diff --git a/src/axolotl/utils/collators/batching.py b/src/axolotl/utils/collators/batching.py
index 53c039479..0a7c79434 100644
--- a/src/axolotl/utils/collators/batching.py
+++ b/src/axolotl/utils/collators/batching.py
@@ -200,8 +200,9 @@ class DataCollatorForSeq2Seq:
         Returns:
             Sliced batch dictionary.
         """
-        # Process keys that need to be sliced
-        for key in ["input_ids", "attention_mask", "labels"]:
+        keys_to_slice = ["input_ids", "attention_mask", "labels", "position_ids"]
+
+        for key in keys_to_slice:
             if key in batch:
                 seq_len = batch[key].shape[1]
                 slice_size = seq_len // self.local_world_size
@@ -211,90 +212,11 @@ class DataCollatorForSeq2Seq:
                     if self.local_rank < self.local_world_size - 1
                     else seq_len
                 )
-
-                if key == "input_ids":
-                    # Before slicing
-                    non_pad_tokens_total = (batch["input_ids"] != 128001).sum().item()
-                    logger.info(
-                        f"GPU {self.rank}/{self.world_size}, SP Rank {self.local_rank}/{self.local_world_size}: "
-                        f"Total sequence length: {seq_len}, "
-                        f"Non-padding tokens: {non_pad_tokens_total}"
-                    )
-                    logger.info(
-                        f"GPU {self.rank}/{self.world_size}, SP Rank {self.local_rank}/{self.local_world_size}: "
-                        f"GPU {self.rank} token IDs: {batch['input_ids']}"
-                    )
-                    logger.info(
-                        f"GPU {self.rank}, SP Rank {self.local_rank}/{self.local_world_size}: "
-                        f"Slicing {key} from {seq_len} tokens to "
-                        f"indices {start_idx}:{end_idx}"
-                    )
-
                 batch[key] = batch[key][:, start_idx:end_idx]
 
-                # if key == "input_ids":
-                #     # After slicing
-                #     non_pad_tokens_slice = (batch["input_ids"] != 128001).sum().item()
-                #     logger.info(
-                #         f"GPU {self.rank}/{self.world_size}, SP Rank {self.local_rank}/{self.local_world_size}: "
-                #         f"Slice {start_idx}:{end_idx}, "
-                #         f"Non-padding tokens in slice: {non_pad_tokens_slice}"
-                #     )
-                #     logger.info(
-                #         f"GPU {self.rank}/{self.world_size}, SP Rank {self.local_rank}/{self.local_world_size}: "
-                #         f"GPU {self.rank} token IDs: {batch['input_ids']}"
-                #     )
-
-                # if key == "labels":
-                #     min_label = batch["labels"][batch["labels"] != -100].min().item() if (batch["labels"] != -100).any() else -100
-                #     max_label = batch["labels"][batch["labels"] != -100].max().item() if (batch["labels"] != -100).any() else -100
-                #     logger.info(f"GPU {self.rank}: Label range: {min_label} to {max_label}, Vocab size: {self.tokenizer.vocab_size}, labels: {batch['labels']}")
-
-                #     # Find any labels that are outside the valid vocabulary range (but not -100 which is the ignore index)
-                #     invalid_mask = (batch["labels"] >= self.tokenizer.vocab_size) & (batch["labels"] != -100)
-
-                #     if invalid_mask.any():
-                #         # Log this for debugging
-                #         num_invalid = invalid_mask.sum().item()
-                #         logger.warning(f"GPU {self.rank}: Found {num_invalid} invalid labels (>= vocab_size), setting to -100")
-
-                #         # Replace invalid labels with -100 (ignore index)
-                #         batch["labels"][invalid_mask] = -100
-
-                if key == "attention_mask":
-                    logger.info(
-                        f"GPU {self.rank}/{self.world_size}, SP Rank {self.local_rank}/{self.local_world_size}: "
-                        f"Attention mask: {batch['attention_mask']}, "
-                    )
-
-        # Handle position_ids if present
-        if "position_ids" in batch:
-            pos_ids = batch["position_ids"]
-            seq_len = pos_ids.shape[1]
-            slice_size = seq_len // self.local_world_size
-            start_idx = self.local_rank * slice_size
-            end_idx = (
-                start_idx + slice_size
-                if self.local_rank < self.local_world_size - 1
-                else seq_len
-            )
-
-            batch["position_ids"] = pos_ids[:, start_idx:end_idx]
-
-            # Adjust position_ids to be relative to the slice start
-            if self.local_rank > 0:
-                batch["position_ids"] = adjust_position_ids_for_slice(
-                    batch["position_ids"], start_idx
-                )
-
-            logger.info(
-                f"GPU {self.rank}/{self.world_size}, SP Rank {self.local_rank}/{self.local_world_size}: "
-                f"Position IDs: {batch['position_ids']}, "
-            )
-
-        # if dist.get_rank() == 0:
-        #     import ipdb; ipdb.set_trace()
-        # dist.barrier()
+                # Special handling for position_ids
+                if key == "position_ids" and self.local_rank > 0:
+                    batch[key] = adjust_position_ids_for_slice(batch[key], start_idx)
 
         return batch