<?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>LLM训练 on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/llm%E8%AE%AD%E7%BB%83/</link><description>Recent content in LLM训练 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/llm%E8%AE%AD%E7%BB%83/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>微调数据准备最佳实践</title><link>https://guijiagi.com/posts/fine-tuning-data-preparation/</link><pubDate>Thu, 02 Jul 2026 11:29:00 +0800</pubDate><guid>https://guijiagi.com/posts/fine-tuning-data-preparation/</guid><description>从数据采集到质量控制，LLM微调数据准备的完整最佳实践</description></item><item><title>LLM数据增强技术：用AI训练更好的AI</title><link>https://guijiagi.com/posts/data-augmentation-llm/</link><pubDate>Thu, 02 Jul 2026 10:47:00 +0800</pubDate><guid>https://guijiagi.com/posts/data-augmentation-llm/</guid><description>系统介绍大语言模型训练中的数据增强技术，从简单变换到AI生成增强</description></item><item><title>LLM持续学习实践：让模型与时俱进</title><link>https://guijiagi.com/posts/continual-learning-llm/</link><pubDate>Thu, 02 Jul 2026 10:46:00 +0800</pubDate><guid>https://guijiagi.com/posts/continual-learning-llm/</guid><description>探讨大语言模型持续学习的方法、挑战与工程实践，防止灾难性遗忘</description></item></channel></rss>