create_safetensors.py

import torch
from safetensors.torch import save_file

weights = {}

# Input order: d0..d15, s3, s2, s1, s0 (20 inputs)
# Layer 1: 16 neurons, each selects di when s = i
# Layer 2: OR gate

layer1_weights = []
layer1_biases = []

for i in range(16):
    w = [0.0] * 20
    # Data input weight
    w[i] = 1.0
    # Select weights: +1 if bit should be 1, -1 if bit should be 0
    s3_bit = (i >> 3) & 1
    s2_bit = (i >> 2) & 1
    s1_bit = (i >> 1) & 1
    s0_bit = i & 1
    w[16] = 1.0 if s3_bit else -1.0  # s3
    w[17] = 1.0 if s2_bit else -1.0  # s2
    w[18] = 1.0 if s1_bit else -1.0  # s1
    w[19] = 1.0 if s0_bit else -1.0  # s0
    # Bias: -(1 + popcount(i))
    bias = -(1 + bin(i).count('1'))
    layer1_weights.append(w)
    layer1_biases.append(bias)

weights['layer1.weight'] = torch.tensor(layer1_weights, dtype=torch.float32)
weights['layer1.bias'] = torch.tensor(layer1_biases, dtype=torch.float32)

# Layer 2: OR gate
weights['layer2.weight'] = torch.tensor([[1.0] * 16], dtype=torch.float32)
weights['layer2.bias'] = torch.tensor([-1.0], dtype=torch.float32)

save_file(weights, 'model.safetensors')

# Verify
def mux16(data, s3, s2, s1, s0):
    inp = torch.tensor([float(d) for d in data] + [float(s3), float(s2), float(s1), float(s0)])
    l1 = (inp @ weights['layer1.weight'].T + weights['layer1.bias'] >= 0).float()
    out = (l1 @ weights['layer2.weight'].T + weights['layer2.bias'] >= 0).float()
    return int(out.item())

print("Verifying MUX16...")
errors = 0
test_count = 0
for s in range(16):
    s3, s2, s1, s0 = (s >> 3) & 1, (s >> 2) & 1, (s >> 1) & 1, s & 1
    # Test with selected data = 1, others = 0
    data = [0] * 16
    data[s] = 1
    result = mux16(data, s3, s2, s1, s0)
    if result != 1:
        errors += 1
        print(f"ERROR: s={s}, d[{s}]=1 -> {result}, expected 1")
    test_count += 1

    # Test with selected data = 0
    data[s] = 0
    result = mux16(data, s3, s2, s1, s0)
    if result != 0:
        errors += 1
        print(f"ERROR: s={s}, d[{s}]=0 -> {result}, expected 0")
    test_count += 1

if errors == 0:
    print(f"All {test_count} test cases passed!")
else:
    print(f"FAILED: {errors} errors")

mag = sum(t.abs().sum().item() for t in weights.values())
print(f"Magnitude: {mag:.0f}")