Rename from tiny-1OutOf8-verified

README.md

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+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+---
+
+# threshold-1outof8
+
+At-least-one detector for 8 inputs. Equivalent to an 8-input OR gate.
+
+## Circuit
+
+```
+  x₀ x₁ x₂ x₃ x₄ x₅ x₆ x₇
+   │  │  │  │  │  │  │  │
+   └──┴──┴──┴──┼──┴──┴──┴──┘
+               ▼
+          ┌─────────┐
+          │ w: all 1│
+          │ b:  -1  │
+          └─────────┘
+               │
+               ▼
+            HW ≥ 1
+```
+
+## Mechanism
+
+With uniform weights of 1 and bias -1, the neuron fires when at least one input is active:
+
+- Sum = (number of 1s) - 1
+- Fires when sum ≥ 0, i.e., when Hamming weight ≥ 1
+
+This is the loosest threshold in the k-out-of-8 family.
+
+## k-out-of-8 Family
+
+| Circuit | Bias | Fires when |
+|---------|------|------------|
+| **1-out-of-8** | **-1** | **HW ≥ 1 (this)** |
+| 2-out-of-8 | -2 | HW ≥ 2 |
+| ... | ... | ... |
+| 8-out-of-8 | -8 | HW = 8 |
+
+All share weights [1,1,1,1,1,1,1,1]. Only the bias differs.
+
+## Parameters
+
+| | |
+|---|---|
+| Weights | [1, 1, 1, 1, 1, 1, 1, 1] |
+| Bias | -1 |
+| Total | 9 parameters |
+
+## Usage
+
+```python
+from safetensors.torch import load_file
+import torch
+
+w = load_file('model.safetensors')
+
+def at_least_1(bits):
+    inputs = torch.tensor([float(b) for b in bits])
+    return int((inputs * w['weight']).sum() + w['bias'] >= 0)
+```
+
+## Files
+
+```
+threshold-1outof8/
+├── model.safetensors
+├── model.py
+├── config.json
+└── README.md
+```
+
+## License
+
+MIT

config.json

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+{
+  "model_type": "threshold_network",
+  "task": "1_out_of_8_threshold",
+  "architecture": "8 -> 1",
+  "input_size": 8,
+  "output_size": 1,
+  "num_neurons": 1,
+  "num_parameters": 9,
+  "threshold": 1,
+  "activation": "heaviside",
+  "weight_constraints": "integer",
+  "verification": {
+    "method": "coq_proof",
+    "exhaustive": true,
+    "inputs_tested": 256
+  },
+  "accuracy": {
+    "all_inputs": "256/256",
+    "percentage": 100.0
+  },
+  "github": "https://github.com/CharlesCNorton/coq-circuits"
+}

model.py

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+"""
+Threshold Network for 1-out-of-8 Gate
+
+A formally verified single-neuron threshold network.
+Outputs 1 when at least 1 of the 8 inputs is true (equivalent to 8-input OR).
+"""
+
+import torch
+from safetensors.torch import load_file
+
+
+class Threshold1OutOf8:
+    """
+    1-out-of-8 threshold gate.
+
+    Circuit: output = (sum of inputs - 1 >= 0)
+    Fires when hamming weight >= 1.
+    """
+
+    def __init__(self, weights_dict):
+        self.weight = weights_dict['weight']
+        self.bias = weights_dict['bias']
+
+    def __call__(self, bits):
+        inputs = torch.tensor([float(b) for b in bits])
+        weighted_sum = (inputs * self.weight).sum() + self.bias
+        return (weighted_sum >= 0).float()
+
+    @classmethod
+    def from_safetensors(cls, path="model.safetensors"):
+        return cls(load_file(path))
+
+
+def forward(x, weights):
+    x = torch.as_tensor(x, dtype=torch.float32)
+    weighted_sum = (x * weights['weight']).sum(dim=-1) + weights['bias']
+    return (weighted_sum >= 0).float()
+
+
+if __name__ == "__main__":
+    weights = load_file("model.safetensors")
+    model = Threshold1OutOf8(weights)
+
+    print("1-out-of-8 Gate Tests:")
+    print("-" * 35)
+    for hw in range(9):
+        bits = [1]*hw + [0]*(8-hw)
+        out = int(model(bits).item())
+        expected = 1 if hw >= 1 else 0
+        status = "OK" if out == expected else "FAIL"
+        print(f"HW={hw}: {out}  [{status}]")

model.safetensors

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+size 164