Upload arcee_fusion_salience_scanner.py

arcee_fusion_salience_scanner.py

@@ -0,0 +1,72 @@
+import torch
+from safetensors import safe_open
+import os
+import re
+from collections import defaultdict
+
+# --- CONFIGURATION ---
+base_model_path = r'B:\12B\models--mistralai--Mistral-Nemo-Instruct-2407'
+merged_model_path = r'C:\Quanter\model_cache\EldritchLabs__DeepWater-Pleroma-12B-v1'
+# ---------------------
+
+def get_tensor_map(path):
+    tensor_map = {}
+    files = [f for f in os.listdir(path) if f.endswith('.safetensors')]
+    for f in files:
+        full_path = os.path.join(path, f)
+        with safe_open(full_path, framework="pt") as st:
+            for k in st.keys():
+                tensor_map[k] = full_path
+    return tensor_map
+
+print("🔍 Indexing model shards...")
+base_map = get_tensor_map(base_model_path)
+merged_map = get_tensor_map(merged_model_path)
+
+# Group results by layer
+layer_stats = defaultdict(lambda: {"changed": 0, "total": 0})
+
+print("📊 Scanning tensors and calculating saliency density...")
+common_tensors = set(base_map.keys()) & set(merged_map.keys())
+
+for k in sorted(common_tensors):
+    # Extract layer number from name (e.g., 'model.layers.5.self_attn...')
+    layer_match = re.search(r'\.layers\.(\0?(\d+))\.', k)
+    layer_id = int(layer_match.group(1)) if layer_match else "Non-Layer"
+
+    with safe_open(base_map[k], framework="pt") as b_st:
+        base_t = b_st.get_tensor(k)
+    with safe_open(merged_map[k], framework="pt") as m_st:
+        merged_t = m_st.get_tensor(k)
+
+    # Arcee Fusion logic: if weights are identical, they came from Base.
+    # If they are different, they are "New Info" from the fusion.
+    # We use a tiny atol to account for potential bf16/f16 casting jitters
+    changed_mask = ~torch.isclose(base_t, merged_t, rtol=1e-05, atol=1e-08)
+    
+    layer_stats[layer_id]["changed"] += torch.count_nonzero(changed_mask).item()
+    layer_stats[layer_id]["total"] += merged_t.numel()
+
+print("\n" + "="*60)
+print(f"{'LAYER':<12} | {'NEW INFO %':<12} | {'VISUAL DENSITY (█ = New, ░ = Base)'}")
+print("="*60)
+
+# Sort layers: Non-Layer first, then 0, 1, 2...
+sorted_keys = sorted([k for k in layer_stats.keys() if isinstance(k, int)])
+if "Non-Layer" in layer_stats:
+    sorted_keys = ["Non-Layer"] + sorted_keys
+
+for lid in sorted_keys:
+    stats = layer_stats[lid]
+    percentage = (stats["changed"] / stats["total"]) * 100
+    
+    # Create ASCII bar
+    bar_width = 30
+    filled = int((percentage / 100) * bar_width)
+    bar = "█" * filled + "░" * (bar_width - filled)
+    
+    label = f"Layer {lid}" if isinstance(lid, int) else lid
+    print(f"{label:<12} | {percentage:>10.2f}% | {bar}")
+
+print("="*60)
+print("Analysis Complete.")