OpenCompass

OpenCompass (also known as "Sinan") is an open source program released by the Shanghai Artificial Intelligence Laboratory.Large Model Reviewsystem designed to provide a fair, open, and replicable benchmark for the evaluation of large models.

Background and overview

• goal: Provide a one-stop solution for large model evaluation, comprehensively quantify the model's capabilities in knowledge, language, understanding, and reasoning, and drive model iteration and optimization.
• specificities: open-source reproducibility, comprehensive capability dimensions, rich model support, distributed and efficient assessment, diverse assessment paradigms, and flexible extensions.

Key Features

• Comprehensive dimensions of competence: Covering five assessment dimensions, including subject, language, knowledge, comprehension, and reasoning, it provides model assessment solutions for about 400,000 questions in 70+ data sets.
• Rich Model Support: Supports 20+ HuggingFace and API models, enabling a comprehensive assessment of the capabilities of large models.
• Distributed Efficient Review: Provide a distributed evaluation solution that supports parallel distribution of computation tasks on the local machine or cluster, realizing a parallel speed-up of evaluation.
• Diverse assessment paradigms: Supports Zero-Shot, Few-Shot, Chain of Thought, and other assessment paradigms, with a variety of built-in Prompt templates to maximize the potential of large models.

Architecture and composition

• CompassRank: As a host platform for various lists in OpenCompass, it maintains neutrality and provides model performance lists under multi-domain and multi-task, and updates them regularly.
• CompassHub: An open source and open benchmark community for large model capability evaluation, providing evaluation benchmarks for different capability dimensions and industry scenarios.
• CompassKit: A full-stack tool chain for large model evaluation, providing complete open source reproducible evaluation code, as well as rich model support and efficient distributed evaluation strategies.

Use and practice

• Installation and use: OpenCompass is implemented based on Python, you can download the project source code and install the required dependencies through the project link on Github. After the installation is complete, download the official evaluation data to start using it.
• evaluation processThe evaluation of large models can be performed by running OpenCompass scripts and specifying the path of the model file and the name of the evaluation data. The evaluation results can be displayed and tracked through a variety of visualization schemes.

Expansion and customization

• Modular design and expandability: OpenCompass supports assessment of new user-defined models or datasets, and the modules can be reused and extended efficiently.
• Customizing Task Segmentation Policies: Users can customize more advanced task splitting policies or even access new cluster management systems as needed.

Application Areas

• teach: For teaching and allowing students to practice AI concepts in an accessible environment.
• examine: Researchers can quickly validate ideas and reduce experimental cycles.
• enterprise development: Help organizations build their own AI solutions to improve efficiency.
• Individual projects: To provide independent developers with a powerful and free tool to realize their innovative visions.

In conclusion, OpenCompass is a powerful, flexible and customizable evaluation platform for large models, which provides strong support for the development and optimization of large models.