Hugging Face in Action

Everything you need to know about using the tools, libraries, and models at Hugging Facefrom 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 youll learn:Utilizing Hugging Face Transformers and Pipelines for NLP tasksApplying Hugging Face techniques for Computer Vision projectsManipulating Hugging Face Datasets for efficient data handlingTraining Machine Learning models with AutoTrain functionalityImplementing AI agents for autonomous task executionDeveloping LLM-based applications using LangChain and LlamaIndexConstructing LangChain applications visually with LangFlowCreating web-based user interfaces using GradioBuilding locally running LLM-based applications with GPT4ALLQuerying local data using Large Language ModelsWant a cutting edge transformer library? Hugging Faces 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 Faces Gradio library makes it easy to build UI using the Python skills you already have. In Hugging Face in Action youll learn how to take full advantage of all of Hugging Faces amazing features to quickly and reliably prototype and productionize AI applications.About the technologyHugging Face is an incredible open-source ecosystem for AI engineers and data scientists, providing hundreds of pre-trained models, datasets, tools, and libraries. Its 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 bookHugging Face in Action teaches you how to build end-to-end AI systems using resources from the Hugging Face community. In it, youll create multiple projects, including an object detection model, a RAG Q&A application, an LLM-powered chatbot, and more. Youll appreciate the clear, accessible explanations, along with thoughtful introductions to key technologies like LangChain, LlamaIndex, and Gradio.What's insideHow to navigate the huge Hugging Face library of models and toolsHow to run LLMs locally using GPT4ALLHow to create web-based user interfaces using GradioHow to improve models using Hugging Face datasetsAbout the readerFor Python programmers familiar with NumPy and Pandas. No AI experience required.About the authorWei-Meng Lee is a technologist and founder of Developer Learning Solutions.Table of Contents1 Introducing Hugging Face2 Getting started3 Using Hugging Face transformers and pipelines for NLP tasks4 Using Hugging Face for computer vision tasks5 Exploring, tokenizing, and visualizing Hugging Face datasets6 Fine-tuning pretrained models and working with multimodal models7 Creating LLM-based applications using LangChain and LlamaIndex8 Building LangChain applications visually using Langflow9 Programming agents10 Building a web-based UI using Gradio11 Building locally running LLM-based applications using GPT4All12 Using LLMs to query your local data13 Bridging LLMs to the real world with the Model Context Protocol