evaluating-llms-harness

This evaluation harness benchmarks LLMs by applying a unified suite of 60+ academic tasks to HuggingFace models, vLLM deployments, and API endpoints, generating standardized metrics for reproducible research and industry quality assurance.

Yes, the harness supports vLLM deployments alongside HuggingFace models and API-based endpoints, allowing you to benchmark LLMs across diverse infrastructure setups and compare results consistently.

You need Python with the lm-evaluation-harness tool and its dependencies, specifically lm-eval, transformers, and vllm installed. You also need access to the target models you intend to evaluate.

Yes, the harness evaluates API-based endpoints alongside HuggingFace and vLLM models. This enables reproducible comparisons across 60+ benchmarks for research papers, industry QA, and progress tracking.

You can track LLM training progress by running the unified evaluation harness against your models at different stages. It measures performance across 60+ benchmarks like ARC and HellaSwag to provide standardized metrics for comparing quality over time.