دانلود کتاب اصول و معماری ChatGPT

As a university computer science researcher and a veteran entrepreneur, I was profoundly impressed by experiencing firsthand the logical reasoning capabilities emerging from ChatGPT. Although many celebrate the efficiency enhancements in multimodal content creation brought by generative artificial intelligence (AI), the reasoning abilities displayed by ChatGPT are often underestimated. This capability enables ChatGPT to serve not only as the core of a new generation of human–computer interaction but also as an intelligent agent to build automated and semiautomated workflows. It can even merge with industrial control or robotics fields, thereby triggering profound social changes. Many underestimate the impact of this transformation. Given the current pace of R&D and commercial application iterations, I expect that this transformation will gradually permeate all aspects of human life and production over the next 3–5 years, greatly enhancing existing productivity and thereby initiating a series of changes. If asked to pinpoint the last era called a “major technological transformation,” many would unhesitatingly refer to the dawn of the internet. This transformation will also reshape business models related to content production, change existing work methods, and even drive changes in production methods. Of course, this still depends on whether the next generation of large language models can achieve breakthroughs in controllability of content output. This book is designed to help readers deeply understand ChatGPT and its related technologies. It consists of 11 chapters that comprehensively explore various aspects. Chapter 1 provides an in-depth analysis of the technological evolution of large language models, supporting technologies, and technology stacks, and discusses their significant impact on society. Chapter 2 elaborates on the theoretical foundations and main components of the Transformer model, revealing the principles and applications behind these technologies. Chapter 3 delves into the generative pretraining process and principles of GPT. Chapter 4 primarily explores technologies such as layer normalization, orthogonal initialization, and reversible tokenization in GPT-2, and provides a detailed analysis of GPT-2 autoregressive generation process. Chapter 5 introduces GPT-3 sparse attention mechanisms, metalearning, and content-based learning concepts, and discusses the application of Bayesian inference in conceptual distributions. Chapter 6 details the pretraining datasets and data processing methods for large language models, as well as distributed training models and architectures. Chapter 7 deeply analyzes the fundamental principles of the proximal policy optimization (PPO) algorithm. Chapter 8 focuses on the fine-tuning datasets of reinforcement learning with human feedback (RLHF) and the application of PPO in InstructGPT, discussing the capabilities of multiturn dialog and the necessity of human feedback reinforcement learning. Chapter 9 explores how to transfer large language models to specific domains at low resource costs. Chapter 10 primarily introduces the middleware technologies involved in the development of large language models. Chapter 11 predicts and prospects the future development trends of large language models.