Khushi Malik contributed to the CSC392-CSC492-Building-AI-ML-systems/ai-identities repository by developing and enhancing evaluation frameworks and data processing pipelines for language model benchmarking. Over two months, Khushi built structured tooling for MathEval and BoolQ, enabling standardized model iteration and results aggregation, and expanded the model catalog to include Qwen and Mistral. Using Python, JavaScript, and TypeScript, Khushi improved prompt retrieval reliability, streamlined data handling, and integrated process ID tracking for model runs. Maintenance work included repository hygiene through .gitignore and DS_Store cleanup. These efforts improved reproducibility, model traceability, and the reliability of performance measurement workflows.
2025-03: Delivered core platform enhancements for the ai-identities project, focusing on data processing, model catalog expansion, evaluation tooling, and repository hygiene. Key features include Ollama process ID handling and logging integrated with llama data processing (with related README tweaks and test-questions CSV trimming), expanded model catalog to include Qwen, Mistral, and new/temporary models, and a comprehensive update to evaluation tooling for vocabulary generation, MMLU, and performance metrics, including API key handling improvements and output organization for evaluation runs. Maintenance work removed macOS DS_Store files to keep the repository clean and version-controlled. Impact: Improved model traceability and data governance, faster onboarding of new models, more reliable and reproducible performance measurements, and a cleaner codebase. Technologies/skills demonstrated: Python tooling for data pipelines, model integration patterns, evaluation framework development, API key management, data handling, and version-control hygiene.
2025-03: Delivered core platform enhancements for the ai-identities project, focusing on data processing, model catalog expansion, evaluation tooling, and repository hygiene. Key features include Ollama process ID handling and logging integrated with llama data processing (with related README tweaks and test-questions CSV trimming), expanded model catalog to include Qwen, Mistral, and new/temporary models, and a comprehensive update to evaluation tooling for vocabulary generation, MMLU, and performance metrics, including API key handling improvements and output organization for evaluation runs. Maintenance work removed macOS DS_Store files to keep the repository clean and version-controlled. Impact: Improved model traceability and data governance, faster onboarding of new models, more reliable and reproducible performance measurements, and a cleaner codebase. Technologies/skills demonstrated: Python tooling for data pipelines, model integration patterns, evaluation framework development, API key management, data handling, and version-control hygiene.
February 2025 — CSC392-CSC492-Building-AI-ML-systems/ai-identities: Focused on strengthening evaluation and reliability for model benchmarking. Delivered an end-to-end Evaluation Framework for MathEval and BoolQ to standardize model iteration, results aggregation, and output formatting. Fixed a critical file path resolution issue in prompt retrieval to ensure robust prompts workflow. Cleaned up the repository by updating ignore rules to exclude test logs, reducing noise in commits. These changes improved reproducibility, reduced runtime errors, and streamlined model iteration, delivering tangible business value by accelerating safe deployment cycles and improving benchmarking reliability.
February 2025 — CSC392-CSC492-Building-AI-ML-systems/ai-identities: Focused on strengthening evaluation and reliability for model benchmarking. Delivered an end-to-end Evaluation Framework for MathEval and BoolQ to standardize model iteration, results aggregation, and output formatting. Fixed a critical file path resolution issue in prompt retrieval to ensure robust prompts workflow. Cleaned up the repository by updating ignore rules to exclude test logs, reducing noise in commits. These changes improved reproducibility, reduced runtime errors, and streamlined model iteration, delivering tangible business value by accelerating safe deployment cycles and improving benchmarking reliability.
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