The cost of running a model is dependent on the number of tokens it processes, and fine-tuning can help reduce these costs by using a shorter input prompt. A fine-tuned GPT-3.5 Turbo model can save developers money in the long run if it's cheaper to run and just as effective, if not more so, in some use cases compared to GPT-4. However, the operational cost of a model is dependent on the size of the context window, which differs depending on the model configuration. OpenAI plans to offer fine-tuning capabilities for GPT-4 later this year.
Key takeaways:
- Developers can now fine-tune OpenAI's GPT-3.5 Turbo model to improve its performance on specific tasks, potentially making it more effective and cheaper to run than the more advanced GPT-4 model.
- Fine-tuning allows users to shape the behaviors and capabilities of an already-trained large language model by further training it on custom data, which can lead to more accurate and effective responses.
- OpenAI charges users for the amount of tokens processed in the input prompt and the number of tokens generated in its output. Fine-tuning can help reduce these costs by using a shorter input prompt.
- OpenAI plans to offer fine-tuning capabilities for GPT-4 later this year, but it's currently unclear what the pricing will be.