ManifoldRG/MultiNet
Sep 2025 – Sep 2025
1 Month active
Languages Used
Python
Technical Skills
Data ProcessingDeep LearningMachine LearningModel EvaluationNatural Language ProcessingPython
Aryan Khurana
PROFILE
Aryan Khurana
Aryan Khurana developed an end-to-end PIQA Evaluation Pipeline for Causal Language Models in the ManifoldRG/MultiNet repository, focusing on automating model evaluation and reporting. Using Python and leveraging skills in data processing and natural language processing, Aryan designed a custom dataset loader, a metrics calculator for multiple-choice questions, and a main script to run inference and generate evaluation reports. The work included refactoring data loading and response parsing to improve robustness and maintainability. By updating dependencies and repository structure, Aryan reduced setup friction and enabled reproducible experiments, establishing a solid foundation for scalable model benchmarking and streamlined evaluation workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
446
Activity Months1
Your Network
10 people
Work History
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 (ManifoldRG/MultiNet) delivered an end-to-end PIQA Evaluation Pipeline for Causal Language Models, enabling automated evaluation, reporting, and reproducibility. Key deliverables include a complete evaluation workflow: a custom dataset loader, a metrics calculator for multiple-choice questions, and a main script to run inference and generate evaluation reports. The work also included updates to .gitignore and dependencies to support the pipeline, as well as refactor enhancements to improve data loading, metrics computation, and response parsing for a more robust evaluation workflow. These efforts reduce manual effort, accelerate model benchmarking, and establish a solid foundation for scalable experimentation.
2 Commits • 1 Features
Sep 1, 2025September 2025 (ManifoldRG/MultiNet) delivered an end-to-end PIQA Evaluation Pipeline for Causal Language Models, enabling automated evaluation, reporting, and reproducibility. Key deliverables include a complete evaluation workflow: a custom dataset loader, a metrics calculator for multiple-choice questions, and a main script to run inference and generate evaluation reports. The work also included updates to .gitignore and dependencies to support the pipeline, as well as refactor enhancements to improve data loading, metrics computation, and response parsing for a more robust evaluation workflow. These efforts reduce manual effort, accelerate model benchmarking, and establish a solid foundation for scalable experimentation.
September 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ProcessingDeep LearningMachine LearningModel EvaluationNatural Language ProcessingPython
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