Table of Contents

"LMVR - Large Language Model via Rust" is a definitive guide for understanding and developing large language models (LLMs) using the Rust programming language. The book is meticulously organized into five parts, each covering foundational, advanced, and practical aspects of LLMs. Whether you’re a researcher, developer, or student, this book offers a comprehensive toolkit for leveraging LLMs in real-world applications, providing a balance of theoretical insights and hands-on implementation with Rust.

Main Sections link

• Introduction
• Preface
• Foreword
• Foreword
• How to Use

Part I: Foundations of Large Language Models link

• Chapter 1 - Introduction to Large Language Models
• Chapter 2 - Mathematical Foundations for LLMs
• Chapter 3 - Neural Networks Architectures for NLP
• Chapter 4 - The Transformer Architecture

Part II: Advanced Transformer Architectures link

• Chapter 5 - Bidirectional Models: BERT and Its Variants
• Chapter 6 - Generative Models: GPT and Beyond
• Chapter 7 - Multitask Learning: T5 and Unified Models
• Chapter 8 - Multimodal Transformers and Extensions

Part III: Training, Fine-Tuning, and Optimization Techniques link

• Chapter 9 - Building a Simple LLM from Scratch Using Rust
• Chapter 10 - Open Foundational LLMs
• Chapter 11 - Retrieval-Augmented Generation (RAG)
• Chapter 12 - Efficient Training Techniques
• Chapter 13 - Inference and Deployment of LLMs

Part IV: Practical Applications of Large Language Models link

• Chapter 14 - LLMs in Healthcare
• Chapter 15 - Financial Applications of LLMs
• Chapter 16 - LLMs in Legal and Compliance
• Chapter 17 - Customer Service and E-commerce
• Chapter 18 - Creative Applications of LLMs
• Chapter 19 - Graph Neural Networks and LLMs

Part V: Prompt Engineering and Advanced Techniques link

• Chapter 20 - Introduction to Prompt Engineering
• Chapter 21 - Few-Shot and Zero-Shot Prompting with LLMs
• Chapter 22 - Advanced Prompt Engineering Techniques
• Chapter 23 - Testing the Quality of Large Language Models
• Chapter 24 - Interpretability and Explainability in LLMs
• Chapter 25 - Bias, Fairness, and Ethics in LLMs
• Chapter 26 - Federated Learning and Privacy-Preserving LLMs
• Chapter 27 - The Future of Large Language Models

Guidance for Students and Lecturers link

For Students: Use this book as a comprehensive resource for mastering LLMs and their implementation in Rust. Start with foundational chapters in Part I to build your mathematical and architectural knowledge. Progress through the chapters to understand advanced architectures, practical applications, and ethical considerations. Engage with the hands-on exercises in Part III to solidify your learning, and explore emerging trends in Part V to stay ahead in the field.

For Lecturers: "LMVR - Large Language Models via Rust" is ideal as a primary or supplementary textbook for AI and software engineering courses. Its structured approach enables a step-by-step exploration of LLMs, making it suitable for various skill levels. Leverage the theoretical and practical sections to create assignments, facilitate hands-on labs, and inspire critical discussions on the role of LLMs in society.