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The Outstanding performance and growing size of Large Language Models has ledto increased attention in parameter efficient learning. The two predominantapproaches are Adapters and Pruning. Adapters are to freeze the model and giveit a new weight matrix on the side, which can significantly reduce the time andmemory of training, but the cost is that the evaluation and testing willincrease the time and memory consumption. Pruning is to cut off some weight andre-distribute the remaining weight, which sacrifices the complexity of trainingat the cost of extremely high memory and training time, making the cost ofevaluation and testing relatively low. So efficiency of training and inferencecan't be obtained in the same time. In this work, we propose a task-orientedPruning-Adapter method that achieve a high memory efficiency of training andmemory, and speeds up training time and ensures no significant decrease inaccuracy in GLUE tasks, achieving training and inference efficiency at the sametime.

Task-oriented Memory-efficient Pruning-Adapter