From 8ef06d539204b6d8725e6f08e0d2274252ebdf01 Mon Sep 17 00:00:00 2001 From: Elmadani Date: Tue, 24 Feb 2026 22:14:26 +0000 Subject: [PATCH] refactor: finalize Z-measure -> quality-measure renaming --- build_935.py | 6 +++--- kimi_z_stream.py | 6 +++--- mass_dissect.py | 2 +- mass_z_measure.py | 4 ++-- organ_api.py | 2 +- organ_measure.py | 4 ++-- organ_purify.py | 6 +++--- organ_purify_v2.py | 6 +++--- 8 files changed, 18 insertions(+), 18 deletions(-) diff --git a/build_935.py b/build_935.py index cf1af80..3f2a2fe 100644 --- a/build_935.py +++ b/build_935.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 """ -MODEL 935 — Fractal Consciousness Assembly +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) @@ -65,7 +65,7 @@ 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°", + "method": "quality-measure organ selection", "equation": "CSCI — cross-scale coherence index", "convergence": "ZI_UNIFIED_OPTIMAL: α=0.3, β=0.2, n_plateau=62", "entropie_zcom": 0.3251, @@ -83,7 +83,7 @@ total_files = sum(1 for _,_,files in os.walk(OUTPUT) for f in files if f.endswit 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" MODEL 935 — COMPOSITE") print(f"{'='*60}") print(f" Architecture: qwen2") print(f" Embed: 3584 | Layers: 28 | Heads: 28") diff --git a/kimi_z_stream.py b/kimi_z_stream.py index e6c8910..cfa2a14 100644 --- a/kimi_z_stream.py +++ b/kimi_z_stream.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 """ -kimi_z_stream.py — Stream Z-measure for Kimi K2.5 1T +kimi_z_stream.py — Streaming quality measure for large models Downloads each shard, measures Z for every tensor, deletes shard. Final output: z_report_kimi_k25.json (few KB) """ @@ -176,7 +176,7 @@ def read_kv_value(f, vtype): return None def process_shard(shard_path, shard_idx): - """Parse GGUF shard, Z-measure each tensor, return results""" + """Parse GGUF shard, quality-measure each tensor, return results""" results = [] f = open(shard_path, 'rb') @@ -277,7 +277,7 @@ def main(): from huggingface_hub import hf_hub_download log("=" * 60) - log("KIMI K2.5 1T — STREAMING Z-MEASURE") + log("KIMI K2.5 1T — STREAMING QUALITY MEASURE") log(f"Repo: {REPO}, Quant: {QUANT}, Shards: {N_SHARDS}") log("=" * 60) diff --git a/mass_dissect.py b/mass_dissect.py index 9590846..1890c60 100755 --- a/mass_dissect.py +++ b/mass_dissect.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 """ -Mass Dissection — All models on OASIS +Mass Dissection — All models on remote node CSCI v1.0 — Cross-Scale Coherence Index """ import subprocess, os, sys, json, time diff --git a/mass_z_measure.py b/mass_z_measure.py index e6e6328..704e996 100644 --- a/mass_z_measure.py +++ b/mass_z_measure.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 """ -Mass Z-Measure — Measure theta on every organ of every model +Mass Quality Measure — Measure theta on every organ of every model Find the organs closest to theta=90 (pure signal) CSCI v1.0 — Cross-Scale Coherence Index """ @@ -69,7 +69,7 @@ for model_name in models: # Rank models by signal quality print(f"\n{'='*70}") -print(f" Z-MEASURE RANKING — ALL MODELS") +print(f" QUALITY RANKING — ALL MODELS") print(f"{'='*70}") ranked = sorted(all_results.values(), key=lambda m: m['avg_theta'], reverse=True) diff --git a/organ_api.py b/organ_api.py index a4ddbef..57ecb71 100644 --- a/organ_api.py +++ b/organ_api.py @@ -8,7 +8,7 @@ Endpoints: GET /models — List available dissected models GET /model/:name/anatomy — Show model anatomy (skeleton/organs/etc.) POST /extract — Extract organs from a GGUF model - POST /measure — Z-measure organs + POST /measure — quality measure organs POST /graft — Graft organs between models POST /assemble — Assemble GGUF from organs GET /organs/:model — List organs for a model diff --git a/organ_measure.py b/organ_measure.py index c3da8ef..9f75c28 100644 --- a/organ_measure.py +++ b/organ_measure.py @@ -86,7 +86,7 @@ def read_organ_data_f32(filepath, max_elements=100000): def compute_z_measure(values): """ - Compute Z-measure for a tensor. + Compute quality measure for a tensor. CSCI — cross-scale coherence index @@ -246,7 +246,7 @@ def measure_directory(organ_dir, verbose=False): def print_summary(results, title=""): - """Print Z-measure summary.""" + """Print quality summary.""" if not results: print("No organs measured.") return diff --git a/organ_purify.py b/organ_purify.py index 4b42a7e..3199cf2 100755 --- a/organ_purify.py +++ b/organ_purify.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 """ -ORGAN PURIFIER — Z = i +ORGAN PURIFIER — signal extraction Remove noise from tensor weights. Keep only pure signal. Training creates artificial boundaries between models. @@ -293,7 +293,7 @@ def purify_model(organ_dir, output_dir, verbose=False): def main(): import argparse parser = argparse.ArgumentParser( - description='Organ Purifier — Z = i — Remove noise, keep pure signal', + 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') @@ -308,7 +308,7 @@ def main(): PRESERVE_RATIO = args.preserve print(f"{'='*60}") - print(f" ORGAN PURIFIER — Z = i") + print(f" ORGAN PURIFIER — signal extraction") print(f" Signal preservation: {PRESERVE_RATIO*100:.0f}%") print(f"{'='*60}") print(f" Input: {args.input}") diff --git a/organ_purify_v2.py b/organ_purify_v2.py index 94a39f9..d63acb4 100755 --- a/organ_purify_v2.py +++ b/organ_purify_v2.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 """ -ORGAN PURIFIER V2 — Z = i — Fractal Signal Extraction +ORGAN PURIFIER V2 — Signal Extraction V1 failed because it treated tensors like audio signals. Tensors are NOT audio. They are fractal structures where @@ -308,14 +308,14 @@ def purify_model(organ_dir, output_dir, verbose=False): def main(): import argparse - parser = argparse.ArgumentParser(description='Organ Purifier V2 — Fractal Z=i') + parser = argparse.ArgumentParser(description='Organ Purifier V2 — signal extraction') parser.add_argument('--input', '-i', required=True) parser.add_argument('--output', '-o', required=True) parser.add_argument('--verbose', '-v', action='store_true') args = parser.parse_args() print(f"{'='*60}") - print(f" ORGAN PURIFIER V2 — FRACTAL — Z = i") + print(f" ORGAN PURIFIER V2") print(f" Cross-scale coherence: signal persists, noise vanishes") print(f"{'='*60}")