Monster-LILA Transformer

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║                      Вход (токены)                           ║
║                     shape: [B, T]                            ║
╚══════════════════════════════════════════════════════════════╝
                              │
                              ▼
╔══════════════════════════════════════════════════════════════╗
║                    Embedding Layer                           ║
║                 shape: [B, T, d_model]                       ║
╚══════════════════════════════════════════════════════════════╝
                              │
                              ▼
╔══════════════════════════════════════════════════════════════╗
║               Замороженное ядро Лича (W_abs)                 ║
║           создаётся один раз, используется всеми слоями      ║
╚══════════════════════════════════════════════════════════════╝
                              │
                              ▼
╔══════════════════════════════════════════════════════════════╗
║                    N × MonsterLayer                          ║
║  ┌──────────────────────────────────────────────────────┐    ║
║  │ 1. MonsterConstantGenerator (α = 1/137)              │    ║
║  │    h = h * (1 + α * sin(α * h))                      │    ║
║  ├──────────────────────────────────────────────────────┤    ║
║  │ 2. LayerNorm (pre‑norm)                              │    ║
║  ├──────────────────────────────────────────────────────┤    ║
║  │ 3. LeechConwayAttention                              │    ║
║  │    Q = h @ W_abs                                     │    ║
║  │    K_raw = h @ W_abs                                 │    ║
║  │    K = K_raw[:, :, conway_perm]                      │    ║
║  │    V = h @ W_v (обучаемый)                           │    ║
║  │    attn = softmax( (Q @ Kᵀ)/√24 )                    │    ║
║  │    out = attn @ V                                    │    ║
║  │    proj = Linear(out)                                │    ║
║  ├──────────────────────────────────────────────────────┤    ║
║  │ 4. Residual: h = h + proj                            │    ║
║  ├──────────────────────────────────────────────────────┤    ║
║  │ 5. LayerNorm (pre‑norm)                              │    ║
║  ├──────────────────────────────────────────────────────┤    ║
║  │ 6. FFN (Linear → GELU → Linear)                      │    ║
║  ├──────────────────────────────────────────────────────┤    ║
║  │ 7. Residual: h = h + ffn_out                         │    ║
║  └──────────────────────────────────────────────────────┘    ║
╚══════════════════════════════════════════════════════════════╝
                              │
                              ▼
╔══════════════════════════════════════════════════════════════╗
║                   Final LayerNorm                            ║
╚══════════════════════════════════════════════════════════════╝
                              │
                              ▼
╔══════════════════════════════════════════════════════════════╗
║          Head (Linear) → logits [B, T, vocab_size]           ║
╚══════════════════════════════════════════════════════════════╝
                              │
                              ▼
╔══════════════════════════════════════════════════════════════╗
║          Детектор монструозного резонанса                    ║
║          (анализирует hidden состояния)                      ║
║          • SVD → спектр σᵢ                                   ║
║          • R_spec = mean(sin(σᵢ * 137))                      ║
║          • monster_factor = min(1, R_spec * d_model/24)      ║
║          → сигнал кристаллизации                             ║
╚══════════════════════════════════════════════════════════════╝

⚖️ Licensing

This project is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later).

Commercial Licensing: For proprietary R&D, integration into private AI stacks, or hardware implementation, please contact the Architect directly.

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This model was trained on free Colab GPU. To improve it further:

What your donation enables:

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Sovereign-Lila-E8 has proven that geometric attention works. Now it's time to scale up.
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Amount	GPU hours	What it covers
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Copyright (C) 2026 This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

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Commercial Licensing: For proprietary R&D, integration into private AI stacks, or hardware implementation, please contact the Architect directly. __

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"What if we treated E8 as the source code for the universe..."

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