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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.

What is Hy4?

Hy4 preview is an innovative open-source Mixture-of-Experts model designed for numerous practical productivity applications, such as software development, office tasks, game creation, and scientific research. With an astounding 770 billion parameters and 49 billion activated per token, it features a remarkable 1 million-token context window, enabling it to adeptly handle extensive codebases, large sets of documents, and intricate multi-step operations. The model's architecture incorporates 78 layers that utilize Gated DeepSeek Sparse Attention and IndexCache for efficient sparse index reuse across layers, while identity Hyper-Connections are implemented to improve information flow within the model. Furthermore, a specialized Multi-Token Prediction layer supports speculative decoding, significantly boosting its performance. Hy4 preview is engineered to understand, strategize, troubleshoot, and verify complex engineering initiatives, all while delivering substantial advancements in the quality of front-end visuals and interaction design, ultimately serving as an essential tool for experts in a wide range of fields. This versatility makes it an outstanding choice for professionals seeking to enhance their productivity and efficiency in various projects.

Media

Media

Integrations Supported

Integrations Supported

Tencent Hy

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Not specified

Training Options

Documentation Hub

Company Facts

Organization Name

Magic AI

Date Founded

2022

Company Location

United States

Company Website

magic.dev/

Company Facts

Organization Name

Tencent

Date Founded

1998

Company Location

China

Company Website

hy.tencent.ai/research/hy4-preview

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

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