How to Finetune GPT-Like Large Language Models on a Custom Dataset (lightning.ai)

Article summary

The article provides a step-by-step guide on how to finetune large language models like GPT on a custom dataset using the Lit-GPT implementation. It covers installation, model weight download, data preparation, and finetuning. The tutorial uses the RedPajama-INCITE model and the Dolly 2.0 instruction dataset as examples. The goal is to adapt the model to a specific task, such as training a chatbot to answer financial questions.

Main themes

• Finetuning large language models
• Terms of service and ownership
• Knowledge distillation and competition
• Data preparation and usage
• Model training and adaptation
• Regulation and moat building

What commenters say

• Using a large language model like GPT-4 to label data and then using that data to train a competing model may be a breach of the terms of service.
• The terms of service of OpenAI are not clear about what constitutes a competing service, and it is unclear how they would enforce their rules.
• Some commenters argue that using GPT-4 to generate training data is a form of knowledge distillation and is allowed, while others believe it is a breach of the terms of service.
• There is a debate about whether OpenAI can claim ownership of the output of their models, and whether they can prevent others from using that output to train competing models.
• Some commenters suggest that setting up a shell company to generate public domain examples of RLHF data could be a way to circumvent the terms of service.
• Others argue that if OpenAI has used copyrighted works to train their models, it is morally fair game to do the same to them, regardless of their terms of service.
• There is a concern that using a teacher model to train a student model can lead to hallucination or ignoring of information if there is a mismatch in training data.