Language Model, Transformers, Deep Learning, Artificial Intelligence, Machine Learning.
earlier layers by the model parameters. This helps minimize the memory overhead. Second, it splits values into chunks, saving memory in FFT layers and make the inference more efficient. Lastly, Reformer uses locality-sensitive hashing to approximate the attention matrix for a more efficient