EmbeddingGemma: Powerful and Lightweight Text Representations
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.
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Mojtaba Seyedhosseini
EmbeddingGemma: Powerful and Lightweight Text Representations
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.
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.
Gemma 3 encoder-decoder initialization geometric embedding distillation spread-out regularizer Massive Text Embedding Benchmark (MTEB) multilingual English code domains quantizing model weights truncating embedding outputs