Fine-Tuning the Qwen2.5-VL Model for Intelligent Applications in the Electrical Domain

Abstract

This study explores the fine-tuning application of the Qwen2.5-VL multi modal large model in the electrical domain. The electrical industry faces numerous challenges in maintaining and managing complex electrical systems. Traditional methods often rely on manual inspection and analysis. With the rapid advancement of artificial intelligence (AI) technologies, there is a growing need to explore how these tools can be applied to improve efficiency and accuracy in the electrical domain. Qwen2.5-VL is a state-of-the-art visual language model. We adopted the LoRA (Low Rank Adaptive) method to fine tune the model, which enables efficient parameter updates in low resource environments while maintaining high performance. This study analyzes the data characteristics and task requirements in the electrical domain, designs fine-tuning strategies with a focus on image-based applications, including data preprocessing, model fine-tuning, and training parameter optimization. The experimental re-sults show that the fine tuned model has achieved significant performance im-provements in tasks such as electrical equipment fault detection, image recogni-tion, and text classification. This study provides new ideas and methods for the application of artificial intelligence in the electrical domain, which is of great significance for promoting the development of electrical intelligence.