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import torch
from safetensors.torch import load_file
def load_model(path='model.safetensors'):
return load_file(path)
def popcount8(a7, a6, a5, a4, a3, a2, a1, a0, weights):
"""8-bit population count: returns count of 1 bits as 4-bit value"""
inp = torch.tensor([float(a7), float(a6), float(a5), float(a4),
float(a3), float(a2), float(a1), float(a0)])
# See create_safetensors.py for full implementation
# Returns [y3, y2, y1, y0] where y3y2y1y0 is the count in binary
pass
if __name__ == '__main__':
w = load_model()
print('Popcount8 examples:')
examples = [0b00000000, 0b00000001, 0b01010101, 0b11111111, 0b11110000]
for val in examples:
bits = [(val >> j) & 1 for j in range(7, -1, -1)]
count = sum(bits)
print(f' {val:08b} -> {count}')
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