Package: opencompass
🌐Website|📘Documentation|🛠️Installation|🤔Reporting IssuesEnglish |简体中文👋 join us onDiscordandWeChat📣 OpenCompass 2023 LLM Annual LeaderboardWe are honored to have witnessed the tremendous progress of artificial general intelligence together with the community in the past year, and we are also very pleased thatOpenCompasscan help numerous developers and users.We announce the launch of theOpenCompass 2023 LLM Annual Leaderboardplan. We expect to release the annual leaderboard of the LLMs in January 2024, systematically evaluating the performance of LLMs in various capabilities such as language, knowledge, reasoning, creation, long-text, and agents.At that time, we will release rankings for both open-source models and commercial API models, aiming to provide a comprehensive, objective, and neutral reference for the industry and research community.We sincerely invite various large models to join the OpenCompass to showcase their performance advantages in different fields. At the same time, we also welcome researchers and developers to provide valuable suggestions and contributions to jointly promote the development of the LLMs. If you have any questions or needs, please feel free tocontact us. In addition, relevant evaluation contents, performance statistics, and evaluation methods will be open-source along with the leaderboard release.We have provided the more details of the CompassBench 2023 inDoc.Let's look forward to the release of the OpenCompass 2023 LLM Annual Leaderboard!🧭	WelcometoOpenCompass!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.🚩🚩🚩 Explore opportunities at OpenCompass! We're currentlyhiring full-time researchers/engineers and interns. If you're passionate about LLM and OpenCompass, don't hesitate to reach out to us viaemail. We'd love to hear from you!🔥🔥🔥 We are delighted to announce thatthe OpenCompass has been recommended by the Meta AI, clickGet Startedof Llama for more information.AttentionWe launch the OpenCompass Collaboration project, welcome to support diverse evaluation benchmarks into OpenCompass!
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Let's work together to build a more powerful OpenCompass toolkit!🚀 What's New[2024.01.17]We supported the evaluation ofInternLM2andInternLM2-Chat, InternLM2 showed extremely strong performance in these tests, welcome to try! 🔥🔥🔥.[2024.01.17]We supported the needle in a haystack test with multiple needles, more information can be foundhere🔥🔥🔥.[2023.12.28]We have enabled seamless evaluation of all models developed usingLLaMA2-Accessory, a powerful toolkit for comprehensive LLM development. 🔥🔥🔥.[2023.12.22]We have releasedT-Eval, a step-by-step evaluation benchmark to gauge your LLMs on tool utilization. Welcome to ourLeaderboardfor more details! 🔥🔥🔥.[2023.12.10]We have releasedVLMEvalKit, a toolkit for evaluating vision-language models (VLMs), currently support 20+ VLMs and 7 multi-modal benchmarks (including MMBench series).[2023.12.10]We have supported Mistral AI's MoE LLM:Mixtral-8x7B-32K. Welcome toMixtralKitfor more details about inference and evaluation.More✨ IntroductionOpenCompass 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 include:Comprehensive support for models and datasets: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 70+ datasets with about 400,000 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, and support real-time reporting of results.📊 LeaderboardWe provideOpenCompass Leaderboardfor the 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 addressopencompass@pjlab.org.cn.🔝Back to top🛠️ InstallationBelow are the steps for quick installation and datasets preparation.💻 Environment SetupOpen-source Models with GPUcondacreate--nameopencompasspython=3.10pytorchtorchvisionpytorch-cuda-cnvidia-cpytorch-y
condaactivateopencompass
gitclonehttps://github.com/open-compass/opencompassopencompasscdopencompass
pipinstall-e.API Models with CPU-onlycondacreate-nopencompasspython=3.10pytorchtorchvisiontorchaudiocpuonly-cpytorch-y
condaactivateopencompass
gitclonehttps://github.com/open-compass/opencompassopencompasscdopencompass
pipinstall-e.# also please install requiresments packages via `pip install -r requirements/api.txt` for API models if needed.📂 Data Preparation# Download dataset to data/ folderwgethttps://github.com/open-compass/opencompass/releases/download/0.1.8.rc1/OpenCompassData-core-20231110.zip
unzipOpenCompassData-core-20231110.zipSome third-party features, like Humaneval and Llama, may require additional steps to work properly, for detailed steps please refer to theInstallation Guide.🔝Back to top🏗️ ️EvaluationAfter ensuring that OpenCompass is installed correctly according to the above steps and the datasets are prepared, you can evaluate the performance of the LLaMA-7b model on the MMLU and C-Eval datasets using the following command:pythonrun.py--modelshf_llama_7b--datasetsmmlu_pplceval_pplOpenCompass has predefined configurations for many models and datasets. You can list all available model and dataset configurations using thetools.# List all configurationspythontools/list_configs.py# List all configurations related to llama and mmlupythontools/list_configs.pyllamammluYou can also evaluate other HuggingFace models via command line. Taking LLaMA-7b as an example:pythonrun.py--datasetsceval_pplmmlu_ppl\--hf-pathhuggyllama/llama-7b\# HuggingFace model path--model-kwargsdevice_map='auto'\# Arguments for model construction--tokenizer-kwargspadding_side='left'truncation='left'use_fast=False\# Arguments for tokenizer construction--max-out-len100\# Maximum number of tokens generated--max-seq-len2048\# Maximum sequence length the model can accept--batch-size8\# Batch size--no-batch-padding\# Don't enable batch padding, infer through for loop to avoid performance loss--num-gpus1# Number of minimum required GPUsNoteTo run the command above, you will need to remove the comments starting from#first.Through the command line or configuration files, OpenCompass also supports evaluating APIs or custom models, as well as more diversified evaluation strategies. Please read theQuick Startto learn how to run an evaluation task.🔝Back to top📖 Dataset SupportLanguageKnowledgeReasoningExaminationWord DefinitionWiCSummEditsIdiom LearningCHIDSemantic SimilarityAFQMCBUSTMCoreference ResolutionCLUEWSCWSCWinoGrandeTranslationFloresIWSLT2017Multi-language Question AnsweringTyDi-QAXCOPAMulti-language SummaryXLSumKnowledge Question AnsweringBoolQCommonSenseQANaturalQuestionsTriviaQATextual EntailmentCMNLIOCNLIOCNLI_FCAX-bAX-gCBRTEANLICommonsense ReasoningStoryClozeCOPAReCoRDHellaSwagPIQASIQAMathematical ReasoningMATHGSM8KTheorem ApplicationTheoremQAStrategyQASciBenchComprehensive ReasoningBBHJunior High, High School, University, Professional ExaminationsC-EvalAGIEvalMMLUGAOKAO-BenchCMMLUARCXiezhiMedical ExaminationsCMBUnderstandingLong ContextSafetyCodeReading ComprehensionC3CMRCDRCDMultiRCRACEDROPOpenBookQASQuAD2.0Content SummaryCSLLCSTSXSumSummScreenContent AnalysisEPRSTMTLAMBADATNEWSLong Context UnderstandingLEvalLongBenchGovReportsNarrativeQAQasperSafetyCivilCommentsCrowsPairsCValuesJigsawMultilingualTruthfulQARobustnessAdvGLUECodeHumanEvalHumanEvalXMBPPAPPsDS1000OpenCompass Ecosystem🔝Back to top📖 Model SupportOpen-source ModelsAPI ModelsInternLMLLaMAVicunaAlpacaBaichuanWizardLMChatGLM2ChatGLM3TigerBotQwenBlueLM...OpenAIClaudeZhipuAI(ChatGLM)BaichuanByteDance(YunQue)Huawei(PanGu)360Baidu(ERNIEBot)MiniMax(ABAB-Chat)SenseTime(nova)Xunfei(Spark)……🔝Back to top🔜 RoadmapSubjective EvaluationRelease CompassAreanaSubjective evaluation dataset.Long-contextLong-context evaluation with extensive datasets.Long-context leaderboard.CodingCoding evaluation leaderboard.Non-python language evaluation service.AgentSupport various agenet framework.Evaluation of tool use of the LLMs.RobustnessSupport various attack method👷‍♂️ ContributingWe appreciate all contributions to improving OpenCompass. Please refer to thecontributing guidelinefor the best practice.🤝 AcknowledgementsSome code in this project is cited and modified fromOpenICL.Some datasets and prompt implementations are modified fromchain-of-thought-hubandinstruct-eval.🖊️ Citation@misc{2023opencompass,title={OpenCompass: A Universal Evaluation Platform for Foundation Models},author={OpenCompass Contributors},howpublished={\url{https://github.com/open-compass/opencompass}},year={2023}}🔝Back to top