evaluating-llms-harness

Benchmark LLMs across 60+ academic and practical tasks using a unified evaluation harness with standardized prompts and metrics for reproducible model comparisons. It supports configurable few-shot settings to ensure fair assessments.

Yes, the evaluation harness supports HuggingFace, vLLM, and API-based models. You can configure tasks, few-shot settings, and batch parameters to evaluate models across your preferred deployment platforms.

Ensure reproducibility in model comparison by applying standardized prompts and unified metrics across 60+ benchmarks. This approach provides consistent evaluation settings for fair, repeatable results across different releases.

Run a baseline evaluation by selecting a model and configuring it against a standard task set within the unified harness. You can then export the resulting benchmark metrics to JSON for reporting and analysis.

Yes, the harness supports 60+ benchmarks including MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag, and ARC. This enables comprehensive model assessment across diverse academic and practical domains.

Use a unified evaluation harness for internal model comparisons to maintain consistent metrics and prompts across releases. It enables accurate performance tracking by evaluating new models against identical benchmark conditions.