Domain-specific small language models / Guglielmo Iozzia.

Format:

Video

Author/Creator:

Iozzia, Guglielmo, author.

Contributor:

Manning Publications, publisher.

Language:

English

Subjects (All):

Natural language generation (Computer science).

Natural language processing (Computer science).

Physical Description:

1 online resource (1 video file (8 hr., 19 min.)) : sound, color.

Edition:

Video Edition.

[First edition].

Place of Publication:

[Shelter Island, New York] : Manning Publications, 2026.

Summary:

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 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.

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1601077990

Publisher Number:

9781633436701VE