phanerozoic / threshold-buffer

huggingface.co
Total runs: 5
24-hour runs: 0
7-day runs: 1
30-day runs: -3
Model's Last Updated: January 24 2026

Introduction of threshold-buffer

Model Details of threshold-buffer

threshold-buffer

4-bit buffer (identity function). Passes input through unchanged.

Function

buffer4(x3, x2, x1, x0) -> (y3, y2, y1, y0)

Output equals input: y_i = x_i for all i.

Truth Table
Input Output
0000 0000
0001 0001
... ...
1111 1111
Architecture

Single-layer, each output independently buffers one input:

x3   x2   x1   x0
│    │    │    │
▼    ▼    ▼    ▼
○    ○    ○    ○   Layer 1
│    │    │    │
▼    ▼    ▼    ▼
y3   y2   y1   y0

Each neuron: w=[...,1,...], b=-1 (single input with weight 1).

Parameters
Inputs 4
Outputs 4
Neurons 4
Layers 1
Parameters 20
Magnitude 8
Purpose

While trivial, buffers serve several purposes:

  • Signal regeneration in long chains
  • Fan-out amplification
  • Timing alignment
  • Isolation between circuit stages
Usage
from safetensors.torch import load_file
import torch

w = load_file('model.safetensors')

def buffer(x3, x2, x1, x0):
    inp = torch.tensor([float(x3), float(x2), float(x1), float(x0)])
    y0 = int((inp @ w['y0.weight'].T + w['y0.bias'] >= 0).item())
    y1 = int((inp @ w['y1.weight'].T + w['y1.bias'] >= 0).item())
    y2 = int((inp @ w['y2.weight'].T + w['y2.bias'] >= 0).item())
    y3 = int((inp @ w['y3.weight'].T + w['y3.bias'] >= 0).item())
    return y3, y2, y1, y0
License

MIT

Runs of phanerozoic threshold-buffer on huggingface.co

5
Total runs
0
24-hour runs
2
3-day runs
1
7-day runs
-3
30-day runs

More Information About threshold-buffer huggingface.co Model

More threshold-buffer license Visit here:

https://choosealicense.com/licenses/mit

threshold-buffer huggingface.co

threshold-buffer huggingface.co is an AI model on huggingface.co that provides threshold-buffer's model effect (), which can be used instantly with this phanerozoic threshold-buffer model. huggingface.co supports a free trial of the threshold-buffer model, and also provides paid use of the threshold-buffer. Support call threshold-buffer model through api, including Node.js, Python, http.

phanerozoic threshold-buffer online free

threshold-buffer huggingface.co is an online trial and call api platform, which integrates threshold-buffer's modeling effects, including api services, and provides a free online trial of threshold-buffer, you can try threshold-buffer online for free by clicking the link below.

phanerozoic threshold-buffer online free url in huggingface.co:

https://huggingface.co/phanerozoic/threshold-buffer

threshold-buffer install

threshold-buffer is an open source model from GitHub that offers a free installation service, and any user can find threshold-buffer on GitHub to install. At the same time, huggingface.co provides the effect of threshold-buffer install, users can directly use threshold-buffer installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

threshold-buffer install url in huggingface.co:

https://huggingface.co/phanerozoic/threshold-buffer

Url of threshold-buffer

threshold-buffer huggingface.co Url

Provider of threshold-buffer huggingface.co

phanerozoic
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Total runs: 37
Run Growth: -8
Growth Rate: -21.62%
Updated:April 28 2026