How to Build Large Language Models (LLMs)

Anand Vemula

ISBN 13: 9798335439497

Editore: Independently Published, 2024

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Dati bibliografici

• Titolo: How to Build Large Language Models (LLMs)
• Editore: Independently Published
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 13: 9798335439497
• Rilegatura: Paperback
• Condizione: New
• Dimensioni: 15,24 L x 0,61 A x 22,86 P centimetri 15,24 centimetri di larghezza per 0,61 centimetri di altezza per 22,86 centimetri di profondità
• Peso: 150 grammi

Descrizione articolo

Riassunto

How to Build Large Language Models (LLMs): From Data Preparation to Deployment and Beyond" provides a comprehensive guide to the entire lifecycle of creating and deploying large language models. This book serves as an essential resource for AI practitioners, data scientists, and machine learning engineers interested in mastering the intricacies of LLMs.

The book begins with an introduction to LLMs, covering foundational concepts and the evolution of language models from early recurrent neural networks (RNNs) to modern transformer architectures. It explores popular LLM architectures, including GPT and BERT, highlighting their unique features and applications.

Part II delves into data preparation and management, a crucial phase for building effective LLMs. It provides detailed guidance on sourcing and curating datasets, addressing biases, and ensuring data diversity. Techniques for data preprocessing, such as tokenization and normalization, are discussed along with methods for handling missing data and generating synthetic data. The section also covers data storage and management strategies to design scalable pipelines and ensure data security.

In Part III, the focus shifts to the technical aspects of building the model. It includes setting up the development environment, choosing appropriate model architectures, and deciding between building from scratch or fine-tuning pre-trained models. The book also provides insights into training LLMs, including distributed training techniques and strategies for addressing common challenges like overfitting and underfitting. Hyperparameter tuning and optimization techniques are also covered to enhance model performance.

Part IV addresses evaluating and fine-tuning the model, emphasizing metrics for assessing model performance, fine-tuning techniques, and debugging strategies. It offers practical solutions for improving model accuracy and adapting it to specific use cases.

Finally, Part V explores deployment and maintenance strategies, including deployment options, monitoring, and securing LLMs in production environments. The book concludes with real-world case studies and examples, demonstrating the practical applications of LLMs in various industries