Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Fine-Tuning Large and Small Language Models

Fine-Tuning Large and Small Language Models

Massaron, Luca

Paperback

Series: Tech Today

Programming

PREORDER - Expected ship date January 5, 2027

ISBN10: 1394430973
ISBN13: 9781394430970
Publisher: Wiley
Published: Jan 5 2027
Pages: 368
Language: English

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:

  • Practical case studies covering the end-to-end process from dataset preparation through model evaluation and production deployment
  • Guidance on selecting base models from the Gemma, Qwen, Phi, and Llama families for specific use cases
  • Techniques for generating synthetic training data where real domain-specific data is scarce or unavailable
  • Configuration walkthroughs for Hugging Face PEFT, TRL, and bitsandbytes to run training on consumer hardware
  • Final chapters extending fine-tuned SLMs toward autonomous agents and domain-specific production applications

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.

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