evaluating-llms-harness

You can benchmark LLMs by running them against 60+ academic tasks like MMLU and HumanEval using a unified harness. This generates standardized evaluation metrics across different runs and projects.

Yes, LLM evaluation supports multi-backend setups including vLLM, OpenAI-like APIs, HuggingFace, and local endpoints. This allows you to standardize benchmark metrics across different serving environments.

The best way to run reproducible LLM evaluation is by using YAML task definitions and frontmatter metadata. This drives consistent results across multiple runs and ensures experiment reproducibility.

Distributed benchmarking works across both local and remote model endpoints. It orchestrates evaluation tasks via YAML definitions to ensure consistent results regardless of the backend.

To start a baseline LLM evaluation, select a model and a set of tasks to generate standardized metrics. This harness orchestrates the tasks to produce consistent baseline results for your project.