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Hugging face transformers documentation, generated from official documentation.

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Common Patterns

Pattern 1: Transformers documentation Configuration Transformers 🏡 View all docsAWS Trainium & InferentiaAccelerateArgillaAutoTrainBitsandbytesChat UIDataset viewerDatasetsDeploying on AWSDiffusersDistilabelEvaluateGoogle CloudGoogle TPUsGradioHubHub Python LibraryHuggingface.jsInference Endpoints (dedicated)Inference ProvidersKernelsLeRobotLeaderboardsLightevalMicrosoft AzureOptimumPEFTReachy MiniSafetensorsSentence TransformersTRLTasksText Embeddings InferenceText Generation InferenceTokenizersTrackioTransformersTransformers.jssmolagentstimm Search documentation mainv5.0.0v4.57.6v4.56.2v4.55.4v4.53.3v4.52.3v4.51.3v4.50.0v4.49.0v4.48.2v4.47.1v4.46.3v4.45.2v4.44.2v4.43.4v4.42.4v4.41.2v4.40.2v4.39.3v4.38.2v4.37.2v4.36.1v4.35.2v4.34.1v4.33.3v4.32.1v4.31.0v4.30.0v4.29.1v4.28.1v4.27.2v4.26.1v4.25.1v4.24.0v4.23.1v4.22.2v4.21.3v4.20.1v4.19.4v4.18.0v4.17.0v4.16.2v4.15.0v4.14.1v4.13.0v4.12.5v4.11.3v4.10.1v4.9.2v4.8.2v4.7.0v4.6.0v4.5.1v4.4.2v4.3.3v4.2.2v4.1.1v4.0.1v3.5.1v3.4.0v3.3.1v3.2.0v3.1.0v3.0.2v2.11.0v2.10.0v2.9.1v2.8.0v2.7.0v2.6.0v2.5.1v2.4.1v2.3.0v2.2.2v2.1.1v2.0.0v1.2.0v1.1.0v1.0.0doc-builder-html ARDEENESFRHIITJAKOPTZH Get started Transformers Installation Quickstart Base classes Inference Training Quantization Ecosystem integrations Resources Contribute API Main Classes Auto Classes Backbones Callbacks Configuration Data Collator Logging Models Text Generation Optimization Model outputs PEFT Pipelines Processors Quantization Tokenizer Trainer DeepSpeed ExecuTorch Feature Extractor Image Processor Video Processor Kernels Models Internal helpers Reference Join the Hugging Face community and get access to the augmented documentation experience Collaborate on models, datasets and Spaces Faster examples with accelerated inference Switch between documentation themes Sign Up to get started Copy page Configuration The base class 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. PreTrainedConfig class transformers.PreTrainedConfig < source > ( output_hidden_states: bool = False output_attentions: bool = False return_dict: bool = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False architectures: list[str] | None = None id2label: dict[int, str] | None = None label2id: dict[str, int] | None = None num_labels: int | None = None problem_type: str | None = None **kwargs ) Parameters name_or_path (str, optional, defaults to "") — Store the string that was passed to PreTrainedModel.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 instead of a plain tuple. is_encoder_decoder (bool, optional, defaults to False) — Whether the model is used as an encoder/decoder or not. 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 < sequence_length embeddings at a time. For more information on feed forward chunking, see How does Feed Forward Chunking work?. Parameters for fine-tuning tasks architectures (list[str], optional) — Model architectures that can be used with the model pretrained weights. 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. problem_type (str, optional) — Problem type for XxxForSequenceClassification models. Can be one of "regression", "single_label_classification" or "multi_label_classification". PyTorch specific parameters 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. Base class for all configuration classes. Handles a few parameters common to all models’ configurations as well as methods for loading/downloading/saving configurations. 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. 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. has_no_defaults_at_init (bool) — Whether the config class can be initialized without providing input arguments. Some configurations requires inputs to be defined at init and have no default values, usually these are composite configs, (but not necessarily) such as EncoderDecoderConfig or ~RagConfig. They have to be initialized from two or more configs of type PreTrainedConfig. 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. base_model_tp_plan (dict[str, Any]) — A dict that maps sub-modules FQNs of a base model to a tensor parallel plan applied to the sub-module when model.tensor_parallel is called. base_model_pp_plan (dict[str, tuple[list[str]]]) — A dict that maps child-modules of a base model to a pipeline parallel plan that enables users to place the child-module on the appropriate device. 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. Setting parameters for sequence generation in the model config is deprecated. For backward compatibility, loading some of them will still be possible, but attempting to overwrite them will throw an exception — you should set them in a [~transformers.GenerationConfig]. Check the documentation of [~transformers.GenerationConfig] for more information about the individual parameters. push_to_hub < source > ( repo_id: str commit_message: str | None = None commit_description: str | None = None private: bool | None = None token: bool | str | None = None revision: str | None = None create_pr: bool = False max_shard_size: int | str | None = '50GB' tags: list[str] | None = None ) Parameters repo_id (str) — The name of the repository you want to push your config to. It should contain your organization name when pushing to a given organization. commit_message (str, optional) — Message to commit while pushing. Will default to "Upload config". commit_description (str, optional) — The description of the commit that will be created private (bool, optional) — Whether to make the repo private. If None (default), the repo will be public unless the organization’s default is private. This value is ignored if the repo already exists. token (bool or str, optional) — The token to use as HTTP bearer authorization for remote files. If True (default), will use the token generated when running hf auth login (stored in ~/.huggingface). revision (str, optional) — Branch to push the uploaded files to. create_pr (bool, optional, defaults to False) — Whether or not to create a PR with the uploaded files or directly commit. max_shard_size (int or str, optional, defaults to "50GB") — Only applicable for models. The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size lower than this size. If expressed as a string, needs to be digits followed by a unit (like "5MB"). tags (list[str], optional) — List of tags to push on the Hub. Upload the configuration file to the 🤗 Model Hub. Examples: Copied from transformers import AutoConfig config = AutoConfig.from_pretrained("google-bert/bert-base-cased") # Push the config to your namespace with the name "my-finetuned-bert". config.push_to_hub("my-finetuned-bert") # Push the config to an organization with the name "my-finetuned-bert". config.push_to_hub("huggingface/my-finetuned-bert") dict_dtype_to_str < source > ( d: dict ) Checks whether the passed dictionary and its nested dicts have a 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. from_dict < source > ( config_dict: dict **kwargs ) → PreTrainedConfig Parameters 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() method. kwargs (dict[str, Any]) — Additional parameters from which to initialize the configuration object. Returns PreTrainedConfig The configuration object instantiated from those parameters. Instantiates a PreTrainedConfig from a Python dictionary of parameters. from_json_file < source > ( json_file: str | os.PathLike ) → PreTrainedConfig Parameters json_file (str or os.PathLike) — Path to the JSON file containing the parameters. Returns PreTrainedConfig The configuration object instantiated from that JSON file. Instantiates a PreTrainedConfig from the path to a JSON file of parameters. from_pretrained < source > ( pretrained_model_name_or_path: str | os.PathLike cache_dir: str | os.PathLike | None = None force_download: bool = False local_files_only: bool = False token: str | bool | None = None revision: str = 'main' **kwargs ) → PreTrainedConfig Parameters 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. a path to a directory containing a configuration file saved using the 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. proxies (dict[str, str], optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. token (str or bool, optional) — The token to use as HTTP bearer authorization for remote files. If True, or not specified, will use the token generated when running hf auth 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. To test a pull request you made on the Hub, you can pass revision="refs/pr/<pr_number>". 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. subfolder (str, optional, defaults to "") — In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here. 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. Returns PreTrainedConfig The configuration object instantiated from this pretrained model. Instantiate a PreTrainedConfig (or a derived class) from a pretrained model configuration. Examples: Copied # We can't instantiate directly the base class PreTrainedConfig so let's show the examples on a # derived class: BertConfig config = BertConfig.from_pretrained( "google-bert/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("google-bert/bert-base-uncased", output_attentions=True, foo=False) assert config.output_attentions == True config, unused_kwargs = BertConfig.from_pretrained( "google-bert/bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True ) assert config.output_attentions == True assert unused_kwargs == {"foo": False} get_config_dict < source > ( pretrained_model_name_or_path: str | os.PathLike **kwargs ) → tuple[Dict, Dict] Parameters pretrained_model_name_or_path (str or os.PathLike) — The identifier of the pre-trained checkpoint from which we want the dictionary of parameters. Returns tuple[Dict, Dict] The dictionary(ies) that will be used to instantiate the configuration object. From a pretrained_model_name_or_path, resolve to a dictionary of parameters, to be used for instantiating a PreTrainedConfig using from_dict. get_text_config < source > ( decoder = None encoder = None ) Parameters decoder (Optional[bool], optional) — If set to True, then only search for decoder config names. encoder (Optional[bool], optional) — If set to True, then only search for encoder config names. Returns the text config related to the text input (encoder) or text output (decoder) of the model. The decoder and encoder input arguments can be used to specify which end of the model we are interested in, which is useful on models that have both text input and output modalities. There are three possible outcomes of using this method: On most models, it returns the original config instance itself. On newer (2024+) composite models, it returns the text section of the config, which is nested under a set of valid names. On older (2023-) composite models, it discards decoder-only parameters when encoder=True and vice-versa. register_for_auto_class < source > ( auto_class = 'AutoConfig' ) Parameters auto_class (str or type, optional, defaults to "AutoConfig") — The auto class to register this new configuration with. 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. save_pretrained < source > ( save_directory: str | os.PathLike push_to_hub: bool = False **kwargs ) Parameters 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. You can specify the repository you want to push to with repo_id (will default to the name of save_directory in your namespace). kwargs (dict[str, Any], optional) — Additional key word arguments passed along to the push_to_hub() method. Save a configuration object to the directory save_directory, so that it can be re-loaded using the from_pretrained() class method. to_dict < source > ( ) → dict[str, Any] Returns dict[str, Any] Dictionary of all the attributes that make up this configuration instance. Serializes this instance to a Python dictionary. to_diff_dict < source > ( ) → dict[str, Any] Returns dict[str, Any] Dictionary of all the attributes that make up this configuration instance. Removes all attributes from the configuration that correspond to the default config attributes for better readability, while always retaining the config attribute from the class. Serializes to a Python dictionary. to_json_file < source > ( json_file_path: str | os.PathLike use_diff: bool = True ) Parameters 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. Save this instance to a JSON file. to_json_string < source > ( use_diff: bool = True ) → str Parameters 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. Returns str String containing all the attributes that make up this configuration instance in JSON format. Serializes this instance to a JSON string. update < source > ( config_dict: dict ) Parameters config_dict (dict[str, Any]) — Dictionary of attributes that should be updated for this class. Updates attributes of this class with attributes from config_dict. update_from_string < source > ( update_str: str ) Parameters update_str (str) — String with attributes that should be updated for this class. 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. Update on GitHub ←Callbacks Data Collator→ Configuration PreTrainedConfig