Overview
Curriculum
In this module, we will learn about the core principles of Large Language Models (LLMs), with a focus on fine-tuning and transfer learning. We will explore methods like prompt tuning, prefix tuning, and low-rank adaptation, while also learning to evaluate the importance of data quality and domain-specific datasets. By the end, we will be equipped to customize and optimize LLMs, through hands-on practice with a Llama2 7B quantized model.
What You'll Learn
- Analyze the differences between fine-tuning and transfer learning for LLMs
- Understand the impact of data quality and domain-specific datasets on model performance
- Explore various fine-tuning methods, including full fine-tuning, low-rank adaptation and advanced optimization strategies
- Perform fine-tuning on a Llama2 7B quantized model for real-world applications through hands-on experience
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