lm-evaluation-harness - LLM Benchmarking

Instruction

Choose the appropriate benchmark suite based on the model's target capability, such as MMLU for reasoning or HumanEval for code.
Configure the model backend (e.g., HuggingFace, vLLM) and specify hardware parameters like device IDs and batch size.
Execute the evaluation using standard settings, such as 5-shot prompts for MMLU to ensure comparability with published papers.
Integrate with vLLM backends for 5-10x faster inference during large-scale evaluation tasks.
Track training progress by running periodic evaluations on checkpoints and plotting learning curves across diverse tasks.
Enable code execution for code-based benchmarks like HumanEval to ensure accurate score calculation.

When benchmarking model quality across academic standards for model release or research papers.
When tracking the performance of an LLM during different stages of training or fine-tuning.
When comparing the relative performance of multiple local or remote models using standardized metrics.

Standardized performance reports containing accuracy scores, standard errors, and sample predictions.
Comparison tables and learning curves showing performance across multiple models or training steps.
Detailed JSON results ready for integration into research documentation or leaderboards.