Large Language Models Projects

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 Learn

• Gain practical experience by working with models from OpenAI and the Hugging Face library
• Use essential libraries relevant to Large Language Models, covering topics such as Chatbots, Code Generation, OpenAI API, Hugging Face, and Vector databases
• Create and implement projects using LLM while understanding the design decisions involved
• Understand the role of Large Language Models in larger corporate settings

Who This Book Is For

Data 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

Table of Contents

Part I: Techniques and Libraries

Chapter 1: Introduction to Large Language Models with OpenAl

Chapter 2: Vector Databases and LLMs

Chapter 3: LangChain and Agents

Chapter 4: Evaluating Models

Chapter 5: Fine-Tuning Models

Part II: Projects

Chapter 6: Natural Language to SQL

Chapter 7: Creating and Publishing Your Own LLM

Part III: Enterprise Solutions

Chapter 8: Architecting a NL2SQL Project for Immense Enterprise Databases

Chapter 9: Decoding Risk: Transforming Banks with Customer Embeddings

About the Author

Pere Martra is a seasoned IT Engineer and AI Enthusiast with years of experience in the financial sector. He is currently pursuing a Master’s in Research on Artificial Intelligence. Initially, he delved into the world of AI through his passion for game development. Applying Reinforcement Learning techniques, he infused video game characters with personality and autonomy, sparking his journey into the realm of AI. Today, AI is not just his passion but a pivotal part of his profession. Collaborating with startups on NLP-based solutions, he plays a crucial role in defining technological stacks, architecting solutions, and guiding team inception. As the author of a course on Large Language Models and their applications, available on GitHub, Pere shares his expertise in this cutting-edge field. He serves as a mentor in the TensorFlow Advanced Techniques Specialization at Deeplearning.AI, assisting students in solving problems within their tasks. He holds the distinction of being one of the few TensorFlow Certified Developers in Spain, complementing this achievement with an Azure Data Scientist Associate certification. Follow Pere on Medium, where he writes about AI, emphasizing Large Language Models and deep learning with TensorFlow, contributing valuable insights to TowardsAI.net. Top skills include Keras, Artificial Intelligence (AI), TensorFlow, Generative AI, and Large Language Models (LLM). Connect with Pere on https://www.linkedin.com/in/pere-martra/ for project collaborations or insightful discussions in the dynamic field of AI.