Journal of East China Normal University(Natural Science) ›› 2024, Vol. 2024 ›› Issue (5): 162-172.doi: 10.3969/j.issn.1000-5641.2024.05.015

• Educational Infrastructure • Previous Articles     Next Articles

Locally lightweight course teaching-assistant system based on IPEX-LLM

Jiarui ZHANG1, Qiming ZHANG1, Fenglin BI1, Yanbin ZHANG1,*(), Wei WANG1, Erjin REN2, Haili ZHANG3   

  1. 1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
    2. Intel Asia Pacific R & D Co. Ltd., Shanghai 200336, China
    3. Yuxing Technology (Zhejiang) Co. Ltd. Shanghai Branch, Shanghai 201821, China
  • Received:2024-06-20 Online:2024-09-25 Published:2024-09-23
  • Contact: Yanbin ZHANG E-mail:ybzhang@dase.ecnu.edu.cn

Abstract:

This study introduces and implements a local, lightweight, intelligent teaching-assistant system. Using the IPEX-LLM (Intel PyTorch extention for large language model) acceleration library, the system can efficiently deploy and execute large language models that are fine-tuned using the QLoRA (quantum-logic optimized resource allocation) framework on devices with limited computational resources. Combining this with enhanced retrieval techniques, the system provides flexible course customization through four major functional modules: intelligent Q&A, automated question generation, syllabus creation, and course PPT generation. This system is intended to assist educators in improving the quality and efficiency of lesson preparation and delivery, safeguarding data privacy, supporting personalized student learning, and offering real-time feedback. Performance tests exemplified by the optimized Chatglm3-6B model show the rapid inference capability of the system via the processing of a 64-token output task within 4.08 s in a resource-constrained environment. A practical case study comparing the functionality of the system with native Chatglm-6B and ChatGPT 4.0 further validates its superior accuracy and practicality.

Key words: intelligent assistance, limited computational resources, IPEX-LLM(Intel PyTorch extention for large language model), fine-tuning, enhanced retrieval

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