123 lines
3.9 KiB
Markdown
123 lines
3.9 KiB
Markdown
[](LICENSE)
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[](https://inference-x.com)
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# Organ Architecture
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**Decompose. Reassemble. Evolve.**
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```
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Skeleton (Attention) = Thought
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Organs (FFN) = Memory
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Adapters (LoRA) = Personality
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```
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## What This Is
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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.
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Organ Architecture breaks models into transplantable parts:
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- **Skeleton** — The attention layers. How the model *thinks*. Shared across all configurations.
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- **Organs** — The feed-forward networks. What the model *knows*. Specialized, swappable, graftable.
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- **Adapters** — LoRA weights. The model's *personality*. Lightweight, trainable by anyone.
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A doctor doesn't rebuild the entire human body to fix a kidney. Why should we rebuild an entire model to change what it knows about medicine?
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## Architecture
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```
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model.gguf (70GB monolith)
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│
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▼
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┌─ skeleton.bin ──── attention layers (shared thought)
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│
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├─ organ_lang.bin ── language FFN (what it knows about language)
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├─ organ_math.bin ── math FFN (what it knows about math)
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├─ organ_code.bin ── code FFN (what it knows about code)
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├─ organ_med.bin ─── medical FFN (what it knows about medicine)
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│
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└─ adapter_fr.bin ── French personality (LoRA)
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adapter_formal.bin ── Formal tone (LoRA)
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```
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## Tools
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| Tool | Purpose |
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|------|---------|
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| `organ_extract.py` | Extract skeleton + organs from any GGUF model |
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| `organ_graft.py` | Transplant organs between models |
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| `organ_measure.py` | Z-measure organ quality (signal vs noise) |
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| `organ_assemble.py` | Assemble custom model from parts |
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| `organ_api.py` | API server for organ operations |
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## Requirements
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- Python 3.10+
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- InferenceX binary (for model loading)
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- GGUF models to dissect
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## Quick Start
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```bash
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# Extract organs from a model
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python3 organ_extract.py --model /path/to/model.gguf --output ./organs/
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# Measure organ quality
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python3 organ_measure.py --organ ./organs/organ_layer_12.bin
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# Graft an organ from model A into model B
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python3 organ_graft.py --source ./organs_A/ --target ./model_B.gguf --layers 12-18
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# Assemble a custom model
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python3 organ_assemble.py --skeleton ./skeleton.bin --organs ./organs/ --output custom.gguf
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```
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## Philosophy
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> Subtract rather than add.
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A 70B monolith is accumulation. A 2B skeleton with specialized organs grafted on demand — that's subtraction. Less weight, more signal.
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> 8 billion contributors, not 3 corporations.
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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.
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## Z-Measure
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Every organ is measured by its Z-vector:
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```
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CSCI — cross-scale coherence index
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θ → 0° : noise (organ adds confusion)
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θ → 90° : pure signal (organ adds knowledge)
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```
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## Part of the IX Ecosystem
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```
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InferenceX ─── The engine (228KB, runs anything)
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Organ Arch ─── The anatomy (decompose, reassemble)
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Atlas Pure ─── The memory (fractal DNA storage)
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Echo ────────── The voice (chat interface)
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Purpose ────── Long-term application domain
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```
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## License
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BSL 1.1 — Same as InferenceX.
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## Signature
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935
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---
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*Ancient builders shaped landscapes through persistent work.*
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*This is the same gesture — channels through intelligence itself.*
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<!-- © SALKA ELMADANI AUTHORSHIP CERTIFICATE
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SHA256: fa9810691f93169fda6d36c1cf7f752b12e0bc44d59bf2da994a9e87af6fc6d4
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SIG-ED25519: TUu6Qp40jhrhXquUzU20iuSHzr0ENB0v+r5FIKYNdJ+TeP9ozqafqW2Mq6U8AJpNPpAram8peGgtnoh5YiQ1AA==
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VERIFY: python3 verify_authorship.py README.md
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-->
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