February 2025
Intermediate
376 pages
9h 25m
Japanese
Content preview from 仕組みからわかる大規模言語モデル 生成AI時代のソフトウェア開発入門
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1
ここで、
P
ij
は出力確率分布
P
の
i
行
j
列の要素、
Z
ij
は入力行列
Z
の
i
行
j
列の要素を表して
います。
この関数は、次のステップで計算されます。
1. 行列
Z
の各要素に対して、指数関数
e
Z
ij
を適用します。これにより、各要素が正の値にな
ります。
2. 各行について、指数関数を適用した値の合計 を計算します。この合計値は、そ
の行の正規化定数になります。
3. 行列
Z
の各要素について、指数関数を適用した値を正規化定数で割ります。これにより、
各行の要素の合計が1 になります。
ソフトマックス関数は、行列の各行に対して独立に適用されます。つまり、各行が独立した
確率分布になります。
1.3.6
パラメータ一覧
ここまでで Transformerの仕組みを詳しく説明してきました。埋め込み層、デコーダスタッ
ク、出力線形層の各層には、それぞれ多くのパラメータが含まれています。これらのパラメー
タをまとめたものを表 1.3.1 に示します。公開されている情報をもとに、表 1.3.1 にはGPT-3
のパラメータ数の推定値を記載しています。合計した値は約1750億個 [Brown et al. 2020]のパ
ラメータとなり、これは公表されているパラメータ数とほぼ同じです。したがって、本章で説
明した Transformerの仕組みは、GPT-3 などの大規模なモデルでも概ね一致しているとも考え
られます。
表1.3.1
レイヤ名 パラメータ名
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.