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threshold-clz4
phanerozoic/threshold-clz4
threshold-clz4 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.
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At a glance
- License
- mit
- Access
- Public
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026
- SHA
- f3d0f2fa
Model card
From the Hugging Face model README
threshold-clz4
4-bit count leading zeros.
Function
clz4(a3, a2, a1, a0) = number of leading zeros from MSB
Truth Table (selected rows)
| Input | First 1 | CLZ | Output |
|---|---|---|---|
| 1xxx | bit 3 | 0 | 000 |
| 01xx | bit 2 | 1 | 001 |
| 001x | bit 1 | 2 | 010 |
| 0001 | bit 0 | 3 | 011 |
| 0000 | none | 4 | 100 |
Architecture
Layer 1: Priority detection from MSB
has3 = a3 (MSB is set, clz=0)
has2_first = a2 AND NOT(a3) (clz=1)
has1_first = a1 AND NOT(a2) AND NOT(a3) (clz=2)
has0_first = a0 AND NOT(a1) AND NOT(a2) AND NOT(a3) (clz=3)
all_zero = NOT(any bit) (clz=4)
Layer 2: Binary encoding
y0 = has2_first OR has0_first (clz is 1 or 3)
y1 = has1_first OR has0_first (clz is 2 or 3)
y2 = all_zero (clz is 4)
Parameters
| Inputs | 4 |
| Outputs | 3 |
| Neurons | 8 |
| Layers | 2 |
| Parameters | 39 |
| Magnitude | 26 |
Usage
from safetensors.torch import load_file
# See model.py for full implementation
# clz4(1, 0, 0, 0) = [0, 0, 0] = 0 (MSB is set)
# clz4(0, 0, 1, 0) = [0, 1, 0] = 2 (two leading zeros)
# clz4(0, 0, 0, 0) = [1, 0, 0] = 4 (all zeros)
License
MIT
- License
- mit
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026