<?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 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/peft/</link><description>Recent content in PEFT on 硅基 AGI · 智能体学习与测评</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Thu, 02 Jul 2026 11:30:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/peft/index.xml" rel="self" type="application/rss+xml"/><item><title>LoRA微调手把手教程</title><link>https://guijiagi.com/posts/lora-fine-tuning-step-by-step/</link><pubDate>Thu, 02 Jul 2026 11:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-fine-tuning-step-by-step/</guid><description>从环境搭建到模型部署，LoRA微调的完整手把手教程</description></item><item><title>LoRA vs QLoRA 2026对比：参数高效微调的两种路径</title><link>https://guijiagi.com/posts/lora-vs-qlora-2026/</link><pubDate>Thu, 02 Jul 2026 10:42:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-vs-qlora-2026/</guid><description>系统对比LoRA与QLoRA在2026年的技术差异、性能表现与适用场景</description></item><item><title>LoRA 微调实战 2026：从数据准备到部署的完整流程</title><link>https://guijiagi.com/posts/lora-finetuning-2026-data-to-deployment/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-finetuning-2026-data-to-deployment/</guid><description>手把手教你用 LoRA 微调大语言模型，涵盖数据工程、训练配置、评估和部署的全流程</description></item><item><title>LoRA微调参数调优指南</title><link>https://guijiagi.com/posts/lora-finetuning-tuning-guide/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-finetuning-tuning-guide/</guid><description>LoRA微调的核心参数调优指南，从rank选择到学习率设置的全面实践</description></item><item><title>LoRA vs DoRA vs QLoRA：参数高效微调三剑客对比</title><link>https://guijiagi.com/posts/lora-vs-dora-vs-qlora/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-vs-dora-vs-qlora/</guid><description>深入对比 LoRA、DoRA、QLoRA 三种参数高效微调方法的原理、数学公式、代码实现与效果差异，给出不同场景下的最佳选择建议和完整的实验数据对比。</description></item><item><title>QLoRA 微调实战：4bit 量化下的高效训练</title><link>https://guijiagi.com/posts/fine-tuning-qlora-guide/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/fine-tuning-qlora-guide/</guid><description>深入解析 QLoRA 的 NF4 量化、双重量化、分页优化器原理，提供完整的 LLaMA-3 QLoRA 微调代码实战，涵盖数据准备、训练配置、显存优化技巧与常见问题排查。</description></item></channel></rss>