OpenCompass is a comprehensive evaluation platform designed to assess the capabilities of large language models (LLMs) and multimodal models. It offers a streamlined workflow encompassing configuration, inference, evaluation, and visualization, enabling users to efficiently evaluate models across various tasks and datasets. By supporting both objective and subjective evaluation methods, OpenCompass provides a holistic understanding of a model's performance, facilitating informed decision-making in model development and deployment. Key Features and Functionality: - Flexible Configuration: Users can easily set up evaluation processes by selecting models, datasets, evaluation strategies, computation backends, and result visualization preferences. - Efficient Inference and Evaluation: OpenCompass manages parallel inference and evaluation tasks, optimizing computational resources to accelerate the evaluation process. - Comprehensive Capability Assessment: The platform evaluates models on general capabilities such as language understanding, knowledge, reasoning, and safety, as well as specialized capabilities like long-text processing, code generation, and tool usage. - Support for Multiple Evaluation Methods: OpenCompass employs both objective evaluations (e.g., multiple-choice questions, fill-in-the-blank tasks) and subjective evaluations (e.g., user satisfaction surveys) to provide a well-rounded assessment of model performance. - Integration with Advanced Inference Tools: The platform supports integration with tools like vLLM and LMDeploy, enabling accelerated inference and efficient deployment of LLMs. Primary Value and Problem Solved: OpenCompass addresses the challenge of systematically and efficiently evaluating large language models by providing a unified platform that combines flexible configuration, efficient execution, and comprehensive assessment capabilities. It simplifies the evaluation process, allowing researchers and developers to gain deep insights into model performance across diverse tasks and datasets, ultimately facilitating the development of more robust and capable language models.