Design and Implementation of a Multi-Modal, Agentic Code Companion System (ICC) using Fine-Tuned Code LLMs and Structured Visualization

This paper proposes the architectural design and implementation strategy for the Intelligent Code Companion (ICC) system, a novel platform engineered to process multi- modal code queries (text and image) and generate comprehensive, structured outputs. The core innovation lies in a hybrid LLM Agent architecture that coordinates specialized tools, specifically Optical Character Recognition (OCR) for handling photographed code inputs and a Structured Output Generator for creating dynamic, animated flowcharts. The system deviates from using underpowered base models, advocating instead for the instruction fine- tuning of a high-performing yet resource- efficient model, such as the StarCoder2-3B model, utilizing Quantized Low-rank Adaptation (QLoRA).