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<!DOCTYPE html>
<html lang="en" class="no-js">
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<title>大规模语言模型：从理论到实践</title>
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<meta name="keywords" content="Natural language Processing, NLP, Deep Learning, Neural Networks" />
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<h2 class="section-title">关于本书</h2>
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大语言模型（Large Language Models，LLM）是一种由包含数百亿以上权重的深度神经网络构建的语言模型，使用自监督学习方法通过大量无标记文本进行训练。自2018年以来，包含Google、OpenAI、Meta、百度、华为等公司和研究机构都纷纷发布了包括BERT， GPT等在内多种模型，并在几乎所有自然语言处理任务中都表现出色。2021年开始大模型呈现爆发式的增长，特别是2022年11月ChatGPT发布后，更是引起了全世界的广泛关注。用户可以使用自然语言与系统交互，从而实现包括问答、分类、摘要、翻译、聊天等从理解到生成的各种任务。大型语言模型展现出了强大的对世界知识掌握和对语言的理解。本书将介绍大语言模型的基础理论包括语言模型、分布式模型训练以及强化学习，并以Deepspeed-Chat框架为例介绍实现大语言模型和类ChatGPT系统的实践。
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<h4>张奇</h4>
<p> 复旦大学，计算机科学技术学院，教授</p>
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<h4>桂韬</h4>
<p> 复旦大学，计算语言学研究院，青年副研究员</p>
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<h4>郑锐</h4>
<p> 复旦大学，计算机科学技术学院，博士研究生</p>
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<img src="images/huangxuanjing.jpg" alt="image">
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<h4>黄萱菁</h4>
<p> 复旦大学，计算机科学技术学院，教授</p>
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<h3 class="section-title">下载当前版本：<a href="./chapter/LLM-TAP.pdf"> 完整版本</a><a href="https://pan.baidu.com/s/1smGQ5ECzDIpvZladuCE59g?pwd=jyz6"> 完整版-百度网盘 </a> </h3>
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<ul>
<li><a href="./slides/ch1.pptx">第一章 绪论</a></li>
<li><a href="./slides/ch2.pptx">第二章 大语言模型基础</a></li>
<li><a href="./slides/ch3.pptx">第三章 大语言模型预训练数据</a></li>
<li><a href="./slides/ch4.pptx">第四章 分布式模型训练</a></li>
<li><a href="./slides/ch5.pptx">第五章 有监督微调</a></li>
<li><a href="./slides/ch6.pptx">第六章 强化学习</a></li>
<li><a href="./slides/ch7.pptx">第七章 大语言模型应用</a></li>
<li><a href="./slides/ch8.pptx">第八章 大语言模型评估</a></li>
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张奇、桂韬、郑锐、黄萱菁，大语言模型理论与实践，https://intro-llm.github.io/, 2023.
</div>
<div class="alert alert-dark" role="alert">
@book{zhang2023introllm, <br>
title = {大规模语言模型：从理论到实践},<br>
publisher = {},<br>
year = {2023},<br>
author = {张奇、桂韬、郑锐、黄萱菁},<br>
address = {上海},<br>
isbn = {},<br>
url = {https://intro-llm.github.io/},<br>
}
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<p>如果您有任何意见、评论以及建议请通过GitHub的<a href="https://github.com/intro-llm/intro-llm.github.io/issues" target="_blank">Issues</a>页面进行反馈。</p>
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