Quick Start Guide to Large Language Models

The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, parameters, and performance. In the second edition, readers will find comprehensive updates and new chapters that reflect the latest advancements in the field. In addition to updating existing code to meet current versions and expectations, this edition significantly expands content on Retrieval-Augmented Generation and AI Agents and introduces new chapters dedicated to manual and automated methods for evaluating LLMs, as well as alignment principles, highlighting the differences and implications of instructional versus value alignment. Additionally, more examples of fine-tuning larger models are included, and all code and model references have been updated to include the latest package versions and AI models like Llama 3 and Mistral v0.2 ensuring the new edition remains at the cutting edge of LLM technology. More content on RAG and AI Agents A new chapter on evaluating LLMs both manually and automatically A new chapter on alignment principles (instructional versus value alignment, etc.) General updates so all code is more current (using the latest package versions + AI models, like Llama 3, etc.) Includes more content on fine-tuning principles