Software and Dataset For Fine-Tuning a local LLM for Thermo-Electric Generators with QLoRA: from generalist to specialist

This document provides a comprehensive guide to the software and datasets contained in
this repository. These resources were developed for the research presented in the article: "[Ar-
ticle Title Fine-Tuning a local LLM for Thermo-Electric Generators with QLoRA: from gener-
alist to specialist:;Preprinthttps://www.preprints.org/manuscript/202511.1348 ]". 

This work presents a complete pipeline for transforming general-purpose
Large Language Models (LLMs) into specialized technical assistants for thermoelectric genera-
tor (TEG) applications. Using QLoRA (Quantized Low-Rank Adaptation), we eciently ne-
tuned two open-source modelsJanV1-expert-TEG and Qwen3-4B-thinking-2507-TEG on a
curated dataset of 202 question-answer pairs covering thermoelectric materials, device physics,
performance optimization, and engineering applications. The methodology enables special-
ization without full model retraining, signicantly reducing computational requirements while
maintaining technical accuracy. We introduce a novel evaluation framework combining human
expert review with LLM-as-a-Judge scoring using state-of-the-art models (GPT-4, Gemini 1.5
Pro). The resulting specialist models demonstrate enhanced performance on technical queries,
achieving higher relevance and accuracy scores compared to their base counterparts. All re-
sources, including trained adapters, merged models, training scripts, and evaluation datasets,
are provided for reproducibility and further research in domain-specic AI applications for
renewable energy technologies.