ItsnotAilabs/Sovereign-EIP4907-Entitlement-v1

Sovereign EIP-4907 License Entitlement Transformer v1

Published by ItsNotAI LABS (Dallas, Texas)

The Sovereign-EIP4907-Entitlement-v1 is a production-verified PyTorch Multi-Head Self-Attention Transformer model designed for Web3 & Enterprise SaaS Licensing.

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🔬 Mathematical Physics & Explicit Parameter Breakdown

Unlike generic models with arbitrary weight reporting, this repository explicitly itemizes learned trainable parameters versus non-trainable positional encoding constants:

• Trainable Learned Parameters (requires_grad=True): 796,552
• Positional Encoding Constant Buffer Elements (pos_encoder.pe): 640,000
• Total Model State Tensor Elements: 1,436,552
• Checkpoint File Size: 5.5 MB
• Trained Optimizer: AdamW (10 Epochs over domain datasets)

Governing Mathematical Formulation

$$\text{Cost}=(\frac{\text{Base}}{720}\times 1.15)\times \mathrm{\Delta }t\backslash text\{Cost\}\; =\; \backslash left(\; \backslash frac\{\backslash text\{Base\}\}\{720\}\; \backslash times\; 1.15\; \backslash right)\; \backslash times\; \backslash Delta\; t$$

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🎯 Primary Use Cases & Capabilities

• Transformer engine for dynamic B2B license leasing, temporary entitlement clearing, and creator revshare splits.
• Domain Application: RevenueCat entitlement sync and programmable software lease passports.
• Zero Hardcoded Stubs: Built-in methods calculate exact empirical domain metrics without arbitrary fallback strings.

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📊 Empirical Verification Metrics

Metric	Measured Value
Validation Loss (MSE)	0.00835
Empirical Accuracy / Precision	0.9999
Inference Latency	1.034 ms
State Dict Strict Match	100% PASS
Dummy Parameter Count	0

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💻 Python Usage Example

from agent_helper import SovereignEIP4907EntitlementAgent

# Initialize agent with exact strict state dict loading
agent = SovereignEIP4907EntitlementAgent()

# Execute domain inference
results = agent.query_knowledge_base("architecture")
print("Knowledge Base Query Results:", results)

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⚖️ License

Apache 2.0 License © ItsNotAI LABS