May 2026
Intermediate
376 pages
9h 52m
English
Overview
Bigger isn’t always better. Train and tune highly focused language models optimized for domain specific tasks.
When you need a language model to respond accurately and quickly about a specific field of knowledge, the sprawling capacity of a LLM may hurt more than it helps. Domain-Specific Small Language Models teaches you to build generative AI models optimized for specific fields.
In Domain-Specific Small Language Models you’ll discover:
Perfect for cost- or hardware-constrained environments, Small Language Models (SLMs) train on domain specific data for high-quality results in specific tasks. In Domain-Specific Small Language Models 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.
What's Inside
About the Reader
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.
The technical editor on this book was Riccardo Mattivi.
Quotes
A rare combination of conceptual clarity and practical guidance.
- From the Foreword by Matthew R. Versaggi
Gives the reader a jumpstart toward making efficient, focused models with well-controlled data.
- Janelle Shane, AI Weirdness
Effectively cuts through the hype to focus on the tangible business value of localized, efficient artificial intelligence.
- Luca Longo, University College Cork
A timely and much-needed reference for both researchers and industry practitioners.
- Ahmed Serag, Weill Cornell Medicine
Excellent collection of tips and techniques.
- Andrew R. Freed, IBM
When you need a language model to respond accurately and quickly about a specific field of knowledge, the sprawling capacity of a LLM may hurt more than it helps. Domain-Specific Small Language Models 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 Domain-Specific Small Language Models 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.
What's Inside
- ONNX and other quantization methods
- Integrate SLMs into end-to-end applications
- Deploy SLMs on laptops, smartphones, and other devices
About the Reader
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.
The technical editor on this book was Riccardo Mattivi.
Quotes
A rare combination of conceptual clarity and practical guidance.
- From the Foreword by Matthew R. Versaggi
Gives the reader a jumpstart toward making efficient, focused models with well-controlled data.
- Janelle Shane, AI Weirdness
Effectively cuts through the hype to focus on the tangible business value of localized, efficient artificial intelligence.
- Luca Longo, University College Cork
A timely and much-needed reference for both researchers and industry practitioners.
- Ahmed Serag, Weill Cornell Medicine
Excellent collection of tips and techniques.
- Andrew R. Freed, IBM
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