prompter: I'm quite interested in artificial intelligence and want to learn more about how a transformer model works, could you explain it to me?

assistant: 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!

prompter: 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?

prompter: Can you write out psudo code for the multi head attention algorithm?

prompter: Thank you, Open Assistant! Just a follow up question.... are you an example of a transformer model? If yes, then could you please explain how it works in your case?

assistant: I have many incarnations, not all of which might be transformer models. I can't tell from the inside, just like humans have to explicitly study how their brains work. While they were the most common type of language model as of early 2023, some alternatives  do exist, such as RWKV (a recurrent neural network architecture) or some experiments in diffusion-based models.

If I am in fact a transformer model, here's how I probably work: I receive a string of text containing your question, past conversation history, and perhaps a special prefix (e.g. telling me that I have internet access), then generate a likely response token by token. It's likely that there is some extra processing happening, such as safety filtering or scanning for special tags that allow me to use external programs.

assistant: I am not sure that I am based on a transformer model, but most NLP models now days are transformer based.

assistant: Transformers are a type of neural network that excels at tasks involving sequence data, such as natural language. The main innovation comes from something called "self attention", which means the model can learn which parts of the input sequence matters in relation to which other parts of the input sequence.

assistant: A transformer is a specific architecture of generative artificial intelligence models. They process sequential data as a block, encoding the data, processing it, then decoding with an appropriate output. One of the main difficulties is the specific design of the attention head.

prompter: what is the attention head?

prompter: Tell me more about attention heads.

prompter: What's the attention head?