evaluating-llms-harness

You can benchmark LLMs by running the lm-evaluation-harness package to execute standardized tasks against multiple models. This generates reproducible results, enabling fair comparisons to assess model quality and track progress across different environments.

Yes, you can evaluate models across API-based providers and local endpoints. The lm-evaluation-harness supports assessing model quality and efficiency uniformly across these environments using standard task sets like MMLU and GSM8K.

To run model evaluation, you need Python 3, the lm-evaluation-harness package installed, and access to your chosen model endpoints and task sets. These prerequisites allow you to execute standardized benchmarks and generate comparisons.

Available standardized benchmarks for comparing LLM capabilities include over 60 tasks such as MMLU, GSM8K, HumanEval, and ARC. These tasks assess various model capabilities to help you evaluate performance for research and product development.

Automate LLM benchmarking in a CI pipeline by integrating the lm-evaluation-harness package. This allows you to continuously run standardized tasks, automate the benchmarking process, and generate reproducible reporting for experiments.