Thanks, good explanation. More questions. Models have the initial N embeddings passed through, processed by the attention+linear blocks which add something to

Thanks, good explanation. More questions. Models have the initial N embeddings passed through, processed by the attention+linear blocks which add something to the input. Sort of memory bus. After the last block we still have array of the same dimensions like N initial embeddings, but they mean something else now. How do we select the next token, based on the last element in that array? Another question, that bus technically doesn't have to have the same width, right? It should be possible, for the same model's size, to trade bus width for the number of heads. Even have sort of U-Net. And the last, on each loop resulting embedding (1) is converted into token, which is being added to the input. Converted to embedding first, it should be possible to just reuse embedding (1), and use token only for user output, right? PS: not sure about every combination tried, we just started. Some of the problems still don't have satisfying solutions. Like hallucinations, online training. Or responsibility.