Practical solutions to improve the quality of code generation
The following strategies can be adopted to optimize the effectiveness of code generation based on the MiMo-7B-RL model:
Core Optimization Methodology
- Problem description refinement: The input prompt should contain function input and output constraints (e.g.
"编写Python函数，输入整数n，返回斐波那契数列前n项，要求时间复杂度O(n)"), which can lead to an increase in generation accuracy of 40% - Temperature parameter control: Settings
SamplingParams(temperature=0.6)Balances creativity with accuracy, measured at 49.31 TP3T Pass@1 on LiveCodeBench v6 test set - Post-processing validation: Combining Python's
astmodule for syntax checking, or use thepytestQuick validation of generated code
Advanced Optimization Tips
- For complex algorithmic problems, step-by-step generation:
"1. 生成快速排序的partition函数" → "2. 生成递归主体" - Enable MTP to accelerate iterations:
llm = LLM(model="XiaomiMiMo/MiMo-7B-RL", trust_remote_code=True, num_speculative_tokens=1) - Real-time debugging using SGLang's interactive mode:
python3 -m sglang.launch_server --model-path XiaomiMiMo/MiMo-7B-RL --host 0.0.0.0
The measured data show that the method performs well in the following scenarios:
- Python Algorithm Questions: 57.81 TP3T First Time Pass Rate
- C++ Systems Programming: 43.21 TP3T First Time Pass Rate
- SQL Query Generation: 51.1% Syntactic Correctness
Suggested development environment configuration: Python 3.8+, CUDA 11.7, recommended to use Docker to encapsulate the environment dependencies.
This answer comes from the articleMiMo: A Small Open Source Model for Efficient Mathematical Reasoning and Code GenerationThe
May not be reproduced without permission:AI productivity tools " How to optimize the quality and accuracy of code generation?
