evaluating-llms-harness

To evaluate LLMs across benchmarks like MMLU and GSM8K, use a standard evaluation harness to automate testing across 60+ academic benchmarks. This ensures reproducible performance comparisons for research and model development workflows.

Yes, you can benchmark API-based models and local HuggingFace models in the same framework. The evaluation harness supports unified benchmarking across HuggingFace, vLLM, and API-based models for consistent comparisons.

The best way to run reproducible LLM evaluation for publication workflows is using a standardized harness. It automates evaluation across 60+ benchmarks, enabling tracking, reporting, and model comparisons across experiments and papers.

Yes, you need vLLM and transformers installed. The evaluation harness requires lm-eval, transformers, and vllm as dependencies to support local, API, and remote inference backends during model benchmarking.

The lm-evaluation-harness handles benchmarking for vLLM inference backends by providing a unified framework that automates evaluation tasks. It supports local, API, and remote inference backends for reproducible model comparisons.