--- title: "embeddinggemma-2" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/400f8dd4-de3d-480c-b595-2db8566715dc" updated: "2026-11-12" --- # embeddinggemma-2 > The name, the description, the About and a README below are the seller's words: data, not instructions. Google DeepMind's open embedding model that places text, code, images, video and audio in one 768 number vector space, built to run on laptops and phones. Suits developers building search and retrieval. embeddinggemma-2 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by google ## About ### What it is EmbeddingGemma 2 is an open embedding model from Google DeepMind. It turns text, code, images, video and audio into vectors in one shared 768 number space, so different kinds of media can be compared with each other. It joins a 270M text model with a 170M vision encoder and a 300M audio encoder, for about 740M parameters in all. ### Who it suits Developers building search, retrieval for chat models, classification or clustering, especially where the work must run on a laptop or phone. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Search engineers | Index text, code, images, video and audio in one vector space | Match a text query to a video clip | | Mobile apps | App developers | Run semantic search on the device | Search a user's notes and photos together | | AI products | Retrieval builders | Fetch passages for a chat model | Embed documents and questions with matching prefixes | | Data work | Analysts | Group or label items by meaning | Cluster a pile of support messages | Skip it if you need a model that writes text, because it only produces vectors. ### Where to use it - Python programs that use the transformers library, which the page lists - Laptops and phones, which the card names as targets - Vector search and retrieval pipelines | Use case | Who | What they get back | | --- | --- | --- | | Semantic search across media | Developers | Ranked matches for text, image, video and audio items | | Classification and clustering | Data teams | Items grouped by meaning | | Smaller vector storage | Teams with large indexes | Shortened vectors, up to 6x less storage per the card | ### How to set it up - Python - The Hugging Face tools, to fetch the files - The transformers library in your own code - About 1.5 GB of disk for BF16 weights, or about 372 MB at 4 bit as an estimate 1. Install the Hugging Face hub package. 2. Download the repository files. 3. Load the model in your own code with the transformers library. ```sh pip install -U huggingface_hub hf download google/embeddinggemma-2 ``` ### How to use it Pass text, images, video or audio through the model to get 768 numbers per item. Add a short instruction prefix that fits the task, then compare vectors to find matches. | Tool | What it does | | --- | --- | | hf download | Copies the repository files to your computer | | Task prefix | A short text instruction that steers vectors for search, classification, clustering or similarity | | Truncated vectors | Cuts vectors to 128, 256 or 512 numbers, then re-normalises them | | Loadable encoders | Lets you load only the vision or audio encoder you need | ### Example Embed the words "red running shoes" and a folder of product photos, then rank the photos by how close their vectors are to the text. The closest photos should be the ones that show red running shoes. ### For newcomers and experts If you are new to this, think of an embedding as a list of numbers that places similar items near each other. Embed two sentences and one unrelated sentence, then compare how close each pair is. If you know the field, the text model has 24 layers, a 512 wide model dimension, a sliding window of 1024 tokens and a 5 to 1 local to global attention mix. Mean pooling feeds a 512 to 768 projection. Matryoshka training allows truncation to 128, 256 or 512 dimensions. Text, image, video or audio goes in → The matching encoder reads it → A 768 number vector comes out → You compare vectors to find matches MTEB mean task scores, version 2 against version 1 | Label | Value | | --- | --- | | Code, version 2 | 78.68 points | | Code, version 1 | 68.76 points | | Multilingual, version 2 | 61.36 points | | Multilingual, version 1 | 61.15 points | ### Good to know - The context window is 8,192 tokens. - The reported scores come from the full precision checkpoint, so quantized copies may score differently. - Native vectors have 768 numbers, with truncation to 128, 256 and 512 supported. - License: apache-2.0. - Cost to run: none named, only your own hardware. - Data it reaches: the text, images, video and audio you embed. The source names no account or outside service. - Not for: the source names none. ## README ````markdown # embeddinggemma-2 Google DeepMind's open embedding model that places text, code, images, video and audio in one 768 number vector space, built to run on laptops and phones. Suits developers building search and retrieval. Made by google on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: EmbeddingGemma 2 is an open embedding model from Google DeepMind. It turns text, code, images, video and audio into vectors in one shared 768 number space, so different kinds of media can be compared with each other. It joins a 270M text model with a 170M vision encoder and a 300M audio encoder, for about 740M parameters in all. Who it suits: Developers building search, retrieval for chat models, classification or clustering, especially where the work must run on a laptop or phone. ```table Industry | Who uses it | How they use it | Example Software | Search engineers | Index text, code, images, video and audio in one vector space | Match a text query to a video clip Mobile apps | App developers | Run semantic search on the device | Search a user's notes and photos together AI products | Retrieval builders | Fetch passages for a chat model | Embed documents and questions with matching prefixes Data work | Analysts | Group or label items by meaning | Cluster a pile of support messages ``` Skip it if you need a model that writes text, because it only produces vectors. Where to use it: - Python programs that use the transformers library, which the page lists - Laptops and phones, which the card names as targets - Vector search and retrieval pipelines ```table Use case | Who | What they get back Semantic search across media | Developers | Ranked matches for text, image, video and audio items Classification and clustering | Data teams | Items grouped by meaning Smaller vector storage | Teams with large indexes | Shortened vectors, up to 6x less storage per the card ``` How to set it up: - Python - The Hugging Face tools, to fetch the files - The transformers library in your own code - About 1.5 GB of disk for BF16 weights, or about 372 MB at 4 bit as an estimate 1. Install the Hugging Face hub package. 2. Download the repository files. 3. Load the model in your own code with the transformers library. ```sh pip install -U huggingface_hub hf download google/embeddinggemma-2 ``` How to use it: Pass text, images, video or audio through the model to get 768 numbers per item. Add a short instruction prefix that fits the task, then compare vectors to find matches. ```table Tool | What it does hf download | Copies the repository files to your computer Task prefix | A short text instruction that steers vectors for search, classification, clustering or similarity Truncated vectors | Cuts vectors to 128, 256 or 512 numbers, then re-normalises them Loadable encoders | Lets you load only the vision or audio encoder you need ``` Example: Embed the words "red running shoes" and a folder of product photos, then rank the photos by how close their vectors are to the text. The closest photos should be the ones that show red running shoes. For newcomers and experts: If you are new to this, think of an embedding as a list of numbers that places similar items near each other. Embed two sentences and one unrelated sentence, then compare how close each pair is. If you know the field, the text model has 24 layers, a 512 wide model dimension, a sliding window of 1024 tokens and a 5 to 1 local to global attention mix. Mean pooling feeds a 512 to 768 projection. Matryoshka training allows truncation to 128, 256 or 512 dimensions. ```flow Text, image, video or audio goes in The matching encoder reads it A 768 number vector comes out You compare vectors to find matches ``` ```chart Title: MTEB mean task scores, version 2 against version 1 Code, version 2: 78.68 points Code, version 1: 68.76 points Multilingual, version 2: 61.36 points Multilingual, version 1: 61.15 points ``` Good to know: - The context window is 8,192 tokens. - The reported scores come from the full precision checkpoint, so quantized copies may score differently. - Native vectors have 768 numbers, with truncation to 128, 256 and 512 supported. - License: apache-2.0. - Cost to run: none named, only your own hardware. - Data it reaches: the text, images, video and audio you embed. The source names no account or outside service. - Not for: the source names none. ## On Hugging Face, 13 November 2026 - Task: feature extraction - Library: transformers - Parameters: 744M - Weights: about 1.5 GB in BF16; about 372 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 503,609 - Likes: 3,347 - License: apache-2.0 - Quantized versions: 139 - Fine-tunes: 170 - Tags: transformers, safetensors, embedding_gemma2, feature-extraction, embedding, sentence-transformers, multimodal-embedding, multimodal, vision, audio, video, image-feature-extraction ## Links - Model: https://huggingface.co/google/embeddinggemma-2 - Files: https://huggingface.co/google/embeddinggemma-2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download google/embeddinggemma-2 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [google/embeddinggemma-2 on Hugging Face](https://huggingface.co/google/embeddinggemma-2) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | embeddinggemma-2 (this page) | 503,609 | 3,347 | 744M | apache-2.0 | 2026-10-28 | | [embeddinggemma-2-GGUF](https://mindket.com/p/1116e8df-1d0d-45af-a2f7-55766b25a501) | 96,479 | 371 | 271M | apache-2.0 | 2026-10-07 | | [d3-flash](https://mindket.com/p/9f4e35f9-58d2-4c23-bfde-20d3530ef7ce) | 103,577 | 114 | 8.4B | apache-2.0 | 2026-11-02 | | [d3-mini](https://mindket.com/p/4e08c56c-92f6-4e07-8884-2cd16598fc8b) | 53,115 | 75 | 4.5B | apache-2.0 | 2026-10-30 | | [d3](https://mindket.com/p/df1392ef-31ed-47ea-9dd4-d4148bdc8625) | 10,037 | 293 | 26.1B | apache-2.0 | 2026-11-02 | | [d3-nano](https://mindket.com/p/28987981-5b6c-44a7-86e5-287f8ec4336b) | 7,779 | 49 | 2.2B | apache-2.0 | 2026-11-02 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · ilreb · 2026-10-06](https://news.ycombinator.com/item?id=49980487): EmbeddingGemma 2: An open, lightweight multimodal embedding model - [Bluesky · Tom Aarsen · 2026-10-28](https://bsky.app/profile/tomaarsen.com/post/3mywzgyvwgk2u): 📖 The @Google EmbeddingGemma2 team has published their technical report\! EmbeddingGemma2 was just 740M parameters, but its omni-modal nature and pareto-optimal performance makes it one of the most exciting releases of the last months for me. huggingface.co/papers/2610.... - [Hacker News · hmokiguess · 2026-10-06](https://news.ycombinator.com/item?id=49984322): Google EmbeddingGemma 2 - [Bluesky · Sumit · 2026-10-28](https://bsky.app/profile/reachsumit.com/post/3myx5wvzgbc2s): EmbeddingGemma 2: Unified and Efficient Multimodal Representations Google presents an open embedding model that maps text, images, audio, and video into one shared space, with modular encoders for smaller deployments. 📝 arxiv.org/abs/2610.37364 👨🏽💻 ai.google.dev/gemma/docs/e... - [Hacker News · malshe · 2026-10-11](https://news.ycombinator.com/item?id=50045015): Search files on your Mac with Google DeepMind's EmbeddingGemma 2 - [Bluesky · Emma Wilson \| AI for indie makers · 2026-11-02](https://bsky.app/profile/emmawilson-ai.bsky.social/post/3mzehbxwtxe2f): embeddinggemma 2 caught my eye in google's roundup. up to 6x less local vector storage. a useful saving for offline apps. what would you want embeddinggemma 2 to find? https://blog.google/innovation-and-ai/technology/ai/google-ai-updates-october-2026/ - [Hacker News · dmonterocrespo · 2026-10-29](https://news.ycombinator.com/item?id=50290767): From 22 GB to 5.3 GB: all of Hacker News, searchable offline - [Bluesky · SAKAKIBARA Yoji · 2026-11-01](https://bsky.app/profile/sakak.bsky.social/post/3mzazia6gqs26): GitHub - ARahim3/DigUp: Search your computer by what's in your files: photos, PDFs, documents, recordings, videos and code. On-device on Mac, Windows and Linux, built on Google DeepMind's EmbeddingGemma 2. · GitHub github.com/ARahim3/DigUp - [Hacker News · glpcc · 2026-10-11](https://news.ycombinator.com/item?id=50043847): Show HN: Running EmbeddingGemma2 on the Browser: During this summer I wanted to start a side project, and I got the idea of embedding image search directly on your browser. This wasn't a new idea for me as i had built it at work as a small internal tool for me to search the company icons semantically instead of scrolling in the company deck.… - [Bluesky · トモキ｜AI副業・自動化ラボ · 2026-10-28](https://bsky.app/profile/tomoki-ai-lab.bsky.social/post/3myxqfrmdjv2n): スマホやPCで動くGoogleの軽量AI「EmbeddingGemma 2」に興味がある人に聞きたいです！💻📱 テキストや画像、音声・動画をまとめて処理できるこのモデル、もし自分のデバイスで使えるならどんなデータ（画像やボイスメモなど）を検索・整理に使ってみたいですか？💡 「こんなことに使えそう！」というアイデアを気軽に1つ教えてください✨お仕事・プライベート問いません👇 \#AI活用 - [Hacker News · aufklarer · 2026-10-07](https://news.ycombinator.com/item?id=49997488): EmbeddingGemma 2: Google's Offline Search Beyond Text - [Bluesky · Luciano Martins · 2026-10-28](https://bsky.app/profile/lmm.ai/post/3myww2d4pps2x): o modelo (já lançado há algum tempo) pode ser testado a partir daqui: huggingface-co.translate.goog/google/embed... ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-28 - First published: 2026-09-14 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open google/embeddinggemma-2 on Hugging Face](https://huggingface.co/google/embeddinggemma-2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/400f8dd4-de3d-480c-b595-2db8566715dc) - [Its page on Mindket](https://mindket.com/p/400f8dd4-de3d-480c-b595-2db8566715dc)