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What is Syn?

Syn is an advanced Japanese large language model, developed through a collaboration between Upstage and Karakuri, featuring nearly 14 billion parameters and specifically designed for enterprise use in various fields such as finance, manufacturing, legal, and healthcare. It excels in benchmark evaluations on the Weights & Biases Nejumi Leaderboard, demonstrating top-tier performance in accuracy and alignment while maintaining cost-effectiveness through its efficient architecture, which draws inspiration from Solar Mini. Furthermore, Syn showcases outstanding capabilities in Japanese "truthfulness" and safety, skillfully understanding intricate expressions and specialized jargon relevant to different industries. Its flexible fine-tuning options allow for the seamless integration of proprietary data and domain knowledge. Built for widespread deployment, Syn is versatile enough to operate in on-premises environments, AWS Marketplace, and cloud infrastructures, reinforced by strong security and compliance protocols tailored to meet enterprise demands. Impressively, by leveraging AWS Trainium, Syn can reduce training costs by approximately 50 percent compared to traditional GPU setups, enabling rapid customization for a variety of applications. This cutting-edge model not only boosts operational efficiency but also opens doors to more agile and responsive solutions for enterprises, ultimately transforming how businesses approach their challenges. Enhancing productivity and innovation, Syn positions itself as a vital tool for organizations looking to thrive in an increasingly competitive landscape.

What is LTM-2-mini?

LTM-2-mini is designed to manage a context of 100 million tokens, which is roughly equivalent to about 10 million lines of code or approximately 750 full-length novels. This model utilizes a sequence-dimension algorithm that proves to be around 1000 times more economical per decoded token compared to the attention mechanism employed by Llama 3.1 405B when operating within the same 100 million token context window. Additionally, the difference in memory requirements is even more pronounced; running Llama 3.1 405B with a 100 million token context requires an impressive 638 H100 GPUs per user just to sustain a single 100 million token key-value cache. In stark contrast, LTM-2-mini only needs a tiny fraction of the high-bandwidth memory available in one H100 GPU for the equivalent context, showcasing its remarkable efficiency. This significant advantage positions LTM-2-mini as an attractive choice for applications that require extensive context processing while minimizing resource usage. Moreover, the ability to efficiently handle such large contexts opens the door for innovative applications across various fields.

Media

Media

Integrations Supported

AWS Marketplace
AWS Trainium
Amazon Web Services (AWS)
Solar Mini
Upstage AI

Integrations Supported

API Availability

API Availability

Pricing Information

$0.1 per 1M tokens

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Not specified

Company Facts

Organization Name

Upstage AI

Date Founded

2020

Company Location

United States

Company Website

www.upstage.ai/news/introducing-upstage-japan

Company Facts

Organization Name

Magic AI

Date Founded

2022

Company Location

United States

Company Website

magic.dev/

Categories and Features

AI Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

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