LoRTA: Low Rank Tensor Adaptation of Large Language Models

• URL: http://arxiv.org/abs/2410.04060v2
• Date: Tue, 15 Oct 2024 16:03:20 GMT
• Title: LoRTA: Low Rank Tensor Adaptation of Large Language Models
• Authors: Ignacio Hounie, Charilaos Kanatsoulis, Arnuv Tandon, Alejandro Ribeiro,
• Abstract summary: Low Rank Adaptation (LoRA) is a popular Efficient Fine Tuning (PEFT) method that effectively adapts large pre-trained models for downstream tasks. We propose a novel approach that employs a low rank tensor parametrization for model updates. Our method is both efficient and effective for fine-tuning large language models, achieving a substantial reduction in the number of parameters while maintaining comparable performance.
• Score: 70.32218116940393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low Rank Adaptation (LoRA) is a popular Parameter Efficient Fine Tuning (PEFT) method that effectively adapts large pre-trained models for downstream tasks. LoRA parameterizes model updates using low-rank matrices at each layer, significantly reducing the number of trainable parameters and, consequently, resource requirements during fine-tuning. However, the lower bound on the number of trainable parameters remains high due to the use of the low-rank matrix model. In this paper, we address this limitation by proposing a novel approach that employs a low rank tensor parametrization for model updates. The proposed low rank tensor model can significantly reduce the number of trainable parameters, while also allowing for finer-grained control over adapter size. Our experiments on Natural Language Understanding, Instruction Tuning, Preference Optimization and Protein Folding benchmarks demonstrate that our method is both efficient and effective for fine-tuning large language models, achieving a substantial reduction in the number of parameters while maintaining comparable performance.

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