Domain-Specific Small Language Models

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

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