LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation

Low-Rank Adaptation (LoRA) introduces auxiliary parameters for each layer to fine-tune the pre-trained model under limited computing resources. But it still faces challenges of resource consumption when scaling up to larger models. Previous studies employ pruning techniques by evaluating the importance of LoRA parameters for different layers to address the problem. However, these efforts only analyzed parameter features to evaluate their importance. Indeed, the output of LoRA related to the parameters and data is the factor that directly impacts the …

challenges computing computing resources consumption cs.cl cs.lg evaluation importance larger models layer lora low low-rank adaptation parameters pruning resources scaling scaling up studies