<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Fine-Tuning - Tag - vo.rs</title><link>https://vo.rs/tags/fine-tuning/</link><description>Fine-Tuning - Tag - vo.rs</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Tue, 16 Jul 2024 09:00:00 +0000</lastBuildDate><atom:link href="https://vo.rs/tags/fine-tuning/" rel="self" type="application/rss+xml"/><item><title>LoRA Fine-Tuning on Consumer Hardware: Adding Skills to a Model Without Retraining It</title><link>https://vo.rs/story/lora-fine-tuning-on-consumer-hardware-adding-skills-to-a-model-without-retraining-it/</link><description>&lt;p&gt;&amp;ldquo;Fine-tuning&amp;rdquo; used to be a word that came with a server room attached. Retraining a multi-billion-parameter model meant a rack of data-centre GPUs, weeks of compute, and a budget that no homelab tinkerer was ever going to sign off on. Then a technique called LoRA quietly changed the maths, and now you can teach a large model a genuinely new skill on the same graphics card you use for gaming. I&amp;rsquo;ve done it on a single 24GB GPU over a long evening, and the result was good enough to be useful.&lt;/p&gt;</description><pubDate>Tue, 16 Jul 2024 09:00:00 +0000</pubDate></item></channel></rss>