LLMNDC: A Novel Approach for Network Device Configuration based on Fine-tuned Large Language Models

As networking demands continue to expand, there is an increasing need for enhanced capabilities in network device configuration. Managing network device configurations has become increasingly critical due to the expansion of network scales and complexity. Correct configurations ensure network security, performance, and reliability, yet traditional methods struggle with numerous parameters and complex dependencies. This complexity is exacerbated by varied configuration syntax and structures across different devices, further complicating management. To address these challenges, we propose a novel approach utilizing specially fine-tuned Large Language Models (LLMs) to assist in automating network device configuration. This study integrates artificial intelligence and machine learning to provide configuration recommendations, enhancing the automation level in network device management. Our experiments demonstrate the model's capability in querying general data communication knowledge and providing specific configuration suggestions tailored to manufacturers such as Huawei and Cisco. These results enhance the automation and intelligence of network device configuration processes, providing crucial data support and introducing new technical approaches to the field.