Introduction to the LLMBook-zh.github.io Project
Overview of Large Language Models
In late 2022, the launch of ChatGPT marked a significant milestone in the evolution of large language models, capturing the interest of both the scientific community and the general public. This leap in artificial intelligence has prompted many to question the underlying technologies that power such models. The technologies behind large language models have progressed through various phases, including statistical language models, neural language models, and pre-trained language models. Each phase represents considerable effort and ingenuity from researchers around the world.
OpenAI has been a trailblazer in this area, pushing the boundaries of what large language models can achieve. However, since the release of GPT-3, detailed technical disclosures from OpenAI have been limited. This has created challenges for academia, where access to the vast resources necessary for training such models is scarce, hampering first-hand exploration and research.
Training large models involves many intricate details that aren't readily available from existing research papers. With the complexity and experimental needs increasing exponentially, gaining expertise in large models remains a formidable task. It's predominantly the industrial sector that leads in developing powerful language models, leaving academic institutions trailing behind.
Despite these challenges, there is a silver lining as both academia and industry recognize the importance of openness and transparency. The release of foundational models, technical codes, and scholarly papers are part of a movement to democratize knowledge and drive AI advancements collaboratively. Publicly available information reveals structured methodologies for training large models, such as data cleaning, instruction fine-tuning, and human preference alignment algorithms. This gradual transparency allows for smoother advances despite the complexity and enormity of resources involved.
Purpose of This Project
The LLMBook-zh.github.io project aims to provide a comprehensive understanding of large language model technologies, covering their fundamental principles, critical technologies, and application prospects. This book is intended to guide readers through the current state and future trends of large language models, offering insights for both research and practical applications. The aspiration is to contribute to AI advancement through shared knowledge and cooperative development.
By offering a detailed examination, this book seeks to empower its readers—primarily advanced undergraduates and early-year graduate students with a background in deep learning—to grasp the overarching framework and roadmap of large language technologies. The book serves as a foundational text for newcomers to the field.
Accessing the Book and Resources
Readers can access this resource in Chinese, aiming to serve as a reference for those new to large model technologies. The comprehensive content was initiated in late 2023 and was recently drafted, targeting deep learning enthusiasts and learners.
- Full Book Download: LLMBook PDF - Link 1, Link 2
In addition, the project offers an English review article, continually updated to reflect the latest technology trends and advancements in large language models.
- English Survey Paper: A Survey of Large Language Models on arXiv
Supporting Tools and Models
The project comes with a robust set of tools and resources to aid in large language model development:
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LLMBox - A comprehensive code library designed to facilitate the training and development of large language models: GitHub Repository
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YuLan Models - A conversational large language model developed by the faculty and students of Renmin University of China, featuring supervised fine-tuning with bilingual data: GitHub Repository
Feedback and Contribution
This book was crafted through extensive exploration of seminal papers, related codebases, and scholarly texts, carefully distilling the essential concepts, prevalent algorithms, and models into a coherent volume. It acknowledges the possibility of omissions or inaccuracies given the vastness of the subject and welcomes constructive feedback from its readers.
Readers are encouraged to share their suggestions and insights via the project's GitHub Issues page or by contacting the authors directly through their provided email addresses. The project seeks to forge a collaborative learning journey, continuing to update and refine its contents.
Feedback Portal: GitHub Issues