Update README.md

README.md

@@ -145,39 +145,66 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import logging
 
-# Initialize logging
+# Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 def load_custom_model(model_name, device):
     try:
+        # Load the model directly from Hugging Face Hub
         model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
         logger.info(f"Model loaded successfully from {model_name}")
         return model
     except Exception as e:
-        logger.error(f"Error loading the model: {e}")
+        logger.error(f"An error occurred while loading the model: {e}")
         raise
 
 def load_tokenizer(tokenizer_name):
     try:
+        # Load the tokenizer
         tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
         logger.info(f"Tokenizer loaded successfully from {tokenizer_name}")
         return tokenizer
     except Exception as e:
-        logger.error(f"Error loading the tokenizer: {e}")
+        logger.error(f"An error occurred while loading the tokenizer: {e}")
         raise
 
+def inspect_model_layers(model):
+    logger.info("Inspecting model layers and weights...")
+    for name, param in model.named_parameters():
+        logger.debug(f"Layer: {name} | Size: {param.size()} | Values : {param[:2]}...\n")
+
 if __name__ == "__main__":
+    # Define device
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model_name = "ayjays132/phillnet"  # Your model's home in Hugging Face Hub
+    logger.info(f"Using {'CUDA' if device.type == 'cuda' else 'CPU'}")
+
+    # Model name or path in Hugging Face Hub
+    model_name = "ayjays132/phillnet"
+    tokenizer_name = model_name  # Assuming tokenizer is at the same path
+
     try:
-        tokenizer = load_tokenizer(model_name)
+        # Load the tokenizer and model
+        tokenizer = load_tokenizer(tokenizer_name)
         model = load_custom_model(model_name, device)
-        logger.info("Model and tokenizer are ready for action!")
+
+        # Inspect the model layers and weights
+        inspect_model_layers(model)
+
+        # Perform a simple test to verify model weights are loaded correctly (Optional)
+        input_ids = tokenizer.encode("Hello, world!", return_tensors="pt").to(device)
+        with torch.no_grad():
+            outputs = model(input_ids)
+        logger.info("Model test run completed successfully.")
+
+        print("Custom model and tokenizer loaded successfully.")
+
     except Exception as e:
-        logger.error(f"An unexpected twist occurred: {e}")
+        logger.error(f"An error occurred: {e}")
 ```
 
+### With `CustomModelLoader.py` at your side, you're not just loading a model; you're unlocking a world of possibilities. Whether you're fine-tuning for accuracy or predicting the unknown, your AI journey is about to get a whole lot smoother. So, scholars and AI enthusiasts, let the odyssey begin! 🎩✨
+
 ### 🛠 How It Works: The Mechanics
 
 1. **Setting the Stage**: Our script begins by checking whether to summon the powers of CUDA or settle in the CPU realm.
@@ -192,7 +219,4 @@ This script isn't just a tool; it's a companion designed to make your AI endeavo
 - **Error Logs**: Detailed logging ensures you're always in the know, making troubleshooting a breeze.
 - **Flexibility**: Designed with customization in mind, feel free to tweak the script to fit the unique needs of your scholarly pursuits.
 
-### 🌟 Final Words of Wisdom:
-
-With `CustomModelLoader.py` at your side, you're not just loading a model; you're unlocking a world of possibilities. Whether you're fine-tuning for accuracy or predicting the unknown, your AI journey is about to get a whole lot smoother. So, scholars and AI enthusiasts, let the odyssey begin! 🎩✨
 ---