Creating Self-Instruct Data Sets for LLM Fine-Tuning with ChatGPT

Learn how to create synthetic instruction datasets for fine-tuning Large Language Models (LLMs) in this 30-minute tutorial that explores the self-instruct methodology. Discover the differences between traditional fine-tuning and instruction fine-tuning, with a focus on using ChatGPT/GPT-4 to generate custom training data. Explore multi-task instruction datasets, break down complex tasks into manageable sub-tasks, and understand model size considerations for self-instruct fine-tuning. Gain practical insights into implementing the ALPACA approach developed by Stanford, and learn how to structure training data for improved model performance across related tasks. Master the technique of leveraging GPT models to create synthetic datasets tailored to specific applications like summarization, translation, and question-answering.