# neuralmagic/sparseml

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

Repository: https://github.com/neuralmagic/sparseml
Canonical: https://ross.abutalabs.com/products/sparseml
Language: Python
License: Apache-2.0
License Family: permissive
Topics: pytorch, keras, sparsification-recipes, tensorflow, smaller-models, deep-learning-library, deep-learning-algorithms, deep-learning-models, automl, pruning-algorithms, sparsity, sparsification, pruning, computer-vision-algorithms, object-detection, image-classification, nlp, onnx, transfer-learning
Archived: true
Last push: 2025-06-02T20:02:18+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 24, release rhythm 8, longevity 100
- inputs: {"age_days": 2091, "days_push": 457, "days_rel": 457, "gap_med": null, "n_releases_24m": 1}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2145, forks 156 (observed 2026-08-28T04:06:18.702457+00:00)

## What it is
SparseML is a Python library for applying sparsification recipes (pruning, quantization, sparsity) to neural networks with a few lines of code, producing faster and smaller models across PyTorch, TensorFlow/Keras, and ONNX. Development ceased and the project was deprecated on June 2, 2025, following Neural Magic's acquisition by Red Hat.

## Use cases
- prune a pytorch model for faster inference
- quantize a neural network to reduce its size
- apply sparsification recipes to deep learning models
- optimize an onnx model for deployment
- compress a computer vision model for edge devices
- speed up nlp model inference with sparsity

## When to choose
- you need to prune or quantize PyTorch, TensorFlow, or ONNX models and are comfortable using an unmaintained library
- you want recipe-driven model sparsification with minimal code changes

## When to avoid
- you need actively maintained tooling - the project was deprecated in June 2025 and receives no updates or support
- you are starting a new model optimization project - consider maintained alternatives or Neural Magic's vLLM-based offerings

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, deep-learning, llm-training
- domain: deep-learning, machine-learning, computer-vision
- platform: python, cross-platform
- tags: model-optimization, pruning, quantization, sparsification, pytorch, tensorflow, onnx, deprecated, neural-magic, natural-language-processing

## Member repositories
- neuralmagic/sparseml (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:18.702457+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:51:12.626400+00:00, confidence not recorded.
  - readme: https://github.com/neuralmagic/sparseml (fetched 2026-08-28T04:06:18.702457+00:00, sha 6a3e077fd584)
  - registry_pypi: https://pypi.org/pypi/sparseml/json (fetched 2026-08-29T10:31:12.337404+00:00, sha f1850423cfce)
- Data as of 2026-08-30T08:39:29.467469+00:00.