<?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>Llama on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/llama/</link><description>Recent content in Llama 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 11:07:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/llama/index.xml" rel="self" type="application/rss+xml"/><item><title>开源大模型生态2026：Llama、Qwen、DeepSeek三足鼎立</title><link>https://guijiagi.com/posts/b1-4b9b3771/</link><pubDate>Thu, 16 Jul 2026 11:07:00 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+0800</pubDate><guid>https://guijiagi.com/posts/llama-4-%E7%B3%BB%E5%88%97%E5%85%A8%E9%9D%A2%E8%AF%84%E6%B5%8B-meta-%E7%9A%84%E5%BC%80%E6%BA%90%E5%8F%8D%E5%87%BB/</guid><description>Meta 发布 Llama 4 系列（Scout/Maverick/Behemoth），全面评测其性能、部署成本与社区生态，分析是否值得从 Llama 3 升级</description></item></channel></rss>