What optimization techniques e g learning rate schedules gradient clipping do you use for fine-tuning large generative models

Question

Can you name some of the famous optimizing techniques for fine-tuning large generative models?

Answer 1

The top 5 most used optimization techniques are as follows:

• Learning Rate Scheduling: Gradually increases the learning rate at the start of training to stabilize initial updates.
• Cosine Annealing: Lower the learning rate following a cosine decay schedule, which helps the model converge smoothly. 
• Gradient Clipping: Caps gradients to a maximum threshold to prevent large updates and improve stability.
• AdamW Optimizer: A variant of Adam with weight decay, often used for large models to prevent overfitting.
• Stochastic Weight Averaging (SWA): Averages weights from different steps, leading to a smoother and more robust model.

These above techniques are widely adopted for their effectiveness in optimizing large model training while maintaining or improving performance.