<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SFT on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/sft/</link><description>Recent content in SFT on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 30 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/sft/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 4: Your First Tunix Fine-Tuning: Supervised Fine-Tuning (SFT)</title><link>https://ai-blog.noorshomelab.dev/tunix-mastery-2026/04-first-sft-model/</link><pubDate>Fri, 30 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/tunix-mastery-2026/04-first-sft-model/</guid><description>&lt;h2 id="chapter-4-your-first-tunix-fine-tuning-supervised-fine-tuning-sft"&gt;Chapter 4: Your First Tunix Fine-Tuning: Supervised Fine-Tuning (SFT)&lt;/h2&gt;
&lt;p&gt;Welcome back, future LLM master! In Chapter 3, we successfully set up our Tunix environment and explored its foundational components. Now, it&amp;rsquo;s time to put that knowledge into action and perform our very first model alignment task: Supervised Fine-Tuning (SFT).&lt;/p&gt;
&lt;p&gt;This chapter is your hands-on guide to taking a pre-trained Large Language Model (LLM) and teaching it a new, specific skill using a carefully curated dataset. We&amp;rsquo;ll walk through everything from preparing your data to configuring Tunix&amp;rsquo;s powerful &lt;code&gt;Trainer&lt;/code&gt; and observing your model learn. By the end, you&amp;rsquo;ll have a practical understanding of SFT and the confidence to apply it to your own projects. Get ready to make some LLMs smarter!&lt;/p&gt;</description></item></channel></rss>