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 customize models that perform better for their use cases and run these custom models at scale. 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. As with all our APIs, data sent in and out of the fine-tuning API is owned by the customer and is not used by OpenAI, or any other organization, to train other models.

Since the release of GPT-3.5 Turbo, developers and businesses have asked for the ability to customize 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.

In our private beta, fine-tuning customers have been able to meaningfully improve model performance across common use cases, such as:

• Improved steerability: Fine-tuning allows businesses to 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.
• Reliable output formatting: 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.
• Custom tone: Fine-tuning is a great way to hone the qualitative feel of the model output, such as its tone, so it better fits the voice of businesses’ brands. A business with a recognizable brand voice can use fine-tuning for the model to be more consistent with their tone.

In addition to increased performance, fine-tuning also enables businesses to shorten their prompts while ensuring similar performance.  Fine-tuning with GPT-3.5-Turbo can also handle 4k tokens—double our previous fine-tuned models. Early testers have reduced prompt size by up to 90% by fine-tuning instructions into the model itself, speeding up each API call and cutting costs.

Fine-tuning is most powerful when combined with other techniques such as prompt engineering, information retrieval, and function calling. Check out our fine-tuning guide to learn more. Support for fine-tuning with function calling and gpt-3.5-turbo-16k will be coming later this fall.

It is very important to us that the deployment of fine-tuning is safe. To preserve the default model's safety features through the  fine-tuning process, fine-tuning training data is passed through our Moderation API and a GPT-4 powered moderation system to detect unsafe training data that conflict with our safety standards.

Fine-tuning costs are broken down into two buckets: the initial training cost and usage cost:

• Training: $0.008 / 1K Tokens
• Usage input: $0.012 / 1K Tokens
• Usage output: $0.016 / 1K Tokens

For example, 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.

In July, we announced that the original GPT-3 base models (ada, babbage, curie, and davinci) would be turned off on January 4th, 2024. Today, we are making babbage-002 and davinci-002 available as replacements for these models, either as base or fine-tuned models. Customers can access those models by querying the Completions API.

These models can be fine-tuned with our new API endpoint /v1/fine_tuning/jobs. This new endpoint offers pagination and more extensibility to support the future evolution of the fine-tuning API. Transitioning from /v1/fine-tunes to the updated endpoint is straightforward and more details can be found in our new fine-tuning guide. This deprecates the old /v1/fine-tunes endpoint, which will be turned off on January 4th, 2024.

Pricing for base and fine-tuned GPT-3 models is as follows: