Run a new open-source language model through math, coding, and general knowledge benchmarks to get standardized scores for a research paper.
Compare GPT-4 against Llama or Qwen across multiple tasks using a consistent evaluation framework instead of ad-hoc tests.
Use a second AI model as a judge to evaluate open-ended text generation quality where there is no single correct answer.
Distribute a large evaluation across multiple GPUs or machines to finish faster.
Requires GPU access and potentially large model weights, distributed evaluation needs multi-machine or multi-GPU configuration.
OpenCompass is a Python platform for testing and comparing AI language models. When researchers or companies build or choose an AI model (the kind that generates text, answers questions, or writes code), they need a consistent way to measure how well it actually performs across a range of tasks. OpenCompass provides that measurement framework, supporting over 100 benchmarks and more than 100 different models, including GPT-4, Llama, Qwen, Mistral, InternLM, Claude, and GLM, among others. The platform runs a model through a series of standardized tests covering areas like general knowledge, mathematical reasoning, coding ability, long-document understanding, and scientific knowledge. It then produces scores that can be compared across models and tracked over time. Results from OpenCompass evaluations are published on a public leaderboard at rank.opencompass.org.cn, and a benchmark hub at hub.opencompass.org.cn collects the datasets used for evaluation. For evaluation that requires judgment calls rather than fixed right-or-wrong answers, the platform includes tools that use a second AI model as a judge. There are also specialized evaluators for mathematical reasoning and for cascading multiple evaluation methods in sequence. The platform supports running evaluations efficiently by distributing work across multiple machines or GPU clusters. Models can be connected through several backends including HuggingFace, vLLM, and LMDeploy, as well as API-based models from OpenAI and other providers. Configuration is file-based, and example scripts for evaluating specific models or benchmarks are included in the repository. The project is maintained by the OpenCompass team at Shanghai AI Laboratory and was recommended by Meta AI as a validation tool for Llama models. Documentation is available at opencompass.readthedocs.io. The full README is longer than what was shown.
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