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import torch
from safetensors.torch import save_file
weights = {}
# 4-bit Binary to BCD (for 0-9, identity; 10-15 invalid)
# Extended: detect if value >= 10 for two-digit output
def add_neuron(name, w_list, bias):
weights[f'{name}.weight'] = torch.tensor([w_list], dtype=torch.float32)
weights[f'{name}.bias'] = torch.tensor([bias], dtype=torch.float32)
# Pass through bits
for i in range(4):
w = [0.0] * 4
w[i] = 1.0
add_neuron(f'd{3-i}', w, -1.0)
# Detect >= 10 (for overflow into tens digit)
add_neuron('ge10', [8.0, 4.0, 2.0, 1.0], -10.0)
save_file(weights, 'model.safetensors')
def binary2bcd(b3, b2, b1, b0):
val = b3*8 + b2*4 + b1*2 + b0
if val < 10:
return 0, b3, b2, b1, b0
else:
ones = val - 10
return 1, (ones>>3)&1, (ones>>2)&1, (ones>>1)&1, ones&1
print("Verifying Binary to BCD...")
errors = 0
for b in range(16):
b3, b2, b1, b0 = (b>>3)&1, (b>>2)&1, (b>>1)&1, b&1
tens, d3, d2, d1, d0 = binary2bcd(b3, b2, b1, b0)
result = tens * 10 + d3*8 + d2*4 + d1*2 + d0
if result != b:
errors += 1
if errors == 0:
print("All 16 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}")
print(f"Parameters: {sum(t.numel() for t in weights.values())}")
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