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🧭 Welcome

to OpenCompass!

Just like a compass guides us on our journey, OpenCompass will guide you through the complex landscape of evaluating large language models. With its powerful algorithms and intuitive interface, OpenCompass makes it easy to assess the quality and effectiveness of your NLP models.

🚀 What's New

• [2023.08.10] OpenCompass is compatible with LMDeploy. Now you can follow this instruction to evaluate the accelerated models provide by the Turbomind. 🔥🔥🔥.
• [2023.08.10] We have supported Qwen-7B and XVERSE-13B ! Go to our leaderboard for more results! More models are welcome to join OpenCompass. 🔥🔥🔥.
• [2023.08.09] Several new datasets(CMMLU, TydiQA, SQuAD2.0, DROP) are updated on our leaderboard! More datasets are welcomed to join OpenCompass.
• [2023.08.07] We have added a script for users to evaluate the inference results of MMBench-dev.
• [2023.08.05] We have supported GPT-4! Go to our leaderboard for more results! More models are welcome to join OpenCompass.
• [2023.07.27] We have supported CMMLU! More datasets are welcome to join OpenCompass.
• [2023.07.21] Performances of Llama-2 are available in OpenCompass leaderboard!
• [2023.07.13] We release MMBench, a meticulously curated dataset to comprehensively evaluate different abilities of multimodality models.

✨ Introduction

OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features includes:

• Comprehensive support for models and datasets: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 50+ datasets with about 300,000 questions, comprehensively evaluating the capabilities of the models in five dimensions.

• Efficient distributed evaluation: One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours.

• Diversified evaluation paradigms: Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue type prompt templates, to easily stimulate the maximum performance of various models.

• Modular design with high extensibility: Want to add new models or datasets, customize an advanced task division strategy, or even support a new cluster management system? Everything about OpenCompass can be easily expanded!

• Experiment management and reporting mechanism: Use config files to fully record each experiment, support real-time reporting of results.

📊 Leaderboard

We provide OpenCompass Leaderbaord for community to rank all public models and API models. If you would like to join the evaluation, please provide the model repository URL or a standard API interface to the email address [email protected].

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📖 Dataset Support

Language	Knowledge	Reasoning	Comprehensive Examination	Understanding
Word Definition WiC SummEdits Idiom Learning CHID Semantic Similarity AFQMC BUSTM Coreference Resolution CLUEWSC WSC WinoGrande Translation Flores	Knowledge Question Answering BoolQ CommonSenseQA NaturalQuestion TrivialQA Multi-language Question Answering TyDi-QA	Textual Entailment CMNLI OCNLI OCNLI_FC AX-b AX-g CB RTE Commonsense Reasoning StoryCloze StoryCloze-CN (coming soon) COPA ReCoRD HellaSwag PIQA SIQA Mathematical Reasoning MATH GSM8K Theorem Application TheoremQA Code HumanEval MBPP Comprehensive Reasoning BBH	Junior High, High School, University, Professional Examinations GAOKAO-2023 CEval AGIEval MMLU GAOKAO-Bench CMMLU ARC	Reading Comprehension C3 CMRC DRCD MultiRC RACE Content Summary CSL LCSTS XSum Content Analysis EPRSTMT LAMBADA TNEWS

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📖 Model Support

Open-source Models	API Models
InternLM LLaMA Vicuna Alpaca Baichuan WizardLM ChatGLM-6B ChatGLM2-6B MPT Falcon TigerBot MOSS ...	OpenAI Claude (coming soon) PaLM (coming soon) ……

🛠️ Installation

Below are the steps for quick installation and datasets preparation.

conda create --name opencompass python=3.10 pytorch torchvision pytorch-cuda -c nvidia -c pytorch -y
conda activate opencompass
git clone https://github.com/InternLM/opencompass opencompass
cd opencompass
pip install -e .
# Download dataset to data/ folder
wget https://github.com/InternLM/opencompass/releases/download/0.1.1/OpenCompassData.zip
unzip OpenCompassData.zip

Some third-party features, like Humaneval and Llama, may require additional steps to work properly, for detailed steps please refer to the Installation Guide.

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🏗️ ️Evaluation

Make sure you have installed OpenCompass correctly and prepared your datasets according to the above steps. Please read the Quick Start to learn how to run an evaluation task.

For more tutorials, please check our Documentation.

👷‍♂️ Contributing

We appreciate all contributions to improve OpenCompass. Please refer to the contributing guideline for the best practice.

🤝 Acknowledgements

Some code in this project is cited and modified from OpenICL.

Some datasets and prompt implementations are modified from chain-of-thought-hub and instruct-eval.

🖊️ Citation

@misc{2023opencompass,
    title={OpenCompass: A Universal Evaluation Platform for Foundation Models},
    author={OpenCompass Contributors},
    howpublished = {\url{https://github.com/InternLM/OpenCompass}},
    year={2023}
}

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