OpenAI: Fine tuning feature now live on GPT-3.5 Turbo

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OpenAI has announced that the fine-tuning for GPT-3.5 Turbo is now available, with fine-tuning for GPT-4 coming this fall.

This update gives developers the ability to customise models that perform better for their use cases and run these custom models at scale.

Meanwhile, a fine-tuned version of GPT-3.5 Turbo “can match, or even outperform, base GPT-4-level capabilities on certain narrow tasks,” according to the company.

Fine-tuning use cases

“Since the release of GPT-3.5 Turbo, developers and businesses have asked for the ability to customise the model to create unique and differentiated experiences for their users. With this launch, developers can now run supervised fine-tuning to make this model perform better for their use cases,” OpenAI said in a blog post.

With fine-tuning, businesses can make the model follow instructions better, such as making outputs terse or always responding in a given language. For instance, developers can use fine-tuning to ensure that the model always responds in German when prompted to use that language.

We’ve just launched fine-tuning for GPT-3.5 Turbo! Fine-tuning lets you train the model on your company’s data and run it at scale. Early tests have shown that fine-tuned GPT-3.5 Turbo can match or exceed GPT-4 on narrow tasks: https://t.co/VaageW9Kaw pic.twitter.com/nndOyxS2xs

— OpenAI (@OpenAI) August 22, 2023

Furthermore, fine-tuning improves the model’s ability to consistently format responses—a crucial aspect for applications demanding a specific response format, such as code completion or composing API calls.

A developer can use fine-tuning to more reliably convert user prompts into high-quality JSON snippets that can be used with their own systems.

In addition, fine-tuning also enables businesses to shorten their prompts. “Early testers have reduced prompt size by up to 90 per cent by fine-tuning instructions into the model itself, speeding up each API call and cutting costs,” the company added in its blog post.

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