Transfer learning

Transfer learning is a technique where a pre-trained model is used as a starting point for a new task. This can be useful for tasks where there is not enough data to train a model from scratch.

To use transfer learning for LLMs, you can fine-tune a pre-trained LLM on your specific task. Fine-tuning involves updating the parameters of the pre-trained LLM to improve its performance on the new task.

Transfer learning can be a very effective way to train LLMs, as it can save you a significant amount of time and resources.