EmbeddingGemma 2Google
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GensimRadim Řehůřek
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Related Products
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About
EmbeddingGemma 2 is an open, lightweight multimodal embedding model designed to map text, code, images, video, and audio into a shared embedding space for search, retrieval, classification, routing, and RAG applications. Built on the Gemma 4 architecture and released under the Apache 2.0 license, it has 740 million parameters and is optimized for on-device inference. Its modular design can use as little as 270M parameters for text-only workloads, with optional vision and audio encoders for full multimodal support. Matryoshka Representation Learning lets developers reduce output vectors from 768 dimensions to 512, 256, or 128, lowering storage and memory requirements for local vector databases. The model supports an 8K-token context window and can process up to 5.5 minutes of audio, 29 images, 58 video frames, or interleaved combinations on local hardware.
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About
Gensim is a free, open source Python library designed for unsupervised topic modeling and natural language processing, focusing on large-scale semantic modeling. It enables the training of models like Word2Vec, FastText, Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), facilitating the representation of documents as semantic vectors and the discovery of semantically related documents. Gensim is optimized for performance with highly efficient implementations in Python and Cython, allowing it to process arbitrarily large corpora using data streaming and incremental algorithms without loading the entire dataset into RAM. It is platform-independent, running on Linux, Windows, and macOS, and is licensed under the GNU LGPL, promoting both personal and commercial use. The library is widely adopted, with thousands of companies utilizing it daily, over 2,600 academic citations, and more than 1 million downloads per week.
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Platforms Supported
Windows
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Mac
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Linux
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Cloud
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On-Premises
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iPhone
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iPad
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Android
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Chromebook
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Not Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Developers and AI teams wanting to build private, efficient, on-device multimodal search, retrieval, RAG, and semantic indexing systems
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Audience
Machine learning practitioners seeking a solution for topic modeling and semantic analysis of large text corpora
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Support
Phone Support
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24/7 Live Support
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Online
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Support
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24/7 Live Support
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Online
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API
Offers API
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API
Offers API
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Pricing
No information available.
Free Version
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Free Trial
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Pricing
Free
Free Version
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Reviews/
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Training
Documentation
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Webinars
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Live Online
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In Person
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Training
Documentation
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Webinars
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Live Online
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Company InformationGoogle
Founded: 1998
United States
blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/
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Company InformationRadim Řehůřek
Founded: 2009
Czech Republic
radimrehurek.com/gensim/
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Categories |
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Integrations
C
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Cython
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NumPy
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Python
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fastText
Not Supported
word2vec
Not Supported
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Integrations
C
Supported
Cython
Supported
NumPy
Supported
Python
Supported
fastText
Supported
word2vec
Supported
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