thunlp/SIR-Bench
Nov 2024 – Apr 2025
4 Months active
Languages Used
MarkdownPythonYAML
Technical Skills
Configuration ManagementData EngineeringData EvaluationData LoadingData ManagementDataset Handling
liushz
PROFILE
Liushz
Over four months, this developer enhanced the thunlp/SIR-Bench repository by integrating new datasets and expanding benchmarking capabilities for complex reasoning and math evaluation tasks. They implemented config-driven dataset loading and evaluation workflows, enabling reproducible assessments of models on benchmarks such as OlymMATH, HLE, and AIME2025. Their work included improving data integrity, parameter handling, and compatibility for both English and Chinese QA datasets. Using Python, YAML, and CI/CD practices, they ensured robust configuration management and seamless dataset integration. The developer’s contributions deepened the platform’s evaluation coverage and maintained high standards for reliability, scalability, and open-source collaboration throughout the project.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
11Total
Bugs
2
Commits
11
Features
5
Lines of code
1,298
Activity Months4
Your Network
15 people
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered the OlymMATH dataset integration for thunlp/SIR-Bench, enabling benchmarking on Olympiad-level math problems with dataset loading, evaluation, and model-judge integration. No major bugs fixed this month. This work expands evaluation capabilities and strengthens the benchmarking platform for math reasoning models. Technologies/skills demonstrated include config-driven dataset loading, evaluation workflow integration, and version-controlled feature delivery (commit 32d6859679539ebbfe8316039f87d095aa8bb4ee).
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered the OlymMATH dataset integration for thunlp/SIR-Bench, enabling benchmarking on Olympiad-level math problems with dataset loading, evaluation, and model-judge integration. No major bugs fixed this month. This work expands evaluation capabilities and strengthens the benchmarking platform for math reasoning models. Technologies/skills demonstrated include config-driven dataset loading, evaluation workflow integration, and version-controlled feature delivery (commit 32d6859679539ebbfe8316039f87d095aa8bb4ee).
April 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for thunlp/SIR-Bench: Expanded benchmarking coverage by adding HLE and AIME2025 dataset support, enabling broader model evaluation on complex reasoning tasks. Implemented dataset loading/config, updated mappings and download URLs, and ensured OSS access for AIME2025. This work enhances evaluation workflows, reproducibility, and opens-source collaboration.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for thunlp/SIR-Bench: Expanded benchmarking coverage by adding HLE and AIME2025 dataset support, enabling broader model evaluation on complex reasoning tasks. Implemented dataset loading/config, updated mappings and download URLs, and ensured OSS access for AIME2025. This work enhances evaluation workflows, reproducibility, and opens-source collaboration.
December 2024
3 Commits • 2 Features
Dec 1, 2024Month: 2024-12. Focused on delivering business value through robustness, scalability, and benchmarking enhancements in thunlp/SIR-Bench. Key outcomes include feature enrichments for longer, compatible QA responses, expanded benchmarking via a new dataset, and reliability improvements in parameter handling and CI thresholds.
3 Commits • 2 Features
Dec 1, 2024Month: 2024-12. Focused on delivering business value through robustness, scalability, and benchmarking enhancements in thunlp/SIR-Bench. Key outcomes include feature enrichments for longer, compatible QA responses, expanded benchmarking via a new dataset, and reliability improvements in parameter handling and CI thresholds.
December 2024
November 2024
5 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for thunlp/SIR-Bench focusing on business value and technical achievements. Delivered notable enhancements to data loading, evaluation, and benchmarking, along with targeted fixes to configuration and data integrity to ensure reliable data processing and reproducible results.
November 2024
5 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for thunlp/SIR-Bench focusing on business value and technical achievements. Delivered notable enhancements to data loading, evaluation, and benchmarking, along with targeted fixes to configuration and data integrity to ensure reliable data processing and reproducible results.
Activity
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Quality Metrics
Correctness90.0%
Maintainability87.2%
Architecture88.2%
Performance78.2%
AI Usage25.4%
Skills & Technologies
Programming Languages
MarkdownPythonYAML
Technical Skills
Benchmark DevelopmentCI/CDConfiguration ManagementData EngineeringData EvaluationData LoadingData ManagementDataset HandlingDataset IntegrationDataset ManagementLLM EvaluationLLM IntegrationMachine Learning EvaluationModel ConfigurationNatural Language Processing
Repositories Contributed To
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