modelscope/evalscope
A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking.
What it solves
EvalScope is a comprehensive framework designed to simplify the evaluation of Large Language Models (LLMs) and other AI models. It provides a unified interface to measure model capabilities, test inference performance under stress, and visualize results, removing the need to run multiple disparate evaluation tools.
How it works
EvalScope acts as a one-stop evaluation hub that integrates multiple backends (such as OpenCompass, VLMEvalKit, and RAGEval) and a vast library of industry-standard benchmarks (like MMLU, C-Eval, and GSM8K). It supports evaluating models via OpenAI-compatible APIs or local model files. For advanced use cases, it includes an "Agent Evaluation Mode" that runs benchmarks within a controlled multi-turn loop using Docker sandboxes and pluggable strategies. It also includes a performance testing module to measure metrics like Time to First Token (TTFT) and Time Per Output Token (TPOT).
Who it’s for
This tool is for AI developers, researchers, and model providers who need to standardize their evaluation pipelines, compare different models across various domains, or stress-test their model services for production readiness.
Highlights
- Broad Model Support: Evaluates LLMs, Vision Language Models (VLM), Embeddings, Rerankers, and AIGC models.
- Agentic Evaluation: Supports multi-turn agent loops with full trace recording and visualization for benchmarks like SWE-bench.
- ** uma Arena Mode**: Enables pairwise battles between models to rank them intuitively.
- Performance Stress Testing: Measures inference efficiency and throughput for model services.
- Interactive Dashboard: A React-based WebUI for multi-dimensional model comparison and detailed prediction inspection.
- Extensible Architecture: Allows developers to easily add custom datasets, models, and metrics.