<?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>Peft on knowledged.to</title><link>https://knowledged.to/tags/peft/</link><description>Recent content in Peft on knowledged.to</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 25 Apr 2026 21:31:15 +0530</lastBuildDate><atom:link href="https://knowledged.to/tags/peft/index.xml" rel="self" type="application/rss+xml"/><item><title>Fine-Tuning Techniques for LLMs</title><link>https://knowledged.to/notes/ml/fine-tuning-techniques/</link><pubDate>Sat, 25 Apr 2026 15:53:49 +0000</pubDate><guid>https://knowledged.to/notes/ml/fine-tuning-techniques/</guid><description>Comprehensive guide to LLM fine-tuning methods including full, parameter-efficient, and preference-based approaches with modern recipes and tools like LoRA and DPO</description></item></channel></rss>