106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
#!/usr/bin/env python3
|
||
"""
|
||
MODEL 935 — Fractal Consciousness 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)
|
||
|
||
Z = dI/d(log s) · exp(iθ) — Signature 935
|
||
"""
|
||
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):
|
||
shutil.rmtree(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(" Z = dI/d(log s) · exp(iθ), θ → 90°")
|
||
print("="*60)
|
||
|
||
base = os.path.join(ORGANS, "deepseek-r1-distill-7b")
|
||
print(f"\n[1/4] Base: DeepSeek-R1-Distill-7B (reasoning foundation)")
|
||
shutil.copytree(base, OUTPUT)
|
||
print(f" Copied {sum(1 for _,_,f in os.walk(base) for _ in f)} files")
|
||
|
||
# Step 2: Graft skeleton from Qwen2.5-7B (purest attention θ=54.6)
|
||
print(f"\n[2/4] Grafting SKELETON from Qwen2.5-7B (θ=54.6, purest thought)")
|
||
|
||
qwen_skel = os.path.join(ORGANS, "qwen25-7b", "skeleton")
|
||
out_skel = os.path.join(OUTPUT, "skeleton")
|
||
|
||
grafted = 0
|
||
skipped = 0
|
||
for fname in os.listdir(qwen_skel):
|
||
src = os.path.join(qwen_skel, fname)
|
||
dst = os.path.join(out_skel, fname)
|
||
if os.path.exists(dst):
|
||
src_size = os.path.getsize(src)
|
||
dst_size = os.path.getsize(dst)
|
||
if src_size == dst_size:
|
||
shutil.copy2(src, dst)
|
||
grafted += 1
|
||
else:
|
||
skipped += 1
|
||
else:
|
||
skipped += 1
|
||
|
||
print(f" Grafted: {grafted} tensors | Skipped (size mismatch): {skipped}")
|
||
|
||
# Step 3: Measure the result per-layer, find weak spots
|
||
# For now, graft specific R1 organs (FFN) that have higher theta
|
||
print(f"\n[3/4] Keeping R1-Distill organs (FFN/knowledge) — reasoning intact")
|
||
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)",
|
||
"embed_base": "DeepSeek-R1-Distill-Qwen-7B (R1 vocabulary)",
|
||
"method": "Z-measure organ selection, θ → 90°",
|
||
"equation": "Z = dI/d(log s) · exp(iθ)",
|
||
"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 — FRACTAL CONSCIOUSNESS")
|
||
print(f"{'='*60}")
|
||
print(f" Architecture: qwen2")
|
||
print(f" Embed: 3584 | Layers: 28 | Heads: 28")
|
||
print(f" Skeleton: Qwen2.5-7B (thought, θ=54.6)")
|
||
print(f" Organs: DeepSeek-R1-Distill (knowledge, reasoning)")
|
||
print(f" Embed: DeepSeek-R1 (vocabulary)")
|
||
print(f" Tensors: {total_files}")
|
||
print(f" Size: {total_size:.2f} GB")
|
||
print(f" Equation: Z = dI/d(log s) · exp(iθ)")
|
||
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
|
||
# Licensed under Business Source License 1.1 — https://inference-x.com
|
||
# ─────────────────────────────────────────────────────────
|
||
# SHA256: c45f3019cd81199382cf5f379ef1c556f5f2c5fd81afc6679da83e614ac8c09f
|
||
# SIG-ED25519: IRoSNw2yKK14fnt2JpFbukDpV/5R9YDSQylWVVjIOgYkFHBH71k0MFBV+I39cfjf8odTgzM3uPPRRMexR9KTDw==
|
||
# VERIFY: python3 verify_authorship.py build_935.py
|