Network Device Language Model Distillation for CLI Assistance
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Solution Overview
Problem
Network administrators face difficulties in efficiently interacting with network devices using command line interfaces (CLIs), which are often clunky, inefficient, and require significant expertise, due to the resource constraints and lack of contextually relevant fine-tuning of large language models (LLMs) on network devices.
Innovation Solution
Fine-tuning a small language model (SLM) optimized for network devices by selecting a pre-trained model, distilling it to fit resource constraints, and augmenting it with locally relevant information to create a contextually relevant chatbot that assists administrators through natural language prompts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large language model is deployed on a network device, then the ability to provide contextually relevant assistance is improved, but the resource constraints of the network device are exceeded
Solution Approach 1:
The patent extracts and removes portions of the large language model that are unrelated to network device functionality, creating a distilled model that retains only the relevant knowledge and capabilities needed for network device assistance, thereby reducing the model size to fit resource constraints while maintaining contextual relevance
Solution Approach 2:
The patent applies fine-tuning to adapt the pre-trained language model specifically for network device contexts by training it on network device manuals, documentation, and operational data, giving the model specialized local knowledge about network devices while maintaining general language capabilities
2Ease of operation
If command line interface is used for network device interaction, then the device can be controlled, but the ease of operation deteriorates due to requiring significant expertise
Solution Approach 1:
The patent introduces a language model-based chatbot as an intermediary between the network administrator and the network device, translating natural language queries into appropriate device commands and operations, thereby eliminating the need for administrators to directly learn complex CLI syntax while maintaining full device control capabilities
3Loss of time
If a pre-trained language model is used without fine-tuning, then the model can be deployed immediately, but the contextual relevance to network devices deteriorates
Solution Approach 1:
The patent performs preliminary fine-tuning of the language model using network device-specific training data before deployment, preparing the model in advance to understand network device contexts, terminology, and operational procedures, so that when deployed, the model is immediately contextualized without requiring runtime adaptation
Data Source
AI summary
Techniques and mechanisms for fine-tuning a language model to be optimized for a network device to which the language model is deployed. A controller for a network may maintain an inventory of network devices in a network, and obtain device information for the network devices. The controller may analyze the device information to determine a device type or role for the network devices. The controller may then select a pre-trained model that is optimal or well-suited for a device type of a particular network device, and perform a distillation function of the language model. Once the language model has been distilled, the controller may augment the language model with locally relevant information such that the language model is contextually relevant for the network device. After fine-tuning the language model, the controller pre-positions the language model on the device so network administrators and other users can access it when necessary.