<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRL on 黄文卓 | DevOps Engineer</title><link>https://socake.github.io/tags/trl/</link><description>Recent content in TRL on 黄文卓 | DevOps Engineer</description><generator>Hugo -- gohugo.io</generator><language>zh-CN</language><managingEditor>17691281867@163.com (Wenzhuo Huang)</managingEditor><webMaster>17691281867@163.com (Wenzhuo Huang)</webMaster><copyright>© 2026 Wenzhuo Huang</copyright><lastBuildDate>Wed, 14 Jan 2026 09:56:00 +0800</lastBuildDate><atom:link href="https://socake.github.io/tags/trl/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM 微调入门：LoRA 让大模型适配私有场景</title><link>https://socake.github.io/posts/llm-finetuning-lora-practice/</link><pubDate>Wed, 14 Jan 2026 09:56:00 +0800</pubDate><author>17691281867@163.com (Wenzhuo Huang)</author><guid>https://socake.github.io/posts/llm-finetuning-lora-practice/</guid><description>什么时候该微调、什么时候该用提示工程？本文给出决策框架，然后用Unsloth+QLoRA实战微调Qwen2.5-7B，覆盖数据格式、训练监控、权重合并、部署到vLLM测试，以及10个真实踩坑记录。</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://socake.github.io/posts/llm-finetuning-lora-practice/featured.jpg"/></item></channel></rss>