swap tempverseformer and tempverse models in readme

README.md

@@ -17,8 +17,8 @@ These models are designed for memory-efficient temporal sequence prediction, par
 
 This repository contains pre-trained weights for the following models, as described in the research article:
 
-* **TempVerseFormer (Rev-Transformer):**  The core Reversible Temporal Transformer architecture, leveraging reversible blocks and time-agnostic backpropagation for memory efficiency.
 * **TempFormer (Vanilla-Transformer):** A standard Vanilla Transformer architecture with temporal chaining, serving as a baseline to compare against TempVerseFormer.
+* **TempVerseFormer (Rev-Transformer):**  The core Reversible Temporal Transformer architecture, leveraging reversible blocks and time-agnostic backpropagation for memory efficiency.
 * **Standard Transformer (Pipe-Transformer):** A standard Transformer model that predicts only one next element at once.
 * **LSTM:** A Long Short-Term Memory network, representing a traditional recurrent sequence modeling approach.
 * **VAE Models:** Variational Autoencoder (VAE) models used for encoding and decoding images to and from a latent space: