<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:media="http://search.yahoo.com/mrss/"><channel><title>llama.cpp on Shaowen Chen's Website</title><link>https://www.chenshaowen.com/en/tags/llama.cpp/</link><description>Recent content in llama.cpp on Shaowen Chen's Website</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>&amp;copy;2016 - {year}, All Rights Reserved.</copyright><lastBuildDate>Tue, 05 Sep 2023 00:00:00 +0000</lastBuildDate><sy:updatePeriod>weekly</sy:updatePeriod><atom:link href="https://www.chenshaowen.com/en/tags/llama.cpp/atom.xml" rel="self" type="application/rss+xml"/><item><title>LLM Deployment Tool llama.cpp</title><link>https://www.chenshaowen.com/en/blog/llama-cpp-that-is-a-llm-deployment-tool.html</link><pubDate>Tue, 05 Sep 2023 00:00:00 +0000</pubDate><atom:modified>Tue, 05 Sep 2023 00:00:00 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/llama-cpp-that-is-a-llm-deployment-tool.html</guid><description>1. LLM Deployment Tool llama.cpp Research on large models is split into two parts: training and inference. The training process is essentially the process of finding model parameters that minimize the model&amp;rsquo;s loss function and optimize the inference results. Once training is complete, the model&amp;rsquo;s parameters are fixed, and at</description><dc:creator>微信公众号</dc:creator><category>AI</category><category>LLM</category><category>Tools</category><category>llama.cpp</category><category>Inference</category><category>Deployment</category></item></channel></rss>