evaluating-llms-harness

You can benchmark LLMs across MMLU and GSM8K using a unified evaluation harness that supports 60+ academic benchmarks. It provides standardized assessment of model quality with configurable tasks, few-shot settings, and batch size controls.

The best way to compare multiple LLM models is using a unified evaluation harness that runs standardized academic benchmarks. This enables easy comparison and tracking of progress across experiments with configurable tasks and few-shot settings.

Yes, the lm-evaluation-harness works with HuggingFace and OpenAI-compatible APIs. It requires Python and the lm-eval package to access models and execute configurable benchmarking tasks across academic evaluations.

Yes, you can configure few-shot settings and batch sizes for LLM evaluation tasks. The harness allows reproducible workflows by adjusting these parameters when benchmarking models on tasks like HumanEval or TruthfulQA.

Yes, you need Python and the lm-eval package to run academic LLM benchmarks. This evaluation harness depends on these alongside transformers and vllm to execute standardized model quality assessments across 60+ tasks.