This paper introduces a novel approach to enhance e-commerce chatbots by 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, the Falcon-7B model excels in natural language understanding and generation. Its 16-bit full quantization transformer ensures efficient computation without compromising scalability or performance. This approach aims to redefine e-commerce chatbot systems, providing businesses with a robust solution for delivering personalized customer experiences. The study addresses the challenges faced by current e-commerce chatbots, which often struggle with complex queries and lack personalization. The Falcon-7B model, combined with 16-bit full quantization, significantly improves the chatbots' ability to understand user queries and provide intelligent, personalized responses. The 16-bit full quantization technique reduces computational and memory requirements, enabling efficient processing of large volumes of data. The paper also presents an evaluation of the Falcon-7B model on the Ecommerce-FAQ-Chatbot-Dataset, demonstrating its superior performance with a BLEU score of 31.62, outperforming other models such as GPT2, GP2-XL, and DistilGPT2. The results highlight the effectiveness of Falcon-7B in natural language understanding and generation tasks within the context of e-commerce chatbots. The study concludes that the Falcon-7B model, with its 16-bit full quantization, represents a significant advancement in e-commerce chatbot technology. It offers a robust solution for delivering personalized and engaging customer experiences, leveraging cutting-edge machine learning techniques and extensive datasets. The approach has the potential to revolutionize customer service in the e-commerce domain.This paper introduces a novel approach to enhance e-commerce chatbots by 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, the Falcon-7B model excels in natural language understanding and generation. Its 16-bit full quantization transformer ensures efficient computation without compromising scalability or performance. This approach aims to redefine e-commerce chatbot systems, providing businesses with a robust solution for delivering personalized customer experiences. The study addresses the challenges faced by current e-commerce chatbots, which often struggle with complex queries and lack personalization. The Falcon-7B model, combined with 16-bit full quantization, significantly improves the chatbots' ability to understand user queries and provide intelligent, personalized responses. The 16-bit full quantization technique reduces computational and memory requirements, enabling efficient processing of large volumes of data. The paper also presents an evaluation of the Falcon-7B model on the Ecommerce-FAQ-Chatbot-Dataset, demonstrating its superior performance with a BLEU score of 31.62, outperforming other models such as GPT2, GP2-XL, and DistilGPT2. The results highlight the effectiveness of Falcon-7B in natural language understanding and generation tasks within the context of e-commerce chatbots. The study concludes that the Falcon-7B model, with its 16-bit full quantization, represents a significant advancement in e-commerce chatbot technology. It offers a robust solution for delivering personalized and engaging customer experiences, leveraging cutting-edge machine learning techniques and extensive datasets. The approach has the potential to revolutionize customer service in the e-commerce domain.