evaluating-llms-harness

You can benchmark LLMs across standard tasks by configuring your model and tasks within the lm-evaluation-harness interface. This Skill automates running evaluations for benchmarks like MMLU and GSM8K, generating standardized metrics for objective comparison.

Yes, you can evaluate API-based models. The Skill supports benchmarking for HuggingFace, vLLM, and OpenAI/Anthropic-style API endpoints, allowing you to generate comparable evaluation metrics across different model hosting environments.

The best way to run HumanEval and TruthfulQA benchmarks is using this Skill's unified interface built on lm-evaluation-harness. It automates rigorous evaluations for research models, generating standardized metrics and comparison reports to facilitate objective evaluation.

Yes, this LLM evaluation tool supports both vLLM and HuggingFace transformers. It integrates these dependencies to provide a unified interface for benchmarking models across 60+ academic tasks and generating reproducible comparison reports.

You get reproducible model comparisons by running this Skill to apply standardized evaluation benchmarks via lm-evaluation-harness. It automates the process across 60+ tasks, generating standardized metrics and comparison reports to facilitate objective decision-making.