Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Banerjee, Atmika M.; Mukhopadhyaya, Keka Title A name given to the resource Fine-Tuning Large Language Models for Personality Development Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 9th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2025;pp.2109-2116 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICECA66444.2025.11382960" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICECA66444.2025.11382960</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034853925?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034853925?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Banerjee A.M., Christ (Deemed to be University), Department of Cse, Bengaluru, India; Mukhopadhyaya K., Cmr Institute of Technology, Department of Ece, Bengaluru, India Description An account of the resource Large Language Models (LLMs) are state-of-the-art in Natural Language Processing (NLP) tasks but are extremely challenging to fine-tune with their size and computational needs. The current research targets the fine-tuning of the OpenLLaMA-3B model for personality development tasks with parameter-efficient fine-tuning (PEFT) via LoRA and QLoRA. To deal with hardware limitations, 4-bit quantization through BitsAndBytes was used, lowering memory without affecting accuracy. A FAISS-based Retrieval-Augmented Generation (RAG) pipeline was also used to improve contextual reliability. Semantic similarity (cosine similarity), BLEURT, ROUGE, and human evaluation were used to evaluate the model. The results of this experiment show that semantic similarity greatly increasing from 0% before fine-tuning to 80% after fine-tuning, demonstrating the benefit of PEFT and quantization in domain adaptation. The work illustrates that by leveraging effective fine-tuning, quantization, and retrieval augmentation, LLMs can be deployed at scale under limited resources while providing high contextual accuracy for personality development purposes. This approach not only enhances knowledge of the job, but it also has practical scalability for educational and self-improvement purposes. The findings highlight a viable path forward for applying small, yet strong, LLMs to personalized learning and adaptive human-AI interaction. 2025 IEEE. Subject The topic of the resource fine tuning; large language models; LoRA; parameter efficient fine tuning; personality development; QLoRA Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159929-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online