Overview
Curriculum
In this module, you’ll learn about the core principles of Large Language Models (LLMs), with a focus on fine-tuning and transfer learning. You’ll 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, you’ll 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.
Courses you might be interested in
This module will equip you with the foundational programming knowledge and theory needed to excel in the bootcamp. By covering essential Python concepts and tools, we aim to ensure a...
-
11 Lessons
Free
In this module, we’ll focus on data exploration, visualization, and feature engineering—essential steps in preparing data for analysis. You’ll learn how to use techniques like summary statistics and visual tools...
-
7 Lessons
$100.00