diff --git a/tuning/sft_trainer.py b/tuning/sft_trainer.py
index fea9a8b81..79d9748e6 100644
--- a/tuning/sft_trainer.py
+++ b/tuning/sft_trainer.py
@@ -67,7 +67,10 @@
     write_termination_log,
 )
 from tuning.utils.logging import set_log_level
-from tuning.utils.tokenizer_data_utils import tokenizer_and_embedding_resize
+from tuning.utils.tokenizer_data_utils import (
+    tokenizer_and_embedding_resize,
+    set_special_tokens_dict,
+)
 
 
 def train(
@@ -260,42 +263,9 @@ def train(
         tokenizer.chat_template = data_args.chat_template
 
     # Add special tokens only when a custom tokenizer is not passed
-    special_tokens_dict = {}
-    if not model_args.tokenizer_name_or_path:
-        # TODO: understand if we need to hardcode these here or just use defaults in model
-        if isinstance(tokenizer, (LlamaTokenizer, LlamaTokenizerFast)):
-            special_tokens_dict["bos_token"] = "<s>"
-            special_tokens_dict["eos_token"] = "</s>"
-            special_tokens_dict["unk_token"] = "<unk>"
-            special_tokens_dict["pad_token"] = "<pad>"
-        elif isinstance(tokenizer, (GPT2Tokenizer, GPTNeoXTokenizerFast)):
-            special_tokens_dict["pad_token"] = "<pad>"
-
-    # add special tokens only when a custom tokenizer is not passed
-    if not model_args.tokenizer_name_or_path:
-        # TODO: we need to change this, perhaps follow what open instruct does?
-        if tokenizer.pad_token is None:
-            logger.warning("PAD token set to default, missing in tokenizer")
-            special_tokens_dict["pad_token"] = configs.DEFAULT_PAD_TOKEN
-        if tokenizer.eos_token is None:
-            logger.warning("EOS token set to default, missing in tokenizer")
-            special_tokens_dict["eos_token"] = configs.DEFAULT_EOS_TOKEN
-        if tokenizer.bos_token is None:
-            logger.warning("BOS token set to default, missing in tokenizer")
-            special_tokens_dict["bos_token"] = configs.DEFAULT_BOS_TOKEN
-        if tokenizer.unk_token is None:
-            logger.warning("UNK token set to default, missing in tokenizer")
-            special_tokens_dict["unk_token"] = configs.DEFAULT_UNK_TOKEN
-        if tokenizer.pad_token == tokenizer.eos_token:
-            logger.warning(
-                "PAD token set to default, to make it different from eos token"
-            )
-            if tokenizer.eos_token != configs.DEFAULT_PAD_TOKEN:
-                tokenizer.pad_token = configs.DEFAULT_PAD_TOKEN
-                special_tokens_dict["pad_token"] = configs.DEFAULT_PAD_TOKEN
-            else:
-                tokenizer.eos_token = configs.DEFAULT_EOS_TOKEN
-                special_tokens_dict["eos_token"] = configs.DEFAULT_EOS_TOKEN
+    special_tokens_dict = set_special_tokens_dict(
+        model_args=model_args, tokenizer=tokenizer
+    )
 
     # TODO: lower priority but understand if resizing impacts inference quality and why its needed.
     # It makes sense if we manipulate tokenizer that we also save it and provide it to inference.
diff --git a/tuning/utils/tokenizer_data_utils.py b/tuning/utils/tokenizer_data_utils.py
index ef0662d59..61ae69c84 100644
--- a/tuning/utils/tokenizer_data_utils.py
+++ b/tuning/utils/tokenizer_data_utils.py
@@ -14,10 +14,71 @@
 
 # Standard
 from typing import Dict
+import logging
 import math
 
 # Third Party
 import transformers
+from tuning.config import configs
+
+logger = logging.getLogger(__name__)
+
+
+def set_special_tokens_dict(
+    model_args: configs.ModelArguments, tokenizer: transformers.PreTrainedTokenizer
+) -> dict:
+    """Creates a special tokens dictionary and sets the special tokens,
+       depending on the tokenizer.
+
+    Args:
+        model_args: configs.ModelArguments.
+        tokenizer: transformers.PreTrainedTokenizer.
+
+    Returns:
+        dict: Special tokens for the tokenizer.
+    """
+
+    special_tokens_dict = {}
+    if not model_args.tokenizer_name_or_path:
+        # TODO: understand if we need to hardcode these here or just use defaults in model
+        if isinstance(
+            tokenizer, (transformers.LlamaTokenizer, transformers.LlamaTokenizerFast)
+        ):
+            special_tokens_dict["bos_token"] = "<s>"
+            special_tokens_dict["eos_token"] = "</s>"
+            special_tokens_dict["unk_token"] = "<unk>"
+            special_tokens_dict["pad_token"] = "<pad>"
+        elif isinstance(
+            tokenizer, (transformers.GPT2Tokenizer, transformers.GPTNeoXTokenizerFast)
+        ):
+            special_tokens_dict["pad_token"] = "<pad>"
+
+    # add special tokens only when a custom tokenizer is not passed
+    if not model_args.tokenizer_name_or_path:
+        # TODO: we need to change this, perhaps follow what open instruct does?
+        if tokenizer.pad_token is None:
+            logger.warning("PAD token set to default, missing in tokenizer")
+            special_tokens_dict["pad_token"] = configs.DEFAULT_PAD_TOKEN
+        if tokenizer.eos_token is None:
+            logger.warning("EOS token set to default, missing in tokenizer")
+            special_tokens_dict["eos_token"] = configs.DEFAULT_EOS_TOKEN
+        if tokenizer.bos_token is None:
+            logger.warning("BOS token set to default, missing in tokenizer")
+            special_tokens_dict["bos_token"] = configs.DEFAULT_BOS_TOKEN
+        if tokenizer.unk_token is None:
+            logger.warning("UNK token set to default, missing in tokenizer")
+            special_tokens_dict["unk_token"] = configs.DEFAULT_UNK_TOKEN
+        if tokenizer.pad_token == tokenizer.eos_token:
+            logger.warning(
+                "PAD token set to default, to make it different from eos token"
+            )
+            if tokenizer.eos_token != configs.DEFAULT_PAD_TOKEN:
+                tokenizer.pad_token = configs.DEFAULT_PAD_TOKEN
+                special_tokens_dict["pad_token"] = configs.DEFAULT_PAD_TOKEN
+            else:
+                tokenizer.eos_token = configs.DEFAULT_EOS_TOKEN
+                special_tokens_dict["eos_token"] = configs.DEFAULT_EOS_TOKEN
+    return special_tokens_dict
 
 
 def tokenizer_and_embedding_resize(
@@ -30,10 +91,10 @@ def tokenizer_and_embedding_resize(
     Args:
         special_tokens_dict: Dict containing special tokens to be added.
         tokenizer: transformers.PreTrainedTokenizer.
-        model: transformers.PreTrainedModel
+        model: transformers.PreTrainedModel.
         multiple_of: int , embeddings are resized to multiple of this.
     Return:
-        dict: Metadata on number of added tokens
+        dict: Metadata on number of added tokens.
     """
     num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
     embedding_size = int(multiple_of * math.ceil(len(tokenizer) / multiple_of))