EvolvingLMMs-Lab/lmms-eval
Jan 2026 – Jan 2026
1 Month active
Languages Used
BashPython
Technical Skills
Deep LearningMachine LearningModel EvaluationPython DevelopmentTransformers
Ye Ruichen
PROFILE
Ye Ruichen
Cedric Ye developed multi-modal evaluation support for the InternVL-HF model within the EvolvingLMMs-Lab/lmms-eval repository, focusing on expanding the evaluation capabilities for next-generation large multi-modal models. He implemented processing for both image and video inputs using HuggingFace transformers, ensuring the architecture remains forward-compatible with future model weights. Cedric’s approach emphasized seamless integration with existing pipelines, reducing setup complexity for researchers and supporting a broader range of evaluation scenarios. His work leveraged deep learning and Python development skills, with careful attention to model evaluation workflows. The solution addressed the need for extensible, maintainable evaluation tools in the evolving multi-modal landscape.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
370
Activity Months1
Your Network
76 people
Work History
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026: Delivered InternVL-HF multi-modal evaluation support in lmms-eval. Implemented processing for image and video inputs via HuggingFace transformers with a forward-compatible design to accommodate future model weights and integrate seamlessly with existing pipelines. This expands evaluation coverage for next-gen multi-modal LMMs and reduces setup effort for researchers.
1 Commits • 1 Features
Jan 1, 2026January 2026: Delivered InternVL-HF multi-modal evaluation support in lmms-eval. Implemented processing for image and video inputs via HuggingFace transformers with a forward-compatible design to accommodate future model weights and integrate seamlessly with existing pipelines. This expands evaluation coverage for next-gen multi-modal LMMs and reduces setup effort for researchers.
January 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
BashPython
Technical Skills
Deep LearningMachine LearningModel EvaluationPython DevelopmentTransformers
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