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Average Ratings 0 Ratings
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
The QVAC SDK serves as a toolkit designed for developers looking to integrate on-device AI inference into their applications crafted with JavaScript/TypeScript or Python. It supplies a unified API to facilitate the execution of various supported language, vision, and speech models across both mobile and desktop platforms, encompassing features like model discovery and downloads, inference APIs, and optional connections to remote providers. With this toolkit, developers can create AI features that function offline, ensuring that prompts and processing occur directly on the device for local inference. Additionally, QVAC provides an OpenAI-compatible server alongside plugins that seamlessly link local models with developer tools. Aimed primarily at those developing AI-enhanced applications, the SDK's documentation outlines processes for setup, choosing models, handling tasks related to text generation, image, and audio, as well as invoking tools; the available functionalities can vary based on the chosen model and platform. Furthermore, the QVAC SDK is accessible as free, open-source software under the Apache 2.0 license, and using local inference does not necessitate a hosted QVAC account, making it even more appealing for developers. This flexibility and ease of use make the QVAC SDK a robust choice for integrating AI capabilities into modern applications.
API Access
Has API
Yes
API Access
Has API
Yes
Screenshots View All
No images available
Integrations
Hugging Face
No
OpenClaw
No
OpenCode
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
$0
Free and open source under Apache 2.0. Local inference has no subscription, API-key, or per-token charge.
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
No
On-Premises
Yes
iPhone App
Yes
iPad App
Yes
Android App
Yes
Windows
Yes
Mac
Yes
Linux
Yes
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
Founded
1998
Country
United States
Website
blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/
Vendor Details
Company Name
Tether Data
Country
El Salvador
Website
qvac.tether.io/products/sdk