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threshold-parity3
phanerozoic/threshold-parity3
threshold-parity3 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.
3-bit parity function. Outputs 1 if odd number of inputs are high.
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.py2.6 KB · 50%
At a glance
- License
- mit
- Access
- Public
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026
- SHA
- 93b53974
Model card
From the Hugging Face model README
threshold-parity3
3-bit parity function. Outputs 1 if odd number of inputs are high.
Function
parity3(a, b, c) = a XOR b XOR c
Truth Table
| a | b | c | out |
|---|---|---|---|
| 0 | 0 | 0 | 0 |
| 0 | 0 | 1 | 1 |
| 0 | 1 | 0 | 1 |
| 0 | 1 | 1 | 0 |
| 1 | 0 | 0 | 1 |
| 1 | 0 | 1 | 0 |
| 1 | 1 | 0 | 0 |
| 1 | 1 | 1 | 1 |
Architecture
Cascade of two XOR2 gates: parity(a,b,c) = XOR(XOR(a,b), c)
Each XOR2 uses OR-NAND-AND structure:
- OR: fires if either input is 1
- NAND: fires if not both inputs are 1
- AND: fires if both OR and NAND fire (XOR condition)
Layers:
- xor1.or, xor1.nand (on inputs a, b)
- xor1.and (combines layer 1)
- xor2.or, xor2.nand (on xor1 output and c)
- xor2.and (final output)
Parameters
| Inputs | 3 |
| Outputs | 1 |
| Neurons | 6 |
| Layers | 4 |
| Parameters | 18 |
| Magnitude | 20 |
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 parity3(a, b, c):
return xor2(xor2(a, b, 'xor1'), c, 'xor2')
print(parity3(1, 0, 1)) # 0 (even parity)
print(parity3(1, 1, 1)) # 1 (odd parity)
License
MIT
- License
- mit
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026