PyMilo: A Python Library for ML I/O
Bibliographic record
Abstract
PyMilo is an open-source Python package that addresses the limitations of existing machine learning (ML) model storage formats by providing a transparent, reliable, end-to-end, and safe method for exporting and deploying trained models.Current tools rely on black-box or executable formats that obscure internal model structures, making them difficult to audit, verify, or safely share.Meanwhile, tensor-centric formats such as Safetensors (Hugging Face, 2025) securely store and transfer numerical tensors but do not capture the internal and structural composition of classical machine-learning models (e.g., scikit-learn pipelines), which remain PyMilo's primary focus.Others apply structural transformations during export that may degrade predictive performance and reduce the model to a limited inference-only interface.In contrast, PyMilo serializes models in a transparent human-readable format that preserves end-to-end model fidelity and enables reliable, safe, and interpretable exchange.Here, transparent refers to the ability to inspect model internals through a human-readable structure without execution, and end-to-end fidelity denotes that a model exported and re-imported with PyMilo retains the exact same signature, functionality, parameters, and internal structure as the original, ensuring complete behavioral and structural equivalence.This package is designed to make the preservation and reuse of trained ML models safer, more interpretable, and easier to manage across different stages of the ML workflow (Figure 1).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.084 | 0.079 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".