Language Model Evaluation Harness

What it does

EleutherAI's harness provides a unified task format and execution layer for dozens of standard benchmarks and hundreds of variants. It supports local and API models, custom tasks, adapters, batching, caching, and distributed inference.

• More than 60 standard benchmarks with hundreds of task variants
• YAML tasks, custom prompts, metrics, filters, answer extraction, CLI, and Python API
• Transformers, vLLM, SGLang, API, adapter, caching, batching, and distributed backends

Where it fits

Reproducible few-shot and zero-shot evaluation of base or instruction-tuned language models on established academic benchmarks.

Use it after defining the behavior that matters, collecting representative cases, and deciding how each case will be graded. A tool can execute and organize an eval, but it cannot decide which failures are costly for your users.

Limitations and cautions

• It is model-benchmark oriented rather than designed for stateful production applications or tool-using environments.
• Chat templates, prompts, few-shot settings, tokenizers, task revisions, and backend versions can materially change scores.

How to evaluate the evaluator

Before using any score as a release gate, create a small evaluator-validation set that humans have labeled. Compare false passes, false failures, disagreement by failure type, run-to-run variance, latency, and cost. Keep deterministic checks for verifiable facts, calibrate model judges against domain experts, and review raw traces when aggregate scores move.