LLM2Vec provides code to transform standard decoder-only language models into bidirectional text encoders. The approach involves three steps: enabling bidirectional attention, training with masked next token prediction, and applying unsupervised contrastive learning. The resulting encoders can be fine-tuned further for various natural language understanding tasks, achieving competitive performance on embedding benchmarks.