A new research approach, budget-aware LoRA merging, challenges the assumption that every layer needs the same rank budget when combining task-specific low-rank adapters, according to the paper's authors. Existing LoRA merging methods also assume the rank budget must be split equally among tasks, the work states. The method aims to eliminate the overhead of swapping task-specific weights at inference time.
Merging low-rank adapters (LoRAs) promises to eliminate the overhead of swapping task-specific weights at inference time. However, existing merging methods assume every layer needs the same rank budget. Further, some methods assume that rank budget needs to be split equally among the tasks too. We s
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