---
license: apache-2.0
base_model: google/embeddinggemma-2
library_name: mlx
pipeline_tag: sentence-similarity
tags:
- mlx
- embeddings
- text-embeddings
---

# EmbeddingGemma 2, text only, 8-bit MLX

[google/embeddinggemma-2](https://huggingface.co/google/embeddinggemma-2) (revision
`914f7f89142e33e77833254d9c9b90c3cef7303b`) with its image and audio encoders removed, for text search on Apple
silicon. 320 MB in all, against about 1.27 GB for the 8-bit conversion that keeps
the encoders.

- Text tower only (271M of the model's 744M parameters).
- Matrices quantized affine, 8 bits, group 64; scales, biases, norms and
  scalars in bfloat16 (never float16: the model overflows in float16).
- 768-dimensional output with Matryoshka prefixes 512, 256 and 128; mean pooling,
  L2 normalization. Use the source model's prompts, for example
  `task: search result | query: …` and `title: none | text: …`.
- The tokenizer and the sentence-transformers configs are Google's files, byte for
  byte.

Converted by RuniT-ChatFramework's `Scripts/convert-embeddinggemma2-text.py`. Its
vectors match the FP32 reference cosines of Google's checkpoint within 0.002 on
multilingual query and document pairs, the same as the 8-bit build with encoders.

License: Apache 2.0, as the source model
(https://ai.google.dev/gemma/docs/gemma_4_license).