AI, as I know, you can now fine-tune GPT 3.5 Turbo using API. This allows developers to bring their own data and customize GPT 3.5 Turbo for their specific use cases. Early tests show that a fine-tuned version of GPT 3.5 Turbo can match or even outperform the base GPT for various tasks.
It is important to note that the data sent in and out of the fine-tuned AI is owned by the customer and is not used by OpenAI or any other organization to train other models.
There are several use cases for fine-tuning GPT 3.5 Turbo. Businesses can use fine-tuning to improve the model’s ability to follow instructions and respond in a specific language. For example, developers can fine-tune the model to always respond in German when prompted in that language. Fine-tuning also allows businesses to have reliable output formatting, such as JSON outputs.
Fine-tuning with GPT 3.5 Turbo also enables businesses to make the model more consistent with their brand voice. This is particularly useful for businesses with a recognizable brand voice. Additionally, fine-tuning with GPT 3.5 Turbo can handle longer context lengths, up to 4K tokens.
The process of fine-tuning is relatively simple. You need to prepare your data in a specific format, which includes system prompts, user prompts, and expected responses. Once you have prepared your data set, you can upload it and create a fine-tuning job. OpenAI provides a fine-tuning guide that explains the process in detail.
The number of training examples required for fine-tuning varies based on the use case. Starting with around 50 well-crafted demonstrations is recommended, and you can add more data if needed. OpenAI typically sees clear improvements from fine-tuning on 50 to 100 training examples with GPT 3.5 Turbo.
The cost of fine-tuning is broken into initial training costs and usage costs. The initial training cost is $0.008 per 1K tokens, and the usage cost is based on the number of input and output tokens.
In the future, OpenAI plans to provide fine-tuning for GPT 4 as well. They are continuously working on improving their fine-tuning capabilities.
I hope this article provides useful information about fine-tuning with GPT 3.5 Turbo. For more details, you can refer to the official fine-tuning guide provided by OpenAI.
