**Fine-Tuning LLMs: Avoiding Catastrophic Forgetting with a

Fine-Tuning LLMs: Avoiding Catastrophic Forgetting with a "Warm-Up" Approach

When adapting pre-trained Large Language Models (LLMs) to specific tasks, a common challenge arises: catastrophic forgetting. This phenomenon occurs when the model's performance on the original task suffers significantly after fine-tuning on a new task. To mitigate this issue, we recommend using a "warm-up" approach with smaller learning rates.

Why "Warm-Up"?

The "warm-up" phase involves gradually increasing the learning rate from an initial small value to a larger one. This approach helps the model to:

1. Stabilize the pre-trained weights: By starting with a small learning rate, you prevent the model from making drastic changes to its pre-trained weights, which are essential for its original performance.
2. Adapt to the new task: As the learning rate increases, the model can learn to incorporate new knowledge without forgetting its original capabilities.
3. Prevent overfitting: The ...

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