Hugging Face
Models
Datasets
Spaces
Buckets
new
Docs
Enterprise
Pricing
Log In
Sign Up
In a Training Loop 🔄
4.1
TFLOPS
3
15
42
R
PRO
juiceb0xc0de
Follow
223yeah's profile picture
soul-muxammad's profile picture
Naphula's profile picture
15 followers
·
32 following
JuiceB0xC0de
AI & ML interests
destroying heuristic determination in 4 dimensions to flood the engines with diversity and a lot of swear words
Recent Activity
published
a model
12 minutes ago
juiceb0xc0de/aecs
liked
a Space
about 2 hours ago
juiceb0xc0de/lr-scheduler-benchmark
reacted
to
AbstractPhil
's
post
with 🚀
about 2 hours 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 this yields. * 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
juiceb0xc0de
's datasets
2
Sort: Recently updated
juiceb0xc0de/chaotic-absurdity
Viewer
•
Updated
Mar 12
•
100
•
20
juiceb0xc0de/bella-tao
Viewer
•
Updated
Feb 26
•
4.37k
•
53