OpenAI has launched fine-tuning for GPT-3.5 Turbo, the company announced on Tuesday, adding that fine-tuning for GPT-4 will also launch this fall. The move allows developers to customize models, optimizing performance and achieving superior results tailored to their specific use cases.

“Early tests have shown a fine-tuned version of GPT-3.5 Turbo can match, or even outperform, base GPT-4-level capabilities on certain narrow tasks,” stated OpenAI in a statement.

There’s a misunderstanding that fine-tuning involves training the model with data and then asking it questions based on that data. That’s not how what it is for. For that, a good product is perhaps Coral by Cohere.

Fine-tuning is basically teaching the model on how to do a task by showing it lots of examples. It is like giving it a ton of practice. It’s like giving it a ton of practice. While one can kind of do this with prompts, fine-tuning is meant for scale. As this quick tutorial explains, it is useful when there are a lot of examples to be used in the prompts and when the model is run often and is needed to run fast.

“You should consider fine-tuning when you have so many that your prompt has become a burden. OpenAI recommends fine-tuning on at least 50 examples to see clear improvements. Fine tuning essentially front-loads the cost and time it takes to train a model, making future API calls faster.”

We’re already starting to see some examples in action on X, and other social media platforms.

OpenAI’s announcement has outlined some common use cases, including enhancing steerability—essentially enabling developers to ensure the model follows instructions accurately; refining output formatting to improve the model’s consistent response presentation; and customizing the tone to, for instance, better fit a business’s voice. These examples illustrate how developers and businesses with private beta access to fine-tuning have utilized it. However, the possibilities for fine-tuning’s applications are certainly broader.

The announcement also underscored the point that the data transmitted into and out of the fine-tuning API is not employed by OpenAI or any other organization to train the model in any manner.

OpenAI would charge developers two types of costs for using fine-tuning; initial training cost, and usage cost. The training cost would be $0.008 per 1,000 tokens, usage input cost $0.012 per 1,000 tokens, and usage output cost $0.016 per 1,000 tokens. Sharing an example in the announcement, the company stated that a gpt-3.5-turbo fine-tuning job with a training file of 100,000 tokens that is trained for 3 epochs would have an expected cost of $2.40.

Following the launch of fine-turning for GPT-3.5, OpenAI has also announced Scale as its preferred partner to help companies use it to customize models.