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TempVerseFormer

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LKyluk commited on Mar 19, 2025

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@@ -5,9 +5,9 @@ license: mit
 # TempVerseFormer - Pre-trained Models
 [![Hugging Face Hub](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-HuggingFace%20Hub-blue?style=flat-square&logo=huggingface)](https://huggingface.co/LKyluk/TempVerseFormer)
-[![GitHub Code](https://img.shields.io/github/v/release/leo27heady/TempVerseFormer?label=TempVerseFormer%20Code&sstyle=flat-square)](https://github.com/leo27heady/TempVerseFormer)
-[![Shape Dataset Toolbox](https://img.shields.io/github/v/release/leo27heady/simple-shape-dataset-toolbox?label=Shape%20Dataset&style=flat-square)](https://github.com/leo27heady/simple-shape-dataset-toolbox)
-[![WandB Log Examples](https://img.shields.io/badge/WandB-Training%20Logs-blue?style=flat-square&logo=wandb)](https://wandb.ai/leo27heady)
 This repository hosts pre-trained models for **TempVerseFormer: Temporal Modeling with Reversible Transformers**, a novel architecture introduced in the research article **"Temporal Modeling with Reversible Transformers"**.
@@ -18,7 +18,7 @@ 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:
 * **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:

 # TempVerseFormer - Pre-trained Models
 [![Hugging Face Hub](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-HuggingFace%20Hub-blue?style=flat-square&logo=huggingface)](https://huggingface.co/LKyluk/TempVerseFormer)
+[![GitHub Code](https://img.shields.io/github/v/release/leo27heady/TempVerseFormer?label=TempVerseFormer&sstyle=flat-square)](https://github.com/leo27heady/TempVerseFormer)
+[![Shape Dataset Toolbox](https://img.shields.io/github/v/release/leo27heady/simple-shape-dataset-toolbox?label=shapekit&style=flat-square)](https://github.com/leo27heady/simple-shape-dataset-toolbox)
+[![WandB Logs](https://img.shields.io/badge/WandB-Training%20Logs-blue?style=flat-square&logo=wandb)](https://wandb.ai/leo27heady/pipe-transformer/reports/TempVerseFormer-Training-Logs--VmlldzoxMTg3OTQ3NQ)
 This repository hosts pre-trained models for **TempVerseFormer: Temporal Modeling with Reversible Transformers**, a novel architecture introduced in the research article **"Temporal Modeling with Reversible Transformers"**.
 This repository contains pre-trained weights for the following models, as described in the research article:
 * **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: