lm-evaluation-harness

Overview

The lm-evaluation-harness provides a unified interface and a large collection of tasks (e.g. Hellaswag, LAMBADA) for evaluating generative LMs. It supports local models, Hugging Face models, and commercial APIs.

Key features

• Extensive benchmark and task library covering common academic and engineering evaluations.
• Support for multiple backends (transformers, vLLM, GPT-NeoX) and deployment modes.
• Configurable evaluation pipelines for reproducible experiments and comparisons.

Use cases

• Researchers comparing model performance and reproducing academic results.
• Engineering teams running regression tests and performance monitoring for models.
• Building leaderboard or benchmarking platforms (e.g. Open LLM Leaderboard).

Technical notes

• Provides CLI lm_eval with documented interface and example configs.
• Supports batching, parallel evaluations, and caching of results for repeatability.
• Integrations for result visualization and reporting (W&B, Zeno, Hugging Face Hub).