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.

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},
}

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

priml-0.1.3.tar.gz (2.8 MB view details)

Uploaded Aug 5, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

priml-0.1.3-py3-none-any.whl (981.5 kB view details)

Uploaded Aug 5, 2026 Python 3

File details

Details for the file priml-0.1.3.tar.gz.

File metadata

Download URL: priml-0.1.3.tar.gz
Upload date: Aug 5, 2026
Size: 2.8 MB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for priml-0.1.3.tar.gz
Algorithm	Hash digest
SHA256	`4ebe0cfb5a2db182b0f33e787e3545a99f411827b5972d351dfa476c54bc5e5b`
MD5	`4632c2ac6e2f9f072d7726cbd9aeaf1d`
BLAKE2b-256	`e4a7820772e14c26e43ba68cbe5a530e2fa2d51ccb68363de5e27f7df37109f0`

See more details on using hashes here.

Provenance

The following attestation bundles were made for priml-0.1.3.tar.gz:

Publisher: publish-pypi.yml on rekursiv-ai/priml

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: priml-0.1.3.tar.gz
- Subject digest: 4ebe0cfb5a2db182b0f33e787e3545a99f411827b5972d351dfa476c54bc5e5b
- Sigstore transparency entry: 2341913796
- Sigstore integration time: Aug 5, 2026
Source repository:
- Permalink: rekursiv-ai/priml@e98a18014c538ab11f3fb30528e2d2c42c7452db
- Branch / Tag: refs/tags/v0.1.3
- Owner: https://github.com/rekursiv-ai
- Access: public
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: publish-pypi.yml@e98a18014c538ab11f3fb30528e2d2c42c7452db
- Trigger Event: release