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2016 -
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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">

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<!doctype html><html lang=en><head><title>Posts · Eric X. Liu's Personal Page</title><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=color-scheme content="light dark"><meta name=author content="Eric X. Liu"><meta name=description content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta name=keywords content="software engineer,performance engineering,Google engineer,tech blog,software development,performance optimization,Eric Liu,engineering blog,mountain biking,Jeep enthusiast,overlanding,camping,outdoor adventures"><meta name=twitter:card content="summary"><meta name=twitter:title content="Posts"><meta name=twitter:description content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta property="og:url" content="/posts/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Posts"><meta property="og:description" content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta property="og:locale" content="en"><meta property="og:type" content="website"><link rel=canonical href=/posts/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.6445a802b9389c9660e1b07b724dcf5718b1065ed2d71b4eeaf981cc7cc5fc46.css integrity="sha256-ZEWoArk4nJZg4bB7ck3PVxixBl7S1xtO6vmBzHzF/EY=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><link rel=alternate type=application/rss+xml href=/posts/index.xml title="Eric X. Liu's Personal Page"></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=/>Eric X. Liu's Personal Page
</a><input type=checkbox id=menu-toggle>
<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container list"><header><h1 class=title><a class=title-link href=/posts/>Posts</a></h1></header><ul><li><span class=date>August 19, 2025</span>
<a class=title href=/posts/a-technical-deep-dive-into-the-transformer-s-core-mechanics/>A Technical Deep Dive into the Transformer's Core Mechanics</a></li><li><span class=date>August 16, 2025</span>
<a class=title href=/posts/a-technical-deep-dive-into-the-transformer-s-core-mechanics/>A Technical Deep Dive into the Transformer's Core Mechanics</a></li><li><span class=date>August 19, 2025</span>
<a class=title href=/posts/quantization-in-llms/>Quantization in LLMs</a></li><li><span class=date>August 16, 2025</span>
<a class=title href=/posts/a-comprehensive-guide-to-breville-barista-pro-maintenance/>A Comprehensive Guide to Breville Barista Pro Maintenance</a></li><li><span class=date>August 9, 2025</span>
<a class=title href=/posts/secure-boot-dkms-and-mok-on-proxmox-debian/>Fixing GPU Operator Pods Stuck in Init: Secure Boot, DKMS, and MOK on Proxmox + Debian</a></li><li><span class=date>August 7, 2025</span>
<a class=title href=/posts/how-rvq-teaches-llms-to-see-and-hear/>Beyond Words: How RVQ Teaches LLMs to See and Hear</a></li><li><span class=date>August 3, 2025</span>
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<a class=title href=/posts/a-deep-dive-into-ppo-for-language-models/>A Deep Dive into PPO for Language Models</a></li><li><span class=date>July 2, 2025</span>
<a class=title href=/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/>Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice</a></li><li><span class=date>June 1, 2025</span>
<a class=title href=/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/>An Architectural Deep Dive of T5</a></li><li><span class=date>May 1, 2025</span>
<a class=title href=/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/>Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso</a></li><li><span class=date>October 26, 2020</span>
<a class=title href=/posts/useful/>Some useful files</a></li></ul></section></div><footer class=footer><section class=container>©
<a class=title href=/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/>Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso</a></li></ul><ul class=pagination><li>1</li><li><a href=/posts/page/2/>2</a></li><li class=hidden><a href=/posts/page/2/>&#8250;</a></li><li><a href=/posts/page/2/>&#187;</a></li></ul></section></div><footer class=footer><section class=container>©
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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>Posts on Eric X. Liu's Personal Page</title><link>/posts/</link><description>Recent content in Posts 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="/posts/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>Posts on Eric X. Liu's Personal Page</title><link>/posts/</link><description>Recent content in Posts 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="/posts/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>
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&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">

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2025
Eric X. Liu
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