How to Fine-Tune GPT-J

Recent research in Natural Language Processing (NLP) has led to the release of multiple large transformer-based language models like OpenAI's GPT-[2,3], EleutherAI's GPT-[Neo, J], and Google's T5. For those not impressed by the leap of tunable parameters in the billions, the ease with which these models could perform on a never before seen task without training a single epoch is something to behold. While it has become evident that the more parameters a model has the better it will generally perform, an exception to this rule applies when one explores fine-tuning. Fine-tuning refers to the practice of further training transformer-based language models on a dataset for a specific task. This practice has led to the 6 billion parameter GPT-J outperforming the 175 billion GPT-3 Davinci on a number of specific tasks. As such, fine-tuning will continue to be the modus operandi when using language models in practice, and, consequently, fine-tuning is the main focus of this post.