When fine-tuning a GPT model like Babbage, you are fine-tuning the GPT-3 base model (not the instruction-oriented variant of GPT-3). Fine-tuning involves taking the pre-trained base model and further training it on your specific dataset or task to enhance its performance. Fine-tuning allows OpenAI API customers to leverage the power of pre-trained GPT-3 language models, such as Babbage, while tailoring them to their specific needs (the fine-tuning process allows a model to specialize in a specific task or context, making it more efficient and effective for a particular use case, which can help to reduce costs and latency for high-volume tasks). You are also able to continue fine-tuning a fine-tuned model to add additional data without having to start from scratch.

Babbage is a medium-sized GPT-3 model. It offers a balance between processing power and computational requirements. It is more capable than Ada but less so than Curie or Davinci. Note: There are two fine-tuning costs to be aware of, a one-time training cost and a pay-as-you-go usage cost.