<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>LoRA on Dylan Ler</title><link>http://dylanler.github.io/tags/lora/</link><description>Recent content in LoRA on Dylan Ler</description><generator>Hugo -- 0.133.0</generator><language>en-us</language><lastBuildDate>Wed, 04 Feb 2026 09:30:00 -0800</lastBuildDate><atom:link href="http://dylanler.github.io/tags/lora/index.xml" rel="self" type="application/rss+xml"/><item><title>Teaching a 0.6B Model to See Physics: Fine-Tuning Qwen3 for p5.js Animations</title><link>http://dylanler.github.io/posts/fine-tuning-qwen3-p5js-physics-animations/</link><pubDate>Wed, 04 Feb 2026 09:30:00 -0800</pubDate><guid>http://dylanler.github.io/posts/fine-tuning-qwen3-p5js-physics-animations/</guid><description>What happens when you take one of the smallest language models available, feed it a thousand physics animations generated by one of the largest, and ask it to teach K-12 students about science?
You get a model that weighs less than a gigabyte, trains in under 3 minutes, and generates interactive physics simulations on demand.
The Premise LLMs are getting bigger. GPT-5, Claude Opus, Gemini Ultra &amp;ndash; they&amp;rsquo;re all racing to hundreds of billions of parameters.</description></item></channel></rss>