AutoTuneX: Interactive Automated Fine-Tuning for Large Language Models

Authors

  • Daniel Karl I. Weidele IBM Research
  • Priyanshu Rai IBM Research
  • Frederico Araujo IBM Research
  • Teryl Taylor IBM Research
  • Radu Marinescu IBM Research

DOI:

https://doi.org/10.1609/aaai.v40i48.42391

Abstract

We present AutoTuneX, a system architecture design and implementation for users to interactively fine-tune large language models (LLMs) based on automated hyperparameter optimization particularly built around Bandit Limited Discrepancy Search. Next to a classical Graphical User Interface (GUI) our system features an agentic runtime to facilitate automated fine-tuning via chat.
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Published

2026-03-14

How to Cite

Weidele, D. K. I., Rai, P., Araujo, F., Taylor, T., & Marinescu, R. (2026). AutoTuneX: Interactive Automated Fine-Tuning for Large Language Models. Proceedings of the AAAI Conference on Artificial Intelligence, 40(48), 41715–41717. https://doi.org/10.1609/aaai.v40i48.42391

Issue

Vol. 40 No. 48: EAAI-26 AI for Education, Model AI Assignments, AAAI-26 Emerging Trends, Doctoral Consortium, Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Demonstration Track