<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Elyza on akenji&#39;s lab</title>
    <link>https://akenji3.github.io/en/tags/elyza/</link>
    <description>Recent content in Elyza on akenji&#39;s lab</description>
    <generator>Hugo</generator>
    <language>en</language>
    <lastBuildDate>Fri, 03 May 2024 00:00:00 +0000</lastBuildDate>
    <atom:link href="https://akenji3.github.io/en/tags/elyza/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Running Elyza models on GPU using llama-cpp-python</title>
      <link>https://akenji3.github.io/en/post/20240503_llama-cpp-python/</link>
      <pubDate>Fri, 03 May 2024 00:00:00 +0000</pubDate>
      <guid>https://akenji3.github.io/en/post/20240503_llama-cpp-python/</guid>
      <description>&lt;h2 id=&#34;motivation&#34;&gt;Motivation&lt;/h2&gt;&#xA;&lt;p&gt;Quantization is essential to run LLM on the local workstation (12-16 GB of GPU memory). In this post, I summarize my attempt to maximize GPU resources using llama-cpp-python.&lt;/p&gt;&#xA;&lt;p&gt;The content includes some of my mistakes, as I got into some areas due to my lack of understanding.&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
