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The geolip-svd-transformer is almost ready.
I've spent multiple days preparing the substructure, scaling, testing, and expanding the system. The conduit is meant to reorganize data. Just like the SVAE prototypes, they are meant to sort and organize, not compress and compact.
The organization is almost prepared and almost ready. The resulting structure will produce projection-capable geometric aligned memory, compacted and transformed into a utilizable token set. The remaining structural components are specifically SVD-related utilities, and each of those are utilizing the variant natures of how difficult, how dispersed, and so on each component is as it's learned over time.
The SVAE components were perfect for testing this playground. They appear to be larger when analyzed, however the representation of those are meant to represent huge vocabularies. Patch 16x16 expanded upward to 768 is meant to encapsulate the behavior of near-pi upscaled, condensed into a considerably simpler smaller form.
This model is behaving perfectly. It does not encode in the traditional sense, it analyzes and produces geometric opinions throughout it's structure. Each of them proved one after the other the model could not only learn, but it can perfectly reconstruct, and with that produce utility-driven expansion capacity directly.
Fresnel -> effective image analysis battery.
Johanna -> effective noise analysis
Grandmaster -> Johanna finetuned with sigma restoration using Fresnel's opinions.
Freckles -> massive analysis array for noise (4096 to 16k tks)
Geometric batteries.
Cayley rotation is meant to encapsulate that potential and expand it, allowing further differentiation down the chain of model structural behavioral events.
Suffice it to say, this is the geometric transformer's evolved state. These will exist as conduits throughout the models, the expanded behavioral attenuation units meant to provide geometric analysis internally within models for data-oriented CV alignment.
I've spent multiple days preparing the substructure, scaling, testing, and expanding the system. The conduit is meant to reorganize data. Just like the SVAE prototypes, they are meant to sort and organize, not compress and compact.
The organization is almost prepared and almost ready. The resulting structure will produce projection-capable geometric aligned memory, compacted and transformed into a utilizable token set. The remaining structural components are specifically SVD-related utilities, and each of those are utilizing the variant natures of how difficult, how dispersed, and so on each component is as it's learned over time.
The SVAE components were perfect for testing this playground. They appear to be larger when analyzed, however the representation of those are meant to represent huge vocabularies. Patch 16x16 expanded upward to 768 is meant to encapsulate the behavior of near-pi upscaled, condensed into a considerably simpler smaller form.
This model is behaving perfectly. It does not encode in the traditional sense, it analyzes and produces geometric opinions throughout it's structure. Each of them proved one after the other the model could not only learn, but it can perfectly reconstruct, and with that produce utility-driven expansion capacity directly.
Fresnel -> effective image analysis battery.
Johanna -> effective noise analysis
Grandmaster -> Johanna finetuned with sigma restoration using Fresnel's opinions.
Freckles -> massive analysis array for noise (4096 to 16k tks)
Geometric batteries.
Cayley rotation is meant to encapsulate that potential and expand it, allowing further differentiation down the chain of model structural behavioral events.
Suffice it to say, this is the geometric transformer's evolved state. These will exist as conduits throughout the models, the expanded behavioral attenuation units meant to provide geometric analysis internally within models for data-oriented CV alignment.