evaluating-llms-harness

To benchmark LLMs across academic tasks like MMLU and GSM8K, you can use a reproducible evaluation harness that evaluates models across 60+ standardized benchmarks and outputs comparable metrics.

Yes, you can evaluate models using vLLM and HuggingFace backends. The evaluation harness supports multiple backends including HuggingFace, vLLM, and API-based models for flexible deployment and scalable benchmarking.

The best way to compare language model performance for publication is generating structured benchmark results. The harness outputs standardized metrics across 60+ academic benchmarks suitable for dashboards, papers, or team reviews.

You can run reproducible LLM evaluations for model tracking by executing the evaluation harness. It applies standardized prompts and metrics across 60+ tasks to provide consistent, comparable results for tracking model progress over time.

Yes, the lm-evaluation harness supports API-based models for benchmarking. It works alongside HuggingFace and vLLM backends, allowing you to evaluate API-based models using the same 60+ academic benchmarks.

Available standardized benchmarks for evaluating language models include MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag, ARC, and MBPP. The harness evaluates models across these tasks using consistent prompts to ensure reproducible metrics.