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+    "model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.norm.weight": "pytorch_model-00002-of-00002.bin"
+  }
+}

tokenization_baichuan.py

@@ -43,7 +43,6 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
 class BaichuanTokenizer(PreTrainedTokenizer):
     """
     Construct a Baichuan tokenizer. Based on byte-level Byte-Pair-Encoding.
-
     Args:
         vocab_file (`str`):
             Path to the vocabulary file.
@@ -72,6 +71,13 @@ class BaichuanTokenizer(PreTrainedTokenizer):
         eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
         unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
         pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+        
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+
         super().__init__(
             bos_token=bos_token,
             eos_token=eos_token,
@@ -82,12 +88,7 @@ class BaichuanTokenizer(PreTrainedTokenizer):
             sp_model_kwargs=self.sp_model_kwargs,
             clean_up_tokenization_spaces=clean_up_tokenization_spaces,
             **kwargs,
-        )
-        self.vocab_file = vocab_file
-        self.add_bos_token = add_bos_token
-        self.add_eos_token = add_eos_token
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(vocab_file)
+        )        
 
     def __getstate__(self):
         state = self.__dict__.copy()
@@ -145,11 +146,9 @@ class BaichuanTokenizer(PreTrainedTokenizer):
     def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
         """
         Save the vocabulary and special tokens file to a directory.
-
         Args:
             save_directory (`str`):
                 The directory in which to save the vocabulary.
-
         Returns:
             `Tuple(str)`: Paths to the files saved.
         """
@@ -186,7 +185,6 @@ class BaichuanTokenizer(PreTrainedTokenizer):
         """
         Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
         special tokens using the tokenizer `prepare_for_model` method.
-
         Args:
             token_ids_0 (`List[int]`):
                 List of IDs.
@@ -194,7 +192,6 @@ class BaichuanTokenizer(PreTrainedTokenizer):
                 Optional second list of IDs for sequence pairs.
             already_has_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not the token list is already formatted with special tokens for the model.
-
         Returns:
             `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
         """
@@ -223,20 +220,16 @@ class BaichuanTokenizer(PreTrainedTokenizer):
         """
         Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
         sequence pair mask has the following format:
-
         ```
         0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
         | first sequence    | second sequence |
         ```
-
         if token_ids_1 is None, only returns the first portion of the mask (0s).
-
         Args:
             token_ids_0 (`List[int]`):
                 List of ids.
             token_ids_1 (`List[int]`, *optional*):
                 Optional second list of IDs for sequence pairs.
-
         Returns:
             `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
         """