The 2D-🪆 model introduces a novel approach that enables you to reduce both the number of layers and the dimensions of embeddings within the model. This dual reduction strategy allows for a more compact model size while still delivering competitive performance compared to leading models, such as Nomic’s embedding model. Specifically, reducing the model’s layers by approximately 50% retains up to 85% of its original performance, even without additional training.