Google 开源 EmbeddingGemma 2：小体积多模态嵌入模型性能超越两倍竞品

出处作者 / 发布主体：Matthias Bastian原标题：Google claims EmbeddingGemma 2 outperforms rival embedding models twice its size

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Google 发布开源多模态嵌入模型 EmbeddingGemma 2，仅 7.4 亿参数即可在文本、图像、视频等基准测试中超越体量两倍的竞品。该模型支持本地运行，内存占用低至 191 MB，显著降低向量数据库存储需求并提升检索效率。

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Google released EmbeddingGemma 2, an open model that converts text, images, video, audio, and code into numerical vectors so similar content can be found and compared more easily. At 740 million parameters, Google says it's the most compact model of its kind and outperforms competing models up to twice its size on multimodal embedding benchmarks.

EmbeddingGemma 2 scores 78.68 on the Massive Text Embedding Benchmark (Code), a jump of nearly 10 points over its predecessor (68.76). That puts it on par with much larger models. | Image: Google

The model runs locally without an API key. Each query takes about 20 to 70 milliseconds via WebGPU in the browser. It needs only around 191 MB of RAM and cuts local vector database storage by up to six times. For text-only tasks, a 270-million-parameter version is enough.

Video 3 Paired with small open models like Gemma 4, EmbeddingGemma 2 can run offline RAG apps without sending data to external servers. The weights are available on Hugging Face and Kaggle, along with a developer guide and documentation.