frankmorales2020/gemma-governed-no-amnesia
frankmorales2020/gemma-governed-no-amnesia is a 2.6 billion parameter Gemma-based model engineered to eliminate catastrophic forgetting during continuous fine-tuning. It achieves zero-drift by anchoring its core long-term memory to a deterministic topological invariant layer derived from the Sieve of Eratosthenes and Arithmetic Spectral Theory. This model excels at lossless multi-task adaptation, preserving original reasoning and mathematical capabilities while mastering new domains like Spanish fluency, advanced physics, and geopolitical facts.
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Gemma Governed No Amnesia: Catastrophic Forgetting Eliminated
This model, developed by frankmorales2020, is a 2.6 billion parameter Gemma-based architecture specifically designed to prevent catastrophic forgetting during continuous adaptation and fine-tuning. Unlike traditional Transformer models that suffer from representational drift, this model maintains a zero-drift baseline profile.
Key Breakthroughs & Capabilities
- Topological Invariant Layer: Achieves weight stabilization by locking onto a universal spectral constant ($C=0.5$) derived from advanced mathematical theories, ensuring perfect preservation of core architectural hashes.
- Zero Anchor Drift: Rigorous verification confirms that the model's core knowledge base remains intact across multiple learning tasks, with no degradation or amnesia of original capabilities.
- Lossless Multi-Task Adaptation: Demonstrates the ability to master entirely new domains (e.g., Spanish language, physics, geopolitical facts) without forgetting its initial reasoning and complex mathematical skills.
- H2E Sheriff Protocol: Incorporates a dual-manifold safety gate for incoming token streams, ensuring alignment and security.
Architectural Foundation
The model's stability is rooted in the Sieve of Eratosthenes and Arithmetic Spectral Theory (AST), utilizing a primary invariant layer for historical knowledge and a dynamic nested sandbox for new data. Every update is validated against the Spectral Trap Criterion to prevent representational decay.
Verification
Verification logs confirm that the model successfully learns new tasks (e.g., Spanish, capitals, physics) while retaining original knowledge (e.g., math) with no amnesia detected.