evaluating-llms-harness

To benchmark a HuggingFace model on MMLU, run the harness with the `hf` model type, specify the pretrained checkpoint name, set `mmlu` as the task, and configure few-shot and batch size parameters to execute evaluation and generate metrics.

Evaluating Large Language Models can be performed across 60+ academic benchmarks including MMLU, GSM8K, and HumanEval, providing standardized prompts and metrics for accurate model comparison and academic research.

Yes, this LLM evaluation harness supports vLLM and custom API endpoints in addition to HuggingFace, requiring the `lm-eval`, `transformers`, and `vllm` dependencies to evaluate and benchmark diverse model deployments.

You can track training progress by evaluating LLM checkpoints during training to measure benchmark performance over time, identify areas for improvement, and generate side-by-side comparison tables.

The best way to compare LLMs side-by-side on academic benchmarks is to evaluate multiple models using the same standardized prompts and metrics harness, which generates comparison tables for accurate performance tracking.

You can evaluate custom tasks using the lm-eval harness by configuring the standardized evaluation framework to support custom evaluation criteria, enabling benchmarking for specialized use cases beyond the 60+ built-in academic datasets.