Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization - Luo, Yang; Wei, Zibu; Xu, Guokun; Li, Zhengning; Xie, Ying; Yin, Yibo (Journal of Theory and Practice of Engineering Science, 2024)
E-commerce chatbots play a crucial role in customer service but often struggle with understanding complex queries. This study introduces a breakthrough approach leveraging the Falcon-7B model, a state-of-the-art Large Language Model (LLM) with 7 billion parameters. Trained on a vast dataset of 1,500 billion tokens from RefinedWeb and curated corpora, the Falcon-7B model excels in natural language understanding and generation. Notably, its 16-bit full quantization transformer ensures efficient computation without compromising scalability or performance. By harnessing cutting-edge machine learning techniques, our method aims to redefine e-commerce chatbot systems, providing businesses with a robust solution for delivering personalized customer experiences.