This project addresses a critical pedagogical need: offering students continuous, on-demand academic assistance beyond conventional reception hours. I present a domain-specific Retrieval-Augmented Generation (RAG) system powered by a quantized Mistral-7B Instruct model and deployed as a Telegram bot. The assistant enhances learning by delivering real-time, personalized responses aligned with the "Introduction to Parallel Processing" course materials. GPU acceleration significantly improves inference latency, enabling practical deployment on consumer hardware. This approach demonstrates how consumer GPUs can enable affordable, private, and effective AI tutoring for HPC education.

翻译：本项目针对一项关键的教学需求：在常规答疑时间之外，为学生提供持续、按需的学术支持。我们提出了一种领域特定的检索增强生成系统，该系统由量化版Mistral-7B Instruct模型驱动，并以Telegram机器人形式部署。该助手通过提供与“并行处理导论”课程材料实时同步的个性化响应，有效促进学习过程。GPU加速显著降低了推理延迟，使得在消费级硬件上实现实际部署成为可能。本方法展示了消费级GPU如何为高性能计算教育提供经济、私密且高效的人工智能辅导。