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 in Production: Your Complete Guide to Customizing, Evaluating, and Deploying Open-Source LLMs for Real-World Applications

Fine-Tuning in Production: Your Complete Guide to Customizing, Evaluating, and Deploying Open-Source LLMs for Real-World Applications

M. Girard, Kevin

Paperback

General Computers

Currently unavailable to order

ISBN13: 9798271916120
Publisher: Independently Published
Published: Oct 28 2025
Pages: 224
Weight: 0.80
Height: 0.47 Width: 6.69 Depth: 9.61
Language: English
Tired of hitting the limits of prompt engineering? Ready to unlock the true potential of open-source Large Language Models like Llama 3 and Mistral for your specific business needs?

While prompt engineering and Retrieval-Augmented Generation (RAG) are powerful starting points, they often fall short when you need deep domain specialization, perfect brand voice alignment, or rock-solid reliability in complex tasks. Simply giving a generalist model a cheat sheet isn't enough; you need to forge a true specialist. That's where fine-tuning comes in.

Fine-Tuning in Production is your complete, hands-on guide to mastering the art and science of customizing open-source LLMs for real-world applications. Written by an experienced AI developer and coach, this book cuts through the hype and provides a pragmatic roadmap, taking you step-by-step through the entire production lifecycle.

Inside, you'll discover:

  • Why and When to Fine-Tune: Understand the crucial differences between prompting, RAG, and fine-tuning, and learn how to justify the investment using a clear decision matrix.
  • The Data Strategy Imperative: Master the most critical aspect - sourcing, curating, cleaning, and formatting high-quality training data (including synthetic generation) that truly teaches your model.
  • Hands-On Training Techniques: Dive into code with practical examples using the Hugging Face ecosystem, mastering Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA and QLoRA to train models efficiently, even on accessible hardware.
  • Beyond Accuracy: Learn modern evaluation techniques, including the LLM-as-Judge pattern and building robust human-in-the-loop validation pipelines, ensuring your model is not just trained, but effective.
  • Production Deployment: Go beyond the lab by learning how to merge adapters, quantize models for inference, and serve them reliably at scale using high-throughput servers like TGI and vLLM within containerized environments (Docker).
  • Closing the Loop (LLMOps): Master the ongoing operational tasks of monitoring for drift and hallucinations, managing GPU costs, and building a data flywheel for continuous retraining and improvement.

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