---
local: configuration
sections:
- local: transformers.PretrainedConfig
  title: PretrainedConfig
title: Configuration
---
<script>
import Tip from "./Tip.svelte";
import Youtube from "./Youtube.svelte";
import Docstring from "./Docstring.svelte";
import CodeBlock from "./CodeBlock.svelte";
import CodeBlockFw from "./CodeBlockFw.svelte";
import DocNotebookDropdown from "./DocNotebookDropdown.svelte";
import IconCopyLink from "./IconCopyLink.svelte";
export let fw: "pt" | "tf"
</script>
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<h1 id="configuration">Configuration</h1>

The base class [PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) implements the common methods for loading/saving a configuration
either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded
from HuggingFace's AWS S3 repository).

Each derived config class implements model specific attributes. Common attributes present in all config classes are:
`hidden_size`, `num_attention_heads`, and `num_hidden_layers`. Text models further implement:
`vocab_size`.


<h2 id="transformers.PretrainedConfig">PretrainedConfig</h2>

<div class="docstring">

<docstring><name>class transformers.PretrainedConfig</name><anchor>transformers.PretrainedConfig</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L53</source><parameters>[{"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **name_or_path** (`str`, *optional*, defaults to `""`) --
  Store the string that was passed to [PreTrainedModel.from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) or
  [TFPreTrainedModel.from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) as `pretrained_model_name_or_path` if the configuration was created
  with such a method.
- **output_hidden_states** (`bool`, *optional*, defaults to `False`) --
  Whether or not the model should return all hidden-states.
- **output_attentions** (`bool`, *optional*, defaults to `False`) --
  Whether or not the model should returns all attentions.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not the model should return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.
- **is_encoder_decoder** (`bool`, *optional*, defaults to `False`) --
  Whether the model is used as an encoder/decoder or not.
- **is_decoder** (`bool`, *optional*, defaults to `False`) --
  Whether the model is used as decoder or not (in which case it's used as an encoder).
- **cross_attention_hidden_size**** (`bool`, *optional*) --
  The hidden size of the cross-attention layer in case the model is used as a decoder in an encoder-decoder
  setting and the cross-attention hidden dimension differs from `self.config.hidden_size`.
- **add_cross_attention** (`bool`, *optional*, defaults to `False`) --
  Whether cross-attention layers should be added to the model. Note, this option is only relevant for models
  that can be used as decoder models within the [EncoderDecoderModel](/docs/transformers/pr_15592/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel) class, which consists of all models
  in `AUTO_MODELS_FOR_CAUSAL_LM`.
- **tie_encoder_decoder** (`bool`, *optional*, defaults to `False`) --
  Whether all encoder weights should be tied to their equivalent decoder weights. This requires the encoder
  and decoder model to have the exact same parameter names.
- **prune_heads** (`Dict[int, List[int]]`, *optional*, defaults to `&amp;lcub;}`) --
  Pruned heads of the model. The keys are the selected layer indices and the associated values, the list of
  heads to prune in said layer.

  For instance `&amp;lcub;1: [0, 2], 2: [2, 3]}` will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
- **chunk_size_feed_forward** (`int`, *optional*, defaults to `0`) --
  The chunk size of all feed forward layers in the residual attention blocks. A chunk size of `0` means that
  the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes `n` &amp;lt;
  sequence_length embeddings at a time. For more information on feed forward chunking, see [How does Feed
  Forward Chunking work?](../glossary.html#feed-forward-chunking).

</paramsdesc><paramsdesc1title>Parameters for sequence generation</paramsdesc1title><paramsdesc1>

- **max_length** (`int`, *optional*, defaults to 20) --
  Maximum length that will be used by default in the `generate` method of the model.
- **min_length** (`int`, *optional*, defaults to 10) --
  Minimum length that will be used by default in the `generate` method of the model.
- **do_sample** (`bool`, *optional*, defaults to `False`) --
  Flag that will be used by default in the `generate` method of the model. Whether or not to use sampling ;
  use greedy decoding otherwise.
- **early_stopping** (`bool`, *optional*, defaults to `False`) --
  Flag that will be used by default in the `generate` method of the model. Whether to stop the beam search
  when at least `num_beams` sentences are finished per batch or not.
- **num_beams** (`int`, *optional*, defaults to 1) --
  Number of beams for beam search that will be used by default in the `generate` method of the model. 1 means
  no beam search.
- **num_beam_groups** (`int`, *optional*, defaults to 1) --
  Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams
  that will be used by default in the `generate` method of the model. 1 means no group beam search.
- **diversity_penalty** (`float`, *optional*, defaults to 0.0) --
  Value to control diversity for group beam search. that will be used by default in the `generate` method of
  the model. 0 means no diversity penalty. The higher the penalty, the more diverse are the outputs.
- **temperature** (`float`, *optional*, defaults to 1) --
  The value used to module the next token probabilities that will be used by default in the `generate` method
  of the model. Must be strictly positive.
- **top_k** (`int`, *optional*, defaults to 50) --
  Number of highest probability vocabulary tokens to keep for top-k-filtering that will be used by default in
  the `generate` method of the model.
- **top_p** (`float`, *optional*, defaults to 1) --
  Value that will be used by default in the `generate` method of the model for `top_p`. If set to float &amp;lt; 1,
  only the most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.
- **repetition_penalty** (`float`, *optional*, defaults to 1) --
  Parameter for repetition penalty that will be used by default in the `generate` method of the model. 1.0
  means no penalty.
- **length_penalty** (`float`, *optional*, defaults to 1) --
  Exponential penalty to the length that will be used by default in the `generate` method of the model.
- **no_repeat_ngram_size** (`int`, *optional*, defaults to 0) -- Value that will be used by default in the --
  `generate` method of the model for `no_repeat_ngram_size`. If set to int > 0, all ngrams of that size can
  only occur once.
- **encoder_no_repeat_ngram_size** (`int`, *optional*, defaults to 0) -- Value that will be used by --
  default in the `generate` method of the model for `encoder_no_repeat_ngram_size`. If set to int > 0, all
  ngrams of that size that occur in the `encoder_input_ids` cannot occur in the `decoder_input_ids`.
- **bad_words_ids** (`List[int]`, *optional*) --
  List of token ids that are not allowed to be generated that will be used by default in the `generate`
  method of the model. In order to get the tokens of the words that should not appear in the generated text,
  use `tokenizer.encode(bad_word, add_prefix_space=True)`.
- **num_return_sequences** (`int`, *optional*, defaults to 1) --
  Number of independently computed returned sequences for each element in the batch that will be used by
  default in the `generate` method of the model.
- **output_scores** (`bool`, *optional*, defaults to `False`) --
  Whether the model should return the logits when used for generation.
- **return_dict_in_generate** (`bool`, *optional*, defaults to `False`) --
  Whether the model should return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a `torch.LongTensor`.
- **forced_bos_token_id** (`int`, *optional*) --
  The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful for
  multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be the target
  language token.
- **forced_eos_token_id** (`int`, *optional*) --
  The id of the token to force as the last generated token when `max_length` is reached.
- **remove_invalid_values** (`bool`, *optional*) --
  Whether to remove possible _nan_ and _inf_ outputs of the model to prevent the generation method to crash.
  Note that using `remove_invalid_values` can slow down generation.

</paramsdesc1><paramsdesc2title>Parameters for fine-tuning tasks</paramsdesc2title><paramsdesc2>

- **architectures** (`List[str]`, *optional*) --
  Model architectures that can be used with the model pretrained weights.
- **finetuning_task** (`str`, *optional*) --
  Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow
  or PyTorch) checkpoint.
- **id2label** (`Dict[int, str]`, *optional*) --
  A map from index (for instance prediction index, or target index) to label.
- **label2id** (`Dict[str, int]`, *optional*) -- A map from label to index for the model.
- **num_labels** (`int`, *optional*) --
  Number of labels to use in the last layer added to the model, typically for a classification task.
- **task_specific_params** (`Dict[str, Any]`, *optional*) --
  Additional keyword arguments to store for the current task.
- **problem_type** (`str`, *optional*) --
  Problem type for `XxxForSequenceClassification` models. Can be one of `"regression"`,
  `"single_label_classification"` or `"multi_label_classification"`.

</paramsdesc2><paramsdesc3title>Parameters linked to the tokenizer</paramsdesc3title><paramsdesc3>

- **tokenizer_class** (`str`, *optional*) --
  The name of the associated tokenizer class to use (if none is set, will use the tokenizer associated to the
  model by default).
- **prefix** (`str`, *optional*) --
  A specific prompt that should be added at the beginning of each text before calling the model.
- **bos_token_id** (`int`, *optional*) -- The id of the _beginning-of-stream_ token.
- **pad_token_id** (`int`, *optional*) -- The id of the _padding_ token.
- **eos_token_id** (`int`, *optional*) -- The id of the _end-of-stream_ token.
- **decoder_start_token_id** (`int`, *optional*) --
  If an encoder-decoder model starts decoding with a different token than _bos_, the id of that token.
- **sep_token_id** (`int`, *optional*) -- The id of the _separation_ token.

</paramsdesc3><paramsdesc4title>PyTorch specific parameters</paramsdesc4title><paramsdesc4>

- **torchscript** (`bool`, *optional*, defaults to `False`) --
  Whether or not the model should be used with Torchscript.
- **tie_word_embeddings** (`bool`, *optional*, defaults to `True`) --
  Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
  model has a output word embedding layer.
- **torch_dtype** (`str`, *optional*) --
  The `dtype` of the weights. This attribute can be used to initialize the model to a non-default `dtype`
  (which is normally `float32`) and thus allow for optimal storage allocation. For example, if the saved
  model is `float16`, ideally we want to load it back using the minimal amount of memory needed to load
  `float16` weights. Since the config object is stored in plain text, this attribute contains just the
  floating type string without the `torch.` prefix. For example, for `torch.float16` ``torch_dtype` is the
  `"float16"` string.

  This attribute is currently not being used during model loading time, but this may change in the future
  versions. But we can already start preparing for the future by saving the dtype with save_pretrained.

</paramsdesc4><paramsdesc5title>TensorFlow specific parameters</paramsdesc5title><paramsdesc5>

- **use_bfloat16** (`bool`, *optional*, defaults to `False`) --
  Whether or not the model should use BFloat16 scalars (only used by some TensorFlow models).</paramsdesc5><paramgroups>5</paramgroups></docstring>

Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
methods for loading/downloading/saving configurations.

<Tip>

A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to
initialize a model does **not** load the model weights. It only affects the model's configuration.

</Tip>

Class attributes (overridden by derived classes):

- **model_type** (`str`) -- An identifier for the model type, serialized into the JSON file, and used to recreate
  the correct object in [AutoConfig](/docs/transformers/pr_15592/en/model_doc/auto#transformers.AutoConfig).
- **is_composition** (`bool`) -- Whether the config class is composed of multiple sub-configs. In this case the
  config has to be initialized from two or more configs of type [PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) like:
  [EncoderDecoderConfig](/docs/transformers/pr_15592/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig) or [~RagConfig](/docs/transformers/pr_15592/en/model_doc/rag#transformers.RagConfig).
- **keys_to_ignore_at_inference** (`List[str]`) -- A list of keys to ignore by default when looking at dictionary
  outputs of the model during inference.
- **attribute_map** (`Dict[str, str]`) -- A dict that maps model specific attribute names to the standardized
  naming of attributes.

Common attributes (present in all subclasses):

- **vocab_size** (`int`) -- The number of tokens in the vocabulary, which is also the first dimension of the
  embeddings matrix (this attribute may be missing for models that don't have a text modality like ViT).
- **hidden_size** (`int`) -- The hidden size of the model.
- **num_attention_heads** (`int`) -- The number of attention heads used in the multi-head attention layers of the
  model.
- **num_hidden_layers** (`int`) -- The number of blocks in the model.





<div class="docstring">
<docstring><name>push\_to\_hub</name><anchor>transformers.file_utils.PushToHubMixin.push_to_hub</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/file_utils.py#L2825</source><parameters>[{"name": "repo_path_or_name", "val": ": typing.Optional[str] = None"}, {"name": "repo_url", "val": ": typing.Optional[str] = None"}, {"name": "use_temp_dir", "val": ": bool = False"}, {"name": "commit_message", "val": ": typing.Optional[str] = None"}, {"name": "organization", "val": ": typing.Optional[str] = None"}, {"name": "private", "val": ": typing.Optional[bool] = None"}, {"name": "use_auth_token", "val": ": typing.Union[bool, str, NoneType] = None"}, {"name": "**model_card_kwargs", "val": ""}]</parameters><paramsdesc>- **repo_path_or_name** (`str`, *optional*) --
  Can either be a repository name for your config in the Hub or a path to a local folder (in which case
  the repository will have the name of that local folder). If not specified, will default to the name
  given by `repo_url` and a local directory with that name will be created.
- **repo_url** (`str`, *optional*) --
  Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
  repository will be created in your namespace (unless you specify an `organization`) with `repo_name`.
- **use_temp_dir** (`bool`, *optional*, defaults to `False`) --
  Whether or not to clone the distant repo in a temporary directory or in `repo_path_or_name` inside the
  current working directory. This will slow things down if you are making changes in an existing repo
  since you will need to clone the repo before every push.
- **commit_message** (`str`, *optional*) --
  Message to commit while pushing. Will default to `"add config"`.
- **organization** (`str`, *optional*) --
  Organization in which you want to push your config (you must be a member of this organization).
- **private** (`bool`, *optional*) --
  Whether or not the repository created should be private (requires a paying subscription).
- **use_auth_token** (`bool` or `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`). Will default to `True` if
  `repo_url` is not specified.</paramsdesc><paramgroups>0</paramgroups><rettype>`str`</rettype><retdesc>The url of the commit of your config in the given repository.</retdesc></docstring>

Upload the configuration file to the 🤗 Model Hub while synchronizing a local clone of the repo in
`repo_path_or_name`.







Examples:

```python
from transformers import AutoConfig

config = AutoConfig.from_pretrained("bert-base-cased")

# Push the config to your namespace with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
config.push_to_hub("my-finetuned-bert")

# Push the config to your namespace with the name "my-finetuned-bert" with no local clone.
config.push_to_hub("my-finetuned-bert", use_temp_dir=True)

# Push the config to an organization with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
config.push_to_hub("my-finetuned-bert", organization="huggingface")

# Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.
config.push_to_hub("my-finetuned-bert", repo_url="https://huggingface.co/sgugger/my-finetuned-bert")
```


</div>
<div class="docstring">
<docstring><name>dict\_torch\_dtype\_to\_str</name><anchor>transformers.PretrainedConfig.dict_torch_dtype_to_str</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L850</source><parameters>[{"name": "d", "val": ": typing.Dict[str, typing.Any]"}]</parameters></docstring>

Checks whether the passed dictionary has a *torch_dtype* key and if it's not None, converts torch.dtype to a
string of just the type. For example, `torch.float32` get converted into *"float32"* string, which can then be
stored in the json format.


</div>
<div class="docstring">
<docstring><name>from\_dict</name><anchor>transformers.PretrainedConfig.from_dict</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L652</source><parameters>[{"name": "config_dict", "val": ": typing.Dict[str, typing.Any]"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config_dict** (`Dict[str, Any]`) --
  Dictionary that will be used to instantiate the configuration object. Such a dictionary can be
  retrieved from a pretrained checkpoint by leveraging the [get_config_dict()](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig.get_config_dict) method.
- **kwargs** (`Dict[str, Any]`) --
  Additional parameters from which to initialize the configuration object.</paramsdesc><paramgroups>0</paramgroups><rettype>[PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig)</rettype><retdesc>The configuration object instantiated from those parameters.</retdesc></docstring>

Instantiates a [PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) from a Python dictionary of parameters.








</div>
<div class="docstring">
<docstring><name>from\_json\_file</name><anchor>transformers.PretrainedConfig.from_json_file</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L690</source><parameters>[{"name": "json_file", "val": ": typing.Union[str, os.PathLike]"}]</parameters><paramsdesc>- **json_file** (`str` or `os.PathLike`) --
  Path to the JSON file containing the parameters.</paramsdesc><paramgroups>0</paramgroups><rettype>[PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig)</rettype><retdesc>The configuration object instantiated from that JSON file.</retdesc></docstring>

Instantiates a [PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) from the path to a JSON file of parameters.








</div>
<div class="docstring">
<docstring><name>from\_pretrained</name><anchor>transformers.PretrainedConfig.from_pretrained</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L445</source><parameters>[{"name": "pretrained_model_name_or_path", "val": ": typing.Union[str, os.PathLike]"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **pretrained_model_name_or_path** (`str` or `os.PathLike`) --
  This can be either:

  - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
    huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
    namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
  - a path to a *directory* containing a configuration file saved using the
    [save_pretrained()](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained) method, e.g., `./my_model_directory/`.
  - a path or url to a saved configuration JSON *file*, e.g., `./my_model_directory/configuration.json`.
- **cache_dir** (`str` or `os.PathLike`, *optional*) --
  Path to a directory in which a downloaded pretrained model configuration should be cached if the
  standard cache should not be used.
- **force_download** (`bool`, *optional*, defaults to `False`) --
  Whether or not to force to (re-)download the configuration files and override the cached versions if
  they exist.
- **resume_download** (`bool`, *optional*, defaults to `False`) --
  Whether or not to delete incompletely received file. Attempts to resume the download if such a file
  exists.
- **proxies** (`Dict[str, str]`, *optional*) --
  A dictionary of proxy servers to use by protocol or endpoint, e.g., `&amp;lcub;'http': 'foo.bar:3128',
  'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
- **use_auth_token** (`str` or *bool*, *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`).
- **revision(`str`,** *optional*, defaults to `"main"`) --
  The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
  git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
  identifier allowed by git.
- **return_unused_kwargs** (`bool`, *optional*, defaults to `False`) --
  If `False`, then this function returns just the final configuration object.

  If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
  dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
  part of `kwargs` which has not been used to update `config` and is otherwise ignored.
- **kwargs** (`Dict[str, Any]`, *optional*) --
  The values in kwargs of any keys which are configuration attributes will be used to override the loaded
  values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
  by the `return_unused_kwargs` keyword parameter.</paramsdesc><paramgroups>0</paramgroups><rettype>[PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig)</rettype><retdesc>The configuration object instantiated from this pretrained model.</retdesc></docstring>

Instantiate a [PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) (or a derived class) from a pretrained model configuration.



<Tip>

Passing `use_auth_token=True` is required when you want to use a private model.

</Tip>





Examples:

```python
# We can't instantiate directly the base class *PretrainedConfig* so let's show the examples on a
# derived class: BertConfig
config = BertConfig.from_pretrained(
    "bert-base-uncased"
)  # Download configuration from huggingface.co and cache.
config = BertConfig.from_pretrained(
    "./test/saved_model/"
)  # E.g. config (or model) was saved using *save_pretrained('./test/saved_model/')*
config = BertConfig.from_pretrained("./test/saved_model/my_configuration.json")
config = BertConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False)
assert config.output_attentions == True
config, unused_kwargs = BertConfig.from_pretrained(
    "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
)
assert config.output_attentions == True
assert unused_kwargs == &amp;lcub;"foo": False}
```

</div>
<div class="docstring">
<docstring><name>get\_config\_dict</name><anchor>transformers.PretrainedConfig.get_config_dict</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L528</source><parameters>[{"name": "pretrained_model_name_or_path", "val": ": typing.Union[str, os.PathLike]"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **pretrained_model_name_or_path** (`str` or `os.PathLike`) --
  The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.</paramsdesc><paramgroups>0</paramgroups><rettype>`Tuple[Dict, Dict]`</rettype><retdesc>The dictionary(ies) that will be used to instantiate the configuration object.</retdesc></docstring>

From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
[PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) using `from_dict`.








</div>
<div class="docstring">
<docstring><name>register\_for\_auto\_class</name><anchor>transformers.PretrainedConfig.register_for_auto_class</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L859</source><parameters>[{"name": "auto_class", "val": " = 'AutoConfig'"}]</parameters><paramsdesc>- **auto_class** (`str` or `type`, *optional*, defaults to `"AutoConfig"`) --
  The auto class to register this new configuration with.</paramsdesc><paramgroups>0</paramgroups></docstring>

Register this class with a given auto class. This should only be used for custom configurations as the ones in
the library are already mapped with `AutoConfig`.

<Tip warning=&amp;lcub;true}>

This API is experimental and may have some slight breaking changes in the next releases.

</Tip>




</div>
<div class="docstring">
<docstring><name>save\_pretrained</name><anchor>transformers.PretrainedConfig.save_pretrained</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L399</source><parameters>[{"name": "save_directory", "val": ": typing.Union[str, os.PathLike]"}, {"name": "push_to_hub", "val": ": bool = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **save_directory** (`str` or `os.PathLike`) --
  Directory where the configuration JSON file will be saved (will be created if it does not exist).
- **push_to_hub** (`bool`, *optional*, defaults to `False`) --
  Whether or not to push your model to the Hugging Face model hub after saving it.

  <Tip warning=&amp;lcub;true}>

  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.

  </Tip>

  kwargs --
  Additional key word arguments passed along to the [push_to_hub()](/docs/transformers/pr_15592/en/main_classes/model#transformers.file_utils.PushToHubMixin.push_to_hub) method.</paramsdesc><paramgroups>0</paramgroups></docstring>

Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the
[from_pretrained()](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained) class method.




</div>
<div class="docstring">
<docstring><name>to\_dict</name><anchor>transformers.PretrainedConfig.to_dict</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L750</source><parameters>[]</parameters><rettype>`Dict[str, Any]`</rettype><retdesc>Dictionary of all the attributes that make up this configuration instance.</retdesc></docstring>

Serializes this instance to a Python dictionary.






</div>
<div class="docstring">
<docstring><name>to\_diff\_dict</name><anchor>transformers.PretrainedConfig.to_diff_dict</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L718</source><parameters>[]</parameters><rettype>`Dict[str, Any]`</rettype><retdesc>Dictionary of all the attributes that make up this configuration instance,</retdesc></docstring>

Removes all attributes from config which correspond to the default config attributes for better readability and
serializes to a Python dictionary.






</div>
<div class="docstring">
<docstring><name>to\_json\_file</name><anchor>transformers.PretrainedConfig.to_json_file</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L788</source><parameters>[{"name": "json_file_path", "val": ": typing.Union[str, os.PathLike]"}, {"name": "use_diff", "val": ": bool = True"}]</parameters><paramsdesc>- **json_file_path** (`str` or `os.PathLike`) --
  Path to the JSON file in which this configuration instance's parameters will be saved.
- **use_diff** (`bool`, *optional*, defaults to `True`) --
  If set to `True`, only the difference between the config instance and the default `PretrainedConfig()`
  is serialized to JSON file.</paramsdesc><paramgroups>0</paramgroups></docstring>

Save this instance to a JSON file.




</div>
<div class="docstring">
<docstring><name>to\_json\_string</name><anchor>transformers.PretrainedConfig.to_json_string</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L770</source><parameters>[{"name": "use_diff", "val": ": bool = True"}]</parameters><paramsdesc>- **use_diff** (`bool`, *optional*, defaults to `True`) --
  If set to `True`, only the difference between the config instance and the default `PretrainedConfig()`
  is serialized to JSON string.</paramsdesc><paramgroups>0</paramgroups><rettype>`str`</rettype><retdesc>String containing all the attributes that make up this configuration instance in JSON format.</retdesc></docstring>

Serializes this instance to a JSON string.








</div>
<div class="docstring">
<docstring><name>update</name><anchor>transformers.PretrainedConfig.update</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L802</source><parameters>[{"name": "config_dict", "val": ": typing.Dict[str, typing.Any]"}]</parameters><paramsdesc>- **config_dict** (`Dict[str, Any]`) -- Dictionary of attributes that should be updated for this class.</paramsdesc><paramgroups>0</paramgroups></docstring>

Updates attributes of this class with attributes from `config_dict`.




</div>
<div class="docstring">
<docstring><name>update\_from\_string</name><anchor>transformers.PretrainedConfig.update_from_string</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/configuration_utils.py#L812</source><parameters>[{"name": "update_str", "val": ": str"}]</parameters><paramsdesc>- **update_str** (`str`) -- String with attributes that should be updated for this class.</paramsdesc><paramgroups>0</paramgroups></docstring>

Updates attributes of this class with attributes from `update_str`.

The expected format is ints, floats and strings as is, and for booleans use `true` or `false`. For example:
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"

The keys to change have to already exist in the config object.




</div></div>
