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Decoding Large Language Models

Decoding Large Language Models

By : Irena Cronin
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Decoding Large Language Models

Decoding Large Language Models

4 (3)
By: Irena Cronin

Overview of this book

Ever wondered how large language models (LLMs) work and how they're shaping the future of artificial intelligence? Written by a renowned author and AI, AR, and data expert, Decoding Large Language Models is a combination of deep technical insights and practical use cases that not only demystifies complex AI concepts, but also guides you through the implementation and optimization of LLMs for real-world applications. You’ll learn about the structure of LLMs, how they're developed, and how to utilize them in various ways. The chapters will help you explore strategies for improving these models and testing them to ensure effective deployment. Packed with real-life examples, this book covers ethical considerations, offering a balanced perspective on their societal impact. You’ll be able to leverage and fine-tune LLMs for optimal performance with the help of detailed explanations. You’ll also master techniques for training, deploying, and scaling models to be able to overcome complex data challenges with confidence and precision. This book will prepare you for future challenges in the ever-evolving fields of AI and NLP. By the end of this book, you’ll have gained a solid understanding of the architecture, development, applications, and ethical use of LLMs and be up to date with emerging trends, such as GPT-5.
Table of Contents (22 chapters)
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Part 1: The Foundations of Large Language Models (LLMs)
4
Part 2: Mastering LLM Development
9
Part 3: Deployment and Enhancing LLM Performance
14
Part 4: Issues, Practical Insights, and Preparing for the Future

Quantization – doing more with less

Quantization is a model optimization technique that converts the precision of the numbers used in a model from higher precision formats, such as 32-bit floating-point, to lower precision formats, such as 8-bit integers. The main goals of quantization are to reduce the model size and to make it run faster during inference, which is the process of making predictions using the model.

When quantizing an LLM, several key benefits and considerations come into play, which we will discuss next.

Model size reduction

Model size reduction via quantization is an essential technique for adapting LLMs to environments with limited storage and memory. The process involves several key aspects:

  • Bit precision: Traditional LLMs often use 32-bit floating-point numbers to represent the weights in their neural networks. Quantization reduces these to lower-precision formats, such as 16-bit, 8-bit, or even fewer bits. The reduction in bit precision...
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