Improving the quality of code generation requires a combination of model characterization and engineering techniques:
- Environment Configuration: Installation of specialized code dependencies
pip install seed-oss[code], which will load the LiveCodeBench optimized tokenizer to handle special symbols. - Tip Engineering: A triple-prompt structure is used:
- The system command specifies the language (
"role":"system", "content":"Generate Python3 code") - User input requirements
- stick
n# Unit Test CasesnSelf-validation of bootstrap models
- The system command specifies the language (
- parameter tuning: Settings
thinking_budget=896+top_p=0.9+temperature=1.05Stimulate creativity while maintaining logic, to key algorithms can be addedstop_sequences=["ndef"]Step-by-step generation. - post-verification mechanism: The built-in
enable-auto-tool-choiceAutomatically call pytest or flake8 to check the generated code, and can trigger regenerate on error.
Empirical tests show that the method improves the pass@1 metric on LiveCodeBench by 32% over the base usage. when dealing with large projects it is recommended to let the model generate the architectural design first (thinking_budget=1024), and then realized in modules.
This answer comes from the articleSeed-OSS: Open Source Large Language Model for Long Context Reasoning and Versatile ApplicationsThe
May not be reproduced without permission:AI productivity tools " How to improve the accuracy of code generation tasks with Seed-OSS?
