In this video, I am going to show the animation of the working of Transformers in recording, encoding, and decoding. Transformers are the building blocks of large language models, such as GPT 3.5 and shared GPT 4 Lambda. These models are used for various tasks, including language translation. Transformers are a type of neural network that utilizes self-attention. A Transformer consists of two main parts: encoding and decoding. In this animation, you can see the working of the Transformer decoding and the self-attention mechanism. The Transformer translates one language to another by focusing on each word or token in the input sentence. The input sentence is divided into different tokens, and the self-attention mechanism is used to generate the output words. The decoding part of the Transformer predicts one word at a time based on the input words. It uses the self-attention mechanism and the contextual information to generate the next word. This animation provides a clear visualization of how Transformers work in language processing tasks.
