( model: typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] config: PretrainedConfig )
(
model_id: typing.Union[str, pathlib.Path]
from_transformers: bool = False
force_download: bool = False
use_auth_token: typing.Optional[str] = None
cache_dir: typing.Optional[str] = None
subfolder: str = ''
config: ForwardRef('PretrainedConfig') = None
local_files_only: bool = False
**kwargs
)
→
OptimizedModel
Parameters
Union[str, Path]
) —
Can be either:bert-base-uncased
, or namespaced under a
user or organization name, like dbmdz/bert-base-german-cased
.~OptimizedModel.save_pretrained
,
e.g., ./my_model_directory/
.bool
, optional, defaults to False
) —
Defines whether the provided model_id
contains a vanilla Transformers checkpoint.
bool
, optional, defaults to True
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
Optional[str]
, optional) —
The token to use as HTTP bearer authorization for remote files. If True
, will use the token generated
when running transformers-cli login
(stored in ~/.huggingface
).
Optional[str]
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
str
, optional, defaults to ""
) —
In case the relevant files are located inside a subfolder of the model repo either locally or on huggingface.co, you can
specify the folder name here.
Optional[transformers.PretrainedConfig]
, optional) —
The model configuration.
bool
, optional, defaults to False
) —
Whether or not to only look at local files (i.e., do not try to download the model).
Returns
OptimizedModel
The loaded optimized model.
Instantiate a pretrained model from a pre-trained model configuration.
( save_directory: typing.Union[str, os.PathLike] push_to_hub: bool = False **kwargs )
Parameters
Union[str, os.PathLike]
) —
Directory to which to save. Will be created if it doesn’t exist.
bool
, optional, defaults to False
) —
Whether or not to push your model to the Hugging Face model hub after saving it.
Using push_to_hub=True
will synchronize the repository you are pushing to with save_directory
,
which requires save_directory
to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along temp_dir=True
to use a temporary directory instead.
Saves a model and its configuration file to a directory, so that it can be re-loaded using the
from_pretrained
class method.