Traceback (most recent call last): File "[sic]/Lib/site-packages/huggingface_hub/dataclasses.py", line 148, in strict_setattr validator(value) File "[sic]/Lib/site-packages/huggingface_hub/dataclasses.py", line 607, in validator type_validator(field.name, value, field.type) File "[sic]/Lib/site-packages/huggingface_hub/dataclasses.py", line 474, in type_validator _validate_simple_type(name, value, expected_type) File "[sic]/Lib/site-packages/huggingface_hub/dataclasses.py", line 597, in _validate_simple_type raise TypeError( TypeError: Field 'embedding_multiplier' expected float, got int (value: 12)

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "[sic]/demo_granite_4_1b_speech.py", line 4, in <module> config = AutoConfig.from_pretrained(model_name) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "[sic]/src/transformers/models/auto/configuration_auto.py", line 1486, in from_pretrained return config_class.from_dict(config_dict, **unused_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "[sic]/src/transformers/configuration_utils.py", line 757, in from_dict config = cls(**config_dict) ^^^^^^^^^^^^^^^^^^ File "[sic]/Lib/site-packages/huggingface_hub/dataclasses.py", line 279, in init_with_validate initial_init(self, *args, **kwargs) # type: ignore [call-arg] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "[sic]/Lib/site-packages/huggingface_hub/dataclasses.py", line 194, in init self.post_init(**additional_kwargs) File "[sic]/src/transformers/models/granite_speech/configuration_granite_speech.py", line 122, in post_init self.text_config = CONFIG_MAPPINGself.text_config["model_type"]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "[sic]/Lib/site-packages/huggingface_hub/dataclasses.py", line 279, in init_with_validate initial_init(self, *args, **kwargs) # type: ignore [call-arg] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "[sic]/Lib/site-packages/huggingface_hub/dataclasses.py", line 179, in init setattr(self, f.name, standard_kwargs[f.name]) File "[sic]/Lib/site-packages/huggingface_hub/dataclasses.py", line 150, in strict_setattr raise StrictDataclassFieldValidationError(field=name, cause=e) from e huggingface_hub.errors.StrictDataclassFieldValidationError: Validation error for field 'embedding_multiplier': TypeError: Field 'embedding_multiplier' expected float, got int (value: 12)

Fix Plan

To resolve the issue, we need to update the embedding_multiplier field in the model configuration to expect an integer value instead of a float.

Here are the steps to fix the issue:

• Update the configuration_granite_speech.py file in the transformers library to change the type of embedding_multiplier from float to int.
• Alternatively, you can modify the model configuration to pass the embedding_multiplier as a float.

Code Changes

You can modify the model configuration as follows:

from transformers import AutoConfig

model_name = "ibm-granite/granite-4.0-1b-speech"
config = AutoConfig.from_pretrained(model_name)

# Update the embedding_multiplier to a float
config.embedding_multiplier = float(config.embedding_multiplier)

However, this is not a recommended solution as it may cause issues with the model's performance.

A better solution would be to update the configuration_granite_speech.py file:

from dataclasses import dataclass, field
from typing import Optional

@dataclass
class GraniteSpeechConfig:
    # ...
    embedding_multiplier: int = field(
        default=12,
        metadata={
            "help": "The multiplier for the embedding size"
        }
    )

Verification

To verify that the fix worked, you can try loading the model configuration again:

from transformers import AutoConfig

model_name = "ibm-granite/granite-4.0-1b-speech"
config = AutoConfig.from_pretrained(model_name)
print(config.embedding_multiplier)  # Should print the updated value

Extra Tips

• Make sure to update the transformers library to the latest version to ensure that you have the latest fixes and features.
• If you are using a custom model, make sure to update the model configuration to match the changes made to the transformers library.