pizdecB3

pizdecB3 (format: n.f.c → name.function.count) is a tiny boolean comparison model.

• n = pizdec
• f = B (Boolean)
• c = 3 (third model in the series)

It takes two numbers and predicts one of three relations:

• > (greater)

• = (equal)

• < (less)

Also supports string input in the format x?y.

Model Details

Property	Value
Parameters	~1731
Input	two floats (a, b) or string "a?b"
Output	<, =, or >
Architecture	small MLP (input features: a, b, a-b)
Framework	PyTorch + Safetensors
License	CC-BY-SA-4.0

Training Dynamics

Here is the visualization of the model's training metrics (Loss and Accuracy):

Intended Use

• Extremely lightweight number comparison

• Edge / constrained environments

• Educational / experimental micro-models

• Fun baseline for boolean tasks

Not intended for high-precision scientific computing or production systems that require perfect floating-point equality handling.

Example inference script

import torch
import torch.nn as nn
from safetensors.torch import load_file
from pathlib import Path

class PizdecB3(nn.Module):
    def __init__(self):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(3, 32),
            nn.ReLU(),
            nn.Linear(32, 32),
            nn.ReLU(),
            nn.Linear(32, 16),
            nn.ReLU(),
            nn.Linear(16, 3)
        )

    def forward(self, x):
        return self.net(x)

    def _prepare(self, a, b):
        a = torch.tensor([[float(a)]], dtype=torch.float32)
        b = torch.tensor([[float(b)]], dtype=torch.float32)
        return torch.cat([a, b, a - b], dim=1)

    def predict(self, a, b) -> str:
        with torch.no_grad():
            x = self._prepare(a, b)
            logits = self.forward(x)
            pred = torch.argmax(logits, dim=-1).item()
        return ["<", "=", ">"][pred]

    def predict_str(self, s: str) -> str:
        try:
            left, right = s.strip().split("?")
            return self.predict(left, right)
        except:
            return "format error"

    @classmethod
    def from_pretrained(cls, path="pizdecB3"):
        model = cls()
        state = load_file(Path(path) / "model.safetensors")
        model.load_state_dict(state)
        model.eval()
        return model

# Load
model = PizdecB3.from_pretrained("pizdecB3")

# Usage
print(model.predict(10, 5))        # >
print(model.predict(3, 3))         # =
print(model.predict_str("8?2"))    # >

Known Limitations

• Struggles with very close floating-point numbers (e.g. 100 vs 99.9, 7.0 vs 7.01)
• Exact equality on large floats can be unstable
• No support for non-numeric input
• Not suitable for high-precision comparison tasks
• Performance degrades when numbers differ by less than ~0.01–0.05 depending on magnitude

Files

• model.safetensors — model weights
• config.json — basic config
• training_metrics.png — training visualization

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Part of the experimental RaspizdAI micro-model collection.