EvolvingLMMs-Lab/lmms-eval
Jul 2025 – Jul 2025
1 Month active
Languages Used
MarkdownPythonYAML
Technical Skills
Benchmark DevelopmentCode QualityCode RefactoringConfiguration ManagementData ProcessingDataset Integration
Tianyuan Qu
PROFILE
Tianyuan Qu
During July 2025, this developer integrated LSDBench support into the lmms-eval repository, expanding its benchmarking capabilities for long-video evaluation tasks. They engineered a new benchmark pipeline that processes LSDBench datasets, updating documentation and evaluation scope to ensure clarity and reproducibility. Their technical approach emphasized code quality and maintainability, applying Python and YAML for configuration management and data processing, while also performing a comprehensive lint pass. The work addressed the need for broader dataset coverage and stable evaluation workflows, resulting in a cohesive, well-documented feature set that aligns with business goals for scalable, reproducible machine learning evaluation pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
1
Lines of code
191
Activity Months1
Your Network
82 peopleSame Organization
@link.cuhk.edu.hk1
Work History
July 2025
6 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) — Delivered a focused set of enhancements to the lmms-eval evaluation toolkit, anchored by LSDBench integration and an associated long-video benchmark. The work extended dataset coverage, upgraded the evaluation scope, and tightened configuration and code quality to improve stability and maintainability. The efforts align with business goals of broader benchmarking support, reproducible results, and faster time-to-value for users deploying longer-video evaluation pipelines.
6 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) — Delivered a focused set of enhancements to the lmms-eval evaluation toolkit, anchored by LSDBench integration and an associated long-video benchmark. The work extended dataset coverage, upgraded the evaluation scope, and tightened configuration and code quality to improve stability and maintainability. The efforts align with business goals of broader benchmarking support, reproducible results, and faster time-to-value for users deploying longer-video evaluation pipelines.
July 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture86.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonYAML
Technical Skills
Benchmark DevelopmentCode QualityCode RefactoringConfiguration ManagementData ProcessingDataset IntegrationDocumentationLintingMachine Learning Evaluation
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