# Architecture

## Model Anatomy

A transformer model has four anatomical systems:

```
┌─────────────────────────────────────────┐
│              GGUF MONOLITH              │
│                                         │
│  ┌─ embed ──────── token_embd.weight   │
│  │                  output.weight       │
│  │                  output_norm.weight  │
│  │                                      │
│  ├─ skeleton ───── attn_q.weight  ×N   │
│  │                  attn_k.weight  ×N   │
│  │                  attn_v.weight  ×N   │
│  │                  attn_output    ×N   │
│  │                                      │
│  ├─ organs ─────── ffn_gate.weight ×N   │
│  │                  ffn_up.weight   ×N   │
│  │                  ffn_down.weight ×N   │
│  │                                      │
│  └─ norm ───────── attn_norm       ×N   │
│                     ffn_norm        ×N   │
└─────────────────────────────────────────┘
```

**Skeleton** (attention) = how the model thinks. Shared thought patterns.
**Organs** (FFN) = what the model knows. Domain knowledge.
**Embed** = input/output translation. The vocabulary interface.
**Norm** = normalization layers. Connective tissue between components.

## Pipeline

```
GGUF file
   │
   ▼ organ_extract.py
   │
   ├── manifest.json (complete anatomy map)
   ├── skeleton/  (attention tensors)
   ├── organs/    (FFN tensors by layer)
   ├── embed/     (embedding + output)
   └── norm/      (normalization)
         │
         ▼ organ_measure.py
         │
    Z-measure per tensor
    θ ∈ [0°, 90°]
         │
         ├──▶ organ_purify_v2.py (fractal signal extraction)
         │
         ├──▶ organ_graft.py (transplant between models)
         │
         └──▶ organ_assemble.py → new GGUF
```

Alternative direct path (no intermediate .bin files):

```
GGUF_A + GGUF_B → transplant_935.py → chimera.gguf
```

## Z-Measure Theory

```
Z = dI/d(log s) · exp(iθ)
```

Three indicators combined into θ:

| Indicator | Measures | Signal | Noise |
|-----------|----------|--------|-------|
| Entropy | Information density | Moderate (0.3-0.7) | Near-maximum (>0.95) |
| Kurtosis | Structural sharpness | High (abs > 3) | Near-zero |
| Scale coherence (CV) | Non-uniform spacing | High (> 1) | Low (< 0.5) |

θ → 90° = pure signal (all three indicators confirm structure)
θ → 0° = pure noise (uniform random distribution)

## Purification Methods

### V1: Spectral (FFT)
- Decompose tensor into frequency domain
- Keep high-energy components (signal), remove low-energy tail (noise)
- Preserve original scale (mean/std)
- Limitation: treats tensors like audio signals

### V2: Fractal (Wavelets)
- Haar wavelet multi-scale decomposition
- Cross-scale coherence: pattern at scale s AND scale 2s = fractal = signal
- Pattern at one scale only = noise
- This IS dI/d(log s) — information that persists across scales
- More theoretically grounded than V1

## Graft Compatibility

Grafting works best between models that share:
- Same base architecture (e.g., Qwen2 family)
- Same embedding dimension
- Same number of layers (or graft specific layer ranges)

Empirical results:
- DeepSeek-R1-Distill-14B ↔ Qwen2.5-14B: **WORKS** (both Qwen2 arch, same dims)
- DeepSeek-R1-Distill-7B ↔ Qwen2.5-7B: **PAD tokens** (7B chimera failed)
- Same architecture + same scale = highest success probability

## File Format

Organ .bin files: `[name_len:u32][name:bytes][n_dims:u32][dims:u64×n][dtype:u32][tensor_data]`
Manifest: JSON with full tensor map, metadata, architecture info, Z-measure results.

## Signature

935