Skeights – Serialize sklearn models to safetensors and JSON, no pickle

Name: Skeights – Serialize sklearn models to safetensors and JSON, no pickle
Availability: InStock
Author: alxhslm

by alxhslm·Jul 6, 2026·3 points·0 comments

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AI Analysis

●●SolidSolve My ProblemBig Brain

Human-readable JSON config beats skops' opaque binary blobs for model versioning.

Strengths

•Separates hyperparameters from weights so models become diffable config files.
•Safetensors format eliminates pickle's arbitrary code execution vulnerability.
•Supports Pipelines, LightGBM, XGBoost, and most common sklearn estimators.

Weaknesses

•skops already solves the security problem for most sklearn users.
•Niche tool—only matters if you're versioning models in Git repositories.

Post Description

We use sklearn in production for time series forecasting in industrial settings. Pickle was causing us constant pain: refactoring our wrapper classes invalidated saved models, and the binary blobs were completely opaque.

We built skeights to decompose a fitted estimator into two files. A JSON file for hyperparameters and structure, and a safetensors file for the numeric arrays. The JSON is human-readable and diffable, and safetensors is a well-established safe format from Hugging Face.

This allows you to trade models as config. The JSON is just data you can inspect, diff, and version alongside the rest of your config, rather than a binary artefact on the side.

It supports most common sklearn estimators (linear models, trees, random forests, gradient boosting, MLPs, pipelines), plus LightGBM and XGBoost. MIT licensed.

Blog post with a worked example: https://alxhslm.github.io/projects/skeights/

Repo: https://github.com/carbon-re/skeights

`pip install skeights`