diff --git a/medcat-v2/medcat/components/addons/meta_cat/meta_cat.py b/medcat-v2/medcat/components/addons/meta_cat/meta_cat.py
index 079497688..caf13908f 100644
--- a/medcat-v2/medcat/components/addons/meta_cat/meta_cat.py
+++ b/medcat-v2/medcat/components/addons/meta_cat/meta_cat.py
@@ -27,6 +27,9 @@
 from medcat.cdb import CDB
 from medcat.vocab import Vocab
 from medcat.utils.defaults import COMPONENTS_FOLDER
+from medcat.utils.defaults import (
+    avoid_legacy_conversion, doing_legacy_conversion_message,
+    LegacyConversionDisabledError)
 from peft import get_peft_model, LoraConfig, TaskType
 
 # It should be safe to do this always, as all other multiprocessing
@@ -173,9 +176,39 @@ def serialise_to(self, folder_path: str) -> None:
         os.mkdir(folder_path)
         self.save(folder_path)
 
+    @classmethod
+    def _create_throwaway_tokenizer(cls) -> BaseTokenizer:
+        from medcat.tokenizing.tokenizers import create_tokenizer
+        from medcat.config import Config
+        logger.warning(
+            "A base tokenizer was not provided during the loading of a "
+            "MetaCAT. The tokenizer is used to register the required data "
+            "paths for MetaCAT to function. Using the default of '%s'. If "
+            "this it not the tokenizer you will end up using, MetaCAT may "
+            "be unable to recover unless a) the paths are registered "
+            "explicitly, or b) there are other MetaCATs created with the "
+            "correct tokenizer. Do note that this will also create "
+            "another instance of the tokenizer, though it should be "
+            "garbage collected soon.", cls.DEFAULT_TOKENIZER
+        )
+        # NOTE: the use of a (mostly) default config here probably won't
+        #       affect anything since the tokenizer itself won't be used
+        gcnf = Config()
+        gcnf.general.nlp.provider = 'spacy'
+        return create_tokenizer(cls.DEFAULT_TOKENIZER, gcnf)
+
     @classmethod
     def deserialise_from(cls, folder_path: str, **init_kwargs
                          ) -> 'MetaCATAddon':
+        if "model.dat" in os.listdir(folder_path):
+            if not avoid_legacy_conversion():
+                doing_legacy_conversion_message(
+                    logger, cls.__name__, folder_path)
+                from medcat.utils.legacy.convert_meta_cat import (
+                    get_meta_cat_from_old)
+                return get_meta_cat_from_old(
+                    folder_path, cls._create_throwaway_tokenizer())
+            raise LegacyConversionDisabledError(cls.__name__,)
         if 'cnf' in init_kwargs:
             cnf = init_kwargs['cnf']
         else:
@@ -191,24 +224,7 @@ def deserialise_from(cls, folder_path: str, **init_kwargs
         if 'tokenizer' in init_kwargs:
             tokenizer = init_kwargs['tokenizer']
         else:
-            from medcat.tokenizing.tokenizers import create_tokenizer
-            from medcat.config import Config
-            logger.warning(
-                "A base tokenizer was not provided during the loading of a "
-                "MetaCAT. The tokenizer is used to register the required data "
-                "paths for MetaCAT to function. Using the default of '%s'. If "
-                "this it not the tokenizer you will end up using, MetaCAT may "
-                "be unable to recover unless a) the paths are registered "
-                "explicitly, or b) there are other MetaCATs created with the "
-                "correct tokenizer. Do note that this will also create "
-                "another instance of the tokenizer, though it should be "
-                "garbage collected soon.", cls.DEFAULT_TOKENIZER
-            )
-            # NOTE: the use of a (mostly) default config here probably won't
-            #       affect anything since the tokenizer itself won't be used
-            gcnf = Config()
-            gcnf.general.nlp.provider = 'spacy'
-            tokenizer = create_tokenizer(cls.DEFAULT_TOKENIZER, gcnf)
+            tokenizer = cls._create_throwaway_tokenizer()
         return cls.load_existing(
             load_path=folder_path,
             cnf=cnf,