transformers/main/ko/main_classes/model.md

모델

기본 클래스 PreTrainedModel, TFPreTrainedModel, FlaxPreTrainedModel는 로컬 파일과 디렉토리로부터 모델을 로드하고 저장하거나 또는 (허깅페이스 AWS S3 리포지토리로부터 다운로드된) 라이브러리에서 제공하는 사전 훈련된 모델 설정을 로드하고 저장하는 것을 지원하는 기본 메소드를 구현하였습니다.

PreTrainedModel과 TFPreTrainedModel은 또한 모든 모델들을 공통적으로 지원하는 메소드 여러개를 구현하였습니다:

• 새 토큰이 단어장에 추가될 때, 입력 토큰 임베딩의 크기를 조정합니다.
• 모델의 어텐션 헤드를 가지치기합니다.

각 모델에 공통인 다른 메소드들은 다음의 클래스에서 정의됩니다.

• [~modeling_utils.ModuleUtilsMixin](파이토치 모델용)
• 텍스트 생성을 위한 [~modeling_tf_utils.TFModuleUtilsMixin](텐서플로 모델용)
• [~generation.GenerationMixin](파이토치 모델용)
• [~generation.FlaxGenerationMixin](Flax/JAX 모델용)

PreTrainedModel[[transformers.PreTrainedModel]]

transformers.PreTrainedModel[[transformers.PreTrainedModel]]

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Base class for all models.

PreTrainedModel takes care of storing the configuration of the models and handles methods for loading, downloading and saving models as well as a few methods common to all models to:

• resize the input embeddings

Class attributes (overridden by derived classes):

• config_class (PreTrainedConfig) -- A subclass of PreTrainedConfig to use as configuration class for this model architecture.
• base_model_prefix (str) -- A string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
• main_input_name (str) -- The name of the principal input to the model (often input_ids for NLP models, pixel_values for vision models and input_values for speech models).
• can_record_outputs (dict):

push_to_hubtransformers.PreTrainedModel.push_to_hubhttps://github.com/huggingface/transformers/blob/main/src/transformers/utils/hub.py#L720[{"name": "repo_id", "val": ": str"}, {"name": "commit_message", "val": ": str | None = None"}, {"name": "commit_description", "val": ": str | None = None"}, {"name": "private", "val": ": bool | None = None"}, {"name": "token", "val": ": bool | str | None = None"}, {"name": "revision", "val": ": str | None = None"}, {"name": "create_pr", "val": ": bool = False"}, {"name": "max_shard_size", "val": ": int | str | None = '50GB'"}, {"name": "tags", "val": ": list[str] | None = None"}]- repo_id (str) -- The name of the repository you want to push your model 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 model".
• 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.0

Upload the model file to the 🤗 Model Hub.

Examples:

from transformers import AutoModel

model = AutoModel.from_pretrained("google-bert/bert-base-cased")

# Push the model to your namespace with the name "my-finetuned-bert".
model.push_to_hub("my-finetuned-bert")

# Push the model to an organization with the name "my-finetuned-bert".
model.push_to_hub("huggingface/my-finetuned-bert")

Parameters:

repo_id (str) : The name of the repository you want to push your model 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 model".

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.

add_model_tags[[transformers.PreTrainedModel.add_model_tags]]

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Add custom tags into the model that gets pushed to the Hugging Face Hub. Will not overwrite existing tags in the model.

Examples:

from transformers import AutoModel

model = AutoModel.from_pretrained("google-bert/bert-base-cased")

model.add_model_tags(["custom", "custom-bert"])

# Push the model to your namespace with the name "my-custom-bert".
model.push_to_hub("my-custom-bert")

Parameters:

tags (Union[list[str], str]) : The desired tags to inject in the model

can_generate[[transformers.PreTrainedModel.can_generate]]

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Returns whether this model can generate sequences with .generate() from the GenerationMixin.

Under the hood, on classes where this function returns True, some generation-specific changes are triggered: for instance, the model instance will have a populated generation_config attribute.

Returns:

bool

Whether this model can generate sequences with .generate().

dequantize[[transformers.PreTrainedModel.dequantize]]

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Potentially dequantize the model in case it has been quantized by a quantization method that support dequantization.

disable_input_require_grads[[transformers.PreTrainedModel.disable_input_require_grads]]

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Removes the _require_grads_hook.

enable_input_require_grads[[transformers.PreTrainedModel.enable_input_require_grads]]

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Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping the model weights fixed.

from_pretrained[[transformers.PreTrainedModel.from_pretrained]]

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Instantiate a pretrained pytorch model from a pre-trained model configuration.

The model is set in evaluation mode by default using model.eval() (Dropout modules are deactivated). To train the model, you should first set it back in training mode with model.train().

The warning Weights from XXX not initialized from pretrained model means that the weights of XXX do not come pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning task.

The warning Weights from XXX not used in YYY means that the layer XXX is not used by YYY, therefore those weights are discarded.

Activate the special "offline-mode" to use this method in a firewalled environment.

Examples:

>>> from transformers import BertConfig, BertModel

>>> # Download model and configuration from huggingface.co and cache.
>>> model = BertModel.from_pretrained("google-bert/bert-base-uncased")
>>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
>>> model = BertModel.from_pretrained("./test/saved_model/")
>>> # Update configuration during loading.
>>> model = BertModel.from_pretrained("google-bert/bert-base-uncased", output_attentions=True)
>>> assert model.config.output_attentions == True

Parameters:

pretrained_model_name_or_path (str or os.PathLike, optional) : Can be either: - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co. - A path to a directory containing model weights saved using save_pretrained(), e.g., ./my_model_directory/. - None if you are both providing the configuration and state dictionary (resp. with keyword arguments config and state_dict).

model_args (sequence of positional arguments, optional) : All remaining positional arguments will be passed to the underlying model's __init__ method.

config (Union[PreTrainedConfig, str, os.PathLike], optional) : Can be either: - an instance of a class derived from PreTrainedConfig, - a string or path valid as input to from_pretrained(). Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when: - The model is a model provided by the library (loaded with the model id string of a pretrained model). - The model was saved using save_pretrained() and is reloaded by supplying the save directory. - The model is loaded by supplying a local directory as pretrained_model_name_or_path and a configuration JSON file named config.json is found in the directory.

state_dict (dict[str, torch.Tensor], optional) : A state dictionary to use instead of a state dictionary loaded from saved weights file. This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

cache_dir (Union[str, 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.

ignore_mismatched_sizes (bool, optional, defaults to False) : Whether or not to raise an error if some of the weights from the checkpoint do not have the same size as the weights of the model (if for instance, you are instantiating a model with 10 labels from a checkpoint with 3 labels).

force_download (bool, optional, defaults to False) : Whether or not to force the (re-)download of the model weights and configuration files, overriding 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.

output_loading_info(bool, optional, defaults to False) : Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.

local_files_only(bool, optional, defaults to False) : Whether or not to only look at local files (i.e., do not try to download the model).

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>".

attn_implementation (str, optional) : The attention implementation to use in the model (if relevant). Can be any of - "eager" (manual implementation of the attention) - "sdpa" (using F.scaled_dot_product_attention) - "flash_attention_2" (using Dao-AILab/flash-attention) - "flash_attention_3" (using Dao-AILab/flash-attention/hopper) - "flash_attention_4" (using Dao-AILab/flash-attention/flash_attn/cute). By default, if available, SDPA will be used. The default is otherwise the manual "eager" implementation. Accept HF kernel references in the form: /[@][:] - and are any non-"/" and non-":" sequences. - "@" is optional (branch, tag, or commit-ish), e.g. "@main", "@v1.2.0", "@abc123". - ":" is optional and selects a function inside the kernel repo. - Both options can appear together and in this order only: @revision first, then :kernel_name. - We intentionally allow a leading "|" prefix (e.g., "flash|...") because the code strips it before loading; '|' is not excluded in the character classes here. Examples that match: "org/model" "org/model@main" "org/model:custom_kernel" "org/model@v1.2.3:custom_kernel"

experts_implementation (str, optional) : The experts implementation to use in the model (if relevant). Can be any of: - "eager" (sequential implementation of the experts matrix multiplications). - "batched_mm" (using torch.bmm). - "grouped_mm" (using torch.nn.functional.grouped_mm). By default, if the model supports it, "grouped_mm" will be used. The default is otherwise the manual "eager" implementation.

get_compiled_call[[transformers.PreTrainedModel.get_compiled_call]]

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Return a torch.compile'd version of self.__call__. This is useful to dynamically choose between non-compiled/compiled forward during inference, especially to switch between prefill (where we don't want to use compiled version to avoid recomputing the graph with new shapes) and iterative decoding (where we want the speed-ups of compiled version with static shapes).

get_decoder[[transformers.PreTrainedModel.get_decoder]]

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Best-effort lookup of the decoder module.

Order of attempts (covers ~85 % of current usages):

1. self.decoder/self.language_model/self.text_model
2. self.base_model (many wrappers store the decoder here)
3. self.base_model.get_decoder() (nested wrappers)
4. fallback: raise for the few exotic models that need a bespoke rule

get_encoder[[transformers.PreTrainedModel.get_encoder]]

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Best-effort lookup of the encoder module. If provided with modality argument, it looks for a modality-specific encoder in multimodal models (e.g. "image_encoder") By default the function returns model's text encoder if any, and otherwise returns self.

Possible modality values are "image", "video" and "audio".

get_expanded_tied_weights_keys[[transformers.PreTrainedModel.get_expanded_tied_weights_keys]]

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Return the expanded tied weight keys (in case they contain modules or regex patterns) for only the current model, or recursively for all submodels if all_submodels=True (i.e. it will re-check the config values for all submodels).

For almost all models, we only require to tie the embeddings, so the model has an internal property _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}. In this case, the mapping is already "expanded", i.e. it already contains full parameters, and this function will simply return a copy of the property. For more complex patterns, e.g. for DFineForObjectDetection, we have the following attribute

_tied_weights_keys = {
    r"bbox_embed.(?![0])\d+": "bbox_embed.0",
    r"class_embed.(?![0])\d+": "class_embed.0",
    "model.decoder.class_embed": "class_embed",
    "model.decoder.bbox_embed": "bbox_embed",
}

In this case, the function looks up all the model's parameters and buffers, and matches all the params,

returning the following:

{
    'bbox_embed.1.layers.0.bias': 'bbox_embed.0.layers.0.bias',
    'bbox_embed.1.layers.0.weight': 'bbox_embed.0.layers.0.weight',
    'bbox_embed.1.layers.1.bias': 'bbox_embed.0.layers.1.bias',
    'bbox_embed.1.layers.1.weight': 'bbox_embed.0.layers.1.weight',
    'bbox_embed.1.layers.2.bias': 'bbox_embed.0.layers.2.bias',
    'bbox_embed.1.layers.2.weight': 'bbox_embed.0.layers.2.weight',
    'bbox_embed.2.layers.0.bias': 'bbox_embed.0.layers.0.bias',
    'bbox_embed.2.layers.0.weight': 'bbox_embed.0.layers.0.weight',
    ...
    'class_embed.1.bias': 'class_embed.0.bias',
    'class_embed.1.weight': 'class_embed.0.weight',
    'class_embed.2.bias': 'class_embed.0.bias',
    'class_embed.2.weight': 'class_embed.0.weight',
    ...
    'model.decoder.class_embed.0.bias': 'class_embed.0.bias',
    'model.decoder.class_embed.0.weight': 'class_embed.0.weight',
    'model.decoder.class_embed.1.bias': 'class_embed.0.bias',
    'model.decoder.class_embed.1.weight': 'class_embed.0.weight',
    ...
    'model.decoder.bbox_embed.0.layers.0.bias': 'bbox_embed.0.layers.0.bias',
    'model.decoder.bbox_embed.0.layers.0.weight': 'bbox_embed.0.layers.0.weight',
    'model.decoder.bbox_embed.0.layers.1.bias': 'bbox_embed.0.layers.1.bias',
    'model.decoder.bbox_embed.0.layers.1.weight': 'bbox_embed.0.layers.1.weight',
    ...
}

i.e. all the parameters matching the regex and modules patterns in _tied_weights_keys

get_memory_footprint[[transformers.PreTrainedModel.get_memory_footprint]]

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Get the memory footprint of a model. This will return the memory footprint of the current model in bytes. Useful to benchmark the memory footprint of the current model and design some tests. Solution inspired from the PyTorch discussions: https://discuss.pytorch.org/t/gpu-memory-that-model-uses/56822/2

Parameters:

return_buffers (bool, optional, defaults to True) : Whether to return the size of the buffer tensors in the computation of the memory footprint. Buffers are tensors that do not require gradients and not registered as parameters. E.g. mean and std in batch norm layers. Please see: https://discuss.pytorch.org/t/what-pytorch-means-by-buffers/120266/2

get_parameter_or_buffer[[transformers.PreTrainedModel.get_parameter_or_buffer]]

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Return the parameter or buffer given by target if it exists, otherwise throw an error. This combines get_parameter() and get_buffer() in a single handy function. If the target is an _extra_state attribute, it will return the extra state provided by the module. Note that it only work if target is a leaf of the model.

gradient_checkpointing_disable[[transformers.PreTrainedModel.gradient_checkpointing_disable]]

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Deactivates gradient checkpointing for the current model.

gradient_checkpointing_enable[[transformers.PreTrainedModel.gradient_checkpointing_enable]]

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Activates gradient checkpointing for the current model.

We pass the __call__ method of the modules instead of forward because __call__ attaches all the hooks of the module. https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2

Parameters:

gradient_checkpointing_kwargs (dict, optional) : Additional keyword arguments passed along to the torch.utils.checkpoint.checkpoint function.

init_weights[[transformers.PreTrainedModel.init_weights]]

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Initialize and tie the weights if needed. If using a custom PreTrainedModel, you need to implement any initialization logic in _init_weights.

initialize_weights[[transformers.PreTrainedModel.initialize_weights]]

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This is equivalent to calling self.apply(self._initialize_weights), but correctly handles composite models. This function dynamically dispatches the correct init_weights function to the modules as we advance in the module graph along the recursion. It can handle an arbitrary number of sub-models. Without it, every composite model would have to recurse a second time on all sub-models explicitly in the outer-most _init_weights, which is extremely error prone and inefficient.

kernelize[[transformers.PreTrainedModel.kernelize]]

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Temporarily register hidden kernel wrappers so kernelize can discover and replace them.

mark_tied_weights_as_initialized[[transformers.PreTrainedModel.mark_tied_weights_as_initialized]]

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Adds the _is_hf_initialized flag on parameters that will be tied, in order to avoid initializing them later as they will be tied (overwritten) anyway. This is very important as most embeddings are tied, and they are huge params (vocabularies are often 256k), so running inits on them is very costly.

named_non_persistent_buffers[[transformers.PreTrainedModel.named_non_persistent_buffers]]

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Similar to named_buffers, but only yield non-persistent ones. It is handy as it's not perfectly straightforward to know if they are persistent or not

post_init[[transformers.PreTrainedModel.post_init]]

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A method executed at the end of each Transformer model initialization, to execute code that needs the model's modules properly initialized (such as weight initialization). It is also used to obtain all correct static properties (parallelism plans, tied_weights_keys, _keep_in_fp32_modules, etc) correctly in the case of composite models (that is, the top level model should know about those properties from its children).

register_for_auto_class[[transformers.PreTrainedModel.register_for_auto_class]]

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

Parameters:

auto_class (str or type, optional, defaults to "AutoModel") : The auto class to register this new model with.

resize_token_embeddings[[transformers.PreTrainedModel.resize_token_embeddings]]

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Resizes input token embeddings matrix of the model if new_num_tokens != config.vocab_size.

Takes care of tying weights embeddings afterwards if the model class has a tie_weights() method.

Parameters:

new_num_tokens (int, optional) : The new number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or None, just returns a pointer to the input tokens torch.nn.Embedding module of the model without doing anything.

pad_to_multiple_of (int, optional) : If set will pad the embedding matrix to a multiple of the provided value.If new_num_tokens is set to None will just pad the embedding to a multiple of pad_to_multiple_of. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. For more details about this, or help on choosing the correct value for resizing, refer to this guide: https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc

mean_resizing (bool) : Whether to initialize the added embeddings from a multivariate normal distribution that has old embeddings' mean and covariance or to initialize them with a normal distribution that has a mean of zero and std equals config.initializer_range. Setting mean_resizing to True is useful when increasing the size of the embeddings of causal language models, where the generated tokens' probabilities won't be affected by the added embeddings because initializing the new embeddings with the old embeddings' mean will reduce the kl-divergence between the next token probability before and after adding the new embeddings. Refer to this article for more information: https://nlp.stanford.edu/~johnhew/vocab-expansion.html

Returns:

torch.nn.Embedding

Pointer to the input tokens Embeddings Module of the model.

save_pretrained[[transformers.PreTrainedModel.save_pretrained]]

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Save a model and its configuration file to a directory, so that it can be re-loaded using the from_pretrained() class method.

Parameters:

save_directory (str or os.PathLike) : Directory to which to save. Will be created if it doesn't exist.

is_main_process (bool, optional, defaults to True) : Whether the process calling this is the main process or not. Useful when in distributed training like TPUs and need to call this function on all processes. In this case, set is_main_process=True only on the main process to avoid race conditions.

state_dict (nested dictionary of torch.Tensor) : The state dictionary of the model to save. Will default to self.state_dict(), but can be used to only save parts of the model or if special precautions need to be taken when recovering the state dictionary of a model (like when using model parallelism).

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).

max_shard_size (int or str, optional, defaults to "50GB") : 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"). If a single weight of the model is bigger than max_shard_size, it will be in its own checkpoint shard which will be bigger than max_shard_size.

variant (str, optional) : If specified, weights are saved in the format model..safetensors.

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).

save_peft_format (bool, optional, defaults to True) : For backward compatibility with PEFT library, in case adapter weights are attached to the model, all keys of the state dict of adapters needs to be prepended with base_model.model. Advanced users can disable this behaviours by setting save_peft_format to False.

save_original_format (bool, optional, defaults to True) : For backward compatibility with the previous versions of transformers you can save the checkpoint with its reverse mapping. The reverse mapping needs to exists even if the model was loaded from a None legacy checkpoint.

kwargs (dict[str, Any], optional) : Additional key word arguments passed along to the push_to_hub() method.

set_attn_implementation[[transformers.PreTrainedModel.set_attn_implementation]]

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Set the requested attn_implementation for this model.

Parameters:

attn_implementation (str or dict) : The attention implementation to set for this model. It can be either a str, in which case it will be dispatched to all submodels if relevant, or a dict where keys are the sub_configs name, in which case each submodel will dispatch the corresponding value.

allow_all_kernels (bool, optional) : Whether to load kernels from unverified hub repos, if attn_implementation is a custom kernel outside of the kernels-community hub repository.

set_decoder[[transformers.PreTrainedModel.set_decoder]]

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Symmetric setter. Mirrors the lookup logic used in get_decoder.

set_encoder[[transformers.PreTrainedModel.set_encoder]]

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Symmetric setter. Mirrors the lookup logic used in get_encoder.

set_experts_implementation[[transformers.PreTrainedModel.set_experts_implementation]]

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Set the requested experts_implementation for this model.

Parameters:

experts_implementation (str or dict) : The experts implementation to set for this model. It can be either a str, in which case it will be dispatched to all submodels if relevant, or a dict where keys are the sub_configs name, in which case each submodel will dispatch the corresponding value.

set_use_kernels[[transformers.PreTrainedModel.set_use_kernels]]

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Set whether or not to use the kernels library to kernelize some layers of the model.

Parameters:

use_kernels (bool) : Whether or not to use the kernels library to kernelize some layers of the model.

kernel_config (KernelConfig, optional) : The kernel configuration to use to kernelize the model. If None, the default kernel mapping will be used.

tie_weights[[transformers.PreTrainedModel.tie_weights]]

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Tie the model weights. If recompute_mapping=False (default when called internally), it will rely on the model.all_tied_weights_keys attribute, containing the {target: source} mapping for the tied params. If recompute_mapping=True, it will re-check all internal submodels and their config to determine the params that need to be tied. This is the default when model.tie_weights() is called on its own, outside of __init__, and from_pretrained, in case the config values were changed somewhere.

Note that during from_pretrained, tying is symmetric: if the mapping says "tie target -> source" but source is missing in the checkpoint while target exists, we swap source and target so we can still tie everything to the parameter that actually exists.

warn_if_padding_and_no_attention_mask[[transformers.PreTrainedModel.warn_if_padding_and_no_attention_mask]]

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Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.

사용자 정의 모델은 초고속 초기화(superfast init)가 특정 모델에 적용될 수 있는지 여부를 결정하는 _supports_assign_param_buffer도 포함해야 합니다. test_save_and_load_from_pretrained 실패 시, 모델이 _supports_assign_param_buffer를 필요로 하는지 확인하세요. 필요로 한다면 False로 설정하세요.

ModuleUtilsMixin[[transformers.modeling_utils.ModuleUtilsMixin]]

transformers.modeling_utils.ModuleUtilsMixin[[transformers.modeling_utils.ModuleUtilsMixin]]

Source

A few utilities for torch.nn.Modules, to be used as a mixin.

get_extended_attention_masktransformers.modeling_utils.ModuleUtilsMixin.get_extended_attention_maskhttps://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L974[{"name": "attention_mask", "val": ": Tensor"}, {"name": "input_shape", "val": ": tuple"}, {"name": "dtype", "val": ": torch.dtype | None = None"}]- attention_mask (torch.Tensor) -- Mask with ones indicating tokens to attend to, zeros for tokens to ignore.

• input_shape (tuple[int]) -- The shape of the input to the model.0torch.Tensor The extended attention mask, with a the same dtype as attention_mask.dtype.

Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

Parameters:

attention_mask (torch.Tensor) : Mask with ones indicating tokens to attend to, zeros for tokens to ignore.

input_shape (tuple[int]) : The shape of the input to the model.

Returns:

torch.Tensor The extended attention mask, with a the same dtype as attention_mask.dtype.

invert_attention_mask[[transformers.modeling_utils.ModuleUtilsMixin.invert_attention_mask]]

Source

Invert an attention mask (e.g., switches 0. and 1.).

Parameters:

encoder_attention_mask (torch.Tensor) : An attention mask.

Returns:

torch.Tensor

The inverted attention mask.

num_parameters[[transformers.modeling_utils.ModuleUtilsMixin.num_parameters]]

Source

Get number of (optionally, trainable or non-embeddings) parameters in the module.

Parameters:

only_trainable (bool, optional, defaults to False) : Whether or not to return only the number of trainable parameters

exclude_embeddings (bool, optional, defaults to False) : Whether or not to return only the number of non-embeddings parameters

Returns:

int

The number of parameters.

허브에 저장하기[[transformers.utils.PushToHubMixin]]

transformers.utils.PushToHubMixin[[transformers.utils.PushToHubMixin]]

Source

A Mixin containing the functionality to push a model or tokenizer to the hub.

push_to_hubtransformers.utils.PushToHubMixin.push_to_hubhttps://github.com/huggingface/transformers/blob/main/src/transformers/utils/hub.py#L720[{"name": "repo_id", "val": ": str"}, {"name": "commit_message", "val": ": str | None = None"}, {"name": "commit_description", "val": ": str | None = None"}, {"name": "private", "val": ": bool | None = None"}, {"name": "token", "val": ": bool | str | None = None"}, {"name": "revision", "val": ": str | None = None"}, {"name": "create_pr", "val": ": bool = False"}, {"name": "max_shard_size", "val": ": int | str | None = '50GB'"}, {"name": "tags", "val": ": list[str] | None = None"}]- repo_id (str) -- The name of the repository you want to push your {object} 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 {object}".
• 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.0

Upload the {object_files} to the 🤗 Model Hub.

Examples:

from transformers import {object_class}

{object} = {object_class}.from_pretrained("google-bert/bert-base-cased")

# Push the {object} to your namespace with the name "my-finetuned-bert".
{object}.push_to_hub("my-finetuned-bert")

# Push the {object} to an organization with the name "my-finetuned-bert".
{object}.push_to_hub("huggingface/my-finetuned-bert")

Parameters:

repo_id (str) : The name of the repository you want to push your {object} 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 {object}".

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.