Welcome EmbeddingGemma, Google's new efficient embedding model

TL;DR

Today, Google releases EmbeddingGemma, a state-of-the-art multilingual embedding model perfect for on-device use cases. Designed for speed and efficiency, the model features a compact size of 308M parameters and a 2K context window, unlocking new possibilities for mobile RAG pipelines, agents, and more. EmbeddingGemma is trained to support over 100 languages and is the highest-ranking text-only multilingual embedding model under 500M on the Massive Text Embedding Benchmark (MTEB) at the time of writing.

Table of Contents

• Introduction
• Evaluation
• Demo
• Usage
  • Sentence Transformers
    • Retrieval
  • LangChain
  • LlamaIndex
  • Haystack
  • txtai
  • Transformers.js
  • Text Embeddings Inference
  • ONNX Runtime
• Finetuning
  • Full Finetuning Script
  • Training
  • Finetuned Evaluation
• Further Reading

Introduction

Text embeddings have become the backbone of modern natural‑language applications, turning words, sentences, and documents into dense vectors that capture meaning, sentiment, and intent. These vectors enable fast similarity search, clustering, classification, and retrieval across massive corpora, powering everything from recommendation engines and semantic search to retrieval-augmented generation and code‑search tools. Embedding models that calculate these embeddings are widely used, with well over 200 million monthly downloads on Hugging Face.

Building on this foundation, Google DeepMind’s EmbeddingGemma arrives as the newest, most capable small multilingual embedding model yet. With just 308M parameters, a 2k‑token context window, and support for over 100 languages, EmbeddingGemma delivers state‑of‑the‑art performance on the Massive Multilingual Text Embedding Benchmark (MMTEB) while staying under 200 MB of RAM when quantized.

The various design choices result in a very practical, open-source tool for computing high-quality multilingual embeddings on everyday devices.

Architecture

Evaluation

MTEB (Multilingual, v2) Performance	MTEB (English, v2) Performance

Your browser does not support the video tag. Click to see non-retrieval code Click to see how to truncate embedding dimensionality for faster and cheaper search Model Dataset Loss Function Training Arguments Evaluator Trainer