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

@article{1711930,
      author = {Kunal Kumar Nath, Samridh Chauhan, Tanmay Jadhav, Pranit Virkar},
      title = {Design and Implementation of a Multi-Modal, Agentic Code Companion System (ICC) using Fine-Tuned Code LLMs and Structured Visualization},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {659-669},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711930.pdf},
      abstract = {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).},
      keywords = {Large Language Models, Code Generation, Multi-Modality, Optical Character Recognition (OCR), Agentic Systems, Structured Output, QLoRA Fine-Tuning, Full- Stack Architecture, FastAPI, Perplexity UI.},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1711930}
  }