embeddinggemma-300m

by google

Google's efficient multilingual text embedding -- 100+ languages in 300M parameters

1.5Mdl/month

308Mparams

HuggingFace Use in Pipeline

Identifiers

Model ID

google/embeddinggemma-300m

Feature URI

mixpeek://text_extractor@v1/google_embeddinggemma_300m_v1

Overview

EmbeddingGemma is Google's compact text embedding model based on the Gemma 3 architecture, designed for deployment on edge devices while maintaining strong multilingual performance. At 308M parameters, it runs in under 200MB of RAM when quantized and achieves sub-22ms inference on EdgeTPU.

Despite its small size, it ranks as the highest-performing open multilingual text embedding model under 500M parameters on MTEB. It supports Matryoshka representations (768 and 128 dimensions) for flexible memory/quality tradeoffs. On Mixpeek, it provides a lightweight embedding option for high-throughput text indexing where GPU resources are limited.

Architecture

Gemma 3 decoder backbone, 308M parameters. 2048 token context. Matryoshka dimensions: 768 (full) and 128 (compressed). Distilled from a larger teacher model. Optimized for TPU/mobile inference.

Mixpeek SDK Integration

import { Mixpeek } from "mixpeek";

const mx = new Mixpeek({ apiKey: "API_KEY" });

// Managed: create a collection over a bucket; Mixpeek runs this model's extractor
const collection = await mx.collections.create({
  namespace_id: "my-namespace",
  collection_name: "my-collection",
  source: { type: "bucket", bucket_ids: ["bkt_your_bucket"] },
  feature_extractor: {
    feature_extractor_name: "text_embedding",
    version: "v1",
    parameters: { model_id: "google/embeddinggemma-300m" },
  },
});