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Description
Snowflake's Arctic Embed 2.0 brings enhanced multilingual functionality to its text embedding models, allowing for efficient global-scale data retrieval while maintaining strong performance in English and scalability. This version builds on the solid groundwork of earlier iterations, offering support for various languages and enabling developers to implement stream-processing pipelines that utilize neural networks and tackle intricate tasks, including tracking, video encoding/decoding, and rendering, thus promoting real-time data analytics across multiple formats. The model employs Matryoshka Representation Learning (MRL) to optimize embedding storage, achieving substantial compression with minimal loss of quality. As a result, organizations can effectively manage intensive workloads such as training expansive models, fine-tuning, real-time inference, and executing high-performance computing operations across different languages and geographical areas. Furthermore, this innovation opens new opportunities for businesses looking to harness the power of multilingual data analytics in a rapidly evolving digital landscape.
Description
Llama (Large Language Model Meta AI) stands as a cutting-edge foundational large language model aimed at helping researchers push the boundaries of their work within this area of artificial intelligence. By providing smaller yet highly effective models like Llama, the research community can benefit even if they lack extensive infrastructure, thus promoting greater accessibility in this dynamic and rapidly evolving domain.
Creating smaller foundational models such as Llama is advantageous in the landscape of large language models, as it demands significantly reduced computational power and resources, facilitating the testing of innovative methods, confirming existing research, and investigating new applications. These foundational models leverage extensive unlabeled datasets, making them exceptionally suitable for fine-tuning across a range of tasks. We are offering Llama in multiple sizes (7B, 13B, 33B, and 65B parameters), accompanied by a detailed Llama model card that outlines our development process while adhering to our commitment to Responsible AI principles. By making these resources available, we aim to empower a broader segment of the research community to engage with and contribute to advancements in AI.
API Access
Has API
API Access
Has API
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Integrations
AICamp
Agenta
Alpaca
Amazon SageMaker Unified Studio
Arch
Batteries Included
BrandRank.AI
Deasie
Deep Infra
Featherless
Integrations
AICamp
Agenta
Alpaca
Amazon SageMaker Unified Studio
Arch
Batteries Included
BrandRank.AI
Deasie
Deep Infra
Featherless
Pricing Details
$2 per credit
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
Snowflake
Founded
2012
Country
United States
Website
www.snowflake.com/en/engineering-blog/snowflake-arctic-embed-2-multilingual/
Vendor Details
Company Name
Meta
Founded
2004
Country
United States
Website
www.llama.com