Create analyze_model.py

analyze_model.py

@@ -0,0 +1,23 @@
+import torch
+from transformers import AutoModel, AutoTokenizer
+import matplotlib.pyplot as plt
+
+def analyze_model(model_path):
+    model = AutoModel.from_pretrained(model_path)
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    
+    print("=== Model Architecture ===")
+    print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}")
+    print(f"Layers: {len(model.encoder.layer) if hasattr(model, 'encoder') else 'N/A'}")
+    
+    # Analyze attention patterns
+    if hasattr(model, 'encoder'):
+        layer = model.encoder.layer[0]
+        print(f"Attention heads: {layer.attention.self.num_attention_heads}")
+    
+    return model, tokenizer
+
+def plot_training_metrics(log_file='training.log'):
+    # Parse training logs and create plots
+    # This would read your training logs and create nice graphs
+    pass