Using smaller m2m100 model

app.py

@@ -18,6 +18,7 @@ from deep_translator import GoogleTranslator
 from model_spacy import nlp_xx
 
 import model_translation
+m2m100 = model_translation.ModelM2M100()
 
 
 def translate_with_Helsinki(
@@ -72,7 +73,6 @@ def translate_text(
         tgt_lang = 'en'
 
     # translate
-    m2m100 = model_translation.ModelM2M100()
     translated_text_m2m100 = m2m100.translate(text, src_lang, tgt_lang)
     translated_text_google_translate = GoogleTranslator(
         source='auto', target='en').translate(text=text)
@@ -102,7 +102,7 @@ with gr.Blocks() as demo:
     # Output
     output_text_m2m100 = gr.Textbox(
         lines=4,
-        label="Facebook m2m100 (1.2B)",
+        label="Facebook m2m100 (418M)",
         render=True
     )
     output_text_google_translate = gr.Textbox(
@@ -136,7 +136,7 @@ with gr.Blocks() as demo:
 
     with gr.Accordion("Documentation", open=False):
         gr.Markdown("""
-            - Models: serving Facebook M2M100 and Google Translate.
+            - Models: serving Facebook M2M100 (418M) and Google Translate.
         """)
 
 if __name__ == "__main__":

model_translation.py

@@ -203,7 +203,7 @@ class ModelM2M100(metaclass=Singleton):
     Model: https://huggingface.co/facebook/m2m100_1.2B
     """
     def __init__(self):
-        self._model_name = "facebook/m2m100_1.2B"
+        self._model_name = "facebook/m2m100_418M"
         self._tokenizer = M2M100Tokenizer.from_pretrained(self._model_name)
         self._model = M2M100ForConditionalGeneration.from_pretrained(
             self._model_name,