Why does T-few fine-tuning in OCI generative AI service reduce costs and training time compared to traditional fine-tuning?

Why does T-few fine-tuning in OCI generative AI service reduce costs and training time compared to traditional fine-tuning?

• A. It avoids dataset partitioning
• B. It selectively updates only a fraction of the model's weights
• C. It uses pre-trained models for all tasks
• D. It eliminates the need for data augmentation

Answer: (B)

Fine-tuning using T-few in the Oracle Cloud Infrastructure (OCI) generative AI service is cost-effective and time-efficient primarily because it selectively updates only a fraction of the model's weights. This approach is in contrast to traditional fine-tuning methods, which typically require retraining the entire model. By focusing on a smaller subset of weights, T-few can quickly adapt the model to new tasks with fewer resources. This also means that less computational power is required, leading to lower overall costs associated with training. The method leverages the existing knowledge encoded in the large pre-trained model, allowing it to adapt to new data without the extensive computational resources that would be necessary if the entire model were updated. This targeted adjustment significantly shortens the training period while still achieving effective performance on specific tasks.

Fine-tuning using T-few in the Oracle Cloud Infrastructure (OCI) generative AI service is cost-effective and time-efficient primarily because it selectively updates only a fraction of the model's weights. This approach is in contrast to traditional fine-tuning methods, which typically require retraining the entire model. By focusing on a smaller subset of weights, T-few can quickly adapt the model to new tasks with fewer resources. This also means that less computational power is required, leading to lower overall costs associated with training.

The method leverages the existing knowledge encoded in the large pre-trained model, allowing it to adapt to new data without the extensive computational resources that would be necessary if the entire model were updated. This targeted adjustment significantly shortens the training period while still achieving effective performance on specific tasks.