FinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation on GPUs

AAAI 2025 Workshop CoLoRAI Submission26 Authors

Keywords: Large Language Models, Finetuning, Low-Rank Adaptation, Quantization, Financial Applications, Sentiment Analysis, Named Entity Recognition, XBRL

Abstract:

Finetuned large language models (LLMs) have shown remarkable performance in financial tasks, such as sentiment analysis and information retrieval. Due to privacy concerns, finetuning and deploying financial LLMs (FinLLMs) locally are crucial for institutions and individuals. In this paper, we employ quantized low-rank adaptation (QLoRA) to finetune FinLLMs, which leverage low-rank structure and quantization technique to significantly reduce computational requirements while maintaining model performance. We also employ data and pipeline parallelism to enable local finetuning on commodity GPUs. Experiments on financial datasets validate the efficacy of our approach in yielding notable improvements over the base models.

Submission Number: 26