<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Self-Attention on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/self-attention/</link><description>Recent content in Self-Attention on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 22 Aug 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/self-attention/index.xml" rel="self" type="application/rss+xml"/><item><title>Decoding Large Language Models: A Deep Dive into LLM Architectures</title><link>https://ai-blog.noorshomelab.dev/ai/llm-architectures/</link><pubDate>Fri, 22 Aug 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai/llm-architectures/</guid><description>&lt;h1 id="decoding-large-language-models-a-deep-dive-into-llm-architectures"&gt;Decoding Large Language Models: A Deep Dive into LLM Architectures&lt;/h1&gt;
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&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Large Language Models (LLMs) have revolutionized the field of Artificial Intelligence, demonstrating unprecedented capabilities in understanding, generating, and manipulating human language. At their core, LLMs are complex neural networks, primarily built upon the Transformer architecture. This document serves as a comprehensive guide to LLM architectures, catering to both beginners and experienced professionals. We will journey from the foundational concepts of Transformer models to the intricate structural details of modern open-source LLMs, exploring their design choices and implications for development and optimization.&lt;/p&gt;</description></item></channel></rss>