Domain-Specific Small Language Models: Efficient AI for Local Deployment

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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. This book teaches you to build generative AI models optimized for specific fields.

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.

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

Foreword by Matthew R. Versaggi.

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.

Table of Contents

Part 1
1 Small language models
Part 2
2 Tuning for a specific domain
3 End-to-end transformer fine-tuning
4 Running inference
5 Exploring ONNX
6 Quantizing for your production environment
Part 3
7 Generating Python code
8 Generating protein structures
Part 4
9 Advanced quantization techniques
10 Profiling insights
11 Deployment and serving
12 Running on your laptop
13 Creating end-to-end LLM applications
14 Advanced components for LLM applications
15 Test-time compute and small language models