Fine-tuning for OpenAI’s GPT-3.5 Turbo model is finally here

Some great news lately for AI developers from OpenAI.

Finally, you can now fine-tune the GPT-3.5 Turbo model using your own data. This gives you the ability to create customized versions of the OpenAI model that perform incredibly well at specific tasks and give responses in a customized format and tone, perfect for your use case.

For example, we can use fine-tuning to ensure that our model always responds in a JSON format, containing Spanish, with a friendly, informal tone. Or we could make a model that only gives one out of a finite set of responses, e.g., rating customer reviews as critical, positive, or neutral, according to how *we* define these terms.

As stated by OpenAI, early testers have successfully used fine-tuning in various areas, such as being able to:

• Make the model output results in a more consistent and reliable format.
• Match a specific brand’s style and messaging.
• Improve how well the model follows instructions.

The company also claims that fine-tuned GPT-3.5 Turbo models can match and even exceed the capabilities of base GPT-4 for certain tasks.

Before now, fine-tuning was only possible with weaker, costlier GPT-3 models, like davinci-002 and babbage-002. Providing custom data for a GPT-3.5 Turbo model was only possible with techniques like few-shot prompting and vector embedding.

OpenAI also assures that any data used for fine-tuning any of their models belongs to the customer, and then don’t use it to train their models.

What is GPT-3.5 Turbo, anyway?

Launched earlier this year, GPT-3.5 Turbo is a model range that OpenAI introduced, stating that it is perfect for applications that do not solely focus on chat. It boasts the capability to manage 4,000 tokens at once, a figure that is twice the capacity of the preceding model. The company highlighted that preliminary users successfully shortened their prompts by 90% after applying fine-tuning on the GPT-3.5 Turbo model.

What can I use GPT-3.5 Turbo fine-tuning for?

• Customer service automation: We can use a fine-tuned GPT model to make virtual customer service agents or chatbots that deliver responses in line with the brand’s tone and messaging.
• Content generation: The model can be used for generating marketing content, blog posts, or social media posts. The fine-tuning would allow the model to generate content in a brand-specific style according to prompts given.
• Code generation & auto-completion: In software development, such a model can provide developers with code suggestions and autocompletion to boost their productivity and get coding done faster.
• Translation: We can use a fine-tuned GPT model for translation tasks, converting text from one language to another with greater precision. For example, the model can be tuned to follow specific grammatical and syntactical rules of different languages, which can lead to higher accuracy translations.
• Text summarization: We can apply the model in summarizing lengthy texts such as articles, reports, or books. After fine-tuning, it can consistently output summaries that capture the key points and ideas without distorting the original meaning. This could be particularly useful for educational platforms, news services, or any scenario where digesting large amounts of information quickly is crucial.

How much will GPT-3.5 Turbo fine-tuning cost?

There’s the cost of fine-tuning and then the actual 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.

OpenAI, GPT 3.5 Turbo fine-tuning and API updates

When will fine-tuning for GPT-4 be available?

This fall.

OpenAI has announced that support for fine-tuning GPT-4, its most recent version of the large language model, is expected to be available later this year, probably during the fall season. This upgraded model has been proven to perform at par with humans across diverse professional and academic benchmarks. It surpasses GPT-3.5 in terms of reliability, creativity, and its capacity to deal with instructions that are more nuanced.