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
Contributed to the lmms-eval repository by integrating LSDBench support and developing a new benchmark for long-video evaluation tasks. This work expanded dataset coverage and enhanced the evaluation toolkit’s scope, enabling users to benchmark models on longer video content with reproducible results. The approach emphasized code quality through a comprehensive lint pass, configuration cleanup, and updated documentation to reflect the new integration. Utilizing Python, YAML, and Markdown, the developer focused on maintainability and stability, aligning the toolkit with broader benchmarking needs. The enhancements delivered a stable, feature-rich evaluation pipeline, supporting faster deployment and more robust machine learning evaluation workflows for users.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
1
Lines of code
191
Activity Months1
Your Network
90 peopleSame Organization
@link.cuhk.edu.hk1
Shared Repositories
89
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
Repositories Contributed To
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