دانلود کتاب افزایش کارایی، تنظیم دقیق و تکنیک‌های استنتاج در LLM

This book provides an in-depth investigation of the dynamic world of large language models (LLMs), which are revolutionizing various facets of research and application within the fields of deep learning, natural language understanding, medical image analysis, predicting the properties of proteins, understanding language acquisition, and many others. We have observed that LLMs not only represent a hot topic but signify a potentially fundamental paradigm shift, one which may lead to the dawn of a new era of research and so merits significant attention. These models pose unique challenges due to their scale, requiring unconventional training and data collection approaches that differ from traditional methods.
Beyond basic language processing, LLMs offer expanded capabilities that warrant further exploration. This broadens their utility and introduces additional challenges, particularly concerning their operation as intelligent agents. Questions of privacy, security, and ethical usage are paramount and highlight the distinct nature of these models compared to their predecessors. It is these differences and the need for a comprehensive understanding that inspired us to compile this book.
This year, following the third annual workshop on Efficient Natural Language and Speech Processing,1 we recognized the opportunity to transform some of the work-shop’s content into a published format for the benefit of the broader community. We realized that researchers provide small but vital techniques to train, fine-tune, optimize, and accelerate LLMs. These incremental but crucial innovations often bypass the traditional publication process, where a theoretical method is enshrouded in a lengthy paper accompanied by an extensive literature review. Instead, today’s researchers lean towards presenting engineering solutions and diving directly into the main techniques without much preliminary exposition. We felt it important that this evolving approach be documented in a book, and one which could appear as quickly as possible in this fast-moving area, so that other practitioners could benefit from the range of techniques contained herein. By doing so, we not only preserve the ingenuity of contemporary researchers but also provide a reliable resource for more traditional learners yet wish to access cutting-edge material. This ensures that valuable practical insights are captured and made accessible to a wider audience. Additionally, by documenting these insights, we make sure that the engineering claims align with the scientific standards and expectations of the research commu-nity. This balance enhances the credibility of the techniques presented and fosters a more robust dialogue between developers of practical applications and researchers interested in adding to our theoretical understanding of how these models work, and how they can be made to work more effectively.
The main theme of this book is efficiency and the pivotal topic is “scale”. More specifically, in this volume, we aim to examine the reasons behind the substantial size of LLMs, investigate the intricacies of their design and the consequent implications. We will discuss the formidable challenges they pose, as well as the unprecedented opportunities they offer. The discussion extends to various technical considerations such as model training, selection of data sets, and the architecture of LLMs. In the first introductory chapter, we lay out a roadmap for the journey ahead, detailing what readers can expect from each subsequent section and chapter of the book. Additionally, we provide the basic fundamentals necessary to understand LLMs, ensuring that regardless of their prior knowledge, readers have a solid foundation from which to explore more advanced concepts throughout the book. The following chapters will not shy away from (sometimes quite deep) detail, as dissecting the intricacies of LLMs is critical to aid understanding of this new paradigm.
This book adopts a direct and focused approach, intentionally putting slightly less weight on traditional literature review and background exposition to immedi-ately engage with proposed techniques and relevant details. It is crafted for a diverse audience, including students, practitioners, and both junior and senior scientists and engineers, whether in academia or industry. Designed to cater to a broad spectrum of readers, it ranges from those seeking detailed technical insights into the workings of LLMs to those interested in understanding the broader implications of these models in practical and theoretical contexts. By maintaining a careful balance between in-depth technical explanations and overarching discussions, this book aims to be both acces-sible and valuable, swiftly moving to enrich the reader’s knowledge and appreciation of LLMs without presupposing extensive prior knowledge of the field.