This repository provides the code and models for a contrastive framework for neural text generation, presented at NeurIPS 2022. The method improves decoding strategies for language models by leveraging contrastive learning principles during generation. It offers an alternative to standard greedy or beam search decoding, addressing issues like repetition and low-quality outputs. The project integrates with Huggingface transformers for easy adoption.