Mostakim, MoinIslam, Md. Tariqul2025-03-162025-03-1620242024-11ID 23173006http://hdl.handle.net/10361/25739Cataloged from the PDF version of the project.Includes bibliographical references (pages 36-38).This project is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2024.Llama-2, an advanced neural network with huge potential in text generation, sentiment analysis and language understanding. This report focuses on the fine-tuning process for build chatbot on custom datasets, specification methods, hyperparameters and training strategies. Experimental results on Guanchu datasets show excellent adaptability of the model, outperforming the baseline model in human evaluation and achieving significant BERT scores for help and safety. The analysis includes an in-depth examination of LAMA-2’s architecture, outlining strengths and suggesting areas for improvement. Parameter-efficient fine-tuning and quantization also investigate the transformative potential of LLMA-2 through low-rank adaptation. The objective is to strike a balance between model complexity and efficiency, addressing challenges in resource-constrained environments.38 pagesenBRAC University projects are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permissionLlama-2Neural networkHyperparametersNatural language processingNeural networks (Computer science).Natural language processing (Computer science).Project Report