evaluating-llms-harness

You can evaluate language models on MMLU and GSM8K using standardized prompts and metrics. This benchmarking suite supports over 60 academic benchmarks, requiring Python libraries like lm-eval, transformers, and vllm for model inference and evaluation.

Tracking training progress for language models is best achieved through standardized benchmarking. By evaluating models on academic benchmarks using consistent metrics, developers can compare performance and track improvements over time during training.

To benchmark an LLM using lm-eval and vllm, run the lm_eval command with specified model arguments, tasks, and batch size. This suite uses vllm for model inference and lm-eval for evaluation across tasks like MMLU and HumanEval.

Yes, you can use this benchmarking suite for model release evaluations. It provides standardized metrics and prompts to ensure fair comparisons across a wide range of over 60 academic benchmarks, suitable for academic research and release tracking.

Yes, you need the transformers Python library installed to evaluate language model performance. This benchmarking suite requires lm-eval, transformers, and vllm to execute model inference and run standardized evaluations on various benchmarks.

Using standardized prompts for language model evaluation ensures fair comparisons across different models. This suite applies consistent prompts and metrics across benchmarks like GSM8K and MMLU, allowing researchers to accurately compare and track model performance.