evaluating-llms-harness

To evaluate LLMs on academic benchmarks like MMLU and GSM8K, you can use this Skill to automatically run models through 60+ established tasks, measuring reasoning and knowledge capabilities to generate objective accuracy scores.

Yes, you can use vLLM and HuggingFace backends for LLM performance testing. This Skill supports various model backends, including HuggingFace transformers and vLLM, facilitating comprehensive quality assessment and direct model comparison.

The best way to compare the reasoning abilities of newly trained models is to run both through academic benchmarks like MMLU and GSM8K using this Skill, which generates objective accuracy scores for direct quality comparison and analysis.

Yes, lm-evaluation-harness supports tracking model improvement during training cycles. It monitors model performance across academic tasks, allowing researchers to assess quality and track training progress through rigorous evaluation.

Besides coding and knowledge tests, LLM benchmarking includes reasoning tasks across 60+ academic benchmarks like MMLU, HumanEval, GSM8K, and TruthfulQA, providing objective measures of diverse model capabilities.