v0.2.0

New compression techniques

• Unstructured pruning: magnitude or random, with optional gradual cubic schedule
• N:M sparsity: default 2:4, for NVIDIA Ampere sparse tensor cores
• Weight-only INT4/INT8 quantization: group-wise, symmetric or asymmetric
• Low-rank decomposition: truncated SVD for Linear layers, with rank-ratio or energy-threshold selection
• Operator fusion: Conv2d + BatchNorm2d folding via torch.fx
• Weight clustering: per-layer k-means codebook

New analysis tools

• cx.analyze_sensitivity(): probes each Conv2d/Linear layer with a prune or noise perturbation, ranks layers by metric drop, and can suggest exclude_layers above a threshold

163 tests passing (up from 91).