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Fine-tune open-source language models with LoRA, QLoRA, and Hugging Face tools
Fine-Tuning Large and Small Language Models walks practitioners through the complete pipeline for customizing open-source SLMs for domain-specific tasks. Written by Luca Massaron, a data scientist with 20+ years in data modelling and nearly a decade building NLP solutions with Transformer architectures, the book covers dataset preparation, synthetic data generation, base model selection from families including Gemma, Qwen, Phi, and Llama, and deployment on consumer-grade hardware.
The book frames fine-tuning against alternatives like retrieval-augmented generation and advanced prompting, helping readers determine when fine-tuning is the right approach. Hands-on coverage of parameter-efficient methods, specifically LoRA and QLoRA, shows how to configure Hugging Face PEFT, TRL, and bitsandbytes for training. Evaluation chapters address detecting whether fine-tuning improved target performance without degrading the model's broader capabilities.
Readers will also find:
Fine-Tuning Large and Small Language Models serves technical practitioners with programming and machine learning experience who want to move beyond off-the-shelf APIs. Data scientists, ML engineers, and AI developers building customized, cost-effective language models will gain the working knowledge to transform general-purpose SLMs into specialized, production-ready tools.
Author: Luca Massaron
ISBN-10: 1394430973
ISBN-13: 9781394430970
Publisher: Wiley
Language: English
Published: 01/05/2027
Pages: 368
Format: Paperback
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