Shubhamsaboo/Qwen3-Coder
Jan 2025 – Jan 2025
1 Month active
Languages Used
PythonShell
Technical Skills
BenchmarkingEvaluation FrameworksLLM IntegrationMachine LearningNatural Language ProcessingPython
Yangjiaxi
PROFILE
Yangjiaxi
Jason Yang developed a reproducible benchmarking framework for the QwQ-32B-Preview model within the Shubhamsaboo/Qwen3-Coder repository. He designed and implemented the LiveCodeBench evaluation framework, which includes runner scripts, evaluation metrics, and prompt formatting to enable reliable code generation benchmarking. Using Python and shell scripting, Jason focused on repository hygiene by updating configurations and maintaining a clean .gitignore, ensuring repeatable and trustworthy results. His work established a robust baseline for data-driven model tuning and cross-version comparisons, providing a foundation for measurable performance improvements. The depth of his engineering enabled faster, more informed decisions in large language model evaluation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
20,692
Activity Months1
Your Network
450 people
Work History
January 2025
1 Commits • 1 Features
Jan 1, 2025Concise monthly summary for 2025-01 focusing on delivering a reproducible benchmarking framework for QwQ-32B-Preview. Key achievement: LiveCodeBench evaluation framework with runner scripts, metrics, and prompt formatting, plus configuration updates and .gitignore hygiene to ensure clean, repeatable benchmarks. These efforts establish a baseline for data-driven model tuning and cross-version comparisons, enabling faster, value-driven decisions and measurable performance gains.
1 Commits • 1 Features
Jan 1, 2025Concise monthly summary for 2025-01 focusing on delivering a reproducible benchmarking framework for QwQ-32B-Preview. Key achievement: LiveCodeBench evaluation framework with runner scripts, metrics, and prompt formatting, plus configuration updates and .gitignore hygiene to ensure clean, repeatable benchmarks. These efforts establish a baseline for data-driven model tuning and cross-version comparisons, enabling faster, value-driven decisions and measurable performance gains.
January 2025
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
PythonShell
Technical Skills
BenchmarkingEvaluation FrameworksLLM IntegrationMachine LearningNatural Language ProcessingPythonShell ScriptingSoftware Engineering
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline