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Description

EmbeddingGemma 2 is a versatile and lightweight multimodal embedding model that facilitates the mapping of text, code, images, video, and audio into a unified embedding space, which is useful for applications such as search, retrieval, classification, routing, and RAG. It is constructed on the Gemma 4 framework and distributed under the Apache 2.0 license, featuring an impressive 740 million parameters while being fine-tuned for efficient on-device inference. Its flexible architecture allows for the use of only 270 million parameters for text-centric tasks, and it includes additional vision and audio encoders for comprehensive multimodal capabilities. Furthermore, the innovative Matryoshka Representation Learning technique enables developers to compress output vectors from 768 dimensions down to 512, 256, or even 128 dimensions, effectively reducing the storage and memory demands for local vector databases. The model is equipped with an 8K-token context window, providing the capability to handle up to 5.5 minutes of audio, 29 images, 58 video frames, or various combinations of these inputs seamlessly on local hardware. This adaptability makes it particularly valuable for developers seeking to enhance their applications with rich multimedia integration.

Description

Marengo is an advanced multimodal model designed to convert video, audio, images, and text into cohesive embeddings, facilitating versatile “any-to-any” capabilities for searching, retrieving, classifying, and analyzing extensive video and multimedia collections. By harmonizing visual frames that capture both spatial and temporal elements with audio components—such as speech, background sounds, and music—and incorporating textual elements like subtitles and metadata, Marengo crafts a comprehensive, multidimensional depiction of each media asset. With its sophisticated embedding framework, Marengo is equipped to handle a variety of demanding tasks, including diverse types of searches (such as text-to-video and video-to-audio), semantic content exploration, anomaly detection, hybrid searching, clustering, and recommendations based on similarity. Recent iterations have enhanced the model with multi-vector embeddings that distinguish between appearance, motion, and audio/text characteristics, leading to marked improvements in both accuracy and contextual understanding, particularly for intricate or lengthy content. This evolution not only enriches the user experience but also broadens the potential applications of the model in various multimedia industries.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

TwelveLabs No 

Integrations

TwelveLabs Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$0.042 per minute
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/

Vendor Details

Company Name

TwelveLabs

Founded

2021

Country

United States

Website

www.twelvelabs.io/product/models-overview#marengo

Product Features

Product Features

Alternatives

No Alternatives

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