DeepSeek in Action LLM Deployment, Fine‐Tuning, and Application (Jing Dai)（Z-Library）

DeepSeek in Action From fundamental concepts to advanced implementations, this book thoroughly explores the DeepSeek‑V3  model, focusing on its Transformer‑based architecture, technological innovations, and applications. This book begins with a thorough examination of theoretical foundations, including self‑attention, positional encoding, the Mixture of Experts mechanism, and distributed training strategies. It then explores DeepSeek‑V3’s technical advancements, including sparse attention mechanisms, FP8  mixed‑precision training, and hierarchical load bal‑ ancing, which optimize memory and energy efficiency. Through case studies and API integration techniques, the model’s high‑performance capabilities in text generation, math‑ ematical reasoning, and code completion are examined. This book highlights DeepSeek’s open platform and covers secure API authentication, concurrency strategies, and real‑time data processing for scalable AI applications. Additionally, this book addresses industry applications, such as chat client development, utilizing DeepSeek’s context caching and callback functions for automation and predictive maintenance. This book is aimed primarily at AI researchers and developers working on large‑scale AI models. It is an invaluable resource for professionals seeking to understand the theo‑ retical underpinnings and practical implementation of advanced AI systems, particularly those interested in efficient, scalable applications. Jing Dai graduated from Tsinghua University with research expertise in data mining, nat‑ ural language processing, and related fields. With over a decade of experience as a techni‑ cal engineer at leading companies including IBM and VMware, she has developed strong technical capabilities and deep industry insight. In recent years, her work has focused on advanced technologies such as large‑scale model training, NLP, and model optimiza‑ tion, with particular emphasis on Transformer architectures, attention mechanisms, and multi‑task learning.

DeepSeek in Action LLM Deployment, Fine‑Tuning, and Application Jing Dai

Designed cover image: Shutterstock First edition published 2026 by CRC Press 2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2026 Jing Dai Translated by DeepL Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978‑750‑8400. For works that are not available on CCC please contact mpkbookspermissions@tandf.co.uk Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. English Version by permission of Posts and Telecom Press Co., Ltd. ISBN: 978‑1‑041‑09000‑7 (hbk) ISBN: 978‑1‑041‑14491‑5 (pbk) ISBN: 978‑1‑003‑67470‑2 (ebk) DOI: 10.1201/9781003674702 Typeset in Minion by codeMantra

v Contents Preface, xiii Part I Theoretical Foundations and Technical Architecture of Generative AI Chapter 1 ◾ Core Principles of Transformer and Attention Mechanisms 3 1.1 BASIC STRUCTURE OF TRANSFORMER 3 1.1.1 Encoder‑Decoder Architecture 4 1.1.2 Self‑Attention Mechanisms vs. Multi‑Head Attention Mechanisms 5 1.1.3 Residual Connection and Layer Normalization 7 1.2 CORE PRINCIPLES OF ATTENTION MECHANISMS 9 1.2.1 Dot‑Product Attention vs. Additive Attention 9 1.2.2 Softmax Normalization Principles 11 1.2.3 Sparse Attention Matrix and Accelerated Optimization 13 1.3 EXTENSION AND OPTIMIZATION OF TRANSFORMER 15 1.3.1 Implementation of Dynamic Attention 15 1.3.2 Long‑Range Attention and Sparse Attention Mechanisms 17 1.3.3 Diversity Positional Encoding 19 1.4 CONTEXT WINDOW 21 1.4.1 Context Window Extensions 21 1.4.2 Balancing Memory and Computational Complexity 23 1.4.3 DeepSeek‑V3 Optimization for Context Window 25 1.5 BALANCING TRAINING COSTS WITH COMPUTATIONAL EFFICIENCY 27 1.5.1 Trends in the Number of Participants and Growth in Computing Needs 27 1.5.2 GPU Computing Architecture in Transformer 30

vi ◾ Contents 1.5.3 How DeepSeek‑V3 Reduces Training Costs 32 1.6 SUMMARY OF THE CHAPTER 33 Chapter 2 ◾ DeepSeek‑V3 Core Architecture and Its Training Techniques in Detail 34 2.1 MoE ARCHITECTURE AND ITS CORE CONCEPTS 34 2.1.1 Introduction to Mixed Expertise (MoE) 35 2.1.2 Working Mechanism of Sigmoid Routing 37 2.1.3 MoE‑Based DeepSeek‑V3 Architecture Design 39 2.2 ADVANTAGES OF FP8 MIXED‑PRECISION TRAINING 40 2.2.1 Fundamentals of Mixed‑Precision Calculations 40 2.2.2 Application of FP8 to Large Model Training 42 2.2.3 FP8‑Based DeepSeek‑V3 Performance Enhancement Strategy 44 2.3 DUALPIPE ALGORITHM AND COMMUNICATION OPTIMIZATION 46 2.3.1 DualPipe Algorithm 46 2.3.2 All‑to‑All Communication Mechanisms across Nodes 49 2.3.3 InfiniBand with NVLink Bandwidth Optimization 50 2.4 DISTRIBUTED TRAINING OF LARGE MODELS 52 2.4.1 Tradeoffs between Data Parallelism and Model Parallelism 53 2.4.2 Distributed Training Architecture for DeepSeek‑V3 55 2.4.3 Design and Optimization of Dynamic Learning Rate Scheduler 56 2.4.4 Auxiliary Loss‑Free Load Balancing Policy 58 2.4.5 Multi‑Token Prediction Strategy 60 2.5 CACHING MECHANISMS AND TOKEN MANAGEMENT 62 2.5.1 Basic Concepts of Cache Hits and Cache Misses 62 2.5.2 Definition and Encoding Process of Token Encoding 64 2.5.3 Efficient Caching Mechanism in DeepSeek‑V3 66 2.6 DEEPSEEK FAMILY OF MODELS 67 2.6.1 DeepSeek LLM 67 2.6.2 DeepSeek‑Coder 69 2.6.3 DeepSeek‑Math 70 2.6.4 DeepSeek‑VL 71 2.6.5 DeepSeek‑V2 72

Contents ◾ vii 2.6.6 DeepSeek‑Coder‑V2 73 2.6.7 DeepSeek‑V3 74 2.7 SUMMARY OF THE CHAPTER 77 Chapter 3 ◾ Introduction to DeepSeek‑V3 Model‑Based Development 78 3.1 LARGE MODEL APPLICATION SCENARIOS 78 3.1.1 Text Generation and Abstraction 78 3.1.2 Question Answering System and Dialogue Generation 79 3.1.3 Multilingual Programming and Code Generation 80 3.2 ADVANTAGES AND APPLICATION DIRECTIONS OF DEEPSEEK‑V3 81 3.2.1 Practical Performance in Different Areas 82 3.2.2 Multilingual Programming Skills (Based on Aider Assessment Cases) 83 3.2.3 Exploring the Application of Code and Math Tasks 84 3.3 SCALING LAWS RESEARCH AND PRACTICE 85 3.3.1 Scaling Laws for Model Size and Performance 85 3.3.2 Experimental Results of Scaling Laws on Small Models 86 3.4 MODEL DEPLOYMENT AND INTEGRATION 90 3.4.1 API Invocation and Real‑Time Generation 90 3.4.2 Local Deployment 93 3.4.3 Performance Optimization Strategies 95 3.5 COMMON PROBLEMS AND SOLUTIONS IN DEVELOPMENT 98 3.5.1 Input Design and Generation Control 98 3.5.2 Model Bias and Robustness Issues 102 3.5.3 Response Techniques for DeepSeek‑V3‑Specific Issues 105 3.6 SUMMARY OF THE CHAPTER 109 Part II Development and Application of Generative AI and Advanced Prompt Design Chapter 4 ◾ A First Look at the DeepSeek‑V3 Big Model 113 4.1 DIALOGUE GENERATION AND SEMANTIC COMPREHENSION CAPABILITIES 113 4.1.1 Single‑Turn Dialogue vs. Multi‑Turn Dialogue 114 4.1.2 Contextual Interaction 114

viii ◾ Contents 4.2 MATHEMATICAL REASONING SKILLS 119 4.2.1 Assessment of Routine Math Topics 119 4.2.2 Complex Puzzle Comprehension and Reasoning 122 4.3 ASSISTED PROGRAMMING CAPABILITIES 128 4.3.1 Assisted Algorithm Development 128 4.3.2 Software Development 130 4.4 SUMMARY OF THE CHAPTER 135 Chapter 5 ◾ DeepSeek Open Platform and API Development Details 136 5.1 INTRODUCTION TO THE DEEPSEEK OPEN PLATFORM 136 5.1.1 Overview of the Platform’s Core Modules and Services 136 5.1.2 Key Players and Collaboration in the Open Ecosystem 138 5.2 BASIC OPERATION OF DEEPSEEK API AND API INTERFACE DETAILS 141 5.2.1 API Invocation Authentication Mechanism and Request Structure 141 5.2.2 Functional Analysis and Examples of Common Interfaces 144 5.3 API PERFORMANCE OPTIMIZATION AND SECURITY STRATEGY 150 5.3.1 Performance Optimization Tips for Reducing Latency 150 5.3.2 Data Protection and Access Control Management 154 5.4 SUMMARY OF THE CHAPTER 157 Chapter 6 ◾ Dialogue Generation, Code Completion, and Customized Model Development 159 6.1 BASIC PRINCIPLES AND IMPLEMENTATION OF DIALOGUE GENERATION 159 6.1.1 Input–Output Design of Dialogue Model 159 6.1.2 Contextual Management in Natural Language Interaction 162 6.2 IMPLEMENTATION LOGIC AND OPTIMIZATION OF CODE COMPLETION 165 6.2.1 Model Adaptation Strategies to Programming Languages 166 6.2.2 Performance Optimization of the Deep Completion Function 169 6.3 CUSTOMIZED MODEL DEVELOPMENT BASED ON DEEPSEEK 173 6.3.1 Model Fine‑Tuning and Task Specialization Techniques 173 6.3.2 Case Studies of Customized Dialogue and Complementary Models 176 6.3.3 Synthesis Case 1: Code Generation and Task Specialization based on the DeepSeek‑V3 Model 180 6.4 SUMMARY OF THE CHAPTER 185

Contents ◾ ix Chapter 7 ◾ Conversation Prefix Completion, FIM, and JSON Output Development Details 186 7.1 TECHNICAL PRINCIPLES AND APPLICATIONS OF CONVERSATIONAL PREFIX COMPLETION 186 7.1.1 Design Logic and Implementation Scheme for Prefix Modeling 187 7.1.2 Control and Implementation of Diverse Continuation Styles 190 7.2 FIM GENERATION MODEL ANALYSIS 193 7.2.1 FIM Task Definition and Generation Flow 193 7.2.2 DeepSeek Optimization for FIM Tasks 196 7.3 JSON FORMAT OUTPUT DESIGN AND GENERATION LOGIC 199 7.3.1 Model Implementation for Structured Data Generation 199 7.3.2 JSON Output in Real‑World Development 202 7.3.3 Synthesis Case 2: DeepSeek Model‑Based Multi‑Turn Dialogue Generation with Structured Data Generation 205 7.4 SUMMARY OF THE CHAPTER 210 Chapter 8 ◾ Callback Functions and Contextual Disk Caching 211 8.1 CALLBACK FUNCTION MECHANISM AND APPLICATION SCENARIOS 211 8.1.1 Principles of Callback Function and Its Design Principles 212 8.1.2 DeepSeek Callback Optimization Techniques 215 8.2 FUNDAMENTALS OF CONTEXTUAL DISK CACHING 218 8.2.1 Impact Analysis of Cache Hits and Misses 219 8.2.2 Hard Disk Cache Implementation 222 8.3 COMBINED APPLICATION OF CALLBACK FUNCTIONS AND CACHING MECHANISMS 226 8.3.1 Context‑Based Design of Intelligent Cache Calls 226 8.3.2 Performance Improvement Case Study of Efficient Cache and Callback Combination 229 8.3.3 Synthesis Case 3: DeepSeek Integration and Optimization of a Smart Power Station Management System 234 8.4 SUMMARY OF THE CHAPTER 238

x ◾ Contents Chapter 9 ◾ The DeepSeek Prompt Library: Exploring More Possibilities for Prompts 239 9.1 CODE‑RELATED APPLICATIONS 239 9.1.1 Code Refactoring 239 9.1.2 Code Annotation 243 9.1.3 Code Generation 245 9.2 CONTENT GENERATION AND CLASSIFICATION 250 9.2.1 Content Classification 250 9.2.2 Structured Output 252 9.3 ROLE‑PLAYING 255 9.3.1 Role‑Playing (Customized Personas) 255 9.3.2 Role‑Playing (Scenario Continuation) 256 9.4 LITERARY CREATION 258 9.4.1 Prose Writing 258 9.4.2 Poetry 261 9.5 COPYWRITING AND PUBLICITY 262 9.5.1 Copywriting Generation 262 9.5.2 Tagline Generation 266 9.6 MODEL PROMPTS AND TRANSLATION EXPERTS 267 9.6.1 Model Prompt Generation 267 9.6.2 Translation Specialists 270 9.7 SUMMARY OF THE CHAPTER 272 Part III Integration of Practical Experience and Advanced Applications Chapter 10 ◾ Integration Practice 1: LLM‑Based Chat Client Development 275 10.1 OVERVIEW OF THE CHAT CLIENT AND ITS FUNCTIONAL FEATURES 275 10.1.1 Chat’s Core Design Philosophy 276 10.1.2 Analysis of Common Application Scenarios 278 10.2 CONFIGURATION AND INTEGRATION OF THE DEEPSEEK API 280 10.2.1 API Key Acquisition and Configuration 280 10.2.2 Common Interface Calls 282 10.2.3 Chat Client API Integration Implementation 287 10.3 MULTI‑MODEL SUPPORT AND SWITCHING 290 10.3.1 Architectural Design for Multi‑Model Support Switching 290

Contents ◾ xi 10.3.2 Model Selection Strategies for Different Task Scenarios 294 10.3.3 Complete Code and System Testing 298 10.4 SUMMARY OF THE CHAPTER 303 Chapter 11 ◾ Integration Hands‑on 2: AI Assistant Development 304 11.1 AI ASSISTANT: THE LAUNCHER OF THE AI ERA 304 11.1.1 Explanation of the Core Functions of AI Assistants 305 11.1.2 Commercialization of AI Assistants 307 11.2 CONFIGURATION AND APPLICATION OF DEEPSEEK API IN AI ASSISTANT 309 11.2.1 API Adaptation Process for AI Assistant with DeepSeek 309 11.2.2 Integrated Application of Speech Recognition and Natural Language Processing (NLP) 311 11.3 IMPLEMENTATION AND OPTIMIZATION OF INTELLIGENT ASSISTANT FUNCTIONS 314 11.3.1 Optimization Strategies for Improving Q&A Accuracy 314 11.3.2 Augmentation Techniques for Continuous Learning and Contextual Understanding 317 11.4 SUMMARY OF THE CHAPTER 320 Chapter 12 ◾ Integration Practice 3: Assisted Programming Plugin Development Based on VS Code 321 12.1 OVERVIEW OF THE ASSISTED PROGRAMMING PLUGIN AND ITS CORE FUNCTIONS 321 12.1.1 Functional Positioning of the Assisted Programming Plugin 322 12.1.2 Explanation of Useful Features for Developers 327 12.2 INTEGRATING THE DEEPSEEK API IN VS CODE 330 12.2.1 Flow of API Invocation in a Plugin 331 12.2.2 Efficiently Managing Caching of API Invocations 334 12.3 IMPLEMENTATION OF CODE AUTO‑COMPLETION AND INTELLIGENT SUGGESTIONS 339 12.3.1 Code Completion Mechanism with Deep Semantic Understanding 339 12.3.2 Personalized Suggestions and Flexible Development Model Configuration 343 12.4 USING ASSISTED PROGRAMMING PLUGINS TO ENHANCE DEVELOPMENT EFFICIENCY 347 12.4.1 Integration of Tools for Rapid Error Localization and Fixes 348 12.4.2 Automated Script Generation 351

xii ◾ Contents 12.4.3 Quickly Generate Large Project Documentation Notes 356 12.4.4 DeepSeek Empowerment Program Construction and Management 360 12.4.5 Code Maintenance for Large Projects 364 12.4.6 Intelligent Code Generation with Multilingual Support 368 12.4.7 Intelligent Debugging Tools for Deeply Integrated Development Environments 371 12.4.8 Intelligent Code Quality Assessment and Optimization Recommendation Generation 374 12.5 SUMMARY OF THE CHAPTER 378 POSTSCRIPT, 379 REFERENCES, 380

xiii Preface Generative AI has made revolutionary progress in recent years and is reshap‑ ing the core framework of AI technology with its outstanding performance in Text Generation, Code Generation, Multimodal Processing, and other areas. As a representa‑ tive architecture of this technology, Transformer lays the theoretical foundation of genera‑ tive AI with its Self‑Attention Mechanism and modular design. Based on the optimization and extension of Transformer, DeepSeek provides powerful support for efficiently process‑ ing large‑scale generative tasks through the Mixture of Experts (MoE) architecture, FP8 Mixed Precision Training, and distributed training optimization. DeepSeek‑V3 is one of the open‑source big models in the DeepSeek series, focusing on tasks such as Dialogue Generation, Code Completion, and Multimodal Generation, which are widely used in Dialogue Systems, Intelligent Assistants, Programming Plugins, and other fields. Its innovation lies in guiding model Optimization through Scaling Laws and combin‑ ing Dynamic Context Window and Sparse Attention mechanisms to significantly improve the model’s performance and efficiency in handling complex tasks. This book is centered around DeepSeek‑V3, combining theoretical analysis and practical application, leading read‑ ers to fully explore the core technology and practical value of this open‑source model. This book aims to provide readers with a systematic learning guide, from the theo‑ retical foundation of generative AI to the technical architecture of DeepSeek‑V3, and then to specific development practices, through a combination of theoretical explanations and practical cases, to help readers master the complete process from principle to application. Whether you are an AI technology researcher or an industry developer, you can quickly understand and utilize DeepSeek’s big model technology through this book and deeply explore its application potential in industrial and commercial scenarios. This book is divided into three parts with 12 chapters covering theoretical analysis and case practice. The first part (Chapters 1–3) starts from the theoretical level, explaining the principles of Transformer and attention mechanism, DeepSeek‑V3 core architecture, and the basics of model development. Through the in‑depth analysis of MoE routing, Context Window optimization, and distributed training strategies, the unique advantages of DeepSeek‑V3 in training cost and computational efficiency are revealed, which lays the theoretical foun‑ dation for the subsequent technical applications. The second part (Chapters 4–9) focuses on the actual performance and development practice of big models, which not only reveals DeepSeek‑V3’s capabilities in the areas of

xiv ◾ Preface Mathematical Reasoning, Dialogue Generation, and Code Completion, but also shows how to utilize the big models to precisely solve the task challenges with the help of detailed code examples. In addition, this part provides systematic explanations on topics such as Dialogue Prefix Completion, FIM Generation Patterns and JSON Output, Function Callback Function and Contextual Disk Caching, and DeepSeek Hinting Library, to help developers achieve customized model development. The third part (Chapters 10–12) focuses on real‑world scenarios, covering a variety of real‑world scenarios with integrated development cases (e.g., Chat Client‑like clients, AI assistants, and programming plugins), demonstrating the powerful application potential of DeepSeek‑V3 in production environments. This book focuses on both theory and practice, helping readers systematically master the core skills of big model development through rich cases and clear technical analysis. Featured contents include practical interpretation of Scaling Laws, advanced implementa‑ tion of Prompt Design, and in‑depth application of big models in industrial scenarios. This book is suitable for researchers and developers in the field of generative AI. It also provides learning and practical guidance for technology enthusiasts and university teachers and students who wish to apply big model technology to real‑world scenarios. We would like to express our gratitude to the open‑source community and technical teams involved in the development and application of DeepSeek‑V3. We thank them for their efforts in driving the rapid development of generative AI technology and providing rich content material for this book. We expect that this book will become a powerful tool for readers to learn and practice in the field of generative AI, and we hope that you will be able to experience its real value in real projects. This book is authored by Jing Dai, under the organization of the Future Intelligence Lab (FIL). The entire content was developed and compiled by Jing Dai, with the Lab providing organizational support throughout the process. All the contents in this book are based on the DeepSeek‑V3 calling method. Readers can easily switch to DeepSeek‑R1 version by simply changing model=’’deepseek‑chat’’ to model=’’deepseek‑reasoner’’ in the code to enjoy its stronger inference ability and performance optimization. In the process of writing the first draft of this book in Chinese, the author made use of ChatGPT‑4o and DeepSeek to touch up the language of some passages to improve the accuracy and fluency of expression. At the same time, the author used Cursor to debug and optimize some of the codes to ensure the correctness and reproducibility of the technical contents. In the process of model analysis and experimental testing, the open capabilities of DeepSeek’s series of models were also referenced and utilized. Jing Dai

1 PART I Theoretical Foundations and Technical Architecture of Generative AI This part (Chapters 1~3) mainly explains the theoretical foundation and technical archi‑ tecture of generative AI, which helps readers lay the theoretical foundation for learning DeepSeek‑V3. Through the in‑depth analysis of Transformer model, this part compre‑ hensively introduces the technical principles of Encoder‑Decoder architecture, attention mechanism, diverse Positional Encoding and Context Window extension. Combined with the key features of Dynamic Attention, Sparse Attention and Long‑Range Attention and Long‑Range Dependency Optimization of DeepSeek‑V3, this part focuses on highlighting the innovations in the design of large models and their Performance Optimization strate‑ gies, which provides a comprehensive guide for readers to understand the technical logic of large models. Meanwhile, this part provides an in‑depth analysis of DeepSeek‑V3’s core architecture and training techniques, including the technical details of MoE‑based expert routing design, FP8 Mixed Precision Training and distributed training. By explaining the GPU architecture, bandwidth optimization and Dynamic Learning Rate Scheduler, this part shows how DeepSeek‑V3 achieves the balance between computational efficiency and train‑ ing cost in large models through technical innovation. In addition, the study of Scaling Laws provides a theoretical basis for exploring the relationship between model scale and Performance Optimization, helping readers understand the technical evolution and opti‑ mization logic of large models more clearly. DOI: 10.1201/9781003674702‑1

3DOI: 10.1201/9781003674702-2 C h a p t e r 1 Core Principles of Transformer and Attention Mechanisms Since the introduction of the Transformer model, its unique Self‑Attention Mechanism and modular design have gradually become the core framework of mod‑ ern Natural Language Processing (NLP), driving the rapid development of large modeling technology. Dynamic Attention provides an efficient solution for modeling complex data by dynamically capturing the dependencies between elements in a sequence, while tech‑ niques such as Multi‑Head Attention and Residual Connection further enhance the scal‑ ability and stability of the model. This chapter will systematically analyze the basic structure and mathematical principles of Transformer, and at the same time, deeply discuss its application and optimization strat‑ egies in long context processing, aiming at laying a solid foundation for readers to under‑ stand the technology of DeepSeek‑V3 and other large models. 1.1 BASIC STRUCTURE OF TRANSFORMER[1] The Transformer model has become a milestone in the field of deep learning by virtue of its flexible modular design and powerful parallel computing capability. Its core architec‑ ture is based on the Encoder‑Decoder model, which combines the innovative design of the Self‑Attention Mechanism and the Multi‑Head Attention Mechanism to achieve accurate modeling of complex sequence relationships. Meanwhile, the introduction of Residual Connection and Layer Normalization effec‑ tively alleviates the problems of vanishing gradient and unstable training. In this section, the core modules of Transformer will be analyzed in detail, laying a technical foundation for readers to deeply understand the architecture of other big models.

4 ◾ DeepSeek in Action 1.1.1 Encoder‑Decoder Architecture 1.1.1.1 Core Concepts of the Encoder‑Decoder Architecture The Encoder‑Decoder architecture is the basis of the Transformer model, which is mainly used to handle sequence‑to‑sequence modeling tasks. The architecture converts the input sequence into an intermediate representation and then decodes the intermediate repre‑ sentation into the target sequence through the cooperation of an Encoder and a Decoder. 1. Function of the encoder: To convert the input sequence into a fixed‑length, high‑dimensional representation that contains semantic and contextual information from the input sequence. 2. Decoder function: To generate the next output in the target sequence based on the intermediate representation generated by the encoder and the history information of the target sequence. This architecture is particularly suitable for tasks such as machine translation and Text Generation, e.g., when translating sentences from one language to another, the encoder extracts features from the source language and the decoder generates content in the target language. 1.1.1.2 How the Encoder Module Works Encoder consists of multiple stacked layers, each containing two parts: the Self‑Attention Mechanism and the Feedforward Neural Network. 1. Self‑Attention Mechanism: This mechanism dynamically adjusts the representation of each element by calculating the relationship between each element in the sequence, so that it can capture the contextual information of the entire input sequence. 2. Feedforward Neural Networks: These networks further process the output of Self‑Attention Mechanisms to generate higher level feature representations. The input to the Encoder can be word vectors or other forms of embedded representa‑ tions, and the output of each layer will be used as the input to the next layer, progressively improving the abstract understanding of the semantics. 1.1.1.3 Core Design of the Decoder Module Decoder is similar to Encoder in that it also consists of multiple layers stacked on top of each other, but its workflow is more complex and consists of three main parts: 1. Self‑Attention Mechanism: Similar to Encoder, the decoder’s Self‑Attention Mechanism is responsible for modeling the relationships within the target sequence to ensure that each word generated is consistent with the previous word.

Core Principles of Transformer and Attention Mechanisms ◾ 5 2. Cross‑attention mechanism: This mechanism combines the intermediate representa‑ tion generated by the encoder with the target sequence representation generated by the decoder ensuring that the decoding process makes full use of the information in the input sequence. 3. Feedforward Neural Network: This network further features extraction and transfor‑ mation of the output of the attention mechanism to provide support for generating the target sequence. 1.1.1.4 Encoder‑Decoder Improvement in DeepSeek‑V3 In DeepSeek‑V3, although the core idea of the Encoder‑Decoder architecture remains the same, several details have been optimized to improve the efficiency and effectiveness of: 1. Enhanced Attention mechanism: DeepSeek‑V3 introduces Multi‑Head Latent Attention (MLA) technology, which improves the ability to capture the details of the input sequence through multiplexed information processing. 2. Auxiliary loss‑free load balancing strategy: In response to the common problem of uneven resource allocation in large model training, DeepSeek‑V3 ensures that the computational resources are fully utilized in both the encoding and decoding phases by adopting an innovative strategy. 3. Multi‑Token Prediction (MTP): The decoder can predict multiple target Tokens at one time to improve the generation speed and show significant performance advan‑ tages in long sequence generation tasks. 1.1.1.5 Practical Implications of the Encoder‑Decoder Architecture The design of Encoder‑Decoder architecture breaks through the limitations of traditional sequence models in long sequence processing, enabling Transformer to efficiently model complex input and output relationships, and laying a technical foundation for the subse‑ quent development of large models. With the further optimization of DeepSeek‑V3, the potential of this architecture is max‑ imized, not only performing well in language modeling tasks, but also providing strong support for Code Generation, Mathematical Reasoning, and other functions. 1.1.2 Self‑Attention Mechanisms vs. Multi‑Head Attention Mechanisms 1.1.2.1 Core Concepts of Self‑Attention Mechanisms Self‑Attention Mechanism is the key mechanism of the Transformer model for capturing the correlation of different elements in an input sequence. It serves to allow each input element (e.g., a word) to dynamically adjust its own representation based on information from other elements, an ability that allows the larger model to provide a deeper under‑ standing of the contextual relationships in the sequence. Its basic workflow consists of three steps: