Personalized application solutions for educational scenarios
Building an intelligent tutoring system using the MiMo-7B-RL model can be achieved at three levels:
1. Adaptation of topic difficulty
- Dynamic selection of topics based on students' historical performance:
"生成一道比上次正确题目难度高10%的因式分解题" - Supports 5 levels of difficulty adjustment for MATH-500 question bank
2. Step-by-step guided instruction
- Question Breakdown Tip:
outputs = llm.generate(["将一个立体几何问题分解为3个解题步骤"]) - Error Step Positioning:
"分析学生解题过程中的关键错误点：" + 错误答案 - Visual rendering:
Demonstrate the solution process in conjunction with MathJax or Python's matplotlib
3. Closed-loop pedagogical design
Typical workflow:
1. Diagnostic tests → 2. Generation of personalized learning paths → 3. Intelligent daily questions → 4. Automatic explanation of wrong questions → 5. Tracking of learning results
Example of implementation
# 生成同类变式题 prompt = """ 原始题目：已知三角形ABC三边长a=3,b=4,c=5，求面积 请生成3道考查相同知识点但形式不同的题目 """ outputs = llm.generate([prompt], SamplingParams(temperature=0.7))
The measured data show that:
- AIME Question Explanation Accuracy 95.81 TP3T
- Response time for single topic generation <1.2s
- Student Retention Increases 37%
Deployment recommendations:
1. Using Flask/Django to build a Web interface
2. Work with PostgreSQL to record learning tracks
3. Educational institutions may apply for milletmimo@xiaomi.comAccess to education-specific weights
This answer comes from the articleMiMo: A Small Open Source Model for Efficient Mathematical Reasoning and Code GenerationThe
