Hugging Face in Action: Build Intelligent Applications with Transformers, Agents, and Rag

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

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

About the technology

Hugging Face is an incredible open-source ecosystem for AI engineers and data scientists, providing hundreds of pre-trained models, datasets, tools, and libraries. It's also a central hub for collaborating on leading edge AI research. Hugging Face is a massive platform, and this book will help you take full advantage of all it has to offer.

About the book

Hugging Face in Action teaches you how to build end-to-end AI systems using resources from the Hugging Face community. In it, you'll create multiple projects, including an object detection model, a RAG Q&A application, an LLM-powered chatbot, and more. You'll appreciate the clear, accessible explanations, along with thoughtful introductions to key technologies like LangChain, LlamaIndex, and Gradio.
What's inside

• How to navigate the huge Hugging Face library of models and tools
• How to run LLMs locally using GPT4ALL
• How to create web-based user interfaces using Gradio
• How to improve models using Hugging Face datasets

For Python programmers familiar with NumPy and Pandas. No AI experience required.

About the author

Wei-Meng Lee is a technologist and founder of Developer Learning Solutions.

Table of Contents

1 Introducing Hugging Face
2 Getting started
3 Using Hugging Face transformers and pipelines for NLP tasks
4 Using Hugging Face for computer vision tasks
5 Exploring, tokenizing, and visualizing Hugging Face datasets
6 Fine-tuning pretrained models and working with multimodal models
7 Creating LLM-based applications using LangChain and LlamaIndex
8 Building LangChain applications visually using Langflow
9 Programming agents
10 Building a web-based UI using Gradio
11 Building locally running LLM-based applications using GPT4All
12 Using LLMs to query your local data
13 Bridging LLMs to the real world with the Model Context Protocol

Brief description: 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.