LTM-2-mini AI technology page Top Builders

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LTM-2-mini

LTM-2-mini (Long-Term Memory Mini Model) is a cutting-edge AI model developed by Magic, designed to process extremely large contexts efficiently. Capable of handling up to 100 million tokens (equivalent to 10 million lines of code), this model is revolutionizing industries that require the analysis of vast datasets. Its innovative sequence-dimension algorithm allows for efficient processing, making it approximately 1,000 times more resource-efficient than traditional attention-based models like Llama 3.1. The LTM-2-mini significantly enhances machine comprehension over longer contexts, enabling better reasoning and recall in tasks like software development, legal document analysis, and database management.

Read more about LTM-2-mini in our article 👉 How Magic.dev’s LTM-2-mini is Redefining AI’s Ability to Handle Vast Contexts

General
Author	Magic (Founded by Eric Steinberger and Sebastian De Ro)
Release Date	August 2024
Website	https://magic.dev/
Technology Type	AI Model, Natural Language Processing (NLP)

Key Features

100 Million Token Context Window: The ability to process up to 100 million tokens simultaneously, surpassing the capabilities of existing models.
Sequence-Dimension Algorithm: Reduces computational and memory overhead by 1,000 times compared to traditional attention mechanisms.
HashHop Evaluation: A unique evaluation method that tests the model's ability to perform multi-hop reasoning and skip-step tasks across vast datasets.
Efficient Resource Usage: Despite handling a massive amount of data, the LTM-2-mini requires minimal GPU power, utilizing a small fraction of the resources needed by comparable models.
Multi-hop and Non-linear Reasoning: Capable of understanding complex relationships and dependencies across entire datasets without relying on semantic cues.

Applications

Software Development: Revolutionizes how developers manage and understand large codebases. The model assists in identifying code dependencies, suggesting optimizations, and automating routine tasks like unit testing or documentation.
Legal Document Analysis: Processes entire legal contracts, cases, and documents to ensure no vital details are overlooked, improving the accuracy and speed of legal analysis.
Database and Large Text Analysis: Ideal for analyzing massive datasets and conversation histories, enabling AI to recall and process information holistically without missing critical details.
Real-time Code Generation: Supports coding platforms by providing intelligent, context-aware suggestions and quality assurance in large, complex projects.

Get Started Building with LTM-2-mini:

Ready to harness the power of ultra-large context windows in your projects? With Magic’s LTM-2-mini, you can revolutionize how AI interacts with large datasets and complex codebases. Whether you're developing software, analyzing legal documents, or building conversational agents, LTM-2-mini opens up new possibilities for AI-assisted efficiency.

👉 Read more about LTM-2-mini and start building intelligent solutions that leverage the unmatched context-handling capabilities of LTM-2-mini.

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LTM-2-mini AI technology page Hackathon projects

Discover innovative solutions crafted with LTM-2-mini AI technology page, developed by our community members during our engaging hackathons.

NetConnect

Public Sector Network Connectivity Analyzer The Public Sector Network Connectivity Analyzer is a comprehensive solution designed to address the critical need for reliable network monitoring across public institutions. Our application serves as an essential tool for IT administrators managing connectivity infrastructure for schools, healthcare facilities, government offices, libraries, and other public service organizations. Core Capabilities Real-Time Network Visualization Interactive diagrams and topology maps provide clear visibility into how public institutions are connected, displaying network elements, connection points, and infrastructure components with intuitive visualization tools. Performance Monitoring System Our platform continuously tracks vital network metrics including uptime percentages, latency measurements, bandwidth utilization, and connection status across the entire public sector network, enabling proactive management. Advanced Simulation Engine IT professionals can run comprehensive simulations to test network resilience under various scenarios such as increased user loads, infrastructure failures, or cyber incidents, helping identify vulnerabilities before they impact critical services. Institution Management Portal Administrators can efficiently manage information about connected institutions, monitor their connection status in real-time, and access detailed performance metrics through a unified dashboard interface. Geographic Mapping Integration Our system incorporates geographic visualization capabilities to display the physical distribution of institutions and network infrastructure across regions, facilitating better resource allocation and planning. Technical Implementation This solution addresses the unique challenges faced by public sector organizations that require reliable connectivity for delivering essential services to communities, while providing the tools needed to ensure network resilience, performance, and security.

Smart Tax Assistant

A three-fold architecture to assume diverse roles for LLAMA-3.2B in order to solve the language barrier problem, generate answers in local language (Urdu), and only cater Pakistan's Tax Ordinance. Now we achieved this first by creating a RAG pipeline, where we used mini-LLM to generate embeddings for our documents, we had two docs, 1 referred to 800 pages of laws governing taxation, other referred to different combination of designations requiring different slabs of tax computation. Now we stored chunks + embeddings in a VectorDB and upon query of user, extracted the relevant context through similarity search, after that we used LLAMA-3.2-11B to match the context with the query and create keyword specific tags, that would help streamline the prompt further ahead on basis of user's query and knowledge base. Then we sent those tags + query + context to LLAMA again to generate the relative pointers to each tag in a detailed simplified manner according to the context. Then we sent this generated text to LLAMA acting as a language converter, to prompt for Urdu Answers such that it can be understood by the majority of native population.