Large language models (LLMs) have become increasingly popular in recent years due to their ability to process and understand human language. However, the cost of training these models can be prohibitively expensive, making it difficult for smaller organizations and researchers to access them. In this article, we will explore some novel approaches that can help reduce the cost of training LLMs.
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Knowledge Distillation
Knowledge distillation is a technique that involves training a small, lightweight model to mimic the behavior of a larger, more complex model. This can be used to reduce the computational cost of training LLMs by creating a smaller, more efficient model that still performs at a high level. The smaller model can then be used for inference, while the larger model can continue to be trained or used for other purposes. -
Transfer Learning
Transfer learning is a technique that involves using a pre-trained model as