In this article, we explore the benefits of LoRA compared to traditional fine-tuning in artificial intelligence models. This approach offers an optimal balance by improving performance on the specific task while reducing the loss of information from the original domain and maintaining response diversity.
The LoRA technique, or Low Rank Adaptation, allows adjusting a model's layers with a reduced number of parameters. This ensures that the model learns new skills without forgetting what was previously learned in the source domain.
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