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Description
The Apple Foundation Models framework enables developers to leverage Apple's on-device model, which excels in language comprehension, organized output, and invoking tools. This framework grants access to the large language model integral to Apple Intelligence, thereby assisting applications in executing intelligent tasks tailored to their specific needs. By recognizing patterns, the text-based on-device model can produce relevant text in response to various prompts and has the capability to call upon developer-written code for targeted functionalities. Developers are empowered to create text content across a multitude of applications, such as summarization, entity extraction, text comprehension, enhancement, game dialogues, creative content crafting, classification, and beyond. Additionally, it offers guided generation features that enable developers to construct complete Swift data structures with robust assurances by utilizing the Generable macro, enhancing the versatility and functionality of the model. Ultimately, this framework significantly streamlines the process of integrating advanced AI capabilities into applications.
Description
EmbeddingGemma is a versatile multilingual text embedding model with 308 million parameters, designed to be lightweight yet effective, allowing it to operate seamlessly on common devices like smartphones, laptops, and tablets. This model, based on the Gemma 3 architecture, is capable of supporting more than 100 languages and can handle up to 2,000 input tokens, utilizing Matryoshka Representation Learning (MRL) for customizable embedding sizes of 768, 512, 256, or 128 dimensions, which balances speed, storage, and accuracy. With its GPU and EdgeTPU-accelerated capabilities, it can generate embeddings in a matter of milliseconds—taking under 15 ms for 256 tokens on EdgeTPU—while its quantization-aware training ensures that memory usage remains below 200 MB without sacrificing quality. Such characteristics make it especially suitable for immediate, on-device applications, including semantic search, retrieval-augmented generation (RAG), classification, clustering, and similarity detection. Whether used for personal file searches, mobile chatbot functionality, or specialized applications, its design prioritizes user privacy and efficiency. Consequently, EmbeddingGemma stands out as an optimal solution for a variety of real-time text processing needs.
API Access
Has API
API Access
Has API
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Apple
Founded
1976
Country
United States
Website
developer.apple.com/documentation/FoundationModels
Vendor Details
Company Name
Founded
1998
Country
United States
Website
ai.google.dev/gemma/docs/embeddinggemma