diff --git a/docs/source/nlp/machine_translation.rst b/docs/source/nlp/machine_translation.rst
index 3635fad5174f..3672eb4bbf36 100644
--- a/docs/source/nlp/machine_translation.rst
+++ b/docs/source/nlp/machine_translation.rst
@@ -561,6 +561,17 @@ To train a Megatron 345M BERT, we would use
   model.encoder.num_layers=24 \
   model.encoder.max_position_embeddings=512 \
 
+If the pretrained megatron model used a custom vocab file, then set:
+
+.. code::
+
+  model.encoder_tokenizer.vocab_file=/path/to/your/megatron/vocab_file.txt
+  model.encoder.vocab_file=/path/to/your/megatron/vocab_file.txt
+
+
+Use ``encoder.model_name=megatron_bert_uncased`` for uncased models with custom vocabularies and
+use ``encoder.model_name=megatron_bert_cased`` for cased models with custom vocabularies.
+
 
 References
 ----------
diff --git a/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py b/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py
index acae5309c22b..b462d4569ebf 100644
--- a/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py
+++ b/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py
@@ -80,6 +80,7 @@ def __init__(self, cfg: MTEncDecModelConfig, trainer: Trainer = None):
             if cfg.encoder_tokenizer.get('bpe_dropout', 0.0) is not None
             else 0.0,
             encoder_model_name=cfg.encoder.get('model_name') if hasattr(cfg.encoder, 'model_name') else None,
+            encoder_tokenizer_vocab_file=cfg.encoder_tokenizer.get('vocab_file', None),
             decoder_tokenizer_library=cfg.decoder_tokenizer.get('library', 'yttm'),
             decoder_tokenizer_model=cfg.decoder_tokenizer.tokenizer_model,
             decoder_bpe_dropout=cfg.decoder_tokenizer.get('bpe_dropout', 0.0)
@@ -379,6 +380,7 @@ def setup_enc_dec_tokenizers(
         encoder_tokenizer_model=None,
         encoder_bpe_dropout=0.0,
         encoder_model_name=None,
+        encoder_tokenizer_vocab_file=None,
         decoder_tokenizer_library=None,
         decoder_tokenizer_model=None,
         decoder_bpe_dropout=0.0,
@@ -397,7 +399,7 @@ def setup_enc_dec_tokenizers(
             tokenizer_model=self.register_artifact("encoder_tokenizer.tokenizer_model", encoder_tokenizer_model),
             bpe_dropout=encoder_bpe_dropout,
             model_name=encoder_model_name,
-            vocab_file=None,
+            vocab_file=self.register_artifact("encoder_tokenizer.vocab_file", encoder_tokenizer_vocab_file),
             special_tokens=None,
             use_fast=False,
         )
diff --git a/nemo/collections/nlp/modules/common/tokenizer_utils.py b/nemo/collections/nlp/modules/common/tokenizer_utils.py
index 8eaa2b3f7707..404b386486e8 100644
--- a/nemo/collections/nlp/modules/common/tokenizer_utils.py
+++ b/nemo/collections/nlp/modules/common/tokenizer_utils.py
@@ -95,6 +95,9 @@ def get_tokenizer(
     elif tokenizer_name == 'char':
         return CharTokenizer(vocab_file=vocab_file, **special_tokens_dict)
 
+    logging.info(
+        f"Getting HuggingFace AutoTokenizer with pretrained_model_name: {tokenizer_name}, vocab_file: {vocab_file}, special_tokens_dict: {special_tokens_dict}, and use_fast: {use_fast}"
+    )
     return AutoTokenizer(
         pretrained_model_name=tokenizer_name, vocab_file=vocab_file, **special_tokens_dict, use_fast=use_fast
     )
@@ -140,7 +143,7 @@ def get_nmt_tokenizer(
         )
     elif library == 'megatron':
         logging.info(
-            f'Getting Megatron tokenizer with pretrained model name: {model_name} and custom vocab file: {vocab_file}'
+            f'Getting Megatron tokenizer for pretrained model name: {model_name} and custom vocab file: {vocab_file}'
         )
         return get_tokenizer(tokenizer_name=model_name, vocab_file=vocab_file)
     else: