LLM in Action - All you need to know to build language based applications

-- Guillaume Alleon, Gerard Dupont

Online version.

Overview

The book is organized to guide you chapter by chapter from core concepts of Large Language Models (LLM) to more sophisticated topics along the way.

The first three chapters will be introducing the fundamentals of LLMs both from the technical and applicative point of view. Chapter 4 will focus on dense retrieval problem to enable neural semantic search using LLM embedding approach. The next chapter will cover the process to fine tune a selected LLM for a specific application. The chapter 6 will then provide the basic solution to serve a model using ray serve platform.

The last two chapters will give you a broader understanding of LLM application with an epxloration of the durrent and future trends as well as a focus on the ethical and societal considerations that represent a key aspects of LLM application problems.

• Chapter 1, Introduction to Large Language Models An overview of the significance and development of large language models (LLMs) in the field of natural language processing (NLP).
• Chapter 2, Understanding the Fundamentals Delve into the core concepts of NLP and the architecture, training, and fine-tuning of LLMs.
• Chapter 3, Applications of LLMs Explore diverse real-world applications of LLMs, from text generation to healthcare, finance, and entertainment.
• Chapter 4, Advanced information retrieval
• Chapter 4.1, Semantic search with Embeddings Learn about semantic search techniques using word, sentence, and document embeddings, neural information retrieval, and applications in various domains.
• Chapter 4.2, Rretrieval augmented generation Build a system that pulls precise answers from a curated set of documents.
• Chapter 5, Building and Finetuning LLMs A practical guide on constructing and refining LLMs for specific tasks, complete with tools and resources for developers and researchers.
• Chapter 6, Serving LLM with Ray Dive into the deployment and serving of LLMs using the Ray framework, covering distributed computing, scalability, and real-time applications.
• Chapter 7, Future Trends and Innovations Predict the future impact of LLMs and emerging trends, such as multilingual models and applications for low-resource languages.
• Chapter 8, Ethical Considerations and Limitations Examine ethical concerns related to LLMs, including bias and privacy, and explore the practical limitations associated with these models.