This project is designed for learning the principles and implementation of the Transformer architecture. It includes various demos that explore different components and use cases of Transformer models.

The project is divided into several parts, each focusing on a specific aspect of the Transformer model:

• A simple sequence-to-sequence (seq2seq) model implemented using the Transformer architecture.
• Demonstrates the core functionality of the Transformer in solving sequence-based tasks.

• A step-by-step breakdown of the key components of a Transformer model.
• Includes demos of attention mechanisms, positional encoding, and other foundational parts of the architecture.

• An encoder-decoder Transformer model, which is typically used for tasks like machine translation and text summarization.
• Shows how the encoder and decoder work together to process input and generate output sequences.

• A decoder-only Transformer model, often used for autoregressive tasks like language modeling or text generation.
• Focuses on how the model generates output step-by-step based on the previous tokens.

• Demos showcasing how to use various distributed frameworks (e.g., PyTorch Distributed, TensorFlow, DeepSpeed, etc.) for training and inference with Transformer models.
• Focuses on scaling up training processes and optimizing inference performance.

• Python 3
• PyTorch
• Other dependencies listed in requirements.txt

To install the necessary dependencies, run the following command:

pip install -r requirements.txt