LLM Engineer's Handbook: Master the art of engineering large language models from concept to production (Paperback)
暫譯: 大型語言模型工程師手冊：從概念到生產掌握大型語言模型工程的藝術 (平裝本)

Iusztin, Paul, Labonne, Maxime

LLM Engineer's Handbook: Master the art of engineering large language models from concept to production (Paperback)

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商品描述

Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practices

Purchase of the print or Kindle book includes a free eBook in PDF format

"This book is instrumental in making sure that as many people as possible can not only use LLMs but also adapt them, fine-tune them, quantize them, and make them efficient enough to deploy in the real world." - Julien Chaumond, CTO and Co-founder, Hugging Face

Book Description

This LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps' best practices. The guide walks you through building an LLM-powered twin that's cost-effective, scalable, and modular. It moves beyond isolated Jupyter Notebooks, focusing on how to build production-grade end-to-end LLM systems.

Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects.

What you will learn

- Implement robust data pipelines and manage LLM training cycles

- Create your own LLM and refine with the help of hands-on examples

- Get started with LLMOps by diving into core MLOps principles like IaC

- Perform supervised fine-tuning and LLM evaluation

- Deploy end-to-end LLM solutions using AWS and other tools

- Explore continuous training, monitoring, and logic automation

- Learn about RAG ingestion as well as inference and feature pipelines

Who this book is for

This book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios.

Table of Contents

- Undersstanding the LLM Twin Concept and Architecture

- Tooling and Installation

- Data Engineering

- RAG Feature Pipeline

- Supervised Fine-tuning

- Fine-tuning with Preference Alignment

- Evaluating LLMs

- Inference Optimization

- RAG Inference Pipeline

- Inference Pipeline Deployment

- MLOps and LLMOps

- Appendix: MLOps Principles

商品描述(中文翻譯)

踏入大型語言模型（LLMs）的世界，這本實用指南將帶您從基礎知識到使用 LLMOps 最佳實踐部署先進應用程式。

購買印刷版或 Kindle 書籍可獲得免費 PDF 格式電子書。

「這本書對於確保盡可能多的人不僅能使用 LLMs，還能適應、微調、量化它們，並使其足夠高效以在現實世界中部署，具有重要意義。」 - Julien Chaumond，Hugging Face 首席技術官及共同創辦人

書籍描述

這本 LLM 書籍提供了在現實場景中設計、訓練和部署 LLMs 的實用見解，利用 MLOps 的最佳實踐。該指南將引導您構建一個具成本效益、可擴展且模組化的 LLM 驅動的雙胞胎系統。它超越了孤立的 Jupyter Notebook，專注於如何構建生產級的端到端 LLM 系統。

在整本書中，您將學習數據工程、監督微調和部署。通過構建 LLM 雙胞胎用例的實踐方法，您將能夠在自己的項目中實施 MLOps 組件。您還將探索該領域的前沿進展，包括推理優化、偏好對齊和實時數據處理，使這本書成為希望在項目中應用 LLMs 的人們的重要資源。

您將學到的內容

- 實施穩健的數據管道並管理 LLM 訓練週期

- 創建自己的 LLM，並通過實踐範例進行精煉

- 通過深入核心 MLOps 原則（如基礎設施即代碼）開始 LLMOps

- 執行監督微調和 LLM 評估

- 使用 AWS 和其他工具部署端到端 LLM 解決方案

- 探索持續訓練、監控和邏輯自動化

- 了解 RAG 數據攝取以及推理和特徵管道

這本書適合誰

這本書適合希望深入了解 LLMs 的 AI 工程師、NLP 專業人士和 LLM 工程師。建議具備 LLMs 和生成 AI 環境的基本知識，以及 Python 和 AWS 的基礎知識。無論您是 AI 新手還是希望提升技能的人，這本書都提供了在現實場景中實施 LLMs 的全面指導。

目錄

- 理解 LLM 雙胞胎概念和架構

- 工具和安裝

- 數據工程

- RAG 特徵管道

- 監督微調

- 偏好對齊的微調

- 評估 LLMs

- 推理優化

- RAG 推理管道

- 推理管道部署

- MLOps 和 LLMOps

- 附錄：MLOps 原則

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