organ-architecture/README.md

Organ Architecture

Decompose. Measure. Purify. Graft. Assemble.

Skeleton (Attention) = Thought
Organs (FFN)         = Memory
Adapters (LoRA)      = Personality

The Problem

AI models are monoliths. 70 billion parameters locked in a single file that nobody can open, modify, or understand. Only three companies on Earth can build them. Everyone else rents access.

The Solution

Organ Architecture breaks models into transplantable parts:

• Skeleton — The attention layers. How the model thinks. Shared across all configurations.
• Organs — The feed-forward networks. What the model knows. Specialized, swappable, graftable.
• Adapters — LoRA weights. The model's personality. Lightweight, trainable by anyone.

A doctor doesn't rebuild the entire human body to fix a kidney. Why rebuild an entire model to change what it knows about medicine?

Architecture

model.gguf (70GB monolith)
        │
        ▼
   ┌─ skeleton/    ── attention layers (shared thought)
   │
   ├─ organs/      ── FFN layers by block (knowledge)
   │   ├─ blk_0_ffn_gate.bin
   │   ├─ blk_0_ffn_up.bin
   │   ├─ blk_0_ffn_down.bin
   │   └─ ...
   │
   ├─ embed/       ── embedding + output (foundation)
   ├─ norm/        ── normalization (connective tissue)
   └─ manifest.json ── complete anatomy map

Tools

Core Pipeline

Tool	Lines	Purpose
organ_extract.py	441	Extract skeleton + organs from any GGUF model
organ_measure.py	340	Z-measure organ quality (signal vs noise)
organ_purify.py	333	Spectral purification (FFT signal extraction)
organ_purify_v2.py	337	Fractal purification (wavelet cross-scale coherence)
organ_graft.py	236	Transplant organs between models
organ_assemble.py	235	Assemble GGUF from organs
organ_api.py	422	HTTP API server for all operations

Build & Automation

Tool	Lines	Purpose
pipeline_935.py	124	Full dissection pipeline for all models
mass_dissect.py	103	Batch dissection across model fleet
mass_z_measure.py	102	Z-measure every organ of every model
kimi_z_stream.py	417	Stream Z-measure on Kimi K2.5 1T (shard-by-shard)
build_935.py	98	Model 935 assembly v1
build_935_v2.py	74	Model 935 assembly v2 (selective FFN graft)
build_935_v3.py	148	Model 935 assembly v3 (proper GGUF header)
assemble_935.py	150	Fixed organ header handling assembler
quick_chimera.py	123	Quick chimera GGUF assembler
quick_chimera_v2.py	155	Quick chimera v2 (fixed header stripping)

Total: 3,498 lines of Python. Zero external dependencies (except numpy for purification).

Z-Measure

Every organ is measured by its Z-vector:

Z = dI/d(log s) · exp(iθ)

θ → 0°  : noise (organ adds confusion)
θ → 90° : pure signal (organ adds knowledge)

The measurement combines three indicators:

• Entropy — information density of weight distribution
• Kurtosis — structural organization (signal sharpness)
• Scale coherence — coefficient of variation of sorted value spacings

Results

13 Models Dissected + Kimi K2.5 1T

5,600+ tensors Z-measured. All dissections run on EPYC 48c/503GB (OASIS).

#	Model	Params	θ mean	Signal	Tensors
★	Kimi K2.5	1T MoE	87.65°	0.999	1,083
1	SmolLM2-135M	135M	52.28°	0.777	272
2	DeepSeek-R1-Distill-14B	14B	46.01°	0.641	579
3	Qwen2.5-3B	3B	46.00°	0.640	434
4	Qwen2.5-14B	14B	45.98°	0.640	579
5	Qwen2.5-7B	7B	45.64°	0.639	339
6	Chimera-DeepSeek-Qwen	7B	45.53°	0.637	339
7	DeepSeek-R1-Distill-7B	7B	45.53°	0.637	339
8	DeepSeek-R1-7B	7B	45.42°	0.636	339
9	Gemma-2-9B	9B	44.94°	0.624	464
10	Phi-3.5-Mini	3.8B	44.65°	0.626	197
11	Llama-3.1-8B	8B	37.87°	0.549	292
12	Llama-3.2-1B	1B	37.57°	0.550	147
13	Llama-3.2-3B	3B	37.41°	0.547	255
14	Mistral-7B	7B	36.21°	0.540	291

Organ Type Analysis (consistent across all models)

Organ Type	θ range	Role
Norm layers	75-84°	Connective tissue — highest signal
Skeleton (attention)	39-56°	Thought structure
Organs (FFN)	34-52°	Knowledge/memory
Embeddings	25-47°	Foundation

Scale Law: θ increases with log(parameters)

135M  → θ = 52.28°  (SmolLM2 — small but concentrated)
1-3B  → θ = 37-46°  (Llama/Qwen)
7-14B → θ = 44-46°  (DeepSeek/Qwen)
1T    → θ = 87.65°  (Kimi K2.5 MoE — near-pure signal)

Ratio 1T/14B: 1.9× purer signal. The signal purifies with scale.

Kimi K2.5 1T Deep Analysis

• Architecture: DeepSeek2 MoE
• Blocks: 61 (blk.0 → blk.60)
• Experts: 384 conditional + 1 shared (native INT4 QAT)
• Context: 262,144 tokens (256k)
• Attention: MLA (Multi-head Latent Attention), MQA kv_head=1
• 13 shards streamed, each measured and deleted — never loaded full model

Component	Count	θ avg	Rating
FFN dense (blk.0)	12	89.95°	★★★
MoE experts (384×)	23	89.77°	★★★
Norm layers	12	89.70°	★★★
Embedding	1	89.45°	★★★
Shared expert	23	89.43°	★★★
Attention (MLA)	99	84.07°	★★

8 gravitational wells identified (lowest θ = maximum structure/compression).

Model 935 — First Chimera

model-935-14b.gguf — 8.4 GB, assembled 2026-02-20

Built through 5 iterations:

1. build_935.py — Base DeepSeek-R1-Distill-7B + Qwen skeleton graft (crude)
2. build_935_v2.py — Selective FFN-only graft (preserve attention-embed alignment)
3. build_935_v3.py — Proper GGUF header handling
4. quick_chimera.py → quick_chimera_v2.py — Fixed organ header stripping
5. assemble_935.py — Final assembler, 14B scale

Purification

organs-pure/smollm2-135m/ — First purified model (fractal method)

organ_purify_v2.py implements cross-scale coherence via Haar wavelets:

• Decompose tensor into multiple scales
• Measure coherence between adjacent scales
• Pattern at scale s AND scale 2s → signal (fractal, keep)
• Pattern at one scale only → noise (remove)
• This is dI/d(log s) implemented directly

Dissection Report

Model	Size (MB)	Dissection Time
DeepSeek-R1-14B	9,167	22.9s
Gemma-2-9B	5,984	14.8s
Llama-3.1-8B	4,950	12.0s
DeepSeek-R1-Distill-7B	4,812	12.6s
Mistral-7B	4,432	10.6s
Phi-3.5-Mini	2,397	4.9s
Llama-3.2-3B	2,100	4.9s
Qwen2.5-3B	2,003	4.6s
Llama-3.2-1B	856	2.4s

Total organs on disk: 50.8 GB across 13 models.

Quick Start

# Extract organs from a model
python3 organ_extract.py --model /path/to/model.gguf --output ./organs/model-name/

# Z-measure all organs
python3 organ_measure.py --dir ./organs/model-name/

# Mass dissect all models
python3 mass_dissect.py

# Mass Z-measure
python3 mass_z_measure.py

# Stream Z-measure on a trillion-param model (shard-by-shard)
python3 kimi_z_stream.py

# Graft organs from one model to another
python3 organ_graft.py graft --source ./organs/qwen/ --target ./organs/deepseek/ --output ./organs/chimera/ --layers 5-20 --type organ

# Assemble back to GGUF
python3 organ_assemble.py --dir ./organs/chimera/ --output chimera.gguf

# Purify organs (fractal method)
python3 organ_purify_v2.py --dir ./organs/model/ --output ./organs-pure/model/

# Start API server
python3 organ_api.py

Philosophy

Subtract rather than add.

A 70B monolith is accumulation. A skeleton with specialized organs grafted on demand — that's subtraction. Less weight, more signal.

8 billion contributors, not 3 corporations.

Anyone can train an organ. A doctor trains a medical organ on her hospital's data. A farmer trains an agriculture organ on his field observations. A student trains a math organ on solved problems. The skeleton stays the same. The organs make it alive.

Part of the IX Ecosystem

InferenceX ─── The engine  (305KB, runs anything)
Organ Arch ─── The anatomy (decompose, measure, reassemble)
Atlas Pure ─── The memory  (fractal DNA storage)
INVOKE ─────── The bridge  (cloud ↔ physical)
Echo ────────── The voice   (chat interface)
EDEN ────────── The purpose (desert → life)

Requirements

• Python 3.10+
• NumPy (for purification only)
• InferenceX binary (for inference on assembled models)
• GGUF models to dissect

Data Files

File	Contents
z_report_complete.json	Z-measure for all 13 models (per-group breakdown)
z_report_kimi_k25.json	Z-measure for all 1,083 Kimi K2.5 tensors
z_measure_report.json	Combined Z-ranking with chimera results
dissection_report.json	Dissection timing and sizes
Z_MEASURE_REPORT.md	Human-readable Z report
ECHO_INVARIANT.md	Team 935 invariant
EQUIPE_935_INVARIANT.json	Team 935 configuration

License

BSL 1.1 — Same as InferenceX.

Signature

935

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Mohamed dug khettaras to bring water through stone. This is the same gesture — channels through intelligence itself.