<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DeepSeek-R1 on Peng Tan's AI Blog</title><link>https://c44db530.hobbytp-github-io.pages.dev/zh/tags/deepseek-r1/</link><description>一个关注 AI 各领域的专题博客</description><atom:link href="https://c44db530.hobbytp-github-io.pages.dev/zh/tags/deepseek-r1/index.xml" rel="self" type="application/rss+xml"/><item><title>微调</title><link>https://c44db530.hobbytp-github-io.pages.dev/zh/training/finetuning/</link><pubDate>Wed, 26 Feb 2025 22:14:00 +0800</pubDate><guid>https://c44db530.hobbytp-github-io.pages.dev/zh/training/finetuning/</guid><description>本文介绍了微调的常见挑战及其克服方法，并详细介绍了如何使用Unsloth在消费级GPU上对DeepSeek-R1进行微调。</description></item><item><title>DeepSeek R1 论文解读</title><link>https://c44db530.hobbytp-github-io.pages.dev/zh/deepseek/deepseek_r1/</link><pubDate>Mon, 10 Feb 2025 20:10:00 +0800</pubDate><guid>https://c44db530.hobbytp-github-io.pages.dev/zh/deepseek/deepseek_r1/</guid><description>本文介绍了深度求索（DeepSeek）公司推出的新一代推理模型DeepSeek-R1，并对其技术原理、主要贡献、论文方法、评估结果和局限性进行了详细解读。</description></item></channel></rss>