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-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Eric X. Liu's Personal Page</title><link>/</link><description>Recent content on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 20 Aug 2025 04:48:53 +0000</lastBuildDate><atom:link href="/index.xml" rel="self" type="application/rss+xml"/><item><title>A Technical Deep Dive into the Transformer's Core Mechanics</title><link>/posts/a-technical-deep-dive-into-the-transformer-s-core-mechanics/</link><pubDate>Tue, 19 Aug 2025 00:00:00 +0000</pubDate><guid>/posts/a-technical-deep-dive-into-the-transformer-s-core-mechanics/</guid><description>&lt;p>The Transformer architecture is the bedrock of modern Large Language Models (LLMs). While its high-level success is widely known, a deeper understanding requires dissecting its core components. This article provides a detailed, technical breakdown of the fundamental concepts within a Transformer block, from the notion of &amp;ldquo;channels&amp;rdquo; to the intricate workings of the attention mechanism and its relationship with other advanced architectures like Mixture of Experts.&lt;/p>
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Eric X. Liu's Personal Page</title><link>/</link><description>Recent content on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 20 Aug 2025 06:02:35 +0000</lastBuildDate><atom:link href="/index.xml" rel="self" type="application/rss+xml"/><item><title>A Technical Deep Dive into the Transformer's Core Mechanics</title><link>/posts/a-technical-deep-dive-into-the-transformer-s-core-mechanics/</link><pubDate>Tue, 19 Aug 2025 00:00:00 +0000</pubDate><guid>/posts/a-technical-deep-dive-into-the-transformer-s-core-mechanics/</guid><description>&lt;p>The Transformer architecture is the bedrock of modern Large Language Models (LLMs). While its high-level success is widely known, a deeper understanding requires dissecting its core components. This article provides a detailed, technical breakdown of the fundamental concepts within a Transformer block, from the notion of &amp;ldquo;channels&amp;rdquo; to the intricate workings of the attention mechanism and its relationship with other advanced architectures like Mixture of Experts.&lt;/p>
 &lt;h3 id="1-the-channel-a-foundational-view-of-d_model">
  1. The &amp;ldquo;Channel&amp;rdquo;: A Foundational View of &lt;code>d_model&lt;/code>
  &lt;a class="heading-link" href="#1-the-channel-a-foundational-view-of-d_model">
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  &lt;span class="sr-only">Link to heading&lt;/span>
  &lt;/a>
 &lt;/h3>
-&lt;p>In deep learning, a &amp;ldquo;channel&amp;rdquo; can be thought of as a feature dimension. While this term is common in Convolutional Neural Networks for images (e.g., Red, Green, Blue channels), in LLMs, the analogous concept is the model&amp;rsquo;s primary embedding dimension, commonly referred to as &lt;code>d_model&lt;/code>.&lt;/p></description></item><item><title>A Comprehensive Guide to Breville Barista Pro Maintenance</title><link>/posts/a-comprehensive-guide-to-breville-barista-pro-maintenance/</link><pubDate>Sat, 16 Aug 2025 00:00:00 +0000</pubDate><guid>/posts/a-comprehensive-guide-to-breville-barista-pro-maintenance/</guid><description>&lt;p>Proper maintenance is critical for the longevity and performance of a Breville Barista Pro espresso machine. Consistent cleaning not only ensures the machine functions correctly but also directly impacts the quality of the espresso produced. This guide provides a detailed, technical breakdown of the essential maintenance routines, from automated cycles to daily upkeep.&lt;/p>
+&lt;p>In deep learning, a &amp;ldquo;channel&amp;rdquo; can be thought of as a feature dimension. While this term is common in Convolutional Neural Networks for images (e.g., Red, Green, Blue channels), in LLMs, the analogous concept is the model&amp;rsquo;s primary embedding dimension, commonly referred to as &lt;code>d_model&lt;/code>.&lt;/p></description></item><item><title>Quantization in LLMs</title><link>/posts/quantization-in-llms/</link><pubDate>Tue, 19 Aug 2025 00:00:00 +0000</pubDate><guid>/posts/quantization-in-llms/</guid><description>&lt;p>The burgeoning scale of Large Language Models (LLMs) has necessitated a paradigm shift in their deployment, moving beyond full-precision floating-point arithmetic towards lower-precision representations. Quantization, the process of mapping a wide range of continuous values to a smaller, discrete set, has emerged as a critical technique to reduce model size, accelerate inference, and lower energy consumption. This article provides a technical overview of quantization theories, their application in modern LLMs, and highlights the ongoing innovations in this domain.&lt;/p></description></item><item><title>A Comprehensive Guide to Breville Barista Pro Maintenance</title><link>/posts/a-comprehensive-guide-to-breville-barista-pro-maintenance/</link><pubDate>Sat, 16 Aug 2025 00:00:00 +0000</pubDate><guid>/posts/a-comprehensive-guide-to-breville-barista-pro-maintenance/</guid><description>&lt;p>Proper maintenance is critical for the longevity and performance of a Breville Barista Pro espresso machine. Consistent cleaning not only ensures the machine functions correctly but also directly impacts the quality of the espresso produced. This guide provides a detailed, technical breakdown of the essential maintenance routines, from automated cycles to daily upkeep.&lt;/p>
 &lt;h4 id="understanding-the-two-primary-maintenance-cycles">
  &lt;strong>Understanding the Two Primary Maintenance Cycles&lt;/strong>
  &lt;a class="heading-link" href="#understanding-the-two-primary-maintenance-cycles">