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English(EN) Introducing EmbeddingGemma 2: A best-in-class open model for natively multimodal embeddings | Google

Google DeepMind 发布 EmbeddingGemma 2 多模态嵌入模型

作者 PulseAugur 编辑部 · [9 个来源] ·

Google DeepMind 推出了 EmbeddingGemma 2，这是一个基于 Gemma 4 架构构建的 7.4 亿参数的开源多模态嵌入模型。该模型能够将文本、代码、图像、视频和音频处理并嵌入到单一的 768 维向量空间中，从而实现跨模态检索和设备端应用。其模块化设计允许灵活部署，仅文本版本需要 2.7 亿参数和 191MB RAM，使其适用于智能手机和笔记本电脑等消费级硬件。EmbeddingGemma 2 已在 Hugging Face 和 Kaggle 等平台上提供，并支持各种推理引擎。 AI

影响 赋能高效的设备端多模态搜索和 RAG，可能加速消费级硬件上 AI 应用的开发。

排序理由 前沿实验室模型发布，附带系统卡。

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AI 生成摘要 · Google Gemini · 来自 9 个来源。 我们如何撰写摘要 →

Google DeepMind 发布 EmbeddingGemma 2 多模态嵌入模型

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报道来源 [9]

  1. Google DeepMind TIER_1 English(EN) ·

    EmbeddingGemma 2：一个开放、轻量级的多模态嵌入模型

  2. MarkTechPost TIER_1 English(EN) · Asif Razzaq ·

    Google DeepMind 发布 EmbeddingGemma 2，一款基于 Gemma 4 的 740M 开源多模态嵌入模型

    <p>Google DeepMind's EmbeddingGemma 2 maps 5 input types into one 768d space and ships today under Apache 2.0.</p> <p>The post <a href="https://www.marktechpost.com/2026/10/06/google-deepmind-releases-embeddinggemma-2-a-740m-open-multimodal-embedding-model-built-on-gemma-4/">Goog…

  3. r/LocalLLaMA TIER_1 English(EN) · /u/sn2006gy ·

    Embedding Gemma 2 是同类最佳的本地多模态嵌入开源模型

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wz9jvx/embeddinggemma_2_is_a_bestinclass_open_model_for/"> <img alt="EmbeddingGemma 2 is a best-in-class open model for natively multimodal embeddings" src="https://external-preview.redd.it/2VS61YcWMMHP9aKp1A…

  4. r/LocalLLaMA TIER_1 English(EN) · /u/Recoil42 ·

    推出 EmbeddingGemma 2：一流的原生多模态嵌入开放模型 | Google

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wz7faa/introducing_embeddinggemma_2_a_bestinclass_open/"> <img alt="Introducing EmbeddingGemma 2: A best-in-class open model for natively multimodal embeddings | Google" src="https://external-preview.redd.it/…

  5. Mastodon — mastodon.social TIER_1 English(EN) · ngate ·

    🚀 隆重推出革命性的 EmbeddingGemma 2：“同类最佳”模型，承诺通过……轻量级？🤔 谁能想到 #AI 的未来是这样的

    🚀 Behold the revolutionary EmbeddingGemma 2: a "best-in-class" model that promises to change everything by being... lightweight? 🤔 Who knew the future of # AI was to just trim some fat and slap on a new name, all while trying to sound important. 😂 Keep innovating, guys. 🌟 https:/…

    链接 blog.google/…/embeddinggemma-2 blog.google/innovation-and-ai
  6. Mastodon — mastodon.social TIER_1 Polski(PL) · aisight ·

    Google DeepMind 以 Apache 2.0 许可发布 EmbeddingGemma 2。得益于其模块化设计，轻量级文本变体可以搜索编码的多媒体数据库

    Google DeepMind udostępniło EmbeddingGemma 2 na licencji Apache 2.0. Dzięki modularnej budowie lekki wariant tekstowy może przeszukiwać bazę multimediów zakodowaną pełnym modelem. # si # ai # sztucznainteligencja # wiadomości # informacje # technologia https:// aisight.pl/technol…

    链接 aisight.pl/…/embeddinggemma-2-modularny-m… aisight.pl/…/generatory-obrazow-ai-stereo…
  7. Mastodon — mastodon.social TIER_1 English(EN) · whatsnew_fyi ·

    transformers v5.19.0 EmbeddingGemma 2 是一款来自 Google 的多模态嵌入模型，基于 Gemma 4 架构构建。它能够编码文本、图像、音频和视频，

    transformers v5.19.0 EmbeddingGemma 2 is a multimodal embedding model from Google built on the Gemma 4 architecture. It encodes text, images, audio, and video, individually or combined in one input, into a shared 768-dimensional vector space for cross-modal retrieval, semantic si…

  8. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    Google 发布 EmbeddingGemma 2，一款开放权重多模态嵌入模型，可在消费级硬件上组织、搜索和连接不同数据流。Al

    Google has released EmbeddingGemma 2, an open-weight multimodal embedding model to organise, search, and connect disparate data streams on consumer hardware. Alongside the release, Google launched the AI Edge Foresight application for Mac, showcasing fully offline semantic proces…

  9. r/singularity TIER_2 English(EN) · /u/HamsterUnfair6313 ·

    推出 EmbeddingGemma 2：一款原生多模态嵌入的开放模型

    <table> <tr><td> <a href="https://www.reddit.com/r/singularity/comments/1wzafmg/introducing_embeddinggemma_2_an_open_model_for/"> <img alt="Introducing EmbeddingGemma 2: An open model for natively multimodal embeddings" src="https://external-preview.redd.it/KFu46TJRwfNErRVqJsWmHr…

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