> Skyfall is "upscaled". How does that work?

It works by using Goddard's mergekit (or equivalent technology) to make something called a "passthrough self-merge". This means making a new model from the first two-thirds of its layers (or thereabouts; it usually needs some trial and error to find the right cut-off) and the last two-thirds of its layers, appended to each other.

This results in a model about 30% larger, because some middle layers have been duplicated. This has two effects:

• Heuristics (generalized knowledge) which are encoded in this middle layer get applied twice, which means they present more strongly in the inference result,

• It adds some redundancy to the model's parameters, so that further training is less likely to obliterate something important (what the field calls "catastrophic forgetting"). The optimizer (AdamW or whatever) is able to repurpose some of those parameters to encode new heuristics without losing the old ones.

The theory of why this works is still very much under development, but David Ng has been developing what he calls RYS theory which describes part of it. You could look him up if you want to learn more about it.