Overview

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.

Full Product Details

9781633436701

Table of Contents

Reviews

The balance between theoretical concepts and hands-on application is excellent, while the specialized domain examples in chemistry and code generation provide unique insight not easily found elsewhere.  Samuel Lawrence, Software Developer  Excellent collection of tips and techniques for optimizing an LLM to run on your own hardware.  Andrew R. Freed, Distinguished Engineer, IBM 

Author Information

Guglielmo Iozzia is Director of ML/AI and Applied Mathematics at MSD, known for turning complex theory into deployable AI solutions. With decades of cross-industry experience, Guglielmo brings pragmatic clarity and code-first rigor to every page. He distills real-world expertise into step-by-step guidance that helps engineers ship reliable, budget-friendly language models.

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