ENGINEERING OF LARGE LANGUAGE MODELS. A HANDBOOK FOR DESIGN, TRAINING, AND IMPLEMENTATION OF LLM

Step into the fascinating world of Large Language Model (LLM) engineering, where theory meets practice and innovation drives the future of technology. This comprehensive handbook is an invaluable resource for anyone looking to delve into the intricacies of designing, training, and deploying LLMs in real-world applications. Whether you're an experienced engineer, researcher, or just starting your journey with AI, this book will provide you with the necessary tools and insights to succeed in this dynamic field.

• Understanding Key Concepts: Learn the fundamentals of LLM engineering, from basic algorithms to advanced techniques.
• Practical Techniques and Expert Advice: Discover proven methods and tips from leading industry experts.
• Inference Optimization: Learn how to increase the performance and efficiency of language models.
• Building Scalable Processing Pipelines: Create robust and flexible systems that meet the demands of real-world projects.
• MLOps Integration: Apply the best MLOps practices to ensure the success of your LLM-based projects.
• Tuning Models for Specific Applications: Learn how to tailor models to specific needs and requirements.
• Increasing Model Performance: Discover techniques that will allow you to squeeze the most out of your language models.

With the growing popularity of large language models, there is an increasing demand for specialists who can effectively deploy them in real-world solutions. LLM engineering is a broad set of tasks, requiring a unique combination of knowledge from many fields. Particularly important here is the MLOps approach, which significantly increases the chances of success in projects based on language models.

This comprehensive guide will show you how to apply best practices when working with LLMs. You will find a discussion of key concepts, practical techniques and advice from experts in data engineering, model tuning and evaluation, inference optimization, as well as building scalable processing pipelines. Step by step, you will follow how to implement a specific product, integrating various aspects of LLM engineering and the MLOps methodology. You will learn how to collect and prepare data, tune models for specific applications, increase their performance, and deploy solutions based on the RAG technique.