QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning
Hossein Rajabzadeh, Mojtaba Valipour, Tianshu Zhu, Marzieh S. Tahaei, Hyock Ju Kwon, Ali Ghodsi, Boxing Chen, Mehdi Rezagholizadeh
Abstract
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.- Anthology ID:
- 2024.emnlp-industry.53
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, US
- Editors:
- Franck Dernoncourt, Daniel Preoţiuc-Pietro, Anastasia Shimorina
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 712–718
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-industry.53/
- DOI:
- 10.18653/v1/2024.emnlp-industry.53
- Cite (ACL):
- Hossein Rajabzadeh, Mojtaba Valipour, Tianshu Zhu, Marzieh S. Tahaei, Hyock Ju Kwon, Ali Ghodsi, Boxing Chen, and Mehdi Rezagholizadeh. 2024. QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 712–718, Miami, Florida, US. Association for Computational Linguistics.
- Cite (Informal):
- QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning (Rajabzadeh et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-industry.53.pdf