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Description
Gemini Diffusion represents our cutting-edge research initiative aimed at redefining the concept of diffusion in the realm of language and text generation. Today, large language models serve as the backbone of generative AI technology. By employing a diffusion technique, we are pioneering a new type of language model that enhances user control, fosters creativity, and accelerates the text generation process. Unlike traditional models that predict text in a straightforward manner, diffusion models take a unique approach by generating outputs through a gradual refinement of noise. This iterative process enables them to quickly converge on solutions and make real-time corrections during generation. As a result, they demonstrate superior capabilities in tasks such as editing, particularly in mathematics and coding scenarios. Furthermore, by generating entire blocks of tokens simultaneously, they provide more coherent responses to user prompts compared to autoregressive models. Remarkably, the performance of Gemini Diffusion on external benchmarks rivals that of much larger models, while also delivering enhanced speed, making it a noteworthy advancement in the field. This innovation not only streamlines the generation process but also opens new avenues for creative expression in language-based tasks.
Description
The Gemini Embedding's inaugural text model, known as gemini-embedding-001, is now officially available through the Gemini API and Gemini Enterprise Agent Platform, having maintained its leading position on the Massive Text Embedding Benchmark Multilingual leaderboard since its experimental introduction in March, attributed to its outstanding capabilities in retrieval, classification, and various embedding tasks, surpassing both traditional Google models and those from external companies. This highly adaptable model accommodates more than 100 languages and has a maximum input capacity of 2,048 tokens, utilizing the innovative Matryoshka Representation Learning (MRL) method, which allows developers to select output dimensions of 3072, 1536, or 768 to ensure the best balance of quality, performance, and storage efficiency. Developers are able to utilize it via the familiar embed_content endpoint in the Gemini API.
API Access
Has API
API Access
Has API
Integrations
Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
Python
WeatherNext
Integrations
Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
Python
WeatherNext
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$0.15 per 1M input tokens
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
Google DeepMind
Founded
2010
Country
United Kingdom
Website
deepmind.google/models/gemini-diffusion/
Vendor Details
Company Name
Founded
1998
Country
United States
Website
developers.googleblog.com/en/gemini-embedding-available-gemini-api/