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NLP with Hugging Face Transformers
Author: Jason Brownlee
language: en
Publisher: Machine Learning Mastery
Release Date: 2025-05-14
Natural language processing has changed a lot recently due to the advances in language models. In the past, helping computers understand human language was a challenging task. Some primitive techniques were used, but they were not very effective. It is because human language is complex and has many nuances. This makes it difficult to model mathematically. For example, the probability model of language with a lot of exceptions would render it useless. The recent advances in transformer-based language models is not to assume anything about the language, but to ask the computer to learn from the data. In this way, you will not get a mathematically clean model. You cannot even write it down as equations. But it works very well in practice. The bloosom of trendy new applications such as ChatGPT is an evidence of this. Creating a transformer-based language model is costily. But using one is not. There are a lot of open source language models available that you can use even on your own computer. However, you must know how to use them. This includes to know what the model can do, what format of data it can accept and what it will produce, how to get the source code of the model and use it, and how to load the model weights. That’s a lot of details. This ebook gives you practical examples of how to use the most popular language models that a commodity computer can run. This uses the Hugging Face Transformers library — probably the simplest way to use the most popular language models. The ebook is not a tutorial on the library, nor how the language models work. As an NLP practitioner, neither of them is important. The focus of this ebook is to give you practical examples on what the language models can do and how to use them for a variety of NLP tasks, without knowing the detailed mechanisms behind them.
Hands-On Generative AI with Transformers and Diffusion Models
Author: Omar Sanseviero
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2024-11-22
Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and creative applications in different domains. This go-to book introduces theoretical concepts followed by guided practical applications, with extensive code samples and easy-to-understand illustrations. You'll learn how to use open source libraries to utilize transformers and diffusion models, conduct code exploration, and study several existing projects to help guide your work. Build and customize models that can generate text and images Explore trade-offs between using a pretrained model and fine-tuning your own model Create and utilize models that can generate, edit, and modify images in any style Customize transformers and diffusion models for multiple creative purposes Train models that can reflect your own unique style
LLM Engineer's Handbook
Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practices Key Features Build and refine LLMs step by step, covering data preparation, RAG, and fine-tuning Learn essential skills for deploying and monitoring LLMs, ensuring optimal performance in production Utilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applications Book DescriptionArtificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers 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. By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.What you will learn Implement robust data pipelines and manage LLM training cycles Create your own LLM and refine it with the help of hands-on examples Get started with LLMOps by diving into core MLOps principles such as orchestrators and prompt monitoring Perform supervised fine-tuning and LLM evaluation Deploy end-to-end LLM solutions using AWS and other tools Design scalable and modularLLM systems Learn about RAG applications by building a feature and inference pipeline 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