Pere Martra – författare

Large language models are powerful, but they can be slow, expensive and too general for specific tasks. Many businesses need smaller and faster models that solve their unique problems without high operational costs. To achieve this, you need to go beyond standard fine-tuning. This book shows you how to structurally change large models to make them simple, efficient and specialised.  Learn to reduce model size with effective pruning techniques.  Use knowledge distillation to train smaller but capable models.  Apply fine-tuning to adapt models for specific tasks.  Work with popular open models like Llama 3, Gemma and Qwen.  Understand the balance between a model’s size, speed and cost.  Explore how to make your models more understandable and explainable. Rearchitecting LLMs teaches you structural techniques for creating efficient models. The book provides a practical guide to making language models smaller, faster and cheaper to run. It explains how to move beyond simple fine-tuning and use advanced methods to alter a model's core architecture. You will work with popular open models and learn to adapt them for real-world business needs. After reading this book, you will be able to create capable and cost-effective models customised to your goals. You will have the skills to build efficient AI systems that deliver value without breaking the budget. This book is for practising AI, ML and data engineers who know Python.

This book offers you a hands-on experience using models from OpenAI and the Hugging Face library. You will use various tools and work on small projects, gradually applying the new knowledge you gain. The book is divided into three parts. Part one covers techniques and libraries. Here, you'll explore different techniques through small examples, preparing to build projects in the next section. You'll learn to use common libraries in the world of Large Language Models. Topics and technologies covered include chatbots, code generation, OpenAI API, Hugging Face, vector databases, LangChain, fine tuning, PEFT fine tuning, soft prompt tuning, LoRA, QLoRA, evaluating models, and Direct Preference Optimization. Part two focuses on projects. You'll create projects, understanding design decisions. Each project may have more than one possible implementation, as there is often not just one good solution. You'll also explore LLMOps-related topics. Part three delves into enterprise solutions. Large Language Models are not a standalone solution; in large corporate environments, they are one piece of the puzzle. You'll explore how to structure solutions capable of transforming organizations with thousands of employees, highlighting the main role that Large Language Models play in these new solutions. This book equips you to confidently navigate and implement Large Language Models, empowering you to tackle diverse challenges in the evolving landscape of language processing. What You Will LearnGain practical experience by working with models from OpenAI and the Hugging Face libraryUse essential libraries relevant to Large Language Models, covering topics such as Chatbots, Code Generation, OpenAI API, Hugging Face, and Vector databasesCreate and implement projects using LLM while understanding the design decisions involvedUnderstand the role of Large Language Models in larger corporate settings Who This Book Is ForData analysts, data science, Python developers, and software professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks