QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large
  Language Model Tuning
Hossein Rajabzadeh Mojtaba Valipour Tianshu Zhu Marzieh Tahaei Hyock Ju Kwon Ali Ghodsi Boxing Chen Mehdi Rezagholizadeh
Finetuning large language models requires huge GPU memory, restricting the
choice to acquire Larger models. While the quantized version of the Low-Rank
Adaptation technique, named QLoRA, significantly alleviates this issue, finding
the efficient LoRA rank is still challenging. Moreover, QLoRA is trained on a
pre-defined rank and, therefore, cannot be reconfigured for its lower ranks
without requiring further fine-tuning steps. This paper proposes QDyLoRA
-Quantized Dynamic Low-Rank Adaptation-, as an efficient quantization approach
for dynamic low-rank adaptation. Motivated by Dynamic LoRA, QDyLoRA is able to
efficiently finetune LLMs on a set of pre-defined LoRA ranks. QDyLoRA enables
fine-tuning Falcon-40b for ranks 1 to 64 on a single 32 GB V100-GPU through one
round of fine-tuning. Experimental results show that QDyLoRA is competitive to
QLoRA and outperforms when employing its optimal rank.