This repository implements the Lite Transformer, a research paper from MIT’s Han Lab published at ICLR 2020. The architecture introduces Long-Short Range Attention to improve efficiency over standard transformers. The implementation builds on fairseq and includes custom CUDA-accelerated convolution layers (lightconv and dynamicconv). It supports standard NLP benchmarks including IWSLT14 and WMT translation tasks.