Build a Large Language Model (From Scratch)

Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up! In Build a Large Language Model (from Scratch), you’ll discover how LLMs work from the inside out. In this insightful book, bestselling author Sebastian Raschka guides you step by step through creating your own LLM, explaining each stage with clear text, diagrams, and examples. You’ll go from the initial design and creation to pretraining on a general corpus, all the way to finetuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to Plan and code all the parts of an LLM Prepare a dataset suitable for LLM training Finetune LLMs for text classification and with your own data Use human feedback to ensure your LLM follows instructions Load pretrained weights into an LLM The large language models (LLMs) that power cutting-edge AI tools like ChatGPT, Bard, and Copilot seem like a miracle, but they’re not magic. This book demystifies LLMs by helping you build your own from scratch. You’ll get a unique and valuable insight into how LLMs work, learn how to evaluate their quality, and pick up concrete techniques to finetune and improve them. The process you use to train and develop your own small-but-functional model in this book follows the same steps used to deliver huge-scale foundation models like GPT-4. Your small-scale LLM can be developed on an ordinary laptop, and you’ll be able to use it as your own personal assistant. about the book Build a Large Language Model (from Scratch) is a one-of-a-kind guide to building your own working LLM. In it, machine learning expert and author Sebastian Raschka reveals how LLMs work under the hood, tearing the lid off the Generative AI black box. The book is filled with practical insights into constructing LLMs, including building a data loading pipeline, assembling their internal building blocks, and finetuning techniques. As you go, you’ll gradually turn your base model into a text classifier welcome 1_Understanding_Large_Language_Models 2_Working_with_Text_Data 3_Coding_Attention_Mechanisms 4_Implementing_a_GPT_model_from_Scratch_To_Generate_Text 5_Pretraining_on_Unlabeled_Data Appendix_A._Introduction_to_PyTorch Appendix_B._References_and_Further_Reading Appendix_C._Exercise_Solutions Appendix_D._Adding_Bells_and_Whistles_to_the_Training_Loop