By tailoring the model to specific use cases and industries, it can better understand and generate contextually relevant responses.
Fine-tuning streamlines performance by reducing token usage and condensing the effectiveness of a larger model into a smaller, more efficient one.
When should I fine-tune my model?
Fine-tuning is recommended when a pre-trained model doesn't perform your task well or when you want to teach it something new.
"The integration of Cohere’s technology marked a significant leap in performance… Cohere's fine-tuned models were easy to test, going live in less than an hour."