This project is a standardized framework for benchmarking large language models across a wide range of academic and reasoning datasets. It provides a platform for executing automated evaluation tasks to measure model accuracy and performance, ensuring consistent assessment through a structured configuration schema.
The framework distinguishes itself by incorporating a dedicated utility for data decontamination, which identifies and removes overlapping training samples from evaluation sets to prevent data leakage. It also features a flexible task builder that allows users to define custom benchmarks by specifying unique data sources, prompt structures, and modular scoring metrics.
The system supports large-scale testing by orchestrating distributed evaluation workloads across multiple compute nodes. It utilizes an abstracted interface to standardize communication with diverse model backends, facilitating systematic validation of model capabilities before deployment.
