<?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>ML on Utensil&#39;s Blog</title>
    <link>https://utensil.tngl.sh/blog/tags/ml/</link>
    <description>Recent content in ML on Utensil&#39;s Blog</description>
    <generator>Hugo -- gohugo.io</generator>
    <language>en</language>
    <lastBuildDate>Thu, 04 May 2023 22:00:00 +0800</lastBuildDate>
    <atom:link href="https://utensil.tngl.sh/blog/tags/ml/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Transformers: from self-attention to performance optimizations</title>
      <link>https://utensil.tngl.sh/blog/posts/transformer/</link>
      <pubDate>Thu, 04 May 2023 22:00:00 +0800</pubDate>
      <guid>https://utensil.tngl.sh/blog/posts/transformer/</guid>
      <description>The purpose of this post is to understand what is under the hood and the performance factors involved when fine-tuning and running local Transformer models, keeping multi-modality in mind, with an emphasis on the decoder-only transformers (e.g. GPT series).
To accomplish this, we first present a brief account of the transformer architecture, including its design intuitions and the underlying mathematics, concretized by illustrative diagrams and code snippets. Then we aim to achieve a comprehensive understanding of the widely adopted performance optimizations for the original transformer architecture.</description>
    </item>
  </channel>
</rss>
