EvolvingLMMs-Lab/lmms-eval
Jun 2026 – Jun 2026
1 Month active
Languages Used
No languages
Technical Skills
HuggingFaceMachine LearningMultimodal LLMsPerformance OptimizationPythonSLURM
Xingrui Wang
PROFILE
Xingrui Wang
Developed a scalable cross-modal evaluation suite for the lmms-eval repository, focusing on the XModBench benchmark to assess omni-modal models. Leveraging Python, HuggingFace, and SLURM, the work introduced a cross-modal multiple-choice framework spanning ten modality-pair subtasks, complete with data processing pipelines, canonical metrics, and resource-aware cluster launchers. Specialized chat wrappers were implemented to support interleaved multimedia prompts, ensuring accurate evaluation without media loss. Integration of XModBench-Lite enabled standardized reporting and robust experiment scaling. Enhancements to data aggregation and environment handling improved reproducibility, while GPU-aware SLURM scripts facilitated concurrent benchmarking, optimizing throughput and resource coordination for model selection workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,110
Activity Months1
Your Network
95 peopleWork History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 focused on delivering a scalable cross-modal evaluation suite for lmms-eval, centered on the XModBench benchmark for omni-modal models. Key deliverables include a cross-modal MCQ framework spanning 10 modality-pair subtasks with data processing, metrics, and resource-aware cluster launch configurations; introduction of interleaved-multimedia chat wrappers to feed full interleaved prompts to models and preserve media across all options; integration of HuggingFace XModBench data with a Lite split, canonical metrics, and Level-2 summaries for robust reporting; enhancements to data processing and results aggregation with per-category breakdown; and scalable, GPU-aware SLURM launchers enabling concurrent benchmarks under cluster constraints. These efforts improve evaluation fidelity, reproducibility, and speed-to-insight for model selection and deployment decisions.
1 Commits • 1 Features
Jun 1, 2026June 2026 focused on delivering a scalable cross-modal evaluation suite for lmms-eval, centered on the XModBench benchmark for omni-modal models. Key deliverables include a cross-modal MCQ framework spanning 10 modality-pair subtasks with data processing, metrics, and resource-aware cluster launch configurations; introduction of interleaved-multimedia chat wrappers to feed full interleaved prompts to models and preserve media across all options; integration of HuggingFace XModBench data with a Lite split, canonical metrics, and Level-2 summaries for robust reporting; enhancements to data processing and results aggregation with per-category breakdown; and scalable, GPU-aware SLURM launchers enabling concurrent benchmarks under cluster constraints. These efforts improve evaluation fidelity, reproducibility, and speed-to-insight for model selection and deployment decisions.
June 2026
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage100.0%
Skills & Technologies
Programming Languages
No languages yet
Technical Skills
HuggingFaceMachine LearningMultimodal LLMsPerformance OptimizationPythonSLURM
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