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What is Tiny Aya?

Tiny Aya is a suite of multilingual language models created by Cohere Labs, designed to deliver powerful and adaptable artificial intelligence capabilities that can operate effectively on local devices like smartphones and laptops, eliminating the necessity for constant cloud connectivity. This pioneering model focuses on improving text understanding and generation across more than 70 languages, with particular emphasis on lower-resource languages that often go overlooked by traditional models. Constructed with an efficient architecture featuring approximately 3.35 billion parameters, Tiny Aya has been optimized for excellent multilingual performance and computational efficiency, making it particularly suitable for use in edge computing environments and offline applications. Additionally, the models are structured to allow for downstream adaptation and instruction tuning, which enables developers to customize the models’ functionalities for various specific applications while maintaining robust performance across different languages. Ultimately, Tiny Aya not only broadens the accessibility of cutting-edge AI technologies but also equips developers with the tools needed to craft tailored applications that cater to a wide array of linguistic requirements, thus fostering greater inclusivity in AI-driven solutions. This capacity for customization ensures that Tiny Aya can evolve alongside the needs of its users, making it a versatile choice in the ever-changing landscape of AI development.

What is EXAONE Deep?

EXAONE Deep is a suite of sophisticated language models developed by LG AI Research, featuring configurations of 2.4 billion, 7.8 billion, and 32 billion parameters. These models are particularly adept at tackling a range of reasoning tasks, excelling in domains like mathematics and programming evaluations. Notably, the 2.4B variant stands out among its peers of comparable size, while the 7.8B model surpasses both open-weight counterparts and the proprietary model OpenAI o1-mini. Additionally, the 32B variant competes strongly with leading open-weight models in the industry. The accompanying repository not only provides comprehensive documentation, including performance metrics and quick-start guides for utilizing EXAONE Deep models with the Transformers library, but also offers in-depth explanations of quantized EXAONE Deep weights structured in AWQ and GGUF formats. Users will also find instructions on how to operate these models locally using tools like llama.cpp and Ollama, thereby broadening their understanding of the EXAONE Deep models' potential and ensuring easier access to their powerful capabilities. This resource aims to empower users by facilitating a deeper engagement with the advanced functionalities of the models.

Media

Media

Integrations Supported

Ollama
OpenAI
OpenAI o1-mini

Integrations Supported

Ollama
OpenAI
OpenAI o1-mini

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Cohere AI

Date Founded

2019

Company Location

Canada

Company Website

cohere.com/blog/cohere-labs-tiny-aya

Company Facts

Organization Name

LG

Date Founded

1947

Company Location

South Korea

Company Website

github.com/LG-AI-EXAONE/EXAONE-Deep

Categories and Features

Categories and Features

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