Hugging Face's logo
AbstractPhila's picture
Open to Collab
19 6 24

AbstractPhila PRO

AbstractPhil
AbstractPowered
dlebedev's profile picture MariaIvanova's profile picture Felldude's profile picture
·
https://civitai.com/user/AbstractPhila

AI & ML interests

datasets, research papers, experimentation, vision, classification, text encoders, tokenization, llms, diffusion, distillation, and more.

Recent Activity

liked a model about 11 hours ago
animetimm/vit_base_patch16_224.dbv4-full
updated a dataset about 12 hours ago
AbstractPhil/diffusion-pretrain-set-ft1
repliedto their post 3 days ago
By trying to disprove the Omega H2 battery I have discovered; * Each topology formed by the H2 battery is deviant, none have a uniformly shared substrate of behavior. They are each uniquely independent per training set all with perfect recon. * Image recon can be tracked and mapped, yielding a consistently mapped and response 16.77m vocabulary potential. In the current spectrum testing at around 5 million unicode bytes. * The model scale shows patch size is related to how much data you want the model to represent within the model itself, and this has yet to see a capacity to this day. The MSE recons and yields - and the more data fed, the more they yield. * The scaling principle shows that the model indefinitely scales upward and each level of the model can be iteratively captured upward to form deviant and uniformly consistent repeatable pathways of implicit codewise response, not just arbitrary bitwise recall. Meaningful implicit learned utility. * Image recon patch size should match the slice of image you want to represent, as it uses patch smoothing per patch internally from identity. * byte trigrams are channel-agnostic, they do not require a channel count just a formula for recall at nGram recall 99.6% for byte-by-byte representations. With those comes an adjacently capable codebook. * sentencepiece preliminary tests show validity and reconstruction just like the byte trigrams, using the new byte trigram this would be arbitrarily convenient to recon a codebook for the structure. * binary trees learn a uniformly potent and powerful gating mechanism that required further exploration, each of them produces direct responsive independent capacity and the responses are controllable. * ternary experiments show the models are directly responsive to -1, 0, +1 behavior, so the quantization is very much a valid potential. * preliminary tests with the H2O1 series of batteries show the models are responding similar to natural universal elements in the universe itself
View all activity

Organizations

DeepGHS's profile picture Blog-explorers's profile picture BangumiBase's profile picture Abstract Powered Research's profile picture