mojolearn

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machine learning ml ai artificial intelligence deep learning gpu metal apple silicon mojo gbdt gradient boosting decision trees mamba samba state space models ssm attention transformer random forest extra trees clustering k-means nearest neighbors dbscan pca svm kernel density hierarchical clustering spectral clustering isolation forest time series metrics bitwise reproducibility determinism reproducibility cuda rocm hip

Metadata

Release files for mojolearn 0.8.28

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for mojolearn 0.8.28
File	Interpreter	ABI	Platform	Reset
mojolearn-0.8.28-py3-none-manylinux_2_35_x86_64.whl 12.1 MB Sep 29, 2026	Python 3	none	Linux glibc 2.35+ x86-64	Details
mojolearn-0.8.28-py3-none-macosx_11_0_arm64.whl 67.0 MB Sep 29, 2026	Python 3	none	macOS 11.0+ ARM64	Details

Total release size: 79.1 MB

Release files / mojolearn-0.8.28-py3-none-manylinux_2_35_x86_64.whl

Download URL	mojolearn-0.8.28-py3-none-manylinux_2_35_x86_64.whl
Size	12.1 MB
Tags	Linux glibc 2.35+ x86-64 Python 3
SHA-256 checksum How to use checksums	`51d9e8e4d3321baacb61513cf48d65b239545a6dde1f65975af15008dc6ebdcb`
BLAKE2b-256 checksum How to use checksums	`57fc5f080002870e068d37157cb0c9b18e3e2b7924e1f8e3c94f8972027942eb`
Upload date	Sep 29, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/mojolearn/mojolearn
Publishing commit: github.com/mojolearn/mojolearn/tree/a50261f2018b91f1f3f8a11c842795abbc235ef3

Workflow

Publishing configuration: github.com/mojolearn/mojolearn/blob/a50261f2018b91f1f3f8a11c842795abbc235ef3/.github/workflows/release-provenance.yml
Publishing logs: github.com/mojolearn/mojolearn/actions/runs/36600926967/attempts/1

Artifact

Subject: mojolearn-0.8.28-py3-none-manylinux_2_35_x86_64.whl
SHA-256 checksum: 51d9e8e4d3321baacb61513cf48d65b239545a6dde1f65975af15008dc6ebdcb Verifying attestations

Release files / mojolearn-0.8.28-py3-none-macosx_11_0_arm64.whl

Download URL	mojolearn-0.8.28-py3-none-macosx_11_0_arm64.whl
Size	67.0 MB
Tags	Python 3 macOS 11.0+ ARM64
SHA-256 checksum How to use checksums	`602e39c79715a088d995bed2d963a269b98d958d25a2086f117f49e332c36e56`
BLAKE2b-256 checksum How to use checksums	`797be78217c5a850ad7f0232cbca5e7dca978966ee10d62e4f6b38dc91f5de4e`
Upload date	Sep 29, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/mojolearn/mojolearn
Publishing commit: github.com/mojolearn/mojolearn/tree/a50261f2018b91f1f3f8a11c842795abbc235ef3

Workflow

Publishing configuration: github.com/mojolearn/mojolearn/blob/a50261f2018b91f1f3f8a11c842795abbc235ef3/.github/workflows/release-provenance.yml
Publishing logs: github.com/mojolearn/mojolearn/actions/runs/36600926967/attempts/1

Artifact

Subject: mojolearn-0.8.28-py3-none-macosx_11_0_arm64.whl
SHA-256 checksum: 602e39c79715a088d995bed2d963a269b98d958d25a2086f117f49e332c36e56 Verifying attestations