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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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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

References

  • Tahori, Gulrajani, Zhang, Dubois, et al.. “Alpaca: A Strong, Replicable Instruction-Following Modelcrfm.stanford.edu, March 13, 2023, https://crfm.stanford.edu/2023/03/13/alpaca.html.
  • Subhabrata Mukherjee et al.. “Orca: Progressive Learning from Complex Explanation Traces of GPT-4.” arXiv preprint arXiv:2306.02707, June 2023.
  • Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and “Teknium”. “Open-Orca/OpenOrca.” huggingface.co, 2023, https://huggingface.co/datasets/Open-Orca/OpenOrca.
  • Weihao Zeng et al.. “Automatic Instruction Evolving for Large Language Models.” arXiv preprint arXiv:2406.00770, June 2024.
  • Chunting Zhou et al.. “LIMA: Less Is More for Alignment.” arXiv preprint arXiv:2305.11206, May 2023
  • 01. AI. “Yi: Open Foundation Models by 01.AI.” arXiv preprint arXiv:2403.04652, March 2024.
  • Alex...
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