Instruction tuning involves training a pretrained language model on a dataset of prompts containing instructions followed by outputs.
This kind of dataset, known as an instruction dataset, is essential but hard to collect.
The Alpaca model addressed this issue by using OpenAI’s text-davinci-003 model to generate synthetic data based upon a small human written seed dataset. They used the OpenAI API, which can be costly.
To solve this, I replicated the Alpaca dataset generation process with the local Meta-Llama-3-8B-Instruct model to reproduce a self-instruct dataset.
This method is cost-effective and efficient for creating high-quality datasets for NLP tasks.
Still, I don't recommend using it directly as I mainly created this notebook for educational purposes.
Sources:
Alpaca: A Strong, Replicable Instruction-Following Model
Self-Instruct: Aligning Language Models with Self-Generated Instructions