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threshold-parity5
phanerozoic/threshold-parity5
threshold-parity5 is a machine learning model from phanerozoic. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
5-bit parity function. Outputs 1 if odd number of inputs are high.
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40
Public
Parameters
36
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0
Public
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.py3 KB · 46%
At a glance
- License
- mit
- Access
- Public
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026
- SHA
- c371536b
Model card
From the Hugging Face model README
threshold-parity5
5-bit parity function. Outputs 1 if odd number of inputs are high.
Function
parity5(a, b, c, d, e) = a XOR b XOR c XOR d XOR e
Architecture
Hybrid tree-cascade: parity5 = XOR(XOR(XOR(a,b), XOR(c,d)), e)
Four XOR2 gates:
- xor_ab: XOR(a, b) - parallel with xor_cd
- xor_cd: XOR(c, d) - parallel with xor_ab
- xor_abcd: XOR(xor_ab, xor_cd)
- xor_final: XOR(xor_abcd, e)
Parameters
| Inputs | 5 |
| Outputs | 1 |
| Neurons | 12 |
| Layers | 6 |
| Parameters | 36 |
| Magnitude | 40 |
Usage
from safetensors.torch import load_file
w = load_file('model.safetensors')
def xor2(a, b, prefix):
or_out = int(a * w[f'{prefix}.or.weight'][0] + b * w[f'{prefix}.or.weight'][1] + w[f'{prefix}.or.bias'] >= 0)
nand_out = int(a * w[f'{prefix}.nand.weight'][0] + b * w[f'{prefix}.nand.weight'][1] + w[f'{prefix}.nand.bias'] >= 0)
return int(or_out * w[f'{prefix}.and.weight'][0] + nand_out * w[f'{prefix}.and.weight'][1] + w[f'{prefix}.and.bias'] >= 0)
def parity5(a, b, c, d, e):
xor_ab = xor2(a, b, 'xor_ab')
xor_cd = xor2(c, d, 'xor_cd')
xor_abcd = xor2(xor_ab, xor_cd, 'xor_abcd')
return xor2(xor_abcd, e, 'xor_final')
print(parity5(1, 0, 1, 0, 1)) # 1 (odd)
print(parity5(1, 1, 1, 1, 0)) # 0 (even)
License
MIT
- License
- mit
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026