<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Model Training on The Coders Blog</title><link>https://thecodersblog.com/tag/model-training/</link><description>Recent content in Model Training on The Coders Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 05 May 2026 15:21:09 +0000</lastBuildDate><atom:link href="https://thecodersblog.com/tag/model-training/index.xml" rel="self" type="application/rss+xml"/><item><title>From Zero to LLM: The Technical Journey of Training Models from Scratch</title><link>https://thecodersblog.com/training-llms-from-scratch-2026/</link><pubDate>Tue, 05 May 2026 15:21:09 +0000</pubDate><guid>https://thecodersblog.com/training-llms-from-scratch-2026/</guid><description>&lt;p&gt;Imagine staring at a blank canvas, not with brushes and paint, but with terabytes of text data and a cluster of GPUs. You want to create a Large Language Model, a true behemoth of artificial intelligence, from the ground up. This isn&amp;rsquo;t about fine-tuning a pre-existing model; it&amp;rsquo;s about building every component yourself. It&amp;rsquo;s a monumental undertaking, often romanticized, but the reality is stark.&lt;/p&gt;
&lt;p&gt;The core problem of training an LLM from scratch is its sheer, unadulterated complexity and resource intensity. You&amp;rsquo;re not just writing a few Python scripts; you&amp;rsquo;re orchestrating a symphony of advanced algorithms, massive datasets, and distributed computing infrastructure.&lt;/p&gt;</description></item></channel></rss>