AbstractPhila PRO

The results are rolling in. The series is coalescing into the necessary implications per structure aligned with the 10m cc12m extracted dataset.

This will help determine the best and fastest utilizable series from the geometric ablation and objective construction systems historically, and with that the organized documented results will be concatenated and organized by Claude Fable to the necessary potentials for each system.

With this, each potential arm for the AMOE-LORA system will be robustly tested for their distillation principles. Faster are ideal for rapid LORA convergence and slower are ideal for anchoring differentiation convergence, while moderate with a bit of MSE overfit are good for generation to an extent, while moderately low generalizable states are more ideal for generalization preservation as gated logical dichotomy structures for the moderate decisions.

It's a bit more complex than that, a lot more complex, but the results are rolling out and will be in the next article based on geometric memory.

Time to expand the arms for the vit into the full sail. We'll be hitting every major vit multi-teacher approach, including the memory anchoring finetune structures as well.

The memory bank systems have been shown to refine trained models within a degree of accuracy. The genetic experiments, the structural berts, and the vit collectives all showcased the possibility of this system's capacity to expand already pretrained systems by attaching expansions to those.

https://huggingface.co/AbstractPhil/geolip-bert-8192
https://huggingface.co/AbstractPhil/geolip-clip-vit-large-patch14-ctx576
https://huggingface.co/AbstractPhil/geolip-clip-vit-large-patch14-ctx576-seq77
https://huggingface.co/AbstractPhil/geolip-clip-vit-bigG-patch14-ctx576-seq77
https://huggingface.co/AbstractPhil/geolip-bertenstein
https://huggingface.co/AbstractPhil/geolip-vit-large-x3 i think?
https://huggingface.co/AbstractPhil/geolip-vit-x34 didn't work, too many vits

https://huggingface.co/AbstractPhil/geolip-captionbert-8192

Each of these are a testament to the utility of this concept.

One of the prototypes will include a multimodal memory bank with directly gated and interconnected shared memory gates speaking another model's language, rather than just embeddings for a singular model. This gate will take in one or multiple types of model inputs and process those inputs into an entirely different model series' responses in the AMOE format.

I will also be experimenting with the AMOE-LORA fused with memory bank processing directly rather than just gate. The constellation was baked from the anchored memory bank originally but it did not meet the same sort of embedding accuracy. However, the constellation results built the AMOE eventually. First things first though, have to step back and hit all the angles with all the necessary tests for robustness.

By stepping back to the earlier memory bank and fusing it with the alephs, the upcoming experiments will provide some solid strong-ended tests. With that the rapid training of captionbert will hopefully be applied to this tiny vit. If surge activates, the process may be strong enough from the memory bank to provide the necessary distillation learning speed required to train the full collective with minimal hardware.

I have many many models to train to create the full Beatrix V3 prototype, however the list is expanding nicely in order to provide a full multimodal type agnostic behavior within a reasonable MOE structure.

COCO InfoNCE feature training vs MSE was strong and better in it's own way.

The larger set of cc12m clip-vit-b laion extractions will yield better results and are now ready for use, roughly 10m~ feature extractions. Will be useful for the first student.
https://huggingface.co/datasets/AbstractPhil/bulk-cc12m-features

https://huggingface.co/datasets/AbstractPhil/tower-probes-results

The current running sweeps are being posted here. Expect many in the coming days, likely hundreds of thousands of more as I probe the hypothesis series.

Claude can't handle manage Colab notebooks directly effectively without letting him control my web browser and I don't plan to rent a pod for this process. That didn't go very well last time so I'll be operating predominantly through Colabs for now.

SO I will be handling everything with Opus until my Fable usage re-ups on Friday.

Fable managing a pod isn't very many tokens, but also Fable becomes super lazy if I do that. More likely to build something that WORKS but isn't optimal, over and over to consume timeslots instead of actually articulating useful optimized systems like when I directly manage the notebooks.