transformers - 💡(How to fix) Fix Processor fails for mistralai/Voxtral-Mini-3B-2507 [3 comments, 2 participants]

Q: Expected behavior

Error on loading of `processor` for `mistralai/Voxtral-Mini-3B-2507` model.

transformers2026-03-19 02:26:39

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huggingface/transformers#44841•Fetched 2026-04-08 01:01:52

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BhavyaShah1234

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Error Message

/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: The secret `HF_TOKEN` does not exist in your Colab secrets. To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session. You will be able to reuse this secret in all of your notebooks. Please note that authentication is recommended but still optional to access public models or datasets. warnings.warn( Converting tekken.json to tokenizer.json: 100%|██████████| 150000/150000 [00:02<00:00, 69142.59it/s]

Exception Traceback (most recent call last) /tmp/ipykernel_7845/2744582412.py in <cell line: 0>() 12 # else: 13 # processor = MODEL_DICT[MODEL]['processor'].from_pretrained(MODEL, trust_remote_code=True) ---> 14 processor = MODEL_DICT[MODEL]['processor'].from_pretrained(MODEL, trust_remote_code=True)

13 frames/usr/local/lib/python3.12/dist-packages/transformers/models/auto/processing_auto.py in from_pretrained(cls, pretrained_model_name_or_path, **kwargs) 409 ) 410 elif processor_class is not None: --> 411 return processor_class.from_pretrained( 412 pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs 413 )

/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py in from_pretrained(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision, **kwargs) 1402 processor_dict, instantiation_kwargs = cls.get_processor_dict(pretrained_model_name_or_path, **kwargs) 1403 args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs) -> 1404 return cls.from_args_and_dict(args, processor_dict, **instantiation_kwargs) 1405 1406 @classmethod

/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py in from_args_and_dict(cls, args, processor_dict, **kwargs) 1171 processor = cls(*args, **valid_kwargs) 1172 -> 1173 logger.info(f"Processor {processor}") 1174 if return_unused_kwargs: 1175 return processor, unused_kwargs

/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py in repr(self) 777 attributes_repr = [f"- {name}: {repr(getattr(self, name))}" for name in self.get_attributes()] 778 attributes_repr = "\n".join(attributes_repr) --> 779 return f"{self.class.name}:\n{attributes_repr}\n\n{self.to_json_string()}" 780 781 def save_pretrained(self, save_directory, push_to_hub: bool = False, **kwargs):

/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py in to_json_string(self) 759 str: String containing all the attributes that make up this feature_extractor instance in JSON format. 760 """ --> 761 dictionary = self.to_dict() 762 763 return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"

/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py in to_dict(self) 697 dict[str, Any]: Dictionary of all the attributes that make up this processor instance. 698 """ --> 699 output = copy.deepcopy(self.dict) 700 701 # Get the kwargs in __init__.

/usr/lib/python3.12/copy.py in deepcopy(x, memo, _nil) 134 copier = _deepcopy_dispatch.get(cls) 135 if copier is not None: --> 136 y = copier(x, memo) 137 else: 138 if issubclass(cls, type):

/usr/lib/python3.12/copy.py in _deepcopy_dict(x, memo, deepcopy) 219 memo[id(x)] = y 220 for key, value in x.items(): --> 221 y[deepcopy(key, memo)] = deepcopy(value, memo) 222 return y 223 d[dict] = _deepcopy_dict

/usr/lib/python3.12/copy.py in deepcopy(x, memo, _nil) 160 y = x 161 else: --> 162 y = _reconstruct(x, memo, *rv) 163 164 # If is its own copy, don't memoize.

/usr/lib/python3.12/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 257 if state is not None: 258 if deep: --> 259 state = deepcopy(state, memo) 260 if hasattr(y, 'setstate'): 261 y.setstate(state)

/usr/lib/python3.12/copy.py in deepcopy(x, memo, _nil) 134 copier = _deepcopy_dispatch.get(cls) 135 if copier is not None: --> 136 y = copier(x, memo) 137 else: 138 if issubclass(cls, type):

/usr/lib/python3.12/copy.py in deepcopy(x, memo, _nil) 160 y = x 161 else: --> 162 y = _reconstruct(x, memo, *rv) 163 164 # If is its own copy, don't memoize.

/usr/lib/python3.12/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 259 state = deepcopy(state, memo) 260 if hasattr(y, 'setstate'): --> 261 y.setstate(state) 262 else: 263 if isinstance(state, tuple) and len(state) == 2:

Exception: Error while attempting to unpickle Tokenizer: Token Ġ<SPECIAL_116> out of vocabulary at line 1 column 9601910

Fix Action

Fix / Workaround

/usr/lib/python3.12/copy.py in deepcopy(x, memo, _nil) 134 copier = _deepcopy_dispatch.get(cls) 135 if copier is not None: --> 136 y = copier(x, memo) 137 else: 138 if issubclass(cls, type):

Code Example

/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: 
The secret `HF_TOKEN` does not exist in your Colab secrets.
To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.
You will be able to reuse this secret in all of your notebooks.
Please note that authentication is recommended but still optional to access public models or datasets.
  warnings.warn(
Converting tekken.json to tokenizer.json: 100%|██████████| 150000/150000 [00:02<00:00, 69142.59it/s]
---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
[/tmp/ipykernel_7845/2744582412.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in <cell line: 0>()
     12 # else:
     13 #     processor = MODEL_DICT[MODEL]['processor'].from_pretrained(MODEL, trust_remote_code=True)
---> 14 processor = MODEL_DICT[MODEL]['processor'].from_pretrained(MODEL, trust_remote_code=True)

13 frames[/usr/local/lib/python3.12/dist-packages/transformers/models/auto/processing_auto.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
    409             )
    410         elif processor_class is not None:
--> 411             return processor_class.from_pretrained(
    412                 pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
    413             )

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in from_pretrained(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision, **kwargs)
   1402         processor_dict, instantiation_kwargs = cls.get_processor_dict(pretrained_model_name_or_path, **kwargs)
   1403         args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs)
-> 1404         return cls.from_args_and_dict(args, processor_dict, **instantiation_kwargs)
   1405 
   1406     @classmethod

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in from_args_and_dict(cls, args, processor_dict, **kwargs)
   1171         processor = cls(*args, **valid_kwargs)
   1172 
-> 1173         logger.info(f"Processor {processor}")
   1174         if return_unused_kwargs:
   1175             return processor, unused_kwargs

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in __repr__(self)
    777         attributes_repr = [f"- {name}: {repr(getattr(self, name))}" for name in self.get_attributes()]
    778         attributes_repr = "\n".join(attributes_repr)
--> 779         return f"{self.__class__.__name__}:\n{attributes_repr}\n\n{self.to_json_string()}"
    780 
    781     def save_pretrained(self, save_directory, push_to_hub: bool = False, **kwargs):

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in to_json_string(self)
    759             `str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
    760         """
--> 761         dictionary = self.to_dict()
    762 
    763         return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in to_dict(self)
    697             `dict[str, Any]`: Dictionary of all the attributes that make up this processor instance.
    698         """
--> 699         output = copy.deepcopy(self.__dict__)
    700 
    701         # Get the kwargs in `__init__`.

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    134     copier = _deepcopy_dispatch.get(cls)
    135     if copier is not None:
--> 136         y = copier(x, memo)
    137     else:
    138         if issubclass(cls, type):

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _deepcopy_dict(x, memo, deepcopy)
    219     memo[id(x)] = y
    220     for key, value in x.items():
--> 221         y[deepcopy(key, memo)] = deepcopy(value, memo)
    222     return y
    223 d[dict] = _deepcopy_dict

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    160                     y = x
    161                 else:
--> 162                     y = _reconstruct(x, memo, *rv)
    163 
    164     # If is its own copy, don't memoize.

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    257     if state is not None:
    258         if deep:
--> 259             state = deepcopy(state, memo)
    260         if hasattr(y, '__setstate__'):
    261             y.__setstate__(state)

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    134     copier = _deepcopy_dispatch.get(cls)
    135     if copier is not None:
--> 136         y = copier(x, memo)
    137     else:
    138         if issubclass(cls, type):

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _deepcopy_dict(x, memo, deepcopy)
    219     memo[id(x)] = y
    220     for key, value in x.items():
--> 221         y[deepcopy(key, memo)] = deepcopy(value, memo)
    222     return y
    223 d[dict] = _deepcopy_dict

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    160                     y = x
    161                 else:
--> 162                     y = _reconstruct(x, memo, *rv)
    163 
    164     # If is its own copy, don't memoize.

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    259             state = deepcopy(state, memo)
    260         if hasattr(y, '__setstate__'):
--> 261             y.__setstate__(state)
    262         else:
    263             if isinstance(state, tuple) and len(state) == 2:

Exception: Error while attempting to unpickle Tokenizer: Token `Ġ<SPECIAL_116>` out of vocabulary at line 1 column 9601910

---

from transformers import VoxtralForConditionalGeneration, AutoProcessor
import torch

device = "cuda"
repo_id = "mistralai/Voxtral-Mini-3B-2507"

processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)

conversation = [
    {
        "role": "user",
        "content": [
            {
                "type": "audio",
                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
            },
        ],
    }
]

inputs = processor.apply_chat_template(conversation)
inputs = inputs.to(device, dtype=torch.bfloat16)

outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)

print("\nGenerated response:")
print("=" * 80)
print(decoded_outputs[0])
print("=" * 80)

---

Converting tekken.json to tokenizer.json: 100%|██████████| 150000/150000 [00:01<00:00, 116597.64it/s]
---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
[/tmp/ipykernel_7845/3351359521.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in <cell line: 0>()
      5 repo_id = "mistralai/Voxtral-Mini-3B-2507"
      6 
----> 7 processor = AutoProcessor.from_pretrained(repo_id)
      8 model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
      9 

13 frames[/usr/local/lib/python3.12/dist-packages/transformers/models/auto/processing_auto.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
    409             )
    410         elif processor_class is not None:
--> 411             return processor_class.from_pretrained(
    412                 pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
    413             )

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in from_pretrained(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision, **kwargs)
   1402         processor_dict, instantiation_kwargs = cls.get_processor_dict(pretrained_model_name_or_path, **kwargs)
   1403         args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs)
-> 1404         return cls.from_args_and_dict(args, processor_dict, **instantiation_kwargs)
   1405 
   1406     @classmethod

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in from_args_and_dict(cls, args, processor_dict, **kwargs)
   1171         processor = cls(*args, **valid_kwargs)
   1172 
-> 1173         logger.info(f"Processor {processor}")
   1174         if return_unused_kwargs:
   1175             return processor, unused_kwargs

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in __repr__(self)
    777         attributes_repr = [f"- {name}: {repr(getattr(self, name))}" for name in self.get_attributes()]
    778         attributes_repr = "\n".join(attributes_repr)
--> 779         return f"{self.__class__.__name__}:\n{attributes_repr}\n\n{self.to_json_string()}"
    780 
    781     def save_pretrained(self, save_directory, push_to_hub: bool = False, **kwargs):

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in to_json_string(self)
    759             `str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
    760         """
--> 761         dictionary = self.to_dict()
    762 
    763         return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in to_dict(self)
    697             `dict[str, Any]`: Dictionary of all the attributes that make up this processor instance.
    698         """
--> 699         output = copy.deepcopy(self.__dict__)
    700 
    701         # Get the kwargs in `__init__`.

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    134     copier = _deepcopy_dispatch.get(cls)
    135     if copier is not None:
--> 136         y = copier(x, memo)
    137     else:
    138         if issubclass(cls, type):

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _deepcopy_dict(x, memo, deepcopy)
    219     memo[id(x)] = y
    220     for key, value in x.items():
--> 221         y[deepcopy(key, memo)] = deepcopy(value, memo)
    222     return y
    223 d[dict] = _deepcopy_dict

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    160                     y = x
    161                 else:
--> 162                     y = _reconstruct(x, memo, *rv)
    163 
    164     # If is its own copy, don't memoize.

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    257     if state is not None:
    258         if deep:
--> 259             state = deepcopy(state, memo)
    260         if hasattr(y, '__setstate__'):
    261             y.__setstate__(state)

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    134     copier = _deepcopy_dispatch.get(cls)
    135     if copier is not None:
--> 136         y = copier(x, memo)
    137     else:
    138         if issubclass(cls, type):

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _deepcopy_dict(x, memo, deepcopy)
    219     memo[id(x)] = y
    220     for key, value in x.items():
--> 221         y[deepcopy(key, memo)] = deepcopy(value, memo)
    222     return y
    223 d[dict] = _deepcopy_dict

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    160                     y = x
    161                 else:
--> 162                     y = _reconstruct(x, memo, *rv)
    163 
    164     # If is its own copy, don't memoize.

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    259             state = deepcopy(state, memo)
    260         if hasattr(y, '__setstate__'):
--> 261             y.__setstate__(state)
    262         else:
    263             if isinstance(state, tuple) and len(state) == 2:

Exception: Error while attempting to unpickle Tokenizer: Token `<SPECIAL_32><SPECIAL_32>` out of vocabulary at line 1 column 9847997

RAW_BUFFERClick to expand / collapse

System Info

I am trying to run inference using mistralai/Voxtral-Mini-3B-2507 on an audio (np.ndarray). On loading the processor using processor = transformers.AutoProcessor.from_pretrained(MODEL, trust_remote_code=True), I am getting the following error:

/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: 
The secret `HF_TOKEN` does not exist in your Colab secrets.
To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.
You will be able to reuse this secret in all of your notebooks.
Please note that authentication is recommended but still optional to access public models or datasets.
  warnings.warn(
Converting tekken.json to tokenizer.json: 100%|██████████| 150000/150000 [00:02<00:00, 69142.59it/s]
---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
[/tmp/ipykernel_7845/2744582412.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in <cell line: 0>()
     12 # else:
     13 #     processor = MODEL_DICT[MODEL]['processor'].from_pretrained(MODEL, trust_remote_code=True)
---> 14 processor = MODEL_DICT[MODEL]['processor'].from_pretrained(MODEL, trust_remote_code=True)

13 frames[/usr/local/lib/python3.12/dist-packages/transformers/models/auto/processing_auto.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
    409             )
    410         elif processor_class is not None:
--> 411             return processor_class.from_pretrained(
    412                 pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
    413             )

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in from_pretrained(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision, **kwargs)
   1402         processor_dict, instantiation_kwargs = cls.get_processor_dict(pretrained_model_name_or_path, **kwargs)
   1403         args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs)
-> 1404         return cls.from_args_and_dict(args, processor_dict, **instantiation_kwargs)
   1405 
   1406     @classmethod

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in from_args_and_dict(cls, args, processor_dict, **kwargs)
   1171         processor = cls(*args, **valid_kwargs)
   1172 
-> 1173         logger.info(f"Processor {processor}")
   1174         if return_unused_kwargs:
   1175             return processor, unused_kwargs

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in __repr__(self)
    777         attributes_repr = [f"- {name}: {repr(getattr(self, name))}" for name in self.get_attributes()]
    778         attributes_repr = "\n".join(attributes_repr)
--> 779         return f"{self.__class__.__name__}:\n{attributes_repr}\n\n{self.to_json_string()}"
    780 
    781     def save_pretrained(self, save_directory, push_to_hub: bool = False, **kwargs):

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in to_json_string(self)
    759             `str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
    760         """
--> 761         dictionary = self.to_dict()
    762 
    763         return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in to_dict(self)
    697             `dict[str, Any]`: Dictionary of all the attributes that make up this processor instance.
    698         """
--> 699         output = copy.deepcopy(self.__dict__)
    700 
    701         # Get the kwargs in `__init__`.

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    134     copier = _deepcopy_dispatch.get(cls)
    135     if copier is not None:
--> 136         y = copier(x, memo)
    137     else:
    138         if issubclass(cls, type):

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _deepcopy_dict(x, memo, deepcopy)
    219     memo[id(x)] = y
    220     for key, value in x.items():
--> 221         y[deepcopy(key, memo)] = deepcopy(value, memo)
    222     return y
    223 d[dict] = _deepcopy_dict

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    160                     y = x
    161                 else:
--> 162                     y = _reconstruct(x, memo, *rv)
    163 
    164     # If is its own copy, don't memoize.

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    257     if state is not None:
    258         if deep:
--> 259             state = deepcopy(state, memo)
    260         if hasattr(y, '__setstate__'):
    261             y.__setstate__(state)

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    134     copier = _deepcopy_dispatch.get(cls)
    135     if copier is not None:
--> 136         y = copier(x, memo)
    137     else:
    138         if issubclass(cls, type):

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _deepcopy_dict(x, memo, deepcopy)
    219     memo[id(x)] = y
    220     for key, value in x.items():
--> 221         y[deepcopy(key, memo)] = deepcopy(value, memo)
    222     return y
    223 d[dict] = _deepcopy_dict

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    160                     y = x
    161                 else:
--> 162                     y = _reconstruct(x, memo, *rv)
    163 
    164     # If is its own copy, don't memoize.

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    259             state = deepcopy(state, memo)
    260         if hasattr(y, '__setstate__'):
--> 261             y.__setstate__(state)
    262         else:
    263             if isinstance(state, tuple) and len(state) == 2:

Exception: Error while attempting to unpickle Tokenizer: Token `Ġ<SPECIAL_116>` out of vocabulary at line 1 column 9601910

I tested out the following standalone code provided in the model card of mistralai/Voxtral-Mini-3B-2507 but it too does not work:

from transformers import VoxtralForConditionalGeneration, AutoProcessor
import torch

device = "cuda"
repo_id = "mistralai/Voxtral-Mini-3B-2507"

processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)

conversation = [
    {
        "role": "user",
        "content": [
            {
                "type": "audio",
                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
            },
        ],
    }
]

inputs = processor.apply_chat_template(conversation)
inputs = inputs.to(device, dtype=torch.bfloat16)

outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)

print("\nGenerated response:")
print("=" * 80)
print(decoded_outputs[0])
print("=" * 80)

Error given by above script:

Converting tekken.json to tokenizer.json: 100%|██████████| 150000/150000 [00:01<00:00, 116597.64it/s]
---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
[/tmp/ipykernel_7845/3351359521.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in <cell line: 0>()
      5 repo_id = "mistralai/Voxtral-Mini-3B-2507"
      6 
----> 7 processor = AutoProcessor.from_pretrained(repo_id)
      8 model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
      9 

13 frames[/usr/local/lib/python3.12/dist-packages/transformers/models/auto/processing_auto.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
    409             )
    410         elif processor_class is not None:
--> 411             return processor_class.from_pretrained(
    412                 pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
    413             )

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in from_pretrained(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision, **kwargs)
   1402         processor_dict, instantiation_kwargs = cls.get_processor_dict(pretrained_model_name_or_path, **kwargs)
   1403         args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs)
-> 1404         return cls.from_args_and_dict(args, processor_dict, **instantiation_kwargs)
   1405 
   1406     @classmethod

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in from_args_and_dict(cls, args, processor_dict, **kwargs)
   1171         processor = cls(*args, **valid_kwargs)
   1172 
-> 1173         logger.info(f"Processor {processor}")
   1174         if return_unused_kwargs:
   1175             return processor, unused_kwargs

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in __repr__(self)
    777         attributes_repr = [f"- {name}: {repr(getattr(self, name))}" for name in self.get_attributes()]
    778         attributes_repr = "\n".join(attributes_repr)
--> 779         return f"{self.__class__.__name__}:\n{attributes_repr}\n\n{self.to_json_string()}"
    780 
    781     def save_pretrained(self, save_directory, push_to_hub: bool = False, **kwargs):

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in to_json_string(self)
    759             `str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
    760         """
--> 761         dictionary = self.to_dict()
    762 
    763         return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"

[/usr/local/lib/python3.12/dist-packages/transformers/processing_utils.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in to_dict(self)
    697             `dict[str, Any]`: Dictionary of all the attributes that make up this processor instance.
    698         """
--> 699         output = copy.deepcopy(self.__dict__)
    700 
    701         # Get the kwargs in `__init__`.

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    134     copier = _deepcopy_dispatch.get(cls)
    135     if copier is not None:
--> 136         y = copier(x, memo)
    137     else:
    138         if issubclass(cls, type):

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _deepcopy_dict(x, memo, deepcopy)
    219     memo[id(x)] = y
    220     for key, value in x.items():
--> 221         y[deepcopy(key, memo)] = deepcopy(value, memo)
    222     return y
    223 d[dict] = _deepcopy_dict

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    160                     y = x
    161                 else:
--> 162                     y = _reconstruct(x, memo, *rv)
    163 
    164     # If is its own copy, don't memoize.

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    257     if state is not None:
    258         if deep:
--> 259             state = deepcopy(state, memo)
    260         if hasattr(y, '__setstate__'):
    261             y.__setstate__(state)

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    134     copier = _deepcopy_dispatch.get(cls)
    135     if copier is not None:
--> 136         y = copier(x, memo)
    137     else:
    138         if issubclass(cls, type):

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _deepcopy_dict(x, memo, deepcopy)
    219     memo[id(x)] = y
    220     for key, value in x.items():
--> 221         y[deepcopy(key, memo)] = deepcopy(value, memo)
    222     return y
    223 d[dict] = _deepcopy_dict

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in deepcopy(x, memo, _nil)
    160                     y = x
    161                 else:
--> 162                     y = _reconstruct(x, memo, *rv)
    163 
    164     # If is its own copy, don't memoize.

[/usr/lib/python3.12/copy.py](https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD#) in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    259             state = deepcopy(state, memo)
    260         if hasattr(y, '__setstate__'):
--> 261             y.__setstate__(state)
    262         else:
    263             if isinstance(state, tuple) and len(state) == 2:

Exception: Error while attempting to unpickle Tokenizer: Token `<SPECIAL_32><SPECIAL_32>` out of vocabulary at line 1 column 9847997

There are many issues with a lot of other APIs in transformers==5.3.0. Please resolve these issues ASAP.

Who can help?

@eustlb @ebezzam @vasqu @Cyrilvallez

Information

The official example scripts
My own modified scripts

Tasks

An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
My own task or dataset (give details below)

Reproduction

Steps to reproduce the issue:

Connect to the correct GPU runtime (I tried on T4 GPU).
Upload a valid audio file with extension .WAV.
Ensure MODEL = "mistralai/Voxtral-Mini-3B-2507" and AUDIO_PATH = "<valid audio path>.WAV".
Run the notebook at https://colab.research.google.com/drive/18tlO4aFg0RxV7hcBf04abb1wKW-B3zbD?usp=sharing.

EMSCall (1).ipynb

Expected behavior

Error on loading of processor for mistralai/Voxtral-Mini-3B-2507 model.

extent analysis

Fix Plan

The error occurs due to an issue with the transformers library version 5.3.0. To resolve this, we can try the following steps:

Update the transformers library: Try updating the transformers library to the latest version using pip: pip install --upgrade transformers
Downgrade the transformers library: If the issue persists, try downgrading the transformers library to a previous version that is known to work with the mistralai/Voxtral-Mini-3B-2507 model: pip install transformers==4.24.0
Use a different model: If the issue is specific to the mistralai/Voxtral-Mini-3B-2507 model, try using a different model that is compatible with the transformers library version 5.3.0.

Here's an example code snippet that demonstrates how to load the processor using the transformers library version 4.24.0:

import torch
from transformers import AutoProcessor

# Load the processor using the transformers library version 4.24.0
processor = AutoProcessor.from_pretrained("mistralai/Voxtral-Mini-3B-2507", trust_remote_code=True)

Verification

To verify that the fix worked, you can try running the following code snippet:

import torch
from transformers import AutoProcessor, VoxtralForConditionalGeneration

# Load the processor and model
processor = AutoProcessor.from_pretrained("mistralai/Voxtral-Mini-3B-2507", trust_remote_code=True)
model = VoxtralForConditionalGeneration.from_pretrained("mistralai/Voxtral-Mini-3B-2507")

# Test the processor and model
conversation = [
    {
        "role": "user",
        "content": [
            {
                "type": "audio",
                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
            },
        ],
    }
]

inputs = processor.apply_chat_template(conversation)
inputs = inputs.to("cuda", dtype=torch.bfloat16)

outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)

print("\nGenerated response:")
print("=" * 80)
print(decoded_outputs[0])
print("=" * 80)

If the code runs without errors and generates a response, it indicates that the fix worked.

Extra Tips

Make sure to check the compatibility of the transformers library version with the mistralai/Voxtral-Mini-3B-2507 model before

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Error on loading of processor for mistralai/Voxtral-Mini-3B-2507 model.

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