In recent years, the training methodologies for NLP (Natural Language Processing) and ML (Machine Learning) models have undergone several transformations. The introduction of pre-trained models like BERT led to the widespread practice of fine-tuning these pre-trained models for specific tasks downstream. The growing capacity of increasingly larger models subsequently facilitated in-context learning by using prompts. Most recently, a novel approach known as instruction tuning has emerged as the latest technique to make Large Language Models (LLMs) more practical and effective. In this blog post, we will delve into some of the most popular datasets employed for instruction tuning. Subsequent editions will delve into the most up-to-date instruction datasets and models that have been fine-tuned using these instructions. What is Instruction Tuning? The primary distinction between instruction tuning and conventional supervised fine-tuning lies in the nature of the training data employed.