model-compression-toolkit
A Model Compression Toolkit for neural networks
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has low install friction, carries a permissive Apache 2.0 license, and addresses a real production need (model compression for edge deployment). It supports both PyTorch and Keras, offers multiple quantization strategies with varying computational costs, and includes practical features like data-free quantization and hardware-aware optimization. Install it if you need to compress trained models for deployment; the main constraint is the Python >=3.10 requirement and the need to provide a pre-trained model as input.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10 and a pre-trained floating-point model (PyTorch or Keras) as input; PyTorch >=2.3 or TensorFlow >=2.14 must be installed separately.
- Low install friction with a pure-Python wheel distribution.
- Active maintenance status and a large dependency set (14 runtime packages including numpy, scikit-learn, tensorboard, protobuf) that are all standard ML libraries, so resolution is straightforward on modern systems.
License · maintenance · safety
permissive license (permissive) — Apache 2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for production deployment in most organizational contexts.
last release 2026-03-04 (163 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 115,244 downloads/mo, #12,262 on PyPI
Alternatives
Verify before relying
pip install model-compression-toolkit
from model_compression_toolkit import pytorch_post_training_quantization
# Quantize a pre-trained PyTorch model
quantized_model = pytorch_post_training_quantization(model, representative_data_gen)- Whether the package supports quantization-aware training (QAT) end-to-end or requires external training loop integration.
- Compatibility with newer PyTorch/TensorFlow versions beyond the minimum specified.
- Performance benchmarks for quantized model inference speed and accuracy trade-offs on target hardware.
- Whether data generation capability works with non-image models or only vision tasks.
What it is and what it does
Model Compression Toolkit is a library for reducing the size and computational cost of neural networks through quantization, pruning, and hardware-aware optimization. It supports three main quantization workflows: post-training quantization (PTQ, low computational cost), gradient-based post-training quantization (GPTQ, moderate cost), and quantization-aware training (QAT, high cost). The toolkit takes a pre-trained floating-point model (PyTorch or Keras) and applies compression techniques to produce a smaller, faster model suitable for edge deployment.
The package includes advanced features like mixed-precision bit-width search per layer, graph optimizations, quantization parameter tuning, data-free synthetic data generation from batch normalization statistics, structured pruning, and hardware-aware optimization via Target Platform Capabilities (TPC). It provides visualization and debugging tools through TensorBoard integration and a network editor for manual quantization configuration. With 14 runtime dependencies covering numerical computing, visualization, and ML frameworks, it integrates into standard ML pipelines.
Use it for
- Reduce inference latency and memory footprint of trained PyTorch or Keras models for deployment on resource-constrained edge devices.
- Perform post-training quantization on existing models without retraining, when representative data is available or can be synthetically generated.
- Search for optimal per-layer bit-widths across a model to balance accuracy and compression for a specific hardware target.
- Generate synthetic training data from model statistics when real representative data is unavailable, enabling quantization without access to original datasets.
- Debug and visualize quantization impact on model layers using TensorBoard and adjust per-layer quantization settings via the network editor.
- Prune redundant channels from neural network layers to reduce model complexity and improve hardware utilization.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has low install friction, carries a permissive Apache 2.0 license, and addresses a real production need (model compression for edge deployment). It supports both PyTorch and Keras, offers multiple quantization strategies with varying computational costs, and includes practical features like data-free quantization and hardware-aware optimization. Install it if you need to compress trained models for deployment; the main constraint is the Python >=3.10 requirement and the need to provide a pre-trained model as input.
Install
model-compression-toolkit on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Active maintenance status and a large dependency set (14 runtime packages including numpy, scikit-learn, tensorboard, protobuf) that are all standard ML libraries, so resolution is straightforward on modern systems.
Requires Python >=3.10 and a pre-trained floating-point model (PyTorch or Keras) as input; PyTorch >=2.3 or TensorFlow >=2.14 must be installed separately.
License in practice
Apache 2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for production deployment in most organizational contexts.
Quickstart
pip install model-compression-toolkit
from model_compression_toolkit import pytorch_post_training_quantization
# Quantize a pre-trained PyTorch model
quantized_model = pytorch_post_training_quantization(model, representative_data_gen)Verify before relying
- Whether the package supports quantization-aware training (QAT) end-to-end or requires external training loop integration.
- Compatibility with newer PyTorch/TensorFlow versions beyond the minimum specified.
- Performance benchmarks for quantized model inference speed and accuracy trade-offs on target hardware.
- Whether data generation capability works with non-image models or only vision tasks.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 14 packagesnetworkxtqdmPillownumpyscikit-imagescikit-learntensorboardPuLPmatplotlibscipyprotobufmct-quantizerspydanticedge-mdt-cl |
| Maintenance | Actively maintained 163 days since the last release |
| First released | |
| Downloads | 115,244 / month, #12,262 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: model_compression_toolkit-2.6.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “neural network quantization”
- model-compression-toolkitCompresses and optimizes neural networks through quantization,…
- qwixQwix is a JAX quantization library that applies Quantization-Aware…
- compressed-tensorsProvides a unified format for storing and loading compressed neural…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also compressed-tensors · edge-mdt-cl · nncf · tensorflow-model-optimization · nvidia-modelopt · ai-edge-quantizer · auto-gptq · qwix · segmentation-models-pytorch · diffq