Updates model.py file.

model.py

@@ -1,19 +1,38 @@
 import torch.nn as nn
-from transformers import AutoModel, PreTrainedModel
+import torch
 
-class LlamaClassificationModel(PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.base_model = AutoModel.from_pretrained(config.model_path, config=config)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-        self.config = config
+from transformers import AutoModel
 
+NUM_LABELS = 4
+
+# Model with frozen LLaMA weights
+class LlamaClassificationModel(nn.Module):
+    def __init__(self, model_path = "meta-llama/Llama-3.2-1B", freeze_weights = True):
+        super(LlamaClassificationModel, self).__init__()
+        self.base_model = AutoModel.from_pretrained(model_path)
+        
+        # For push to hub.
+        self.config = self.base_model.config
+        print(self.base_model.config)
+
+        # Freeze the base model's weights
+        if freeze_weights:
+            for param in self.base_model.parameters():
+                param.requires_grad = False
+        
+        # Add a classification head
+        self.classifier = nn.Linear(self.base_model.config.hidden_size, NUM_LABELS)
+    
     def forward(self, input_ids, attention_mask, labels=None):
-        outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask)
-        summed_representation = outputs.last_hidden_state.sum(dim=1)
-        logits = self.classifier(summed_representation)
+        with torch.no_grad():  # No gradients for the base model
+            outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask)
+        
+        # Sum hidden states over the sequence dimension
+        summed_representation = outputs.last_hidden_state.sum(dim=1)  # Summing over sequence length
+        
+        logits = self.classifier(summed_representation)  # Pass the summed representation to the classifier
         loss = None
         if labels is not None:
             loss_fn = nn.BCEWithLogitsLoss()
             loss = loss_fn(logits, labels.float())
-        return {"loss": loss, "logits": logits}
+        return {"loss": loss, "logits": logits}