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LLM Engineer's Handbook

You're reading from   LLM Engineer's Handbook Master the art of engineering large language models from concept to production

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781836200079
Length 522 pages
Edition 1st Edition
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Authors (3):
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Maxime Labonne Maxime Labonne
Author Profile Icon Maxime Labonne
Maxime Labonne
Paul Iusztin Paul Iusztin
Author Profile Icon Paul Iusztin
Paul Iusztin
Alex Vesa Alex Vesa
Author Profile Icon Alex Vesa
Alex Vesa
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Toc

Table of Contents (15) Chapters Close

Preface 1. Understanding the LLM Twin Concept and Architecture FREE CHAPTER 2. Tooling and Installation 3. Data Engineering 4. RAG Feature Pipeline 5. Supervised Fine-Tuning 6. Fine-Tuning with Preference Alignment 7. Evaluating LLMs 8. Inference Optimization 9. RAG Inference Pipeline 10. Inference Pipeline Deployment 11. MLOps and LLMOps 12. Other Books You May Enjoy
13. Index
Appendix: MLOps Principles

Tooling and Installation

This chapter presents all the essential tools that will be used throughout the book, especially in implementing and deploying the LLM Twin project. At this point in the book, we don’t plan to present in-depth LLM, RAG, MLOps, or LLMOps concepts. We will quickly walk you through our tech stack and prerequisites to avoid repeating ourselves throughout the book on how to set up a particular tool and why we chose it. Starting with Chapter 3, we will begin exploring our LLM Twin use case by implementing a data collection ETL that crawls data from the internet.

In the first part of the chapter, we will present the tools within the Python ecosystem to manage multiple Python versions, create a virtual environment, and install the pinned dependencies required for our project to run. Alongside presenting these tools, we will also show how to install the LLM-Engineers-Handbook repository on your local machine (in case you want to try out the code yourself...

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