https://answer.freetools.me/tags/peft/ Recent content in PEFT on Answer Hugo -- 0.152.2 zh-cn Thu, 12 Mar 2026 03:43:50 +0800 https://answer.freetools.me/%E5%8F%82%E6%95%B0%E9%AB%98%E6%95%88%E5%BE%AE%E8%B0%83%E4%B8%BA%E4%BB%80%E4%B9%880.1%E7%9A%84%E5%8F%82%E6%95%B0%E8%83%BD%E5%81%9A%E5%88%B0%E5%85%A8%E5%8F%82%E6%95%B0%E5%BE%AE%E8%B0%8399%E7%9A%84%E6%95%88%E6%9E%9C/ Thu, 12 Mar 2026 03:43:50 +0800 https://answer.freetools.me/%E5%8F%82%E6%95%B0%E9%AB%98%E6%95%88%E5%BE%AE%E8%B0%83%E4%B8%BA%E4%BB%80%E4%B9%880.1%E7%9A%84%E5%8F%82%E6%95%B0%E8%83%BD%E5%81%9A%E5%88%B0%E5%85%A8%E5%8F%82%E6%95%B0%E5%BE%AE%E8%B0%8399%E7%9A%84%E6%95%88%E6%9E%9C/ 从全参数微调的资源困境出发，深入解析Adapter Tuning、Prefix Tuning、Prompt Tuning、LoRA及其变体的技术原理、数学基础与性能权衡。基于NeurIPS 2024最新研究揭示LoRA与全参数微调的本质差异，并提供实践中的超参数选择指南。 https://answer.freetools.me/lora%E4%BD%8E%E7%A7%A9%E9%80%82%E9%85%8D%E4%B8%BA%E4%BD%95%E8%83%BD%E4%BB%A5%E5%8D%83%E5%88%86%E4%B9%8B%E4%B8%80%E5%8F%82%E6%95%B0%E9%87%8F%E5%AE%9E%E7%8E%B0%E9%AB%98%E6%95%88%E5%BE%AE%E8%B0%83/ Mon, 09 Mar 2026 02:52:14 +0800 https://answer.freetools.me/lora%E4%BD%8E%E7%A7%A9%E9%80%82%E9%85%8D%E4%B8%BA%E4%BD%95%E8%83%BD%E4%BB%A5%E5%8D%83%E5%88%86%E4%B9%8B%E4%B8%80%E5%8F%82%E6%95%B0%E9%87%8F%E5%AE%9E%E7%8E%B0%E9%AB%98%E6%95%88%E5%BE%AE%E8%B0%83/ 深入解析LoRA低秩适配技术如何通过内在维度假设实现参数高效微调。从2021年微软原始论文到QLoRA、DoRA等变体演进，涵盖低秩分解的数学原理、参数选择最佳实践、与全参数微调的性能对比，以及工程应用中的权衡考量。