Have you ever wondered what it takes to implement a research paper in machine learning from scratch? I recently took on the challenge of implementing the ‘Memorizing Transformers’ paper, and I’m excited to share my experience with you.
The original paper introduces a memory-based mechanism that allows the model to attend to information beyond its context window, enabling long-term context handling. I decided to take it a step further by making some major modifications to the model architecture and hyperparameters, aiming for improved performance. The entire model is built from scratch using PyTorch.
One of the key modifications I made was replacing the default positional encoding with Rotary Positional Embeddings (RoPE). I also altered the attention mechanism to use Grouped Query Attention and customized the DataLoader to support sharded datasets and data parallelism. Additionally, I implemented Mixed Precision Training along with Distributed Data Parallel (DDP) support and tweaked several training and model hyperparameters for better adaptability.
The result is a model that can attend to information beyond its context window, enabling long-term context handling. You can check out my implementation on Hugging Face, where I’ve shared the model and training code.
If you’re interested in learning more about implementing research papers in machine learning, I hope this inspires you to take on the challenge!