Begin implementing a training edge case to pickup where the training …

…left off if interrupeted.

train.py

@@ -139,6 +139,7 @@ def train(
     tokenizer,
     epochs=10,
     gradient_accumulation_steps=4,
+    resume_from_checkpoint=None,
 ):
     """
     Train the LongRoPE model.
@@ -153,6 +154,7 @@ def train(
         tokenizer: Tokenizer for encoding/decoding text.
         epochs (int): Number of training epochs.
         gradient_accumulation_steps (int): Number of steps to accumulate gradients.
+        resume_from_checkpoint (str): Path to a checkpoint to resume training from.
 
     Returns:
         None
@@ -164,6 +166,7 @@ def train(
     best_val_loss = float("inf")
     patience = 0
     max_patience = 3
+    start_epoch = 0
 
     for epoch in range(epochs):
         model.train()