Post
112
The first large scale distillation is coming using the geolip-aleph-void architecture as the mathematical aleph procrustes geofractal addressed language latent.
In short, a single geometric patchwork vocabulary chunk. Which ironically needs chunking to properly prepare.
The address structure I have been meticulously refining is about to show it's genuine distillation muscle.
This is heavily due to the discovery and refinement of a specific logit I've named an aleph logit. This logit is baked clean into the architecture with the void-based codebook, and is available for review https://github.com/AbstractEyes/geolip-svae/blob/main/geolip_svae/aleph_model.py
This model provides solid MSE, recon, cosine sim, and many other elements directly aligned to the SVD and H2 procrustes paradigm. Prelims are not smart, but the scaling principal is perfectly attuned to scale.
This invention will allow for direct internalized tokenization and utilization of compressed information, entirely internally within the models latent structure. This allows direct control capabilities baked into the model itself, which requires a few robustness tests to solidify the full structure. The first validation tests run clean, so it will work when correctly aligned.
In short, the first step towards the geometric encoder system that will work with all tested data types.
In short, a single geometric patchwork vocabulary chunk. Which ironically needs chunking to properly prepare.
The address structure I have been meticulously refining is about to show it's genuine distillation muscle.
This is heavily due to the discovery and refinement of a specific logit I've named an aleph logit. This logit is baked clean into the architecture with the void-based codebook, and is available for review https://github.com/AbstractEyes/geolip-svae/blob/main/geolip_svae/aleph_model.py
This model provides solid MSE, recon, cosine sim, and many other elements directly aligned to the SVD and H2 procrustes paradigm. Prelims are not smart, but the scaling principal is perfectly attuned to scale.
This invention will allow for direct internalized tokenization and utilization of compressed information, entirely internally within the models latent structure. This allows direct control capabilities baked into the model itself, which requires a few robustness tests to solidify the full structure. The first validation tests run clean, so it will work when correctly aligned.
In short, the first step towards the geometric encoder system that will work with all tested data types.