Open Daily: 10am - 10pm | Alley door 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

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Fine-Tuning LLMs Crash Course: An Advanced Practical Guide to Optimizing Large Language Models for Domain-Specific Intelligence, Task Alignment, and S

Fine-Tuning LLMs Crash Course: An Advanced Practical Guide to Optimizing Large Language Models for Domain-Specific Intelligence, Task Alignment, and S

Tech, Bitforge

Paperback

General Computers

Currently unavailable to order

ISBN13: 9798279196029
Publisher: Independently Published
Published: Dec 20 2025
Pages: 114
Weight: 0.31
Height: 0.24 Width: 5.50 Depth: 8.50
Language: English
This book will only be available for a short time. I just need 10 serious readers to grab it before I take it down to revise for private release. Be one of the few.

Fine-Tuning LLMs Crash Course is your advanced, practical guide to mastering the art and science of optimizing large language models (LLMs) for real-world performance. Whether you're a machine learning engineer, AI researcher, or developer exploring custom AI solutions, this crash course gives you everything you need to train, align, and deploy smarter, domain-specific models at scale.

What's Inside:

  • - Step-by-step workflows for fine-tuning models like GPT, LLaMA, and Falcon
  • - Techniques for aligning models with specific tasks, tone, and business goals
  • - Strategies for dataset preparation, prompt engineering, and hyperparameter tuning
  • - Real-world examples from industries like finance, healthcare, customer service, and more
  • - Deployment tips for scalable inference, safety, and monitoring in production environments
Key Features:
  • - Covers both open-source and proprietary LLMs
  • - Code examples using Hugging Face, OpenAI API, and custom training loops
  • - Best practices for reducing hallucinations and improving model accuracy
  • - Insights into evaluation, benchmarking, and responsible AI fine-tuning
Who This Book Is For:
  • - AI engineers and data scientists ready to move beyond off-the-shelf models
  • - Developers building intelligent apps, agents, or internal AI tools
  • - Startups and enterprises looking to tailor LLMs to their domain
  • - Anyone interested in deploying AI that actually fits their users and context
Why You Should Get This Book:

Also in

General Computers

View All