Chapter 1
Overview & Concepts
Fine-tuning large language models: overview, concepts, benefits, and when to use fine-tuning instead of prompting.
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Chapter 2
Improving Domain Performance
What is fine-tuning in large language models and how it improves domain-specific performance and reliability.
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Chapter 3
The Fine-Tuning Process
Steps to fine-tune large language models, including dataset preparation, training, validation, and evaluation.
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Chapter 4
Fine-Tuning Techniques
Fine-tuning techniques for LLMs, including supervised fine-tuning, instruction tuning, and parameter-efficient tuning (PEFT).
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Chapter 5
Best Practices & Deployment
Best practices for fine-tuning LLMs, including dataset quality, evaluation, monitoring, and model deployment strategies.
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