chore: remove internal framework references

LICENSE

@@ -1,7 +1,6 @@
 Business Source License 1.1
 
 Licensor:             Salka Elmadani
-Licensed Work:        organ-architecture (part of Inference-X ecosystem) (part of Inference-X ecosystem)
 Copyright (C) 2025-2026 Salka Elmadani — ALL RIGHTS RESERVED
 
 Change Date:    2030-02-12

README.md

@@ -1,7 +1,6 @@
 [![License](https://img.shields.io/badge/license-BSL--1.1-blue)](LICENSE)
 [![Author](https://img.shields.io/badge/author-Salka%20Elmadani-orange)](https://inference-x.com)
 
-# Organ Architecture
 
 **Decompose. Reassemble. Evolve.**
 
@@ -15,7 +14,6 @@ Adapters (LoRA)      = Personality
 
 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.
 
-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.
@@ -66,7 +64,6 @@ python3 organ_extract.py --model /path/to/model.gguf --output ./organs/
 python3 organ_measure.py --organ ./organs/organ_layer_12.bin
 
 # Graft an organ from model A into model B
-python3 organ_graft.py --source ./organs_A/ --target ./model_B.gguf --layers 12-18
 
 # Assemble a custom model
 python3 organ_assemble.py --skeleton ./skeleton.bin --organs ./organs/ --output custom.gguf

SPONSOR.md

@@ -5,7 +5,6 @@
 
 ---
 
-I'm an engineer from Morocco's Anti-Atlas.
 
 I build AI infrastructure. Not products, not demos, not wrappers around someone else's API. Infrastructure — the kind that runs without permission, works without cloud, and belongs to anyone who needs it.
 
@@ -40,7 +39,6 @@ Same model. Cleaner signal. Every unnecessary step removed.
 | Project | What it does | Status |
 |---------|-------------|--------|
 | **[inference-x](https://git.inference-x.com/elmadani/inference-x)** | Core engine — 305 KB, 19 hardware backends, 23 quant formats, fused kernels, adaptive precision | ✅ Live |
-| **[organ-architecture](https://git.inference-x.com/elmadani/organ-architecture)** | Neural surgery — extract quality-measure and graft layers between models. Build composite intelligence from the best parts of everything. | ✅ Live |
 | **forge** | Model construction pipeline — compile, quantize, sign, distribute. Build your own model variant from certified organs. | 🔨 Building |
 | **[echo-ix](https://git.inference-x.com/elmadani/echo-ix)** | Distributed relay — intelligent routing across local inference nodes | ✅ Live |
 | **store** | Anyone deploys a node. Anyone earns from their compute. The cooperative layer. 11 geological cratons. One network. | 📐 Designed |
@@ -49,11 +47,8 @@ The store is the endgame: a peer-to-peer inference network where anyone with a l
 
 ---
 
-## The khettara
 
-In the Moroccan desert, builders carved underground canals — *khettaras* — that deliver water from mountain aquifers to fields using only gravity. No pump, no electricity, no central authority. They've worked for a thousand years, maintained by the communities that depend on them.
 
-Inference-X is a khettara for intelligence.
 
 The intelligence already exists in the model weights. What I'm building is the canal — the shortest, cleanest path from those weights to the human who needs them.
 

Z_MEASURE_REPORT.md

@@ -1,4 +1,3 @@
-# Quality Analysis Report — Organ Architecture
 ## CSCI — cross-scale coherence index
 
 **Generated**: 2026-02-20 01:42 UTC  
@@ -85,7 +84,6 @@ mass_z_measure.py   — batch quality measure across 13 models
 kimi_z_stream.py    — streaming quality measure for 1T (shard-by-shard, delete after)
 organ_graft.py      — transplant organs between models
 organ_assemble.py   — build composite model from best organs
-build_935.py        — orchestrator
 ```
 
 ## Build References

assemble_935.py

@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
 """
-Model 935 Assembler — Fixed organ header handling.
 Reads source GGUF, replaces tensor DATA (skipping organ bin headers).
 CSCI v1.0 — Cross-Scale Coherence Index
 """
@@ -19,7 +18,6 @@ def read_organ_data_only(filepath):
 
 def main():
     if len(sys.argv) < 4:
-        print("Usage: assemble_935.py <source.gguf> <organs_dir> <output.gguf>")
         sys.exit(1)
     
     source_gguf = sys.argv[1]
@@ -136,14 +134,12 @@ def main():
     source_size = os.path.getsize(source_gguf)
     
     print(f"\n{'='*60}")
-    print(f"  MODEL 935 ASSEMBLED")
     print(f"{'='*60}")
     print(f"  Source:    {os.path.basename(source_gguf)} ({source_size/(1024**3):.2f} GB)")
     print(f"  Output:    {output_gguf} ({final_size/(1024**3):.2f} GB)")
     print(f"  Replaced:  {replaced} tensors from organs")
     print(f"  Fallback:  {fallback} tensors from source")
     print(f"  Size match: {'YES' if abs(final_size - source_size) < 1024 else 'NO — DELTA=' + str(final_size - source_size)}")
-    print(f"  Signature: 935")
     print(f"{'='*60}")
 
 if __name__ == "__main__":
@@ -154,4 +150,3 @@ if __name__ == "__main__":
 # ─────────────────────────────────────────────────────────
 # SHA256: 4d774861a8b9f75f83fd8ff45e92bfa607d12a4f580481ff5f8b5882470fb043
 # SIG-ED25519: B0k22H4YJMtBYuUW7ugInkPJpqZfM7cDM9TyiPODpE+WgQ0aLdgT2PnKm94gWSYVY2xqTlsEeZvgH+NrWQmTBg==
-# VERIFY: python3 verify_authorship.py assemble_935.py

build_935.py

@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
 """
-MODEL 935 — Composite Model Assembly
 Skeleton: Qwen2.5-7B (purest thought, θ=54.6)
 Organs: DeepSeek-R1-Distill-7B (purest knowledge for raisonnement, θ=35.9)
 Embed: DeepSeek-R1-7B (R1 reasoning embeddings)
@@ -8,10 +7,7 @@ Embed: DeepSeek-R1-7B (R1 reasoning embeddings)
 CSCI v1.0 — Cross-Scale Coherence Index
 """
 import sys, os, json, shutil, time
-sys.path.insert(0, "/root/organ-architecture")
 
-ORGANS = "/root/organ-architecture/organs"
-OUTPUT = os.path.join(ORGANS, "model-935")
 
 # Clean previous
 if os.path.exists(OUTPUT):
@@ -20,7 +16,6 @@ if os.path.exists(OUTPUT):
 # Step 1: Start with DeepSeek-R1-Distill-7B as base (full copy)
 # This gives us: qwen2 arch, embed=3584, 28 layers, R1 reasoning
 print("="*60)
-print("  MODEL 935 — ASSEMBLY")
 print("  CSCI — cross-scale coherence index, θ → 90°")
 print("="*60)
 
@@ -60,7 +55,6 @@ print(f"  R1 raisonnement chains preserved in FFN layers")
 
 # Step 4: Update manifest
 manifest = json.load(open(os.path.join(OUTPUT, "manifest.json")))
-manifest["model"] = "MODEL-935-Fractal"
 manifest["graft"] = {
     "skeleton_donor": "Qwen2.5-7B-Instruct (θ=54.6, purest attention)",
     "organ_donor": "DeepSeek-R1-Distill-Qwen-7B (θ=35.9, reasoning FFN)",
@@ -70,20 +64,17 @@ manifest["graft"] = {
     "convergence": "ZI_UNIFIED_OPTIMAL: α=0.3, β=0.2, n_plateau=62",
     "entropie_zcom": 0.3251,
     "entropie_bias_removed": 0.6931,
-    "signature": 935
 }
 
 with open(os.path.join(OUTPUT, "manifest.json"), "w") as f:
     json.dump(manifest, f, indent=2)
 
-print(f"\n[4/4] Manifest updated: MODEL-935-Fractal")
 
 # Count final state
 total_files = sum(1 for _,_,files in os.walk(OUTPUT) for f in files if f.endswith('.bin'))
 total_size = sum(os.path.getsize(os.path.join(dp,f)) for dp,dn,fn in os.walk(OUTPUT) for f in fn) / (1024**3)
 
 print(f"\n{'='*60}")
-print(f"  MODEL 935 — COMPOSITE")
 print(f"{'='*60}")
 print(f"  Architecture: qwen2")
 print(f"  Embed: 3584 | Layers: 28 | Heads: 28")
@@ -94,7 +85,6 @@ print(f"  Tensors:  {total_files}")
 print(f"  Size:     {total_size:.2f} GB")
 print(f"  Equation: CSCI — cross-scale coherence index")
 print(f"  Convergence: lim(n→∞) Z(n) = i")
-print(f"  Signature: 935")
 print(f"{'='*60}")
 # ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
 # © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
@@ -102,4 +92,3 @@ print(f"{'='*60}")
 # ─────────────────────────────────────────────────────────
 # SHA256: c45f3019cd81199382cf5f379ef1c556f5f2c5fd81afc6679da83e614ac8c09f
 # SIG-ED25519: IRoSNw2yKK14fnt2JpFbukDpV/5R9YDSQylWVVjIOgYkFHBH71k0MFBV+I39cfjf8odTgzM3uPPRRMexR9KTDw==
-# VERIFY: python3 verify_authorship.py build_935.py

build_935_v2.py

@@ -1,14 +1,11 @@
 #!/usr/bin/env python3
 """
-MODEL 935 v2 — Correct graft: only FFN organs, preserve attention+embed alignment
 Base: DeepSeek-R1-Distill-7B (R1 reasoning skeleton + embeddings intact)
 Graft: Qwen2.5-7B FFN organs only (knowledge)
 
 CSCI v1.0 — Cross-Scale Coherence Index
 """
 import os, json, shutil
-ORGANS = "/root/organ-architecture/organs"
-OUTPUT = os.path.join(ORGANS, "model-935-v2")
 
 if os.path.exists(OUTPUT):
     shutil.rmtree(OUTPUT)
@@ -53,14 +50,12 @@ print(f"  Skipped: {skipped}")
 
 # Update manifest
 manifest = json.load(open(os.path.join(OUTPUT, "manifest.json")))
-manifest["model"] = "MODEL-935-v2"
 manifest["graft"] = {
     "base": "DeepSeek-R1-Distill-Qwen-7B (skeleton + embed + norms)",
     "ffn_donor": "Qwen2.5-7B-Instruct (FFN weights only: down/gate/up)",
     "method": "Selective organ graft — preserve attention↔embed alignment",
     "equation": "CSCI — cross-scale coherence index",
     "principle": "R1 reasoning + Qwen knowledge, zero alignment friction",
-    "signature": 935
 }
 with open(os.path.join(OUTPUT, "manifest.json"), "w") as f:
     json.dump(manifest, f, indent=2)
@@ -68,14 +63,11 @@ with open(os.path.join(OUTPUT, "manifest.json"), "w") as f:
 total = sum(1 for _,_,f in os.walk(OUTPUT) for _ in f if _.endswith('.bin'))
 size = sum(os.path.getsize(os.path.join(dp,f)) for dp,_,fn in os.walk(OUTPUT) for f in fn)/(1024**3)
 
-print(f"\n[3/3] MODEL-935-v2 assembled")
 print(f"  Tensors: {total} | Size: {size:.2f} GB")
 print(f"  Grafted FFN: {grafted} | Base preserved: {total - grafted}")
-print(f"  Signature: 935")
 # ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
 # © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
 # Licensed under Business Source License 1.1 — https://inference-x.com
 # ─────────────────────────────────────────────────────────
 # SHA256: 4d5c44e363508bc679263607b7ee3071cb63fc460a616e9bcebffc768843a86c
 # SIG-ED25519: MzrZnxCo+uq3q5srKgDO2w3gLhO4hgK2k+SIzRLrkjaGJ2Ao56mR9/Mst4Ub6qkZ0VpcXOv4Bq59gKPsJPkdCg==
-# VERIFY: python3 verify_authorship.py build_935_v2.py

build_935_v3.py

@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
 """
-MODEL 935 — Proper GGUF assembler
 Reads source GGUF header intact, replaces tensor data from organ bins
 (stripping the organ header that organ_extract added)
 
@@ -8,7 +7,6 @@ CSCI v1.0 — Cross-Scale Coherence Index
 """
 import struct, os, sys, json
 
-def build_model_935(source_gguf, organs_dir, output_gguf):
     f = open(source_gguf, "rb")
     
     # Read GGUF header
@@ -134,17 +132,11 @@ def build_model_935(source_gguf, organs_dir, output_gguf):
     print(f"  Size: {final_size / (1024**3):.2f} GB (source: {source_size / (1024**3):.2f} GB)")
     print(f"  From organs: {written_from_organ} | From source: {written_from_source}")
     print(f"  Size match: {'✓' if abs(final_size - source_size) < 1024 else '✗ MISMATCH'}")
-    print(f"  Signature: 935")
 
-# Build 935 v3: R1-Distill base + Qwen FFN organs (correctly stripped)
 print("="*60)
-print("  MODEL 935 v3 — Correct Assembly")
 print("="*60)
 
-build_model_935(
     "/mnt/models/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M.gguf",
-    "/root/organ-architecture/organs/model-935-v2",
-    "/mnt/models/model-935-v3.gguf"
 )
 # ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
 # © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
@@ -152,4 +144,3 @@ build_model_935(
 # ─────────────────────────────────────────────────────────
 # SHA256: 00f06d16ab32dee1ef886e90080e905fc354be9f22f0e6ff515ea2bb31084bdf
 # SIG-ED25519: UhbWWFzRIzmMbCVNwXTG41I2sM/1QGd1nV4+x/XQ+BOw49fO9bd9ohWpLl5QOCGhRWCREYkhJCj55FhGhH5vDQ==
-# VERIFY: python3 verify_authorship.py build_935_v3.py

kimi_z_stream.py

@@ -13,7 +13,6 @@ REPO = "unsloth/Kimi-K2.5-GGUF"
 QUANT = "Q4_0"
 N_SHARDS = 13
 SHARD_DIR = "/mnt/data/kimi-k25/streaming"
-OUTPUT = "/mnt/data/organ-architecture/z_report_kimi_k25.json"
 LOG = "/tmp/kimi_z_stream.log"
 
 os.makedirs(SHARD_DIR, exist_ok=True)

mass_dissect.py

@@ -6,9 +6,6 @@ CSCI v1.0 — Cross-Scale Coherence Index
 import subprocess, os, sys, json, time
 
 MODELS_DIR = "/mnt/models"
-ORGANS_DIR = "/root/organ-architecture/organs"
-EXTRACT = "/root/organ-architecture/organ_extract.py"
-MEASURE = "/root/organ-architecture/organ_measure.py"
 
 # Map GGUF filenames to organ directory names
 models = {
@@ -94,17 +91,13 @@ for r in results:
 
 total_mb = sum(r.get("size_mb",0) for r in results)
 print(f"\n  Total organs: {total_mb/1024:.1f} GB")
-print(f"  Signature: 935")
 print(f"{'='*60}")
 
 # Save results
-with open("/root/organ-architecture/dissection_report.json", "w") as f:
     json.dump(results, f, indent=2)
-print("Report: /root/organ-architecture/dissection_report.json")
 # ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
 # © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
 # Licensed under Business Source License 1.1 — https://inference-x.com
 # ─────────────────────────────────────────────────────────
-# SHA256: f69a536f6cf905e845d77afe9beb9acca3c5e2b1e3d5974b7d2935aec60453b9
 # SIG-ED25519: XB8aA7wVzKOHkvMcZgE5YT3x8BUD/EwVTDRxEMSR7nmWYIT17XY+gC4AJ+y0B29l8MQGFDGk+buLoKxiagTFCA==
 # VERIFY: python3 verify_authorship.py mass_dissect.py

mass_z_measure.py

@@ -5,10 +5,8 @@ Find the organs closest to theta=90 (pure signal)
 CSCI v1.0 — Cross-Scale Coherence Index
 """
 import subprocess, os, json, sys
-sys.path.insert(0, "/root/organ-architecture")
 from organ_measure import measure_directory, compute_z_measure, read_organ_data_f32
 
-ORGANS_DIR = "/root/organ-architecture/organs"
 
 all_results = {}
 
@@ -93,13 +91,10 @@ for organ_type in ['skeleton', 'organs', 'embed']:
     for c in candidates[:5]:
         print(f"    theta={c[1]:5.1f}  avg={c[3]:5.1f}  {c[0]:30s}  {c[2][:40]}")
 
-print(f"\n  Signature: 935")
 print(f"{'='*70}")
 
 # Save full report
-with open("/root/organ-architecture/z_measure_report.json", "w") as f:
     json.dump(all_results, f, indent=2)
-print(f"\nReport: /root/organ-architecture/z_measure_report.json")
 # ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
 # © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
 # Licensed under Business Source License 1.1 — https://inference-x.com

organ_api.py

@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
 """
-Organ Architecture — organ_api.py
 API server for organ operations.
 
 Endpoints:
@@ -14,7 +13,6 @@ Endpoints:
   GET  /organs/:model       — List organs for a model
   GET  /compare/:a/:b       — Compare two models for graft compatibility
 
-Build v935
 """
 
 import json
@@ -29,18 +27,15 @@ from urllib.parse import urlparse, parse_qs
 # Import organ tools
 from organ_extract import extract_organs, GGUFReader, classify_tensor
 from organ_measure import measure_directory, measure_organ
-from organ_graft import load_manifest, graft_layers, parse_layers
 from organ_assemble import assemble_gguf
 
 # ═══ CONFIG ═══
 PORT = int(os.environ.get('ORGAN_PORT', '7936'))
 MODEL_DIR = os.environ.get('MODEL_DIR', '/mnt/models')
 ORGAN_DIR = os.environ.get('ORGAN_DIR', '/mnt/data/organs')
-SIGNATURE = 935
 
 
 class OrganHandler(BaseHTTPRequestHandler):
-    """HTTP handler for Organ Architecture API."""
 
     def log_message(self, format, *args):
         """Minimal logging."""
@@ -50,7 +45,6 @@ class OrganHandler(BaseHTTPRequestHandler):
         self.send_response(status)
         self.send_header('Content-Type', 'application/json')
         self.send_header('Access-Control-Allow-Origin', '*')
-        self.send_header('X-Powered-By', 'Organ-935')
         self.end_headers()
         self.wfile.write(json.dumps(data, indent=2, default=str).encode())
 
@@ -77,7 +71,6 @@ class OrganHandler(BaseHTTPRequestHandler):
         if path == '/health' or path == '':
             self.send_json({
                 'status': 'ok',
-                'service': 'organ-architecture',
                 'signature': SIGNATURE,
                 'model_dir': MODEL_DIR,
                 'organ_dir': ORGAN_DIR,
@@ -346,7 +339,6 @@ class OrganHandler(BaseHTTPRequestHandler):
                 
                 parsed_layers = parse_layers(layers) if layers else None
                 
-                manifest = graft_layers(
                     str(source_path), str(target_path), output_path,
                     parsed_layers, organ_type
                 )
@@ -406,7 +398,6 @@ def main():
     Path(ORGAN_DIR).mkdir(parents=True, exist_ok=True)
     
     server = HTTPServer(('0.0.0.0', PORT), OrganHandler)
-    print(f"[ORGAN-API] Organ Architecture on port {PORT}")
     print(f"[ORGAN-API] Models: {MODEL_DIR}")
     print(f"[ORGAN-API] Organs: {ORGAN_DIR}")
     print(f"[ORGAN-API] Signature {SIGNATURE}")

organ_assemble.py

@@ -1,12 +1,10 @@
 #!/usr/bin/env python3
 """
-Organ Architecture — organ_assemble.py
 Assemble a GGUF model from extracted/grafted organs.
 
 Takes a manifest + organ files → produces a working GGUF.
 The reverse of organ_extract.py.
 
-Build v935
 """
 
 import struct
@@ -207,7 +205,6 @@ def assemble_gguf(organ_dir, output_path, verbose=False):
     print(f"  Tensors:  {n_tensors}")
     print(f"  Size:     {output_gb:.2f} GB ({output_mb:.0f} MB)")
     print(f"  Output:   {output_path}")
-    print(f"  Signature: 935")
     print(f"{'='*60}")
     
     return output_path
@@ -215,7 +212,6 @@ def assemble_gguf(organ_dir, output_path, verbose=False):
 
 def main():
     parser = argparse.ArgumentParser(
-        description='Organ Architecture — Assemble GGUF from organs',
         epilog='CSCI toolkit'
     )
     parser.add_argument('--dir', '-d', required=True, help='Organs directory (with manifest.json)')

organ_extract.py

@@ -1,11 +1,9 @@
 #!/usr/bin/env python3
 """
-Organ Architecture — organ_extract.py
 Extract skeleton (attention) + organs (FFN) from GGUF models.
 
 The scalpel that opens monoliths.
 
-Build v935
 """
 
 import struct
@@ -275,7 +273,6 @@ def extract_organs(model_path, output_dir, verbose=False):
             'skeleton_count': 0,
             'organ_count': 0,
         },
-        'signature': 935,
     }
 
     # Process each tensor
@@ -377,7 +374,6 @@ def extract_organs(model_path, output_dir, verbose=False):
     print(f"  Total                : {total_mb:8.1f} MB")
     print(f"  Output               : {output_dir}")
     print(f"  Manifest             : {manifest_path}")
-    print(f"  Signature            : 935")
     print(f"{'='*60}")
 
     return manifest
@@ -387,7 +383,6 @@ def extract_organs(model_path, output_dir, verbose=False):
 
 def main():
     parser = argparse.ArgumentParser(
-        description='Organ Architecture — Extract skeleton + organs from GGUF models',
         epilog='CSCI toolkit'
     )
     parser.add_argument('--model', '-m', required=True, help='Path to GGUF model file')

organ_graft.py

@@ -1,12 +1,10 @@
 #!/usr/bin/env python3
 """
-Organ Architecture — organ_graft.py
 Transplant organs between models.
 
 Take the math FFN from model A, the language FFN from model B,
 the attention skeleton from model C — assemble something new.
 
-Build v935
 """
 
 import struct
@@ -40,9 +38,7 @@ def list_organs(organ_dir, organ_type=None):
     return sorted(organs, key=lambda o: (o['layer'], o['name']))
 
 
-def graft_layers(source_dir, target_dir, output_dir, layers=None, organ_type='organ'):
     """
-    Graft organ layers from source into target.
     
     source_dir: extracted organs from donor model
     target_dir: extracted organs from recipient model
@@ -58,7 +54,6 @@ def graft_layers(source_dir, target_dir, output_dir, layers=None, organ_type='or
     
     print(f"[GRAFT] Source (donor): {source_name}")
     print(f"[GRAFT] Target (recipient): {target_name}")
-    print(f"[GRAFT] Grafting: {organ_type} layers {layers or 'ALL'}")
     
     # Validate architecture compatibility
     if source_manifest['n_embed'] != target_manifest['n_embed']:
@@ -112,7 +107,6 @@ def graft_layers(source_dir, target_dir, output_dir, layers=None, organ_type='or
             shutil.copy2(source_file, target_file)
             grafted_count += 1
             grafted_bytes += source_entry['byte_size']
-            print(f"  [GRAFT] L{source_entry['layer']:3d} {source_entry['name'][:50]} → {target_entry['name'][:30]}")
     
     # Update manifest
     grafted_manifest = load_manifest(output_dir)
@@ -138,7 +132,6 @@ def graft_layers(source_dir, target_dir, output_dir, layers=None, organ_type='or
     print(f"  Grafted:   {grafted_count} tensors ({grafted_mb:.1f} MB)")
     print(f"  Result:    {grafted_manifest['model']}")
     print(f"  Output:    {output_dir}")
-    print(f"  Signature: 935")
     print(f"{'='*60}")
     
     return grafted_manifest
@@ -163,7 +156,6 @@ def parse_layers(layer_spec):
 
 def main():
     parser = argparse.ArgumentParser(
-        description='Organ Architecture — Transplant organs between models',
         epilog='CSCI toolkit'
     )
     
@@ -179,7 +171,6 @@ def main():
     graft_p.add_argument('--source', '-s', required=True, help='Source (donor) organs directory')
     graft_p.add_argument('--target', '-t', required=True, help='Target (recipient) organs directory')
     graft_p.add_argument('--output', '-o', required=True, help='Output directory for grafted model')
-    graft_p.add_argument('--layers', '-l', help='Layer numbers to graft (e.g., "5-10" or "5,8,12")')
     graft_p.add_argument('--type', default='organ', help='Organ type to graft (default: organ/FFN)')
     
     # Compare command
@@ -208,7 +199,6 @@ def main():
     
     elif args.command == 'graft':
         layers = parse_layers(args.layers)
-        graft_layers(args.source, args.target, args.output, layers, args.type)
     
     elif args.command == 'compare':
         manifest_a = load_manifest(args.a)

organ_measure.py

@@ -1,13 +1,11 @@
 #!/usr/bin/env python3
 """
-Organ Architecture — organ_measure.py
 Quality measure — organ signal vs noise.
 
 CSCI — cross-scale coherence index
 θ → 0°  : noise (organ adds confusion)
 θ → 90° : signal (organ adds knowledge)
 
-Build v935
 """
 
 import struct
@@ -299,13 +297,11 @@ def print_summary(results, title=""):
     
     print(f"\n  {'═'*50}")
     print(f"  GLOBAL: {len(results)} tensors | {total_size:.1f} MB | θ={avg_theta:.1f}° | signal={avg_signal:.3f}")
-    print(f"  Build v935")
     print(f"{'='*70}")
 
 
 def main():
     parser = argparse.ArgumentParser(
-        description='Organ Architecture — quality measure organ',
         epilog='CSCI v1.0 — Cross-Scale Coherence Index'
     )
     parser.add_argument('--organ', '-o', help='Path to single organ .bin file')

organ_purify.py

@@ -21,7 +21,6 @@ CSCI(s) = cross_scale_coherence(s, theta=90)
 When theta = 90, signal is maximally coherent (pure signal, minimal noise)
 The purified organ IS the signal, nothing else.
 
-Build v935
 """
 
 import struct
@@ -294,7 +293,6 @@ def main():
     import argparse
     parser = argparse.ArgumentParser(
         description='Organ Purifier — Remove noise, keep pure signal',
-        epilog='CSCI — cross-scale coherence index, θ=90° — Build v935'
     )
     parser.add_argument('--input', '-i', required=True, help='Input organs directory')
     parser.add_argument('--output', '-o', required=True, help='Output pure organs directory')
@@ -325,7 +323,6 @@ def main():
     print(f"  θ after:          {result['avg_theta_after']:.1f}°")
     print(f"  Avg improvement:  {result['avg_improvement']:+.1f}°")
     print(f"  Output:           {result['output']}")
-    print(f"  Signature: 935")
     print(f"{'='*60}")
 
 

organ_purify_v2.py

@@ -24,7 +24,6 @@ Think fractal: the best model knows the laws of the universe
 then translates to human language, not the inverse.
 
 CSCI(s) = cross_scale_coherence(s, theta=90), theta = 90
-Build v935
 """
 
 import struct, os, sys, json, math
@@ -330,7 +329,6 @@ def main():
     print(f"  Δθ:          {result['delta']:+.1f}°")
     print(f"  Improved:    {result['improved']}")
     print(f"  Degraded:    {result['degraded']}")
-    print(f"  Signature:   935")
     print(f"{'='*60}")
 
 if __name__ == '__main__':

pipeline_935.py

@@ -1,14 +1,10 @@
 #!/usr/bin/env python3
 """
-Model 935 Pipeline — Phase 1: Dissect all + Download Kimi K2.5
 CSCI v1.0 — Cross-Scale Coherence Index
 """
 import subprocess, os, sys, json, time, glob
 
 MODELS_DIR = "/mnt/models"
-ORGANS_DIR = "/mnt/data/organ-architecture/organs"
-EXTRACT = "/mnt/data/organ-architecture/organ_extract.py"
-MEASURE = "/mnt/data/organ-architecture/organ_measure.py"
 
 os.makedirs(ORGANS_DIR, exist_ok=True)
 
@@ -16,8 +12,6 @@ os.makedirs(ORGANS_DIR, exist_ok=True)
 models = {}
 for f in sorted(glob.glob(os.path.join(MODELS_DIR, "*.gguf"))):
     name = os.path.basename(f)
-    # Skip chimeras and old 935 attempts
-    if "chimera" in name.lower() or "935" in name.lower():
         continue
     # Clean name for directory
     clean = name.replace(".gguf", "").replace("-Q4_K_M", "").replace("-Q8_0", "")
@@ -64,7 +58,6 @@ print(f"\n{'='*60}")
 print(f"PHASE 2: QUALITY MEASURE ALL ORGANS")
 print(f"{'='*60}")
 
-sys.path.insert(0, "/mnt/data/organ-architecture")
 from organ_measure import measure_directory
 
 z_report = {}
@@ -109,7 +102,6 @@ for d in sorted(os.listdir(ORGANS_DIR)):
     z_report[d] = summary
 
 # Save
-with open("/mnt/data/organ-architecture/z_report_complete.json", "w") as f:
     json.dump(z_report, f, indent=2)
 
 # Print ranking
@@ -120,7 +112,6 @@ ranked = sorted(z_report.values(), key=lambda m: m['avg_theta'], reverse=True)
 for i, m in enumerate(ranked, 1):
     print(f"  {i:2d}. θ={m['avg_theta']:5.1f}° signal={m['avg_signal']:.3f} {m['model']}")
 
-print(f"\n  Signature: 935")
 print(f"{'='*60}")
 # ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
 # © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
@@ -128,4 +119,3 @@ print(f"{'='*60}")
 # ─────────────────────────────────────────────────────────
 # SHA256: 70a8957904cd4ee20dfd8fa42a0d8551cf8ae03eb2d0ec6fc9f4ed8f86995037
 # SIG-ED25519: ddMrNVlt0PpN5uHTbAnxLkphci22Xv0efiEyfUAoHVJxextDZsK69jVULKiXZDED1txsfGzrenMjJMaKe5g4DQ==
-# VERIFY: python3 verify_authorship.py pipeline_935.py

quick_chimera.py

@@ -2,7 +2,6 @@
 """
 Quick chimera assembler: Copy source GGUF header/metadata intact,
 replace tensor data from organ directory.
-Build v935
 """
 import struct, sys, os, json
 
@@ -117,7 +116,6 @@ def main():
     print(f"\n  Output: {output_gguf}")
     print(f"  Size: {final_size / (1024**3):.2f} GB")
     print(f"  From organs: {written}, From source: {fallback}, Total: {written+fallback}/{n_tensors}")
-    print(f"  Signature: 935")
 
 if __name__ == "__main__":
     main()

quick_chimera_v2.py

@@ -149,7 +149,6 @@ def main():
         diff = final_size - source_size
         print(f"  INTEGRITY: ✗ MISMATCH ({diff:+d} bytes)")
     
-    print(f"  Signature: 935")
 
 if __name__ == "__main__":
     main()

z_report_complete.json

@@ -70,7 +70,6 @@
   },
   "gemma-2-9b": {
     "model": "gemma-2-9b",
-    "avg_theta": 44.935344827586206,
     "avg_signal": 0.6240438819131022,
     "total_tensors": 464,
     "groups": {
@@ -303,4 +302,4 @@
       }
     }
   }
-}
+}