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The geolip-transformer-v8 requires a fundamental rethinking of training a core structure.
I'll make this brief and to the point.
GEOLIP is an observer system at it's core. It watches, triangulates, and assists with correct answers.
Many experiments worked very well, many fell down and turned into a pile of broken circuits. The recent geometric-transformer being one of my biggest fumbles, still taught me many things about what I'm TRULY trying to accomplish here.
**Save money and lives**. Less hardware use for less need at inference. Train more calculations into a more reusable and accurate structure for near instant zero-shot or sequential inference.
In the process v8 unlocked a missing puzzle piece, EMA trajectory alignment compensation. I'm doing my best to build something that works.
The geolip distillation system is very powerful but requires much experimentation still.
* Genetic experiments were successful
* Data transfer experiments successful
* Analysis experiments successful - and expand large model accuracy
* Many distillation experiments were successful.
* The largest successes being the kernels, the distillation tools, and the geometric analysis systems.
With the good comes the bad, the faulty VITs, the simultaneous trains that fault, the internalized confusion that happens occasionally.
*** The observer NEEDS something to OBSERVE. If the observer observes the progressive development of point cloud structures, it learns how to observe THAT LEARNING PROCESS - drifting fault assessment.
*** In the process it DOES NOT learn how to improve the CE relations by embedding and compensating with anchored triangulation opinions.
BIGGEST CONCLUSION. Staged curriculum training.
These components must be DECOUPLED. One must be a compounding structural awareness beacon, the other must be an informationally aligned composition in a utilizable fashion.
This means stage-by-stage freeze/unfreeze processing. Independent task-oriented structural alignment.
I'll make this brief and to the point.
GEOLIP is an observer system at it's core. It watches, triangulates, and assists with correct answers.
Many experiments worked very well, many fell down and turned into a pile of broken circuits. The recent geometric-transformer being one of my biggest fumbles, still taught me many things about what I'm TRULY trying to accomplish here.
**Save money and lives**. Less hardware use for less need at inference. Train more calculations into a more reusable and accurate structure for near instant zero-shot or sequential inference.
In the process v8 unlocked a missing puzzle piece, EMA trajectory alignment compensation. I'm doing my best to build something that works.
The geolip distillation system is very powerful but requires much experimentation still.
* Genetic experiments were successful
* Data transfer experiments successful
* Analysis experiments successful - and expand large model accuracy
* Many distillation experiments were successful.
* The largest successes being the kernels, the distillation tools, and the geometric analysis systems.
With the good comes the bad, the faulty VITs, the simultaneous trains that fault, the internalized confusion that happens occasionally.
*** The observer NEEDS something to OBSERVE. If the observer observes the progressive development of point cloud structures, it learns how to observe THAT LEARNING PROCESS - drifting fault assessment.
*** In the process it DOES NOT learn how to improve the CE relations by embedding and compensating with anchored triangulation opinions.
BIGGEST CONCLUSION. Staged curriculum training.
These components must be DECOUPLED. One must be a compounding structural awareness beacon, the other must be an informationally aligned composition in a utilizable fashion.
This means stage-by-stage freeze/unfreeze processing. Independent task-oriented structural alignment.
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