Introduction to LLM Instruction Fine-tuning | Loading Dataset | Alpaca Prompt format

In this lecture, we start learning about instruction fine-tuning.

We download the dataset of instructions and then convert it into Alpaca prompt format.

This is a lecture explained through detailed whiteboard notes and live coding.

The key reference book which this video series very closely follows is Build a Large Language Model from Scratch by Manning Publications. All schematics and their descriptions are borrowed from this incredible book!

This book serves as a comprehensive guide to understanding and building large language models, covering key concepts, techniques, and implementations.

Affiliate links for purchasing the book will be added soon. Stay tuned for updates!

0:00 What is instruction fine-tuning?
3:28 Instruction fine-tuning examples
8:35 Steps for instruction fine-tuning
9:06 Preparing and loading the dataset
13:11 Converting instructions into Alpaca prompt format
22:04 Splitting dataset into train-test-validation

Code file:
https://drive.google.com/file/d/13jgqjSY-GOKhLoOsedUuZPXHTzQXrnt1/view?usp=sharing

Instruction data link:
https://github.com/rasbt/LLMs-from-scratch/blob/main/ch07/01_main-chapter-code/instruction-data.json

Stanford Alpaca link:
https://github.com/tatsu-lab/stanford_alpaca

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