<?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>Model-Adaptation on AI Charcha</title><link>https://www.aicharcha.com/tags/model-adaptation/</link><description>Recent content in Model-Adaptation on AI Charcha</description><image><title>AI Charcha</title><url>https://www.aicharcha.com/images/aicharcha-logo-refresh-1.svg</url><link>https://www.aicharcha.com/images/aicharcha-logo-refresh-1.svg</link></image><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 17 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.aicharcha.com/tags/model-adaptation/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM Fine-Tuning Best Practices for 2026: When and How to Adapt Models</title><link>https://www.aicharcha.com/research/llm-fine-tuning-best-practices-2026/</link><pubDate>Sat, 13 Jun 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/llm-fine-tuning-best-practices-2026/</guid><description>Comprehensive guide to fine-tuning large language models, including cost-benefit analysis, techniques, tools, and practical implementation patterns for teams.</description></item></channel></rss>