When you need a language model to respond accurately and quickly about a specific field of knowledge, the sprawling capacity of an LLM may hurt more than it helps. This book teaches you to build generative AI models optimized for specific fields.

In “Domain-Specific Small Language Models,” you’ll discover:

• Model sizing best practices

• Open source libraries, frameworks, utilities, and runtimes

• Fine-tuning techniques for custom datasets

• Hugging Face’s libraries for SLMs

• Running SLMs on commodity hardware

• Model optimization or quantization

Perfect for cost- or hardware-constrained environments, Small Language Models (SLMs) train on domain-specific data for high-quality results in specific tasks. In this book you’ll develop SLMs that can generate everything from Python code to protein structures and antibody sequences—all on commodity hardware.

About the technology:

Small-footprint language models trained on custom data sets and hosted locally can perform as well as large generalist models in speed and accuracy, often at a fraction of the cost. “Domain-Specific Small Language Models” shows you how to build privacy-preserving and regulation-compliant SLMs for agentic systems, specialist applications, and deployment on the edge.

About the book:

This is a practical book that shows you how to adapt pretrained open-source models to your domain using transfer learning and parameter-efficient fine-tuning. You’ll learn to minimize cost through optimization and quantization, develop secure APIs to serve your models, and deploy SLMs on commodity hardware—including small devices. The hands-on examples include integrating SLMs into RAG systems and agentic workflows.

About the listener:

For AI engineers familiar with Python.

About the author:

Guglielmo Iozzia is a Director of AI and Applied Mathematics at Merck & Co. and a Distinguished Member of the American Society for Artificial Intelligence. He specializes in AI biomedical applications.

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.