Introduction to LLM Instruction Fine Tuning: Hands-On Session

Join us for an engaging session on mastering the art of LLM fine-tuning! We'll guide you through every step, from downloading and tokenizing datasets to testing models with zero-shot inference. Dive into instruction fine-tuning, parameter-efficient fine-tuning (PEFT), and hands-on techniques to tailor models to your needs. Explore qualitative evaluation with human feedback and quantitative metrics like ROUGE to measure performance. By the end, you'll be equipped with the skills to fine-tune and optimize LLMs, unlocking their full potential for real-world applications.

Key Takeaways:

• Understand the basics of LLM fine-tuning and instruction fine-tuning for enhanced performance.
• Learn the steps to download, tokenize datasets, and test models with zero-shot inference.
• Gain hands-on experience in fine-tuning models and evaluating them qualitatively and quantitatively using ROUGE.
• Explore Parameter Efficient Fine-Tuning (PEFT) techniques to optimize LLM performance efficiently.

Youssef Hosni is a senior data scientist and machine learning researcher. He has worked as a data scientist, using customers' data to gain a better understanding of their behavior. Youssef is passionate about data and believes in AI's power to improve people's lives. He hopes to transfer his passion to others and guide them into this wide field through his writings. You can reach him on LinkedIn.