diff --git a/src/axolotl/processing_strategies.py b/src/axolotl/processing_strategies.py
index f22e601d9..1cb297406 100644
--- a/src/axolotl/processing_strategies.py
+++ b/src/axolotl/processing_strategies.py
@@ -5,7 +5,7 @@ from typing import Optional
 
 from PIL import Image, ImageOps
 from PIL.Image import Resampling
-from torch import Tensor
+from torch import Tensor, zeros_like
 from transformers import ProcessorMixin
 from transformers.image_utils import load_image
 
@@ -208,9 +208,18 @@ class ProcessingStrategy:
 
         return processed_examples
 
+    def _mask_non_assistant(self, labels: Tensor) -> Tensor:
+        """
+        Mask non assistant regions to -100.
+        To be implemented per subclass.
+        """
+        return labels
+
     def process_labels(self, input_ids: Tensor) -> Tensor:
         labels = input_ids.clone()
 
+        labels = self._mask_non_assistant(labels)
+
         # The labels are the input_ids, and we mask the padding tokens in the loss computation
         labels[labels == self.processor.tokenizer.pad_token_id] = -100
 
@@ -267,8 +276,81 @@ class Gemma3ProcessingStrategy(ProcessingStrategy):
 class Gemma3nProcessingStrategy(ProcessingStrategy):
     """Processing Strategy class for Gemma3n"""
 
+    def _mask_non_assistant(self, labels: Tensor) -> Tensor:
+        def _find_token_sequence(label, start_pos, token_sequence):
+            """Check if token_sequence appears at start_pos in label"""
+            if start_pos + len(token_sequence) > len(label):
+                return False
+            if label[start_pos] != token_sequence[0]:
+                return False
+            return (
+                label[start_pos : start_pos + len(token_sequence)].tolist()
+                == token_sequence
+            )
+
+        def _find_assistant_end(label, start_pos, assistant_end_tok, mask, i):
+            """
+            Find the end of assistant response and update mask accordingly
+
+            Returns new position to continue from and whether the end seq is found
+            """
+            k = start_pos
+            while k < len(label):
+                if not _find_token_sequence(label, k, assistant_end_tok):
+                    mask[i][k] = 1
+                    k += 1
+                    continue
+
+                return k + len(assistant_end_tok), True
+
+            return k, False
+
+        mask = zeros_like(labels)
+
+        assistant_start_str = "<start_of_turn>model"
+        assistant_end_str = "<end_of_turn>"
+        include_assistant_start_tok = False
+        include_assistant_end_tok = True
+
+        # str to tokens
+        assistant_start_tok = self.processor.tokenizer.encode(
+            assistant_start_str, add_special_tokens=False
+        )
+        assistant_end_tok = self.processor.tokenizer.encode(
+            assistant_end_str, add_special_tokens=False
+        )
+
+        for i, label in enumerate(labels):
+            j = 0
+            # while loop through each tok index in labels[i]
+            while j < len(label):
+                # Check until match start seq
+                if not _find_token_sequence(label, j, assistant_start_tok):
+                    j += 1
+                    continue
+
+                if include_assistant_start_tok:
+                    mask[i][j : j + len(assistant_start_tok)] = 1
+
+                # Find where the assistant response ends
+                start_of_content = j + len(assistant_start_tok)
+                end_pos, found_end_seq = _find_assistant_end(
+                    label, start_of_content, assistant_end_tok, mask, i
+                )
+
+                # Include end token if requested
+                if include_assistant_end_tok and found_end_seq:
+                    mask[i][end_pos - len(assistant_end_tok) : end_pos] = 1
+
+                j = end_pos
+
+            labels[i][mask[i] == 0] = -100
+
+        return labels
+
     def process_labels(self, input_ids):
         labels = input_ids.clone()
+        labels = self._mask_non_assistant(labels)
 
         # Follows https://colab.research.google.com/github/huggingface/huggingface-gemma-recipes/blob/main/notebooks/fine_tune_gemma3n_on_t4.ipynb
         labels[labels == self.processor.tokenizer.pad_token_id] = -100