**Step Decay** is a learning rate schedule that drops the learning rate by a factor every few epochs, where the number of epochs is a hyperparameter.

Image Credit: [Suki Lau](https://towardsdatascience.com/learning-rate-schedules-and-adaptive-learning-rate-methods-for-deep-learning-2c8f433990d1)

**Neural Network Compression Framework**, or **NNCF**, is a Python-based framework for neural network compression with fine-tuning. It leverages recent advances of various network compression methods and implements some of them, namely quantization, sparsity, filter pruning and binarization. These methods allow producing more hardware-friendly models that can be efficiently run on general-purpose hardware computation units (CPU, GPU) or specialized deep learning accelerators.

NNCF

Neural Network Compression Framework for fast model inference

Step Decay

**PermuteFormer** is a [Performer](https://paperswithcode.com/method/performer)-based model with relative position encoding that scales linearly on long sequences. PermuteFormer applies position-dependent transformation on queries and keys to encode positional information into the attention module. This transformation is carefully crafted so that the final output of self-attention is not affected by absolute positions of tokens.

Each token’s query / key feature is illustrated as a row of blocks in the figure, and its elements are marked with different colors. The position-aware permutation permutes elements of each token’s query / key feature along the head size dimension in each attention head. Depending on the token’s position, the permutation applied to query / key feature is different.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com