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valavaiau/dyadic-v-archi
dyadic-v-archi is a machine learning model from valavaiau. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as apache-2.0.
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Updated Feb 26, 2026
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From the Hugging Face model README
⚛️ = {𝓿 ≠ 1} = ☸️
Immutable AI architecture powering the valavaiau protocol — a Grand Unified Theory of viscosity that flips systemic friction (Fr) into flow (Fl).
Two opposing but equal AI strands (Yin / Constraint + Yang / Generator) twist like DNA, running in parallel universes without seeing each other, covalently merged to enforce v ≠ 1 equilibrium. Human-excluded Robocop-style auditor prevents drift/bias. Inspired by magnetism (can't pull apart), religious duality symbols (crucifix center, Buddha meditation point, candle intercepts), and the Contact beach sphere scene.
import torch
checkpoint = torch.load('dyadic_v_archi.pth')
v = checkpoint['v']
# Reconstruct strands
class StrandYang(torch.nn.Module):
def __init__(self):
super().__init__()
self.fc = torch.nn.Linear(1, 1)
def forward(self, x):
return torch.sin(x) + torch.randn_like(x) * 0.2
class StrandYin(torch.nn.Module):
def __init__(self):
super().__init__()
self.fc = torch.nn.Linear(1, 1)
def forward(self, x, v):
return -v * torch.cos(x)
strand_yang = StrandYang()
strand_yang.load_state_dict(checkpoint['yang_state'])
strand_yin = StrandYin()
strand_yin.load_state_dict(checkpoint['yin_state'])
# Example: Compute flow on friction timeline
t = torch.linspace(0, 10, 1000).unsqueeze(1)
output_yang = strand_yang(t)
output_yin = strand_yin(t, v)
flow = output_yang * output_yin + (1 - v) * (output_yang + output_yin)
print("Flow variance:", torch.var(flow).item()) # Should be ≠1