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<!doctype html><html lang=en><head><title>Transformer's Core Mechanics · 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 http-equiv=Content-Security-Policy content="upgrade-insecure-requests; block-all-mixed-content; default-src 'self'; child-src 'self'; font-src 'self' https://fonts.gstatic.com https://cdn.jsdelivr.net/; form-action 'self'; frame-src 'self' https://www.youtube.com; img-src 'self'; object-src 'none'; style-src 'self' 'unsafe-inline' https://fonts.googleapis.com/ https://cdn.jsdelivr.net/; script-src 'self' 'unsafe-inline' https://www.google-analytics.com https://cdn.jsdelivr.net/ https://pagead2.googlesyndication.com https://static.cloudflareinsights.com https://unpkg.com https://ericxliu-me.disqus.com https://disqus.com https://*.disqus.com https://*.disquscdn.com https://unpkg.com; connect-src 'self' https://www.google-analytics.com https://pagead2.googlesyndication.com https://cloudflareinsights.com ws://localhost:1313 ws://localhost:* wss://localhost:*;"><meta name=author content="Eric X. Liu"><meta name=description content="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 &ldquo;channels&rdquo; to the intricate workings of the attention mechanism and its relationship with other advanced architectures like Mixture of Experts.
<!doctype html><html lang=en><head><title>Transformer's Core Mechanics · 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 http-equiv=Content-Security-Policy content="upgrade-insecure-requests; block-all-mixed-content; default-src 'self'; child-src 'self'; font-src 'self' https://fonts.gstatic.com https://cdn.jsdelivr.net/; form-action 'self'; frame-src 'self' https://www.youtube.com https://disqus.com; img-src 'self' https://referrer.disqus.com https://c.disquscdn.com https://*.disqus.com; object-src 'none'; style-src 'self' 'unsafe-inline' https://fonts.googleapis.com/ https://cdn.jsdelivr.net/; script-src 'self' 'unsafe-inline' https://www.google-analytics.com https://cdn.jsdelivr.net/ https://pagead2.googlesyndication.com https://static.cloudflareinsights.com https://unpkg.com https://ericxliu-me.disqus.com https://disqus.com https://*.disqus.com https://*.disquscdn.com https://unpkg.com; connect-src 'self' https://www.google-analytics.com https://pagead2.googlesyndication.com https://cloudflareinsights.com ws://localhost:1313 ws://localhost:* wss://localhost:* https://links.services.disqus.com https://*.disqus.com;"><meta name=author content="Eric X. Liu"><meta name=description content="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 &ldquo;channels&rdquo; to the intricate workings of the attention mechanism and its relationship with other advanced architectures like Mixture of Experts.
1. The &ldquo;Channel&rdquo;: A Foundational View of d_model
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2025
Eric X. Liu
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