priml✴️

ML building blocks for training experiments.

Quick Start

# Mac:
#   # Required for quick install.
#   brew install uv

# Ubuntu/Debian:
#   # Required for quick install.
#   sudo apt-get install -y curl
#   curl -LsSf https://astral.sh/uv/install.sh | sh

uv add priml

# Alternatively: python -m pip install priml

What's inside

• model -- composable model definitions and building blocks.
• optimizers -- optimizer implementations for training.
• loss -- loss functions.
• metrics -- evaluation metrics.
• math -- numerical and math utilities.
• train -- the training loop and experiment scaffolding.
• data -- the data pipeline and dataset utilities.
• inference -- inference helpers.

Development

See CONTRIBUTING.md for local validation and the public contribution flow.

See also

Sibling projects in the rekursiv-ai family:

• sagent — The self-mutating multi-provider coding-agent CLI and typed Python library.
• trackinizer — Centralized agent database for tracking inquiries, work, and the evidence behind conclusions.
• wesearch — Web search, resilient page fetch, and scholarly-paper lookup without a browser stack.
• madcatter — Rich-based Markdown renderer for the terminal; ships the mdcat CLI.
• configgle — Hierarchical experiment configuration in typed pure-Python dataclasses instead of YAML.
• copybarista — Bidirectional source sync for publishing OSS-ready trees from a monorepo.
• sudoku — Sudoku-Extreme solved end to end with a 7M-parameter recursive transformer.

Citing

If you find our work useful, please consider citing:

@misc{rekursivai2026priml,
      title={Priml - ML building blocks for training experiments.},
      author={Joshua V. Dillon and Dan Kondratyuk},
      year={2026},
      howpublished={Github},
      url={https://github.com/rekursiv-ai/priml},
}