Quick Start Guide to Large Language Models: Strategies and Best Practices for Using Chatgpt and Other Llms (Paperback)

The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products

Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems.

Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, parameters, and performance. You'll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), Cohere (the Command family), and Meta (BART and the LLaMA family).

• Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more
• Use APIs and Python to fine-tune and customize LLMs for your requirements
• Build a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generation
• Master advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot prompting
• Customize LLM embeddings to build a complete recommendation engine from scratch with user data
• Construct and fine-tune multimodal Transformer architectures using opensource LLMs
• Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF)
• Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind

"By balancing the potential of both open- and closed-source models, Quick Start Guide to Large Language Models stands as a comprehensive guide to understanding and using LLMs, bridging the gap between theoretical concepts and practical application."
--Giada Pistilli, Principal Ethicist at HuggingFace

"A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field."
--Pete Huang, author of The Neuron

Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

《大型語言模型快速入門指南》是一本實用的、逐步指導如何在項目和產品中規模化使用LLM的指南。像ChatGPT這樣的大型語言模型展示了令人驚嘆的能力，但它們的大小和複雜性使許多從業者望而卻步。在這本書中，資深數據科學家和人工智能企業家Sinan Ozdemir消除了這些障礙，提供了一個指南，教你如何使用、整合和部署LLM來解決實際問題。

Ozdemir匯集了一切你需要入門的內容，即使你對LLM沒有直接經驗：逐步指導、最佳實踐、實際案例研究、實踐練習等等。在此過程中，他分享了對LLM內部運作的見解，幫助你優化模型選擇、數據格式、參數和性能。在附帶的網站上，你還可以找到更多資源，包括與OpenAI（GPT-4和ChatGPT）、Google（BERT、T5和Bard）、EleutherAI（GPT-J和GPT-Neo）、Cohere（Command系列）和Meta（BART和LLaMA系列）等開源和專有LLM一起使用的樣本數據集和代碼。

本書的內容包括：
- 學習關鍵概念：預訓練、遷移學習、微調、注意力、嵌入、分詞等等
- 使用API和Python對LLM進行微調和自定義，以滿足你的需求
- 構建完整的神經/語義信息檢索系統，並將其連接到對話型LLM以進行檢索增強生成
- 掌握高級提示工程技術，如輸出結構化、思維鏈和語義少樣本提示
- 自定義LLM嵌入，從頭開始構建完整的推薦引擎，使用用戶數據
- 使用開源LLM構建和微調多模態Transformer架構
- 使用人類和AI反饋的強化學習（RLHF/RLAIF）對齊LLM
- 部署提示和自定義微調的LLM到雲端，考慮可擴展性和評估流程

《大型語言模型快速入門指南》通過平衡開源和專有模型的潛力，成為了一本全面理解和使用LLM的指南，填補了理論概念和實際應用之間的差距。

Sinan Ozdemir is currently the founder and CTO of Shiba Technologies. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master's degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, CA.

Sinan Ozdemir目前是Shiba Technologies的創始人兼首席技術官。Sinan曾是約翰霍普金斯大學的數據科學講師，並撰寫了多本關於數據科學和機器學習的教科書。此外，他還是最近被收購的Kylie.ai的創始人，該平台是一個具有RPA功能的企業級對話式人工智能平台。他擁有約翰霍普金斯大學的純數學碩士學位，目前居住在加利福尼亞州舊金山。