Update README.md

README.md

@@ -92,31 +92,7 @@ pip install transformers sentencepiece torch ctranslate2
 ```
 
 ### 2. Inference
-## Vanilla model
-```Python
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-model_path = "anzorq/m2m100_418M_ft_ru-kbd_44K"  
-tgt_lang="zu"
-
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
-
-def translate(text, num_beams=4, num_return_sequences=4):
-    inputs = tokenizer(text, return_tensors="pt")
-    num_return_sequences = min(num_return_sequences, num_beams)
-
-    translated_tokens = model.generate(
-        **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang], num_beams=num_beams, num_return_sequences=num_return_sequences
-    )
-
-    translations = [tokenizer.decode(translation, skip_special_tokens=True) for translation in translated_tokens]
-    return text, translations
-
-# Test the translation
-text = "Текст для перевода"
-print(translate(text))
-```
 
 ## CTranslate2 model (quantized model, much faster inference)
 First, download the files for the model in ctranslate2 format:
@@ -158,6 +134,32 @@ def translate(text, num_beams=4, num_return_sequences=4):
     
     return text, translations
 
+# Test the translation
+text = "Текст для перевода"
+print(translate(text))
+```
+
+## Vanilla model
+```Python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+model_path = "anzorq/m2m100_418M_ft_ru-kbd_44K"  
+tgt_lang="zu"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
+
+def translate(text, num_beams=4, num_return_sequences=4):
+    inputs = tokenizer(text, return_tensors="pt")
+    num_return_sequences = min(num_return_sequences, num_beams)
+
+    translated_tokens = model.generate(
+        **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang], num_beams=num_beams, num_return_sequences=num_return_sequences
+    )
+
+    translations = [tokenizer.decode(translation, skip_special_tokens=True) for translation in translated_tokens]
+    return text, translations
+
 # Test the translation
 text = "Текст для перевода"
 print(translate(text))