Updates model.py.

model.py

@@ -1,38 +1,19 @@
 import torch.nn as nn
-import torch
+from transformers import AutoModel, PreTrainedModel
 
-from transformers import AutoModel
+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
 
-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):
-        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
+        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)
         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}