Fine-Tuning

Fine-tuning is the process of further training a pre-trained AI model on a specific dataset to specialize it for a particular task or domain. It adjusts the model's weights to improve performance on your use case.

How It Works

Pre-trained models are generalists. Fine-tuning makes them specialists. You provide a dataset of input/output examples that represent your desired behavior, and the model adjusts its parameters to match. Fine-tuning is useful when prompt engineering alone cannot achieve the desired output quality. However, it requires data preparation, compute resources, and technical knowledge. It is a trade-off: better performance on your specific task, but a model that may be less flexible on general tasks.

Fine-Tuning in Chapeta

Chapeta does not perform fine-tuning directly, but it can connect to fine-tuned models hosted on OpenRouter. If you have fine-tuned a model through a provider like OpenAI or Together, and that model is accessible via OpenRouter, you can use it in Chapeta. For most users, Chapeta's Skills system (which uses system prompts) achieves similar specialization without the cost and complexity of fine-tuning.

See Fine-Tuning in action with Chapeta