{"product_id":"9781633436701","title":"Domain-Specific Small Language Models: Efficient AI for local deployment","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eIozzia, Guglielmo\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e5\/26\/2026\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eGet the eBook free when you register your print book at Manning.\u003c\/b\u003e\u003cbr\u003e\u003cbr\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. This book teaches you to build generative AI models optimized for specific fields.\u003cbr\u003e \u003cbr\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 this book you’ll develop SLMs that can generate everything from Python code to protein structures and antibody sequences—all on commodity hardware.\u003cbr\u003e \u003cbr\u003eIn \u003ci\u003eDomain-Specific Small Language Models\u003c\/i\u003e you’ll discover:\u003cbr\u003e \u003cbr\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\u003cbr\u003e \u003cbr\u003eForeword by Matthew R. Versaggi.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 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. \u003ci\u003eDomain-Specific Small Language Models\u003c\/i\u003e shows you how to build privacy-preserving and regulation-compliant SLMs for agentic systems, specialist applications, and deployment on the edge.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 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.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e • ONNX and other quantization methods\u003cbr\u003e • Integrate SLMs into end-to-end applications\u003cbr\u003e • Deploy SLMs on laptops, smartphones, and other devices\u003cbr\u003e \u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e For AI engineers familiar with Python.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003cb\u003eGuglielmo Iozzia\u003c\/b\u003e is a Director of AI and Applied Mathematics at Merck \u0026amp; Co. and a Distinguished Member of the American Society for Artificial Intelligence. He specializes in AI biomedical applications.\u003cbr\u003e \u003cbr\u003eThe technical editor on this book was \u003cb\u003eRiccardo Mattivi\u003c\/b\u003e.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e Part 1\u003cbr\u003e 1 Small language models\u003cbr\u003e Part 2\u003cbr\u003e 2 Tuning for a specific domain\u003cbr\u003e 3 End-to-end transformer fine-tuning\u003cbr\u003e 4 Running inference\u003cbr\u003e 5 Exploring ONNX\u003cbr\u003e 6 Quantizing for your production environment\u003cbr\u003e Part 3\u003cbr\u003e 7 Generating Python code\u003cbr\u003e 8 Generating protein structures\u003cbr\u003e Part 4\u003cbr\u003e 9 Advanced quantization techniques\u003cbr\u003e 10 Profiling insights\u003cbr\u003e 11 Deployment and serving\u003cbr\u003e 12 Running on your laptop\u003cbr\u003e 13 Creating end-to-end LLM applications\u003cbr\u003e 14 Advanced components for LLM applications\u003cbr\u003e 15 Test-time compute and small language models","brand":"Manning","offers":[{"title":"Default Title","offer_id":47086814462207,"sku":"9781633436701","price":59.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/9781633436701_s600x595.jpg?v=1779980387","url":"https:\/\/massivebookshop.com\/de\/products\/9781633436701","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}