<?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>Gemma4 on orndorff.dev</title>
    <link>https://orndorff.dev/tags/gemma4/</link>
    <description>Recent content in Gemma4 on orndorff.dev</description>
    <generator>Hugo -- 0.138.0</generator>
    <language>en-us</language>
    <lastBuildDate>Fri, 07 Aug 2026 00:00:00 +0000</lastBuildDate>
    <atom:link href="https://orndorff.dev/tags/gemma4/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Fine tuning a small model that sounds like me, mostly</title>
      <link>https://orndorff.dev/posts/creating-bots-that-sound-like-a-nerd/</link>
      <pubDate>Fri, 07 Aug 2026 00:00:00 +0000</pubDate>
      <guid>https://orndorff.dev/posts/creating-bots-that-sound-like-a-nerd/</guid>
      <description>&lt;p&gt;I’ve been experimenting with how much of our personality is encoded in the data we generate every day. We all have a digital exhaust—years of emails, texts, and chat logs. I figured, if I could distill 10 years of my own communication into a small, specialized model, I could create a personal AI that sounds like me.&lt;/p&gt;
&lt;p&gt;The result is a 73 MB LoRA &amp;ldquo;voice cartridge&amp;rdquo; that makes stock Gemma 4 E4B text like me—blind-judged 31/40 times over its base model.&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
