Introduction
According to a blog post by OpenAI, they have rolled out the fine-tuning capability for their GPT-3.5 Turbo model, with an extension of this feature to GPT-4 planned for the upcoming fall. This enhancement, as OpenAI states, allows developers to modify models to better cater to specific use cases and to deploy these custom models on a larger scale.
OpenAI’s preliminary tests suggest that a fine-tuned GPT-3.5 Turbo might match or even exceed the base GPT-4’s performance in certain specialized tasks. OpenAI emphasizes that data exchanged via the fine-tuning API remains the property of the customer and is not repurposed by OpenAI or any other entity for training subsequent models.
Fine-Tuning Use Cases
Since the introduction of GPT-3.5 Turbo, OpenAI reports that there has been a significant demand from developers and businesses to tailor the model for distinct user experiences. OpenAI highlights the following benefits of the recent update:
- Improved Steerability: OpenAI suggests that fine-tuning enhances the model’s ability to adhere to specific instructions, such as generating concise outputs or consistently responding in a designated language.
- Reliable Output Formatting: According to OpenAI, fine-tuning can bolster the model’s consistency in response formatting, which is crucial for applications like code completion or API call generation.
- Custom Tone: OpenAI mentions that businesses can adjust the model’s tone to resonate more with their brand voice.
OpenAI also notes that fine-tuning allows for a reduction in prompt lengths while maintaining similar performance levels. They claim that GPT-3.5 Turbo, when fine-tuned, can manage up to 4k tokens, which is double the capacity of their previous models.
Safety Considerations
OpenAI places a strong emphasis on the safe deployment of fine-tuning. They state that to ensure the model’s inherent safety features remain intact during the fine-tuning process, the training data undergoes scrutiny via OpenAI’s Moderation API and a GPT-4 powered moderation system.
Pricing Breakdown
OpenAI provides a detailed cost structure associated with fine-tuning, categorized into initial training and usage:
- Training: $0.008 per 1K Tokens
- Usage input: $0.012 per 1K Tokens
- Usage output: $0.016 per 1K Tokens