usage: ct2-fairseq-converter [-h] --model_path MODEL_PATH --data_dir DATA_DIR
                             [--user_dir USER_DIR] [--fixed_dictionary FIXED_DICTIONARY]
                             [--source_lang SOURCE_LANG] [--target_lang TARGET_LANG]
                             [--no_default_special_tokens] --output_dir OUTPUT_DIR
                             [--vocab_mapping VOCAB_MAPPING]
                             [--quantization {int8,int8_float32,int8_float16,int8_bfloat16,int16,float16,bfloat16,float32}]
                             [--force]

options:
  -h, --help            show this help message and exit
  --model_path MODEL_PATH
                        Model path. (default: None)
  --data_dir DATA_DIR   Data directory containing the source and target vocabularies. (default:
                        None)
  --user_dir USER_DIR   Directory containing custom extensions. (default: None)
  --fixed_dictionary FIXED_DICTIONARY
                        Fixed dictionary for multilingual models. (default: None)
  --source_lang SOURCE_LANG
                        Source language. This argument is used to find dictionary file from
                        `data_dir`. (default: None)
  --target_lang TARGET_LANG
                        Target language. This argument is used to find dictionary file from
                        `data_dir`. (default: None)
  --no_default_special_tokens
                        Require all special tokens to be provided by the user during inference,
                        including the decoder start token. (default: False)
  --output_dir OUTPUT_DIR
                        Output model directory. (default: None)
  --vocab_mapping VOCAB_MAPPING
                        Vocabulary mapping file (optional). (default: None)
  --quantization {int8,int8_float32,int8_float16,int8_bfloat16,int16,float16,bfloat16,float32}
                        Weight quantization type. (default: None)
  --force               Force conversion even if the output directory already exists. (default:
                        False)