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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
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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

  • Lianmin Zheng et al.. “Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.” arXiv preprint arXiv:2306.05685, June 2023.
  • Aymeric Roucher. “Using LLM-as-a-judge for an automated and versatile evaluation - Hugging Face Open-Source AI Cookbook.” huggingface.co, No date found, https://huggingface.co/learn/cookbook/en/llm_judge.
  • LangChain. “Aligning LLM-as-a-Judge with Human Preferences.” blog.langchain.dev, June 26, 2024, https://blog.langchain.dev/aligning-llm-as-a-judge-with-human-preferences/.
  • Dan Hendrycks et al.. “Measuring Massive Multitask Language Understanding.” arXiv preprint arXiv:2009.03300, September 2020.
  • Jeffrey Zhou et al.. “Instruction-Following Evaluation for Large Language Models.” arXiv preprint arXiv:2311.07911, November 2023.
  • Yann Dubois et al.. “Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.” arXiv preprint...
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