v0.1.4

• Add Kahan Summation with use_kahan to optimizers.
• Add basic CPU offload with offload_buffers to optimizers.
• Add torch.compile support with use_torch_compile to optimizers.
• Add minimum ndim and numel checks to optimizers.
• Add more guards against NaNs on optimizers.
• Add more broadcast, view, split and select ops to SDNQTensor for DeepSpeed compatibility.
• Add SDNQLayer, SDNQLinear and SDNQConv layer wrappers.
• Add GLM Image and LTX 2 keys to the model specific keys.
• Disable stochastic rounding on backward pass with quantized matmul.
• Set the default max_shard_size to 5GB.
• Set the minimum supported Python version to 3.10.
• Set skip compile on dynamic quantization.
• Register sdnq_training as a backend to diffusers and transformers.
• Refactor optimizers again to make them more easily expandable.
• Fix compatibility with outdated PyTorch versions.
• Fix quantized matmul with packed weights.
• Fix stochastic rounding with float type weights.

Full Changelog: v0.1.3...v0.1.4