I'm quite interested in artificial intelligence and want to learn more about how a transformer model works, could you explain it to me?
Sure! A transformer model is a type of neural network architecture that was introduced in the paper Attention Is All You Need by Google in 2017. It's called a transformer because it's based on the idea of self-attention, which allows the model to focus on different parts of the input when processing it.

The transformer architecture is primarily used for tasks such as natural language processing (NLP) that involve sequential input, such as text. The model is composed of an encoder and a decoder. The encoder takes in the input sequence and produces a set of hidden states, which are then passed to the decoder to produce the output sequence.

One key component of the transformer model is the self-attention mechanism. Self-attention allows the model to weigh the importance of different parts of the input when processing it. The model does this by computing a set of attention weights, which are used to weigh the contribution of each input element to the output.

Another important component of the transformer model is the use of multi-head attention. Multi-head attention allows the model to attend to multiple parts of the input at once. This improves the model's ability to understand relationships between different parts of the input.

In the original paper the transformer was compromised from an encoder component and a decoder component. The input of the encoder is the the embedding of a conditioning text, such as a question, or text meant to be translated to a different language. The decoder receives the output of the encoder and the tokens it generated so far. It then predicts the next token to generate.

Finally, the transformer model also uses positional encoding, which encodes the position of each element in the input sequence. This allows the model to understand the order of the input elements, which is important for tasks like language understanding.

if you have more questions, feel free to ask!
Could you give a general overview (assume I'm a software developer who knowns nothing about ML) of how the decoder and encoder actually work?