Fine-Tuning a Local LLM for Thermoelectric Generators with QLoRA: From Generalist to Specialist

This work establishes a large language model (LLM) specialized in the domain of thermoelectric generators (TEGs), for deployment on local hardware. Starting with the generalist JanV1-4B model, an efficient fine-tuning (FT) methodology (QLoRA) was employed, modifying only 3.18% of the total parameters of this base model. The key to the process is the use of a custom-designed dataset, which merges deep theoretical knowledge with rigorous instruction tuning to refine behavior and mitigate catastrophic forgetting. Performance evaluation, conducted using a questionnaire of increasing complexity, revealed that the FT JanV1-4B-expert-TEG modelled in this work achieves an overall accuracy of 81%, demonstrating capabilities ranging from the correct formulation of equations to critical design reasoning. This study validates the specialization of LLMs using QLoRA as an effective and accessible strategy for developing highly competent engineering support tools, eliminating dependence on large-scale computing infrastructures.