Hugging Face in Action [Minkštas viršelis]

Everything you need to know about using the tools, libraries, and models at Hugging Face—from transformers, to RAG, LangChain, and Gradio.

Hugging Face is the ultimate resource for machine learning engineers and AI developers. It provides hundreds of pretrained and open source models for dozens of different domains—from natural language processing to computer vision. Plus, you’ll find a popular platform for hosting your models and datasets. Hugging Face in Action reveals how to get the absolute best out of everything Hugging Face, from accessing state-of-the-art models to building intuitive frontends for AI apps.

With Hugging Face in Action you’ll learn:

• Utilizing Hugging Face Transformers and Pipelines for NLP tasks
• Applying Hugging Face techniques for Computer Vision projects
• Manipulating Hugging Face Datasets for efficient data handling
• Training Machine Learning models with AutoTrain functionality
• Implementing AI agents for autonomous task execution
• Developing LLM-based applications using LangChain and LlamaIndex
• Constructing LangChain applications visually with LangFlow
• Creating web-based user interfaces using Gradio
• Building locally running LLM-based applications with GPT4ALL
• Querying local data using Large Language Models

Want a cutting edge transformer library? Hugging Face’s open source offering is best in class. Need somewhere to host your models? Hugging Face Spaces has you covered. Do your users need an intuitive frontend for your AI app? Hugging Face’s Gradio library makes it easy to build UI using the Python skills you already have. In Hugging Face in Action you’ll learn how to take full advantage of all of Hugging Face’s amazing features to quickly and reliably prototype and productionize AI applications.

About the book

Hugging Face in Action provides in-depth hands-on tutorials that will help you take advantage of all that Hugging Face offers for AI developers. You’ll build multiple different AI projects—including an object detection model, RAG applications that can answer questions based on local datasets, chatbots with web frontends, and even code-free machine learning models built with AutoTrain. Each chapter is full of step-by-step instructions and clear tips and advice. You’ll soon be productive and proficient with all of Hugging Face’s tools, pretrained models, and datasets!

About the reader

For Python programmers familiar with NumPy and Pandas. No previous experience of machine learning required!

About the author

Wei-Meng Lee is a technologist and founder of Developer Learning Solutions, a company specializing in helping companies adopt the latest IT technologies. Wei-Meng provides consultancy services to companies on adopting blockchain and AI solutions for their businesses.

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