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Prerequisites

  • Python 3.10+
  • A GPU (for local HF/vLLM models) or an API key (for LiteLLM backends)

1. Install

Install straight from GitHub — no clone required:

2. Run a text evaluation

Mill streams progress to your terminal and writes a Feather file to ./results/ when done.

3. View results

The collect command renders a table of scores in your terminal. Pass --metric to choose which metric to show — MMLU reports acc:

4. Browse available tasks

This opens a full-screen TUI browser. Use ↑ ↓ to navigate, Tab to switch between Benchmarks and Tasks, and Enter to copy a task name to your clipboard.

Next steps

Text evaluation guide

Few-shot, custom metrics, and n-shot sweeps.

Vision evaluation guide

Multimodal models with image/video inputs.

Distributed scheduling

Scale across a SLURM cluster.

CLI reference

Full flag documentation.