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Synopsis
Get the eBook free when you register your print book at Manning.
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. This book teaches you to build generative AI models optimized for specific fields.
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.
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
Foreword by Matthew R. Versaggi.
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.
Table of Contents
Part 1
1 Small language models
Part 2
2 Tuning for a specific domain
3 End-to-end transformer fine-tuning
4 Running inference
5 Exploring ONNX
6 Quantizing for your production environment
Part 3
7 Generating Python code
8 Generating protein structures
Part 4
9 Advanced quantization techniques
10 Profiling insights
11 Deployment and serving
12 Running on your laptop
13 Creating end-to-end LLM applications
14 Advanced components for LLM applications
15 Test-time compute and small language models
"synopsis" may belong to another edition of this title.
About the Author
Guglielmo Iozzia 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.
"About this title" may belong to another edition of this title.
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Hardback. Condition: New. 1st. Want LLM power without the LLM price tag? Crave models that fit your data, laptop, and budget? Stop renting GPUs you cannot afford. Start building Domain-Specific Small Language Models today. Own your AI stack, end to end. Model sizing best practices: pick the smallest architecture that still delivers top-tier accuracy. Open-source toolchain: leverage Hugging Face, PEFT, and quantization libraries for zero-license freedom. Fine-tuning workflows: adapt existing checkpoints to niche datasets in hours, not weeks. Commodity hardware deployment: run chat, code, or bio models locally on a single GPU or CPU. Retrieval-augmented generation: fuse SLMs with RAG pipelines for grounded, up-to-date answers. Cost-control checklists: slash cloud spends and eliminate dependency on expensive foundation APIs. Domain-Specific Small Language Models, by AI director Guglielmo Iozzia, is a field guide packed with runnable Python code and real-world engineering insight. Step-by-step chapters demystify transformer architecture, quantization, and PEFT fine-tuning, then walk you through building RAG systems and autonomous agents that rely solely on SLMs. Clear diagrams, annotated notebooks, and troubleshooting tips keep learning smooth. You will finish with reusable templates, deployment scripts, and the confidence to deliver performant language models under tight hardware and budget constraints. Perfect for Python-savvy machine-learning engineers, data scientists, and technical leads who need domain-tuned AI now. Seller Inventory # LU-9781633436701
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Hardcover. Condition: new. Hardcover. Want LLM power without the LLM price tag? Crave models that fit your data, laptop, and budget? Stop renting GPUs you cannot afford. Start building Domain-Specific Small Language Models today. Own your AI stack, end to end. Model sizing best practices: pick the smallest architecture that still delivers top-tier accuracy. Open-source toolchain: leverage Hugging Face, PEFT, and quantization libraries for zero-license freedom. Fine-tuning workflows: adapt existing checkpoints to niche datasets in hours, not weeks. Commodity hardware deployment: run chat, code, or bio models locally on a single GPU or CPU. Retrieval-augmented generation: fuse SLMs with RAG pipelines for grounded, up-to-date answers. Cost-control checklists: slash cloud spends and eliminate dependency on expensive foundation APIs. Domain-Specific Small Language Models, by AI director Guglielmo Iozzia, is a field guide packed with runnable Python code and real-world engineering insight. Step-by-step chapters demystify transformer architecture, quantization, and PEFT fine-tuning, then walk you through building RAG systems and autonomous agents that rely solely on SLMs. Clear diagrams, annotated notebooks, and troubleshooting tips keep learning smooth. You will finish with reusable templates, deployment scripts, and the confidence to deliver performant language models under tight hardware and budget constraints. Perfect for Python-savvy machine-learning engineers, data scientists, and technical leads who need domain-tuned AI now. 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 youll develop SLMs that can generate everything from Python code to protein structures and antibody sequencesall on commodity hardware. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781633436701
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