EvolvingLMMs-Lab/lmms-eval
Jul 2025 – Jul 2025
1 Month active
Languages Used
MarkdownPythonYAML
Technical Skills
API IntegrationConfiguration ManagementData ProcessingMachine Learning EvaluationNatural Language Processing
wutaiqiang
PROFILE
Wutaiqiang
Developed PhyX Benchmark Support for the EvolvingLMMs-Lab/lmms-eval repository, enabling comprehensive evaluation of models’ physics-grounded reasoning across both multiple-choice and open-ended PhyX subsets. The work involved designing configuration scaffolding and implementing evaluation logic to integrate the benchmark seamlessly into the existing pipeline. Leveraging Python and YAML, the developer established a reproducible workflow that supports future experiments and validation efforts. The project drew on skills in API integration, configuration management, and data processing, enhancing the repository’s capacity for machine learning evaluation in natural language processing tasks. No bug fixes were recorded during this period, focusing solely on feature development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
556
Activity Months1
Your Network
89 peopleShared Repositories
89
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for EvolvingLMMs-Lab/lmms-eval: Delivered PhyX Benchmark Support enabling physics-grounded evaluation across PhyX MCQ and open-ended subsets, with configuration scaffolding and evaluation logic. Minor bug fixes were not recorded in this period. The work enhances model assessment capabilities and supports data-driven improvements in physics-based reasoning evaluation.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for EvolvingLMMs-Lab/lmms-eval: Delivered PhyX Benchmark Support enabling physics-grounded evaluation across PhyX MCQ and open-ended subsets, with configuration scaffolding and evaluation logic. Minor bug fixes were not recorded in this period. The work enhances model assessment capabilities and supports data-driven improvements in physics-based reasoning evaluation.
July 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
MarkdownPythonYAML
Technical Skills
API IntegrationConfiguration ManagementData ProcessingMachine Learning EvaluationNatural Language Processing
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