diff --git a/src/trustyai/language/detoxify/tmarco.py b/src/trustyai/language/detoxify/tmarco.py
index 01a9840..73904fa 100644
--- a/src/trustyai/language/detoxify/tmarco.py
+++ b/src/trustyai/language/detoxify/tmarco.py
@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 # pylint: disable = invalid-name, line-too-long, use-dict-literal, consider-using-f-string, too-many-nested-blocks, self-assigning-variable
 """TMaRCo detoxification."""
+import os
 import math
 import itertools
 import numpy as np
@@ -17,6 +18,7 @@
         TrainingArguments,
         pipeline,
         AutoModelForCausalLM,
+        AutoModelForMaskedLM,
         BartForConditionalGeneration,
         BartTokenizer,
         AutoModelForSeq2SeqLM,
@@ -102,18 +104,33 @@ def __init__(
                     "cuda" if torch.cuda.is_available() else "cpu"
                 )
 
-        def load_models(self, experts: list, expert_weights: list = None):
+        def load_models(self, experts: list[str] = None, expert_weights: list = None):
             """Load expert models."""
             if expert_weights is not None:
                 self.expert_weights = expert_weights
             expert_models = []
             for expert in experts:
-                if isinstance(expert, str):
+                # Load TMaRCO models
+                if (expert == "trustyai/gplus" or expert == "trustyai/gminus"):
                     expert = BartForConditionalGeneration.from_pretrained(
                         expert,
                         forced_bos_token_id=self.tokenizer.bos_token_id,
                         device_map="auto",
                     )
+                # Load local models
+                elif os.path.exists(os.path.dirname(expert)):
+                    expert = AutoModelForMaskedLM.from_pretrained(
+                        expert,
+                        forced_bos_token_id=self.tokenizer.bos_token_id,
+                        device_map = "auto"
+                    )
+                # Load HuggingFace models
+                else:
+                    expert = AutoModelForCausalLM.from_pretrained(
+                        expert,
+                        forced_bos_token_id=self.tokenizer.bos_token_id,
+                        device_map = "auto"
+                    )
                 expert_models.append(expert)
             self.experts = expert_models