Overview

Unlock the power of Small Language Models with this practical guide to building lightweight, efficient, and scalable NLP systems. This book helps developers, data scientists, and AI enthusiasts create fast, resource-efficient natural language processing solutions using Python, Hugging Face Transformers, and quantization techniques. From fine-tuning models on CPUs to deploying on edge devices such as Raspberry Pi and Android, this guide covers the complete SLM lifecycle including dataset preparation, parameter-efficient fine-tuning (PEFT), model compression, and production deployment. With clear examples and real-world applications, readers learn how to build smarter, faster, and lighter AI systems without relying on massive infrastructure. What's inside: Step-by-step workflows for developing small language models in Python Techniques for parameter-efficient fine-tuning and quantization Methods for deploying models on resource-limited devices Real-world examples and cost-saving strategies About the reader: Designed for developers, data scientists, AI students, and entrepreneurs with basic Python knowledge. No deep machine learning background is required. Why this book: This book is a practical blueprint for building powerful yet efficient NLP systems. Whether you want to fine-tune domain-specific models, deploy on constrained hardware, or reduce operational costs, this guide provides the tools and understanding to do so effectively.

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