Shubhamsaboo/Qwen3-Coder
Jan 2025 – Jan 2025
1 Month active
Languages Used
PythonShell
Technical Skills
BenchmarkingEvaluation FrameworksLLM IntegrationMachine LearningNatural Language ProcessingPython
Yangjiaxi
PROFILE
Yangjiaxi
Developed a reproducible benchmarking framework within the Shubhamsaboo/Qwen3-Coder repository, focusing on evaluating code generation performance for the QwQ-32B-Preview model. Designed and implemented the LiveCodeBench evaluation framework, which included runner scripts, custom evaluation metrics, and prompt formatting to ensure consistent and reliable measurement of model outputs. Leveraged Python and shell scripting to automate benchmarking processes and maintain repository hygiene through configuration updates and .gitignore improvements. This work established a robust baseline for data-driven model tuning and facilitated cross-version comparisons, enabling more informed decisions and measurable performance improvements in large language model integration and evaluation workflows.
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
592 peopleWork 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
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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
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