The Transformer model uses attention to boost the speed of neural machine translation applications and outperforms the Google Neural Machine Translation model in specific tasks. It consists of an encoding component, a decoding component, and connections between them, with the encoder using self-attention and feed-forward neural networks to process input sequences.