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arxiv:2509.20354
EmbeddingGemma: Powerful and Lightweight Text Representations
Abstract
EmbeddingGemma, a lightweight text embedding model based on Gemma 3, achieves state-of-the-art performance with fewer parameters through encoder-decoder initialization, geometric embedding distillation, and spread-out regularization.
AI-generated summary
We introduce EmbeddingGemma, a new lightweight, open text embedding model based on the Gemma 3 language model family. Our innovative training recipe strategically captures knowledge from larger models via encoder-decoder initialization and geometric embedding distillation. We improve model robustness and expressiveness with a spread-out regularizer, and ensure generalizability by merging checkpoints from varied, optimized mixtures. Evaluated on the Massive Text Embedding Benchmark (MTEB) across multilingual, English, and code domains, EmbeddingGemma (300M) achieves state-of-the-art results. Notably, it outperforms prior top models, both proprietary and open, with fewer than 500M parameters, and provides performance comparable to models double its size, offering an exceptional performance-to-cost ratio. Remarkably, this lead persists when quantizing model weights or truncating embedding outputs. This makes EmbeddingGemma particularly well-suited for low-latency and high-throughput use cases such as on-device applications. We provide ablation studies exploring our key design choices. We release EmbeddingGemma to the community to promote further research.
Community
We introduce EmbeddingGemma, a new lightweight, open text embedding model based on the Gemma 3 language model family. Our innovative training recipe strategically captures knowledge from larger models via encoder-decoder initialization and geometric embedding distillation. We improve model robustness and expressiveness with a spread-out regularizer, and ensure generalizability by merging checkpoints from varied, optimized mixtures. Evaluated on the Massive Text Embedding Benchmark (MTEB) across multilingual, English, and code domains, EmbeddingGemma (300M) achieves state-of-the-art results. Notably, it outperforms prior top models, both proprietary and open, with fewer than 500M parameters, and provides performance comparable to models double its size, offering an exceptional performance-to-cost ratio. Remarkably, this lead persists when quantizing model weights or truncating embedding outputs. This makes EmbeddingGemma particularly well-suited for low-latency and high-throughput use cases such as on-device applications. We provide ablation studies exploring our key design choices. We release EmbeddingGemma to the community to promote further research.
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Granite Embedding R2 Models (2025)
- Training LLMs to be Better Text Embedders through Bidirectional Reconstruction (2025)
- Conan-Embedding-v2: Training an LLM from Scratch for Text Embeddings (2025)
- Causal2Vec: Improving Decoder-only LLMs as Versatile Embedding Models (2025)
- On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey (2025)
- Context-Adaptive Multi-Prompt Embedding with Large Language Models for Vision-Language Alignment (2025)
- MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction (2025)
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