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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 2. Tooling and Installation FREE CHAPTER 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

Gathering raw data into the data warehouse

ZenML orchestrates the data collection pipeline. Thus, leveraging ZenML, the data collection pipeline can be run manually, scheduled, or triggered by specific events. Here, we will show you how to run it manually, while we will discuss the other scenarios in Chapter 11 when digging deeper into MLOps.

We configured a different pipeline run for each author. We provided a ZenML configuration file for Paul Iusztin’s or Maxime Labonne’s data. To call the data collection pipeline to collect Maxime’s data, for example, you can run the following CLI command:

poetry poe run-digital-data-etl-maxime

That will call the pipeline with the following ZenML YAML configuration file:

parameters:
  user_full_name: Maxime Labonne # [First Name(s)] [Last Name]
  links:
    # Personal Blog
    - https://mlabonne.github.io/blog/posts/2024-07-29_Finetune_Llama31.html
    - https://mlabonne.github.io/blog/posts/2024-07-15_The_Rise_of_Agentic_Data_Generation...
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