Opencompass

OpenCompass is a comprehensive evaluation platform, benchmarking suite, and distributed model evaluator designed to measure the performance and accuracy of large language models. It provides a framework for benchmarking both open-source and API-based models against diverse datasets using standardized metrics and reproducible pipelines.

The project features an automated judging framework that uses language models as judges to score and verify the quality of generated text. It includes a performance leaderboard system for comparing the relative capabilities of various models across industry-standard benchmarks.

The platform covers a broad range of capabilities, including multimodal model assessment, mathematical reasoning verification, and model robustness assessment. It manages the full evaluation lifecycle through dataset acquisition, experiment management, and the application of various prompting paradigms.

To handle large-scale assessments, the system utilizes distributed evaluation workloads and GPU hardware scaling to process billion-scale models across computing clusters.

Features

Model Evaluation Frameworks - Provides a comprehensive framework for running model inference and validation on standardized datasets.

LLM Evaluation - Provides a comprehensive platform for measuring the quality of LLM outputs using automated judges and custom metrics.

Multi-Node Inference Scaling - Distributes evaluation tasks across multiple GPUs and nodes to handle workloads exceeding single-device memory.

Inference Scaling - Distributes the computational workload of evaluating massive models across multiple GPUs and clusters to reduce processing time.

LLM Benchmarking - Measures the accuracy and capabilities of large language models using standardized datasets and reproducible metrics.

LLM Evaluation Frameworks - Offers a framework for benchmarking large language models against diverse datasets using standardized metrics and reproducible pipelines.

Model Performance Benchmarking - Employs standardized tests to evaluate model speed and accuracy across diverse datasets.

Model Benchmarking Suites - Ships a collection of tools to evaluate the accuracy, reasoning, and performance of LLMs against standardized datasets.

Model Performance Leaderboards - Features a performance leaderboard system to compare the relative capabilities of open-source and proprietary models.

Provider-Agnostic Model Interfaces - Provides a unified interface that wraps diverse model APIs and local weights for consistent input and output handling.

Distributed Task Orchestration - Provides a system for defining and executing evaluation workloads across clusters of computing resources to reduce inference time.

Prediction Workload Distribution - Splits massive evaluation workloads across computing clusters to process billion-scale models efficiently.

LLM-As-A-Judge Scoring - Implements an automated judging framework where high-capability language models score generated responses based on predefined rubrics.

Model Evaluation - Provides a scalable infrastructure for running massive model assessments across multiple GPUs and computing clusters.

Model-Based Extraction - Employs secondary AI models to parse and isolate model outputs for a more accurate representation of capabilities.

Answer Extraction Logics - Uses specialized models or regular expressions to isolate final answers from verbose model outputs for metric calculation.

LLM Experiment Management - Records full experiment details via configuration files and reports resulting metrics in real time.

Adversarial Robustness Testing - Tests model stability and security by applying various attack methods and evaluating tool-use capabilities.

Evaluation Workflow Orchestrations - Sequences multiple evaluators in a custom workflow to assess complex scenarios through a multi-stage mechanism.

Evaluation Configurations - Configures zero-shot, few-shot, and chain-of-thought prompting templates to standardize and maximize model performance during testing.

Reasoning Verifications - Validates the logical steps and final answers of mathematical or complex reasoning tasks using specialized verification tools.

Architecture Benchmarking - Evaluates model performance across a wide range of architectures to assess general capabilities.

Dataset Preparation Tools - Automates the downloading and preparation of required evaluation datasets from remote storage servers or third-party hubs.

Benchmark Dataset Loaders - Includes utilities for loading and preprocessing diverse benchmark datasets from remote hubs via a standardized interface.

Evaluation Chains - Sequences prompting, generation, and verification steps into linear chains to assess complex reasoning tasks.

Mathematical Verification - Validates the logic and accuracy of mathematical solutions through specialized verification steps.

Configuration-Driven Pipelines - Defines evaluation workflows and dataset parameters through static files to ensure reproducibility across experiments.

Vision-Language Model Benchmarking - Includes toolkits for the standardized evaluation of accuracy and reasoning in vision-language models.

Unified Model Wrappers - Standardizes diverse open-source and API-based model interfaces under a single consistent configuration.

Model Evaluation - One-stop evaluation platform supporting multiple datasets and distributed testing.

InternLMopencompass

Features

Star history