opencompass: a one-stop platform for fair and reproducible large model evaluation across diverse benchmarks

What it solves

OpenCompass provides a unified, fair, and reproducible platform for evaluating large language models (LLMs) and large vision-language models. It eliminates the complexity of manually managing various benchmarks by offering a one-stop system to assess model quality across multiple dimensions using a vast library of datasets.

How it works

The platform allows users to run evaluations via a command-line interface (CLI) or Python scripts. It integrates with various inference backends (such as HuggingFace, vLLM, and LMDeploy) to handle model execution and supports multiple evaluation paradigms, including zero-shot, few-shot, and chain-of-thought. It can handle both open-source models and API-based models (like GPT-4o) interchangeably. For complex assessments, it uses a modular design that supports custom evaluators and sequential evaluation pipelines (CascadeEvaluator).

Who it’s for

It is designed for AI researchers and engineers who need to benchmark the performance of their NLP models, compare different LLMs on a standardized leaderboard, or validate the capabilities of a specific model on reasoning, knowledge, or long-context tasks.

Highlights

• Extensive Library: Pre-supports over 20 models and 70+ datasets containing approximately 400,000 questions.
• Distributed Evaluation: Supports one-click distributed task division to evaluate billion-scale models in a few hours.
• Flexible Inference: One-click switching between acceleration backends like vLLM and LMDeploy.
• Diverse Paradigms: Supports zero-shot, few-shot, and CoT evaluations with customizable prompt templates.
• LLM-as-Judge: Includes tools like GenericLLMEvaluator for using LLMs to judge other model outputs.