 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/`](./tree/main/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`](https://huggingface.co/google/embeddinggemma-300m/tree/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](https://huggingface.co/google/embeddinggemma-300m). |
| These packages and tokenizer assets are Gemma model derivatives and are subject |
| to the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). |
|
|
