EvalScope Tutorial: Benchmark Your LLM in 5 Minutes

You have a model, and you need numbers. In this tutorial you'll run your first benchmark with EvalScope — from a clean environment to a scored report — in about five minutes.

Step 1 — Install

EvalScope is a Python package: pip install evalscope. It pulls in the evaluation backends and the CLI. Python 3.8+ is all you need to get started.

Step 2 — Evaluate with One Command

Point the CLI at a model and a benchmark set. The framework handles dataset download, prompt construction, inference, and metric computation:

evalscope eval --model qwen2.5-7b-instruct \
  --benchmarks mmlu gsm8k human_eval \
  --api-url http://localhost:8000/v1

Using an OpenAI-compatible endpoint? Pass the base URL and EvalScope adapts automatically — no model-specific code.

Step 3 — Read the Report

Results are written as structured files you can compare across runs. That makes regression testing trivial: upgrade a model, rerun, and see exactly which capability moved.

Going Further

Beyond text LLMs, EvalScope evaluates vision-language models and AI agents, and its Arena mode collects human preference votes with a review GUI. For reproducibility, pin the framework version and commit your eval config — evaluation should be as disciplined as training.

That's it — install, run one command, and read the report. EvalScope turns model evaluation from a chore into a repeatable, trustworthy process.