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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 06:04:36 +0000</lastBuildDate><atom:link href="/index.xml" rel="self" type="application/rss+xml"/><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>Transformer's Core Mechanics</title><link>/posts/transformer-s-core-mechanics/</link><pubDate>Tue, 19 Aug 2025 00:00:00 +0000</pubDate><guid>/posts/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">
&lt;i class="fa-solid fa-link" aria-hidden="true" title="Link to heading">&lt;/i>
&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>Breville Barista Pro Maintenance</title><link>/posts/breville-barista-pro-maintenance/</link><pubDate>Sat, 16 Aug 2025 00:00:00 +0000</pubDate><guid>/posts/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>
<?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 06:28:39 +0000</lastBuildDate><atom:link href="/index.xml" rel="self" type="application/rss+xml"/><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>Breville Barista Pro Maintenance</title><link>/posts/breville-barista-pro-maintenance/</link><pubDate>Sat, 16 Aug 2025 00:00:00 +0000</pubDate><guid>/posts/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">
@@ -33,6 +25,14 @@
&lt;p>&lt;strong>The Problem:&lt;/strong>
Many routing mechanisms, especially &amp;ldquo;Top-K routing,&amp;rdquo; involve a discrete, hard selection process. A common function is &lt;code>KeepTopK(v, k)&lt;/code>, which selects the top &lt;code>k&lt;/code> scoring elements from a vector &lt;code>v&lt;/code> and sets others to $-\infty$ or $0$.&lt;/p></description></item><item><title>An Architectural Deep Dive of T5</title><link>/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/</link><pubDate>Sun, 01 Jun 2025 00:00:00 +0000</pubDate><guid>/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/</guid><description>&lt;p>In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the &amp;ldquo;decoder-only&amp;rdquo; model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.&lt;/p>
&lt;p>But to truly understand the field, we must look at the pivotal models that explored different paths. Google&amp;rsquo;s T5, or &lt;strong>Text-to-Text Transfer Transformer&lt;/strong>, stands out as one of the most influential. It didn&amp;rsquo;t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices.&lt;/p></description></item><item><title>Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso</title><link>/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/</link><pubDate>Thu, 01 May 2025 00:00:00 +0000</pubDate><guid>/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/</guid><description>&lt;p>Are you ready to transform your home espresso game from good to genuinely great? The Breville Barista Pro is a fantastic machine, but unlocking its full potential requires understanding a few key principles. This guide will walk you through the systematic process of dialing in your espresso, ensuring every shot is delicious and repeatable.&lt;/p>
&lt;p>Our overarching philosophy is simple: &lt;strong>isolate and change only one variable at a time.&lt;/strong> While numbers are crucial, your palate is the ultimate judge. Dose, ratio, and time are interconnected, but your &lt;strong>grind size&lt;/strong> is your most powerful lever.&lt;/p></description></item><item><title>Some useful files</title><link>/posts/useful/</link><pubDate>Mon, 26 Oct 2020 04:14:43 +0000</pubDate><guid>/posts/useful/</guid><description>&lt;ul>
&lt;p>Our overarching philosophy is simple: &lt;strong>isolate and change only one variable at a time.&lt;/strong> While numbers are crucial, your palate is the ultimate judge. Dose, ratio, and time are interconnected, but your &lt;strong>grind size&lt;/strong> is your most powerful lever.&lt;/p></description></item><item><title>Transformer's Core Mechanics</title><link>/posts/transformer-s-core-mechanics/</link><pubDate>Tue, 01 Apr 2025 00:00:00 +0000</pubDate><guid>/posts/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">
&lt;i class="fa-solid fa-link" aria-hidden="true" title="Link to heading">&lt;/i>
&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>Some useful files</title><link>/posts/useful/</link><pubDate>Mon, 26 Oct 2020 04:14:43 +0000</pubDate><guid>/posts/useful/</guid><description>&lt;ul>
&lt;li>&lt;a href="/rootCA.crt" >rootCA.pem&lt;/a>&lt;/li>
&lt;/ul></description></item><item><title>About</title><link>/about/</link><pubDate>Fri, 01 Jun 2018 07:13:52 +0000</pubDate><guid>/about/</guid><description/></item></channel></rss>