  The advent of large language models (LLMs) has revolutionized natural
language processing, enabling unprecedented capabilities in understanding and
generating human-like text. However, the computational cost and convergence
times associated with fine-tuning these models remain significant challenges.
Low-Rank Adaptation (LoRA) has emerged as a promising method to mitigate these
issues by introducing efficient fine-tuning techniques with a reduced number of
trainable parameters. In this paper, we present OLoRA, an enhancement to the
LoRA method that leverages orthonormal matrix initialization through QR
decomposition. OLoRA significantly accelerates the convergence of LLM training
while preserving the efficiency benefits of LoRA, such as the number of
trainable parameters and GPU memory footprint. Our empirical evaluations
demonstrate that OLoRA not only converges faster but also exhibits improved
performance compared to standard LoRA across a variety of language modeling
tasks. This advancement opens new avenues for more efficient and accessible
fine-tuning of LLMs, potentially enabling broader adoption and innovation in
natural language applications.
