Foreword

As the Head of the Data Science Center at the University of Indonesia, I have witnessed the rapid advancements in Generative AI (GenAI) and the profound impact it is having on both academia and industry. Large language models (LLMs) have emerged as a cornerstone of these advancements, revolutionizing natural language processing and enabling machines to understand and generate human language with unprecedented accuracy. However, as you embark on your journey to master LLMs, it is crucial to recognize that the true power of these models lies in the foundational mathematical concepts that underpin them, coupled with the choice of the right tools and frameworks for implementation.

For students in the Mathematics faculty, understanding the mathematics behind LLMs is not just important—it is essential. Linear algebra, calculus, probability, and optimization form the backbone of transformer architectures and neural networks. These mathematical principles are what make LLMs function effectively, and mastering them is key to unlocking the full potential of these models. But beyond the mathematical foundation, the choice of programming language and tools plays a critical role in how these models are implemented and deployed.

In the realm of AI, Rust has emerged as the best companion for developing the low-level systems that power LLM applications. Unlike more widely adopted languages like Python, Rust offers unmatched advantages in terms of memory safety, concurrency, and performance optimization. These features are especially important when building and deploying LLMs at scale, where efficiency and reliability are paramount. Rust’s growing ecosystem, with powerful crates like burn and Hugging Face’s candle, provides the necessary tools to implement sophisticated neural architectures while ensuring that the underlying systems are robust and efficient.

However, it is important to remember that tools and frameworks are only as powerful as the mathematical understanding that drives their use. Rust’s capabilities in handling low-level system complexities make it an ideal choice for those who are serious about pushing the boundaries of LLM development. But to truly harness this power, one must first have a deep understanding of the mathematical abstractions and architectural intricacies that define these models.

This book, LMVR - Large Language Model via Rust, is designed to guide you through both the theoretical and practical aspects of LLMs. It emphasizes the importance of a strong mathematical foundation while introducing you to the practicalities of implementing LLMs using Rust. By doing so, it bridges the gap between high-level mathematical concepts and low-level system implementation, offering a comprehensive guide for those looking to excel in this field.

I encourage you to approach this book with a deep appreciation for both the mathematics and the technology that make LLMs possible. Rust is more than just a tool; it is a language that allows you to build efficient, reliable, and scalable systems that are at the core of modern AI applications. As you progress through this book, let it inspire you to not only learn but also innovate and contribute to the future of AI technology.

With a strong mathematical foundation and Rust as your companion, you are well-equipped to navigate the complexities of LLM development and make meaningful contributions to this exciting and rapidly evolving field.