<?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>QLoRA on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/qlora/</link><description>Recent content in QLoRA 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, 16 Jul 2026 11:04:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/qlora/index.xml" rel="self" type="application/rss+xml"/><item><title>大模型微调实战：LoRA、QLoRA与全参微调的选择策略</title><link>https://guijiagi.com/posts/b1-ca24c427/</link><pubDate>Thu, 16 Jul 2026 11:04:00 +0800</pubDate><guid>https://guijiagi.com/posts/b1-ca24c427/</guid><description>对比分析LoRA、QLoRA和全参微调的适用场景、技术细节与工程实践指南</description></item><item><title>LoRA微调实战指南：参数高效微调的原理、实践与陷阱</title><link>https://guijiagi.com/posts/b2-dae4b598/</link><pubDate>Thu, 16 Jul 2026 10:03:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-dae4b598/</guid><description>从LoRA数学原理到工程实现，覆盖秩选择、学习率配置、QLoRA及常见微调陷阱的完整指南</description></item><item><title>Hermes 4微调实战：从数据准备到模型部署全流程</title><link>https://guijiagi.com/posts/hermes4-finetuning-guide/</link><pubDate>Wed, 08 Jul 2026 13:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/hermes4-finetuning-guide/</guid><description>Nous Hermes 4微调完整指南：数据准备、LoRA训练、评估调优、量化部署的企业级实践</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>QLoRA量化微调实战：显存减半效果不减</title><link>https://guijiagi.com/posts/qlora-quantized-finetuning-guide/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/qlora-quantized-finetuning-guide/</guid><description>QLoRA让7B模型微调只需8GB显存，70B模型只需24GB，本文详解QLoRA原理与实战配置</description></item><item><title>大模型微调成本分析：LoRA/QLoRA/全参数的费用对比</title><link>https://guijiagi.com/posts/finetuning-cost-analysis-lora-qlora-full-parameter/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/finetuning-cost-analysis-lora-qlora-full-parameter/</guid><description>深入分析三种主流微调方法的成本结构，涵盖显存、计算、存储和部署的全面费用对比</description></item><item><title>QLoRA量化微调指南</title><link>https://guijiagi.com/posts/qlora-finetune-guide/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/qlora-finetune-guide/</guid><description>QLoRA量化微调指南</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><item><title>LoRA/QLoRA 微调实战指南：显存省 10 倍</title><link>https://guijiagi.com/posts/lora-qlora-finetune-guide/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-qlora-finetune-guide/</guid><description>从原理到实战，全面解析 LoRA 和 QLoRA 参数高效微调技术，包含 Rank 选择、模块配置和 Unsloth 加速方案</description></item><item><title>LoRA/QLoRA 高效微调实践：单卡训练大模型</title><link>https://guijiagi.com/posts/lora-qlora-practice/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-qlora-practice/</guid><description>深入讲解 LoRA 低秩分解原理、QLoRA 量化、PEFT 库使用及从数据准备到部署的完整实践</description></item></channel></rss>