DiagHW: A Compact LLM for Hardware Failure Diagnosis via a Novel Knowledge Distillation Pipeline - Caminha, Carlos; Silva, Maria de Lourdes M.; Chaves, Iago C.; Brito, Felipe T.; Farias, Victor A. E.; Machado, Javam C. (Anais da Brazilian Conference on Intelligent Systems (BRACIS), 2025)
Hardware failure diagnosis from textual user-reported issues presents significant challenges due to the ambiguous and non-technical nature of these reports. While Large Language Models show promise in this domain, state-of-the-art models with billions of parameters pose practical limitations for deployment on low-power consumer devices. This work introduces a novel knowledge distillation pipeline for hardware failure detection, leveraging a large teacher model to generate synthetic training data for a smaller student model. Then, we present the DiagHW model, a compact 1.2B parameter fine-tuned LLaMA-3.2-1b-instruct model, which achieves diagnostic accuracy comparable to much larger models (up to 72B parameters). Our extensive experimental validation involved 32,414 inferences across 28 baseline models and 10 fine-tuned variants, processing a total of 19,692,210 tokens for these evaluations, complemented by 105,925,876 tokens processed during the fine-tuning stages.