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packt:工程师手册掌握从概念到产品的大语言模型工程艺术(英文版)(523页).pdf

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1、LLM Engineers HandbookMaster the art of engineering large language models from concept to productionPaul IusztinMaxime LabonneLLM Engineers HandbookCopyright 2024 Packt PublishingAll rights reserved.No part of this book may be reproduced,stored in a retrieval system,or transmitted in any form or by

2、any means,without the prior written permission of the publisher,except in the case of brief quotations embedded in critical articles or reviews.Every effort has been made in the preparation of this book to ensure the accuracy of the information presented.However,the information contained in this boo

3、k is sold without warranty,either express or implied.Neither the authors,nor Packt Publishing or its dealers and distributors,will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.Packt Publishing has endeavored to provide trademark information

4、 about all of the companies and products mentioned in this book by the appropriate use of capitals.However,Packt Publishing cannot guarantee the accuracy of this information.Senior Publishing Product Manager:Gebin GeorgeAcquisition Editor Peer Reviews:Swaroop SinghProject Editor:Amisha VathareConten

5、t Development Editor:Tanya DcruzCopy Editor:Safis EditingTechnical Editor:Karan SonawaneProofreader:Safis EditingIndexer:Manju ArasanPresentation Designer:Rajesh ShirsathDeveloper Relations Marketing Executive:Anamika SinghFirst published:October 2024Production reference:2171024Published by Packt Pu

6、blishing Ltd.Grosvenor House11 St Pauls SquareBirmingham B3 1RB,UK.ISBN 978-1-83620-007-ForewordsAs my co-founder at Hugging Face,Clement Delangue,and I often say,AI is becoming the default way of building technology.Over the past 3 years,LLMs have already had a profound impact on technology,and the

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本文主要介绍了LLM Twin的概念和架构。LLM Twin是一个AI角色,它将某人的写作风格、声音和个性融入大型语言模型(LLM)中,成为一个数字版的自己。作者通过构建LLM Twin,展示了如何将个人数据(如LinkedIn、Medium、Substack和GitHub个人资料)收集、处理并输入到LLM中,以实现个性化的内容生成。作者还讨论了使用ChatGPT等聊天机器人的局限性,并强调了构建LLM Twin的必要性,以保持个人品牌的原创性和真实性。此外,作者还介绍了如何使用特征/训练/推理(FTI)管道设计模式来构建可扩展的ML系统,并将其应用于LLM Twin架构。
什么是LLM Twin? 如何构建LLM Twin? LLM Twin有哪些应用场景?
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