{"product_id":"domain-specific-small-language-models-guglielmo-iozzia-9781633436701","title":"Domain-Specific Small Language Models","description":"\u003cb\u003eBigger isn't always better. Train and tune highly focused language models optimized for domain specific tasks.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eWhen 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. \u003ci\u003eDomain-Specific Small Language Models\u003c\/i\u003e teaches you to build generative AI models optimized for specific fields. \u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eDomain-Specific Small Language Models\u003c\/i\u003e you'll discover: \u003cp\u003e\u003c\/p\u003e - Model sizing best practices\u003cbr\u003e - Open source libraries, frameworks, utilities and runtimes\u003cbr\u003e - Fine-tuning techniques for custom datasets\u003cbr\u003e - Hugging Face's libraries for SLMs\u003cbr\u003e - Running SLMs on commodity hardware\u003cbr\u003e - Model optimization or quantization \u003cp\u003e\u003c\/p\u003e Perfect for cost- or hardware-constrained environments, Small Language Models (SLMs) train on domain specific data for high-quality results in specific tasks. In \u003ci\u003eDomain-Specific Small Language Models\u003c\/i\u003e you'll develop SLMs that can generate everything from Python code to protein structures and antibody sequences--all on commodity hardware. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003ci\u003eDomain-Specific Small Language Models\u003c\/i\u003e teaches you how to create language models that deliver the power of LLMs for specific areas of knowledge. You'll learn to minimize the computational horsepower your models require, while keeping high-quality performance times and output. You'll appreciate the clear explanations of complex technical concepts alongside working code samples you can run and replicate on your laptop. Plus, you'll learn to develop and deliver RAG systems and AI agents that rely solely on SLMs, and without the costs of foundation model access. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For machine learning engineers familiar with Python. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eGuglielmo Iozzia\u003c\/b\u003e is a Director, ML\/AI and Applied Mathematics at MSD. He studied Electronic and Biomedical Engineering at the University of Bologna, has an extensive background in Software and ML\/AI Engineering applied to real-life use cases across different industries, such as Biotech Manufacturing, Healthcare, Cloud Operations, and Cyber Security. \u003cp\u003e\u003c\/p\u003e Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book. \u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Guglielmo Iozzia\u003cbr\u003e\u003cb\u003eISBN-10:\u003c\/b\u003e 1633436705\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9781633436701\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Manning Publications\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 12\/30\/2025\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 300\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.79lbs","brand":"Guglielmo Iozzia","offers":[{"title":"Paperback","offer_id":47201170653439,"sku":"9781633436701","price":59.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_c9384996-2e4a-4c29-abb1-e061a3d896e0.jpg?v=1756789088","url":"https:\/\/www.whiterainbookhouse.com\/products\/domain-specific-small-language-models-guglielmo-iozzia-9781633436701","provider":"WR Book House","version":"1.0","type":"link"}