Post
209
Cardinality cardinality CARDINALITY! As I restructure the wordnet's multi-definition structure, I've found a fair assessment capability that minimizes column recall requirement while simultaneously maximizing recall speed. So it will be fast.
Research shows, the most intelligent and most intellectually-driven LLMs require the most intelligent and carefully curated solid representative vocabularies - with the most intelligent and carefully curated training regiments.
Class simultaneously loaded hierarchical structures built with variants of vocabulary dimensions do not help this. Multiple dimensions of imagenet do not help this. Reshaping does not help. Solidification processes through pulverizing using Alucard do not help - though they did show some interesting potentials for pretraining the full geometric clip from the ground floor.
The experimentations with the multitude of clip features and imagenet - showcase that not only can this tiny 4meg classification tool can handle imagenet from clip features AT AROUND 76% no matter the hyperparams using linear, but expanding this system upward and including hundreds of different formula variants DOES NOT HELP SCALE IT AT ALL! The largest ones only house 76%, and the medium-sized ones house about 86% instead of 76% when using clip-vit-b-patch16 and clip-vit-b-patch32. If you check the big number valuations for the clip-vit-b laion and openai, you'll find nearly identical classifications.
So I only taught it, to understand geometry - the more training and more steps only brings it closer incorrectly.
So, this tells me one simple principle; geometry and linear have an upward capacity based on the information extracted from the linear model. Meaning... We need more places to extract and more curative potentials to solidify that access with, rather than simply EXPANDING it and making it bigger.
Next experiment includes a full cardinality subset of unicode to wordnet vocabulary translation matrices. Today. Within the hour.
Research shows, the most intelligent and most intellectually-driven LLMs require the most intelligent and carefully curated solid representative vocabularies - with the most intelligent and carefully curated training regiments.
Class simultaneously loaded hierarchical structures built with variants of vocabulary dimensions do not help this. Multiple dimensions of imagenet do not help this. Reshaping does not help. Solidification processes through pulverizing using Alucard do not help - though they did show some interesting potentials for pretraining the full geometric clip from the ground floor.
The experimentations with the multitude of clip features and imagenet - showcase that not only can this tiny 4meg classification tool can handle imagenet from clip features AT AROUND 76% no matter the hyperparams using linear, but expanding this system upward and including hundreds of different formula variants DOES NOT HELP SCALE IT AT ALL! The largest ones only house 76%, and the medium-sized ones house about 86% instead of 76% when using clip-vit-b-patch16 and clip-vit-b-patch32. If you check the big number valuations for the clip-vit-b laion and openai, you'll find nearly identical classifications.
So I only taught it, to understand geometry - the more training and more steps only brings it closer incorrectly.
So, this tells me one simple principle; geometry and linear have an upward capacity based on the information extracted from the linear model. Meaning... We need more places to extract and more curative potentials to solidify that access with, rather than simply EXPANDING it and making it bigger.
Next experiment includes a full cardinality subset of unicode to wordnet vocabulary translation matrices. Today. Within the hour.