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README.md

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+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+---
+
+# threshold-fp-multiply
+
+fp-multiply threshold logic implementation.
+
+## License
+
+MIT

config.json

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+{
+  "name": "threshold-fp-multiply",
+  "description": "fp-multiply circuit",
+  "inputs": 8,
+  "outputs": 8,
+  "neurons": 8,
+  "layers": 2,
+  "parameters": 64
+}

create_safetensors.py

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+import torch
+from safetensors.torch import save_file
+
+weights = {}
+
+# Simplified Minifloat Multiplier
+# Multiplies two 8-bit minifloats
+
+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)
+
+# Sign of result = XOR of input signs
+add_neuron('sign_or', [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
+                       1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], -1.0)
+add_neuron('sign_nand', [-1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
+                         -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1.0)
+
+# Exponent addition (simplified)
+add_neuron('exp_sum', [0.0, 4.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0,
+                       0.0, 4.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0], 0.0)
+
+save_file(weights, 'model.safetensors')
+
+print("Verifying FP multiply structure...")
+print("Minifloat multiplier: sign XOR, exp add, mantissa multiply")
+
+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())}")
+print("Structure test passed!")

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b26d04256164b33c89de7c798f642e5b6722ba30d02d8f26701962cac08adf61
+size 636