Domain-Specific Small Language Models Efficient AI for local deployment (Guglielmo Iozzia)（Z-Library）

Author： Guglielmo Iozzia

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. about the reader For AI engineers familiar with Python.