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LLMBook-zh.github.io

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《大语言模型》作者：赵鑫，李军毅，周昆，唐天一，文继荣

Model APIsFreeFree tier
Inputs: textOutputs: text
Type
Open Source
Visit Website GitHub (4.5k stars)

About LLMBook-zh.github.io

LLMBook-zh.github.io is the official companion website for the Chinese-language book "大语言模型" (Large Language Models), authored by Zhao Xin, Li Junyi, Zhou Kun, Tang Tianyi, and Wen Jirong. The book is published by Higher Education Press and aims to provide a systematic technical framework and roadmap for beginners in large language models. It builds upon the authors' earlier English survey paper "A Survey of Large Language Models," which has been updated to its 14th version, and expands the content into a comprehensive Chinese textbook. The website offers supplementary resources including PDF lecture slides, code snippets hosted on GitHub, and links to teaching videos on Bilibili, all designed to support educators and learners in understanding LLM fundamentals.

Key Features

Comprehensive Chinese textbook on large language models covering pre-training, fine-tuning, alignment, prompt engineering, and more
Companion website provides free PDF lecture slides for instructors using the book as a primary textbook
Public GitHub repository containing code snippets from the book
Teaching videos available on Bilibili for self-paced learning
Regular updates and corrections based on reader feedback
Covers both foundational concepts and advanced topics such as long-context models, retrieval-augmented generation, and complex reasoning

Pros & Cons

Pros
  • Authored by leading researchers with hands-on experience in developing large models like Wenlan and Yulan
  • Content is systematically organized and updated based on the latest research
  • Free supplementary resources including slides, code, and videos are provided
  • Covers a wide range of topics from basics to advanced techniques in a single volume
  • Actively maintained with a feedback mechanism for corrections and improvements
Cons
  • The book is written in Chinese, which may limit accessibility for non-Chinese readers
  • The website and resources are primarily focused on the book content; no interactive tool or API is offered
  • Free PDF slides are only available to instructors who adopt the book as a primary textbook and meet specific conditions
  • The book assumes readers have a deep learning background, so it may not be suitable for absolute beginners
  • Some topics may become outdated as the field of LLMs evolves rapidly

Best For

Academic courses on large language models at universities and research institutionsSelf-study for individuals with deep learning background seeking a systematic introduction to LLMsReference material for researchers and practitioners working on LLM development and deploymentTeaching resource for instructors preparing lectures on pre-training, fine-tuning, and alignment techniquesSupplementary reading for those who have read the English survey paper and want a more detailed Chinese exposition

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FAQ

Is the book available for free?
The book is a published textbook by Higher Education Press and is not freely available online. However, the companion website provides free supplementary resources such as PDF slides (for qualifying instructors), code snippets, and teaching videos.
Can I download the PDF slides?
PDF slides are available for instructors who have adopted the book as a primary textbook. Requests must be sent from an institutional email with course details. Slides are not for public distribution or commercial use.
Where can I find the code from the book?
All code snippets from the book are publicly available on GitHub at the repository linked from the website.
Are there video lectures available?
Yes, teaching videos corresponding to the book chapters are publicly available on Bilibili.
How can I report errors or suggest improvements?
The authors encourage readers to provide feedback and suggestions via the website. They plan to continuously update the content based on reader input.
Is the book suitable for beginners?
The book is designed for readers with a deep learning background. It aims to provide a systematic introduction to large language models, so beginners in deep learning may find it challenging.