Downloads · 30 days
13
27% of all-time downloads
threshold-negator4bit
phanerozoic/threshold-negator4bit
threshold-negator4bit 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.
4-bit bitwise NOT (one's complement negation).
All-time downloads
48
Public
Parameters
20
Likes
0
Public
Repo makeup
Click a slice to open those files.
.py2 KB · 51%
At a glance
- License
- mit
- Access
- Public
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026
- SHA
- 4b2d5f99
Model card
From the Hugging Face model README
threshold-negator4bit
4-bit bitwise NOT (one's complement negation).
Function
negator4bit(a3, a2, a1, a0) = [NOT(a3), NOT(a2), NOT(a1), NOT(a0)]
Inverts each bit independently.
Truth Table (selected rows)
| Input | Output |
|---|---|
| 0000 | 1111 |
| 0001 | 1110 |
| 0101 | 1010 |
| 1111 | 0000 |
Architecture
Single layer with 4 independent NOT neurons.
| Output | Weight on input | Bias |
|---|---|---|
| y3 | a3: -1 | 0 |
| y2 | a2: -1 | 0 |
| y1 | a1: -1 | 0 |
| y0 | a0: -1 | 0 |
Each neuron fires when its input is 0.
Parameters
| Inputs | 4 |
| Outputs | 4 |
| Neurons | 4 |
| Layers | 1 |
| Parameters | 8 |
| Magnitude | 4 |
Usage
from safetensors.torch import load_file
import torch
w = load_file('model.safetensors')
def negator4(a3, a2, a1, a0):
inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)])
return [int((inp * w[f'y{i}.weight']).sum() + w[f'y{i}.bias'] >= 0)
for i in range(4)]
print(negator4(0, 1, 0, 1)) # [1, 0, 1, 0]
License
MIT
- License
- mit
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026