| import torch | |
| from safetensors.torch import save_file | |
| # iszero4: outputs 1 if all 4 bits are 0 (4-input NOR) | |
| weights = { | |
| 'neuron.weight': torch.tensor([[-1.0, -1.0, -1.0, -1.0]], dtype=torch.float32), | |
| 'neuron.bias': torch.tensor([0.0], dtype=torch.float32) | |
| } | |
| save_file(weights, 'model.safetensors') | |
| def iszero4(a, b, c, d): | |
| inp = torch.tensor([float(a), float(b), float(c), float(d)]) | |
| return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item()) | |
| print("Verifying iszero4...") | |
| errors = 0 | |
| for i in range(16): | |
| bits = [(i >> j) & 1 for j in range(4)] | |
| result = iszero4(*bits) | |
| expected = 1 if i == 0 else 0 | |
| if result != expected: | |
| errors += 1 | |
| if errors == 0: | |
| print("All 16 test cases passed!") | |
| print(f"Magnitude: {sum(t.abs().sum().item() for t in weights.values()):.0f}") | |