{"library":"mct-quantizers","title":"MCT Quantizers","description":"MCT Quantizers is a Python library that provides infrastructure for supporting neural network compression through quantization-aware training and post-training quantization. It is part of the Model Compression Toolkit (MCT) ecosystem. Version 1.7.0 requires Python >=3.10. The library is actively maintained with regular releases.","language":"python","status":"active","last_verified":"Sat May 09","install":{"commands":["pip install mct-quantizers"],"cli":null},"imports":["from mct_quantizers import QuantizationConfig","from mct_quantizers.pytorch import PostTrainingQuantization","from mct_quantizers.logger import Logger"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import torch\nfrom mct_quantizers.pytorch import QuantizationConfig, PostTrainingQuantization\n\n# Create a simple model\nmodel = torch.nn.Sequential(torch.nn.Linear(10, 5), torch.nn.ReLU())\n\n# Define quantization configuration\nconfig = QuantizationConfig(n_bits=8, per_channel=True)\n\n# Apply post-training quantization\nptq = PostTrainingQuantization(model, config)\nquantized_model = ptq.quantize()\n\n# Save the quantized model\ntorch.save(quantized_model.state_dict(), 'quantized_model.pth')","lang":"python","description":"Basic usage: quantize a PyTorch model with post-training quantization.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}