<?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>LoRA on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/lora/</link><description>Recent content in LoRA 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/lora/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2026：从数据准备到部署的全流程</title><link>https://guijiagi.com/posts/lora-finetuning-2026-guide/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-finetuning-2026-guide/</guid><description>2026年LoRA微调的最新实践指南，涵盖数据工程、训练配置、评估优化到生产部署的完整流程</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>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>大模型微调工具链 2026：LLaMA-Factory vs Axolotl vs Unsloth</title><link>https://guijiagi.com/posts/finetuning-toolchain-2026-llamafactory-axolotl-unsloth/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/finetuning-toolchain-2026-llamafactory-axolotl-unsloth/</guid><description>三大微调工具链全面对比：LLaMA-Factory、Axolotl、Unsloth的功能、性能与易用性评测</description></item><item><title>LoRA微调参数调优指南</title><link>https://guijiagi.com/posts/lora-finetune-tuning/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-finetune-tuning/</guid><description>LoRA微调参数调优指南</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>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>