evaluating-llms-harness

Benchmark LLMs on MMLU and GSM8K by running standardized evaluation tasks through lm-evaluation-harness workflows. This skill executes reproducible academic benchmarks across HuggingFace models, vLLM inference, and API deployments to generate comparative performance metrics.

Compare multiple LLM checkpoints by running standardized evaluation frameworks that track model quality across training progress. This skill generates reproducible benchmark results for MMLU, GSM8K, HumanEval, and TruthfulQA to evaluate checkpoint performance differences.

Yes, vLLM inference supports benchmark evaluation through this skill. It integrates with lm-evaluation-harness to evaluate models served via vLLM, HuggingFace, or API-based deployments, enabling flexible model quality assessment across different inference backends.

Yes, distributed multi-GPU evaluation is supported. This skill handles advanced evaluation workflows for large language models using lm-evaluation-harness, enabling scalable benchmark execution across multiple GPUs for comprehensive model quality assessment.

Add custom benchmark tasks by integrating them into lm-evaluation-harness workflows. This skill supports custom task integration alongside standard benchmarks like HellaSwag and HumanEval, allowing reproducible execution of specialized evaluation criteria for model comparison.