Pip install the model compression toolkit package in a Python>=3.9 environment with PyTorch>=2.1 or Tensorflow>=2.12.
pip install model-compression-toolkit
For installing the nightly version or installing from source, refer to the installation guide.
Important note: In order to use MCT, you’ll need to provide a floating point .pt or .keras model as an input.
Our tutorials section will walk you through the basics of the MCT tool, covering various compression techniques for both Keras and PyTorch models. Access interactive notebooks for hands-on learning with popular models/tasks or move on to Resources section.
MCT supports various quantization methods as appears below.
| Quantization Method | Complexity | Computational Cost | API | Tutorial |
|---|---|---|---|---|
| PTQ (Post Training Quantization) | Low | Low (~1-10 CPU minutes) | PyTorch API / Keras API |   |
| GPTQ (parameters fine-tuning using gradients) | Moderate | Moderate (~1-3 GPU hours) | PyTorch API / Keras API |  |
| QAT (Quantization Aware Training) | High | High (~12-36 GPU hours) | QAT API | |
For each flow, Quantization core utilizes various algorithms and hyper-parameters for optimal hardware-aware quantization results. For further details, please see Supported features and algorithms.
Required input: Floating point model - 32bit model in either .pt or .keras format
Optional input: Representative dataset - can be either provided by the user, or generated utilizing the Data Generation capability
MCT offers a range of powerful features to optimize models for efficient edge deployment. These supported features include:
🏆 Mixed-precision search 
📈 Graph optimizations. Transforming the model to be best fitted for quantization process.
🔎 Quantization parameter search
🧮 Advanced quantization algorithms
🎯 TPC (Target Platform Capabilities). Describes the target hardware’s constrains, for which the model optimization is targeted. See TPC Readme for more information.
Generates synthetic images based on the statistics stored in the model's batch normalization layers, according to your specific needs, for when image data isn’t available. See Data Generation Library for more. The specifications of the method are detailed in the paper: "Data Generation for Hardware-Friendly Post-Training Quantization" [5].
Reduces model size/complexity and ensures better channels utilization by removing redundant input channels from layers and reconstruction of layer weights. Read more (Pytorch API / Keras API).
🎛️ Network Editor (Modify Quantization Configurations)
🖥️ Visualization. Observe useful information for troubleshooting the quantized model's performance using TensorBoard. Read more.
🔑 XQuant (Explainable Quantization)
As part of the GPTQ capability, we provide an advanced optimization algorithm called EPTQ. The specifications of the algorithm are detailed in the paper: "EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise Optimization" [4]. More details on how to use EPTQ via MCT can be found in the GPTQ guidelines.
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User Guide contains detailed information about MCT and guides you from installation through optimizing models for your edge AI applications.
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MCT's API Docs is separated per quantization methods:
- Post-training quantization | PTQ API docs
- Gradient-based post-training quantization | GPTQ API docs
- Quantization-aware training | QAT API docs
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Debug – modify optimization process or generate an explainable report
Currently, MCT is being tested on various Python, Pytorch and TensorFlow versions:
Supported Versions Table
| PyTorch 2.2 | PyTorch 2.3 | PyTorch 2.4 | PyTorch 2.5 | |
|---|---|---|---|---|
| Python 3.9 |  |
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| Python 3.10 |  |
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| Python 3.11 |  |
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| Python 3.12 |  |
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| TensorFlow 2.12 | TensorFlow 2.13 | TensorFlow 2.14 | TensorFlow 2.15 | |
|---|---|---|---|---|
| Python 3.9 |  |
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| Python 3.10 |  |
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| Python 3.11 |  |
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MCT can quantize an existing 32-bit floating-point model to an 8-bit fixed-point (or less) model without compromising accuracy. Below is a graph of MobileNetV2 accuracy on ImageNet vs average bit-width of weights (X-axis), using single-precision quantization, mixed-precision quantization, and mixed-precision quantization with GPTQ.
For more results, please see [1]
Results for applying pruning to reduce the parameters of the following models by 50%:
| Model | Dense Model Accuracy | Pruned Model Accuracy |
|---|---|---|
| ResNet50 [2] | 75.1 | 72.4 |
| DenseNet121 [3] | 74.44 | 71.71 |
If you encountered a large accuracy degradation with MCT, check out the Quantization Troubleshooting for common pitfalls and some tools to improve the quantized model's accuracy.
Check out the FAQ for common issues.
You are welcome to ask questions and get support on our issues section and manage community discussions under the discussions section.
We'd love your input! MCT would not be possible without help from our community, and welcomes contributions from anyone!
*Checkout our Contribution guide for more details.
Thank you 🙏 to all our contributors!
MCT is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
[1] Habi, H.V., Peretz, R., Cohen, E., Dikstein, L., Dror, O., Diamant, I., Jennings, R.H. and Netzer, A., 2021. HPTQ: Hardware-Friendly Post Training Quantization. arXiv preprint.
[4] Gordon, O., Cohen, E., Habi, H. V., & Netzer, A., 2024. EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise Optimization, European Conference on Computer Vision Workshop 2024, Computational Aspects of Deep Learning (CADL)
[5] Dikstein, L., Lapid, A., Netzer, A., & Habi, H. V., 2024. Data Generation for Hardware-Friendly Post-Training Quantization, Accepted to IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025
