DeepEdu-v1: Efficient and Scalable Agentic LLMs for Vietnamese Education· DeepEdu-v1：越南教育的高效代理型大语言模型
AI tutoring could markedly improve learning outcomes for students in developing regions such as Vietnam, yet the two obvious paths both fall short. Cloud assistants such as ChatGPT route sensitive student data to foreign servers---violating data-sovereignty laws such as Vietnam's Decree 53---and, pre-trained on Western-centric corpora, are not organized around the national textbook curriculum, so their knowledge of local content is unsystematic and frequently hallucinated. Self-hosting an open model keeps data on-premise but hits a two-fold wall: post-training quantization (AWQ, GPTQ) tames the static weight footprint, yet the dynamic KV cache and prefill latency of long tutoring contexts still cause out-of-memory failures and slow responses on consumer GPUs, while the model keeps hallucin
针对越南教育的高效代理型大语言模型，解决数据主权和本地内容问题。
- 核心方法
- 开发DeepEdu-v1，通过本地部署优化减少内存消耗和延迟，同时加强本地内容的学习和理解能力。
- 适合谁读
- 研究者、工程师、教育工作者
- 要解决的问题
- 越南等发展中国家的学生使用AI辅导时面临数据主权问题和本地教材内容准确性问题，以及本地部署大模型时的内存和延迟问题。
- 关键实验
- 未提供
- 主要贡献
- 提供了一种符合越南数据主权法律的高效AI教育辅导模型，增强了对本地教材内容的准确性和系统性。
- 意义与局限
- DeepEdu-v1有助于提高越南学生的个性化学习体验，但可能受限于训练数据量和模型复杂度。