> Another very smart thing they did is to use what is known as a Mixture-of-Experts (MOE) Transformer architecture, but with key innovations around load balancing. As you might know, the size or capacity of an AI model is often measured in terms of the number of parameters the model contains. A parameter is just a number that stores some attribute of the model; either the "weight" or importance a particular artificial neuron has relative to another one, or the importance of a particular token depending on its context (in the "attention mechanism").
Has a wide-scale model analysis been performed inspecting the parameters and their weights for all popular open / available models yet? The impact and effects of disclosed inbound data and tuning parameters on individual vector tokens will prove highly informative and clarifying.
Such analysis will undoubtedly help semi-literate AI folks level up and bridge any gaps.
Has a wide-scale model analysis been performed inspecting the parameters and their weights for all popular open / available models yet? The impact and effects of disclosed inbound data and tuning parameters on individual vector tokens will prove highly informative and clarifying.
Such analysis will undoubtedly help semi-literate AI folks level up and bridge any gaps.