Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs (4th Rough Cuts)КНИГИ » ПРОГРАММИНГ
Название: Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs (4th Rough Cuts) Автор: Sinan Ozdemir Издательство: Addison-Wesley/Pearson Education Год: 2023 Страниц: 213 Язык: английский Формат: epub Размер: 11.3 MB
The advancement of Large Language Models (LLMs) has revolutionized the field of Natural Language Processing in recent years. Models like BERT, T5, and ChatGPT have demonstrated unprecedented performance on a wide range of NLP tasks, from text classification to machine translation. Despite their impressive performance, the use of LLMs remains challenging for many practitioners. The sheer size of these models, combined with the lack of understanding of their inner workings, has made it difficult for practitioners to effectively use and optimize these models for their specific needs.
This practical guide to the use of LLMs in NLP provides an overview of the key concepts and techniques used in LLMs and explains how these models work and how they can be used for various NLP tasks. The book also covers advanced topics, such as fine-tuning, alignment, and information retrieval while providing practical tips and tricks for training and optimizing LLMs for specific NLP tasks.
This work addresses a wide range of topics in the field of Large Language Models, including the basics of LLMs, launching an application with proprietary models, fine-tuning GPT3 with custom examples, prompt engineering, building a recommendation engine, combining Transformers, and deploying custom LLMs to the cloud. It offers an in-depth look at the various concepts, techniques, and tools used in the field of Large Language Models.
Ever since an advanced Artificial Intelligence (AI) deep learning model called the Transformer was introduced by a team at Google Brain in 2017, it has become the standard for tackling various Natural Language Processing (NLP) tasks in academia and industry. It is likely that you have interacted with a transformer today without even realizing it, as Google uses BERT to enhance its search engine by better understanding users’ search queries. The GPT family of models from OpenAI have also received attention for their ability to generate human-like text and images. These transformers now power applications such as GitHub’s Copilot, which can convert comments into source code that runs yet another LLM from Facebook.
Large Language Models (LLMs) are AI models that are usually (but not necessarily) derived from the Transformer architecture and are designed to understand and generate human language, code, and much more. These models are trained on vast amounts of text data, allowing them to capture the complexities and nuances of human language. LLMs are capable of performing a wide range of language tasks, from simple text classification to text generation, with high accuracy, fluency, and style. With the rapid advancement of Transformers and the growing demand for AI solutions, LLMs have become an essential tool in various industries and applications.
LLMs are advanced AI models that have revolutionized the field of NLP. LLMs are highly versatile and are used for a variety of NLP tasks, including text classification, text generation, and machine translation. They are pre-trained on large corpora of text data and can then be fine-tuned for specific tasks. Using LLMs in this fashion has become a standard step in the development of NLP models. In our first case study, we will explore the process of launching an application with proprietary models like GPT-3 and ChatGPT. We will get a hands-on look at the practical aspects of using LLMs for real-world NLP tasks, from model selection and fine-tuning to deployment and maintenance.
Preface Part I: Introduction to Large Language Models 1. Overview of Large Language Models 2. Launching an Application with Proprietary Models 3. Prompt Engineering with GPT3 4. Fine-Tuning GPT3 with Custom Examples Part II: Getting the most out of LLMs 5. Advanced Prompt Engineering Techniques 6. Building a Recommendation Engine 7. Combining Transformers 8. Fine-Tuning Open-Source LLMs 9. Deploying Custom LLMs to the Cloud
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Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs (4th Rough Cuts) 
