Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization

Authors

  • Yang Luo Computer Science, China CITIC Bank Software Development Center, Beijing, China
  • Zibu Wei Computer Science, University of California, Los Angeles, Los Angeles, USA
  • Guokun Xu Computer Science, Beijing Foreign Studies University, Beijing, China
  • Zhengning Li Computer Science, Georgetown University, Washington, D.C. USA
  • Ying Xie Computer Science, San Francisco Bay University, Fremont, USA
  • Yibo Yin Computer Science, Contemporary Amperex Technology USA Inc, Auburn Hills, USA

DOI:

https://doi.org/10.53469/jtpes.2024.04(02).08

Keywords:

E-commerce Chatbot, Large Language Models (LLM), Falcon-7B

Abstract

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.

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Published

2024-02-28

How to Cite

Luo, Y., Wei, Z., Xu, G., Li, Z., Xie, Y., & Yin, Y. (2024). Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization. Journal of Theory and Practice of Engineering Science, 4(02), 52–57. https://doi.org/10.53469/jtpes.2024.04(02).08

Issue

Vol. 4 No. 02 (2024): JTPES-2023-4-2

Section

Articles

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Copyright (c) 2024 Yang Luo, Zibu Wei, Guokun Xu, Zhengning Li, Ying Xie, Yibo Yin

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.