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PEFT documentation

Models

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Models

PeftModel is the base model class for specifying the base Transformer model and configuration to apply a PEFT method to. The base PeftModel contains methods for loading and saving models from the Hub.

PeftModel

class peft.PeftModel

< >

( model: PreTrainedModel peft_config: PeftConfig adapter_name: str = 'default' )

Parameters

  • model (PreTrainedModel) — The base transformer model used for Peft.
  • peft_config (PeftConfig) — The configuration of the Peft model.
  • adapter_name (str, optional) — The name of the adapter, defaults to "default".

Base model encompassing various Peft methods.

Attributes:

  • base_model (torch.nn.Module) — The base transformer model used for Peft.
  • peft_config (PeftConfig) — The configuration of the Peft model.
  • modules_to_save (list of str) — The list of sub-module names to save when saving the model.
  • prompt_encoder (PromptEncoder) — The prompt encoder used for Peft if using PromptLearningConfig.
  • prompt_tokens (torch.Tensor) — The virtual prompt tokens used for Peft if using PromptLearningConfig.
  • transformer_backbone_name (str) — The name of the transformer backbone in the base model if using PromptLearningConfig.
  • word_embeddings (torch.nn.Embedding) — The word embeddings of the transformer backbone in the base model if using PromptLearningConfig.

add_adapter

< >

( adapter_name: str peft_config: PeftConfig )

Parameters

  • adapter_name (str) — The name of the adapter to be added.
  • peft_config (PeftConfig) — The configuration of the adapter to be added.

Add an adapter to the model based on the passed configuration.

The name for the new adapter should be unique.

The new adapter is not automatically set as the active adapter. Use PeftModel.set_adapter() to set the active adapter.

create_or_update_model_card

< >

( output_dir: str )

Updates or create model card to include information about peft:

  1. Adds peft library tag
  2. Adds peft version
  3. Adds base model info
  4. Adds quantization information if it was used

disable_adapter

< >

( )

Context manager that disables the adapter module. Use this to run inference on the base model.

Example:

>>> with model.disable_adapter():
...     model(inputs)

forward

< >

( *args: Any **kwargs: Any )

Forward pass of the model.

from_pretrained

< >

( model: torch.nn.Module model_id: Union[str, os.PathLike] adapter_name: str = 'default' is_trainable: bool = False config: Optional[PeftConfig] = None **kwargs: Any )

Parameters

  • model (torch.nn.Module) — The model to be adapted. For 🤗 Transformers models, the model should be initialized with the from_pretrained.
  • model_id (str or os.PathLike) — The name of the PEFT configuration to use. Can be either:
    • A string, the model id of a PEFT configuration hosted inside a model repo on the Hugging Face Hub.
    • A path to a directory containing a PEFT configuration file saved using the save_pretrained method (./my_peft_config_directory/).
  • adapter_name (str, optional, defaults to "default") — The name of the adapter to be loaded. This is useful for loading multiple adapters.
  • is_trainable (bool, optional, defaults to False) — Whether the adapter should be trainable or not. If False, the adapter will be frozen and can only be used for inference.
  • config (PeftConfig, optional) — The configuration object to use instead of an automatically loaded configuation. This configuration object is mutually exclusive with model_id and kwargs. This is useful when configuration is already loaded before calling from_pretrained. kwargs — (optional): Additional keyword arguments passed along to the specific PEFT configuration class.

Instantiate a PEFT model from a pretrained model and loaded PEFT weights.

Note that the passed model may be modified inplace.

get_base_model

< >

( )

Returns the base model.

get_nb_trainable_parameters

< >

( )

Returns the number of trainable parameters and the number of all parameters in the model.

get_prompt

< >

( batch_size: int task_ids: Optional[torch.Tensor] = None )

Returns the virtual prompts to use for Peft. Only applicable when using a prompt learning method.

get_prompt_embedding_to_save

< >

( adapter_name: str )

Returns the prompt embedding to save when saving the model. Only applicable when using a prompt learning method.

load_adapter

< >

( model_id: str adapter_name: str is_trainable: bool = False **kwargs: Any )

Parameters

  • adapter_name (str) — The name of the adapter to be added.
  • peft_config (PeftConfig) — The configuration of the adapter to be added.
  • is_trainable (bool, optional, defaults to False) — Whether the adapter should be trainable or not. If False, the adapter will be frozen and can only be used for inference. kwargs — (optional): Additional arguments to modify the way the adapter is loaded, e.g. the token for Hugging Face Hub.

Load a trained adapter into the model.

The name for the new adapter should be unique.

The new adapter is not automatically set as the active adapter. Use PeftModel.set_adapter() to set the active adapter.

print_trainable_parameters

< >

( )

Prints the number of trainable parameters in the model.

save_pretrained

< >

( save_directory: str safe_serialization: bool = True selected_adapters: Optional[List[str]] = None save_embedding_layers: Union[str, bool] = 'auto' is_main_process: bool = True **kwargs: Any )

Parameters

  • save_directory (str) — Directory where the adapter model and configuration files will be saved (will be created if it does not exist).
  • safe_serialization (bool, optional) — Whether to save the adapter files in safetensors format, defaults to True.
  • selected_adapters (List[str], optional) — A list of adapters to be saved. If None, will default to all adapters.
  • save_embedding_layers (Union[bool, str], optional, defaults to "auto") — If True, save the embedding layers in addition to adapter weights. If auto, checks the common embedding layers peft.utils.other.EMBEDDING_LAYER_NAMES in config’s target_modules when available. and automatically sets the boolean flag. This only works for 🤗 transformers models.
  • is_main_process (bool, optional) — Whether the process calling this is the main process or not. Will default to True. Will not save the checkpoint if not on the main process, which is important for multi device setups (e.g. DDP).
  • kwargs (additional keyword arguments, optional) — Additional keyword arguments passed along to the push_to_hub method.

This function saves the adapter model and the adapter configuration files to a directory, so that it can be reloaded using the PeftModel.from_pretrained() class method, and also used by the PeftModel.push_to_hub() method.

set_adapter

< >

( adapter_name: str )

Parameters

  • adapter_name (str) — The name of the adapter to be set as active. The adapter must be loaded first.

Sets the active adapter.

Only one adapter can be active at a time.

PeftModelForSequenceClassification

A PeftModel for sequence classification tasks.

class peft.PeftModelForSequenceClassification

< >

( model: torch.nn.Module peft_config: PeftConfig adapter_name: str = 'default' )

Parameters

Peft model for sequence classification tasks.

Attributes:

  • config (PretrainedConfig) — The configuration object of the base model.
  • cls_layer_name (str) — The name of the classification layer.

Example:

>>> from transformers import AutoModelForSequenceClassification
>>> from peft import PeftModelForSequenceClassification, get_peft_config

>>> config = {
...     "peft_type": "PREFIX_TUNING",
...     "task_type": "SEQ_CLS",
...     "inference_mode": False,
...     "num_virtual_tokens": 20,
...     "token_dim": 768,
...     "num_transformer_submodules": 1,
...     "num_attention_heads": 12,
...     "num_layers": 12,
...     "encoder_hidden_size": 768,
...     "prefix_projection": False,
...     "postprocess_past_key_value_function": None,
... }

>>> peft_config = get_peft_config(config)
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased")
>>> peft_model = PeftModelForSequenceClassification(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117

PeftModelForTokenClassification

A PeftModel for token classification tasks.

class peft.PeftModelForTokenClassification

< >

( model: torch.nn.Module peft_config: PeftConfig = None adapter_name: str = 'default' )

Parameters

Peft model for token classification tasks.

Attributes:

  • config (PretrainedConfig) — The configuration object of the base model.
  • cls_layer_name (str) — The name of the classification layer.

Example:

>>> from transformers import AutoModelForSequenceClassification
>>> from peft import PeftModelForTokenClassification, get_peft_config

>>> config = {
...     "peft_type": "PREFIX_TUNING",
...     "task_type": "TOKEN_CLS",
...     "inference_mode": False,
...     "num_virtual_tokens": 20,
...     "token_dim": 768,
...     "num_transformer_submodules": 1,
...     "num_attention_heads": 12,
...     "num_layers": 12,
...     "encoder_hidden_size": 768,
...     "prefix_projection": False,
...     "postprocess_past_key_value_function": None,
... }

>>> peft_config = get_peft_config(config)
>>> model = AutoModelForTokenClassification.from_pretrained("bert-base-cased")
>>> peft_model = PeftModelForTokenClassification(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117

PeftModelForCausalLM

A PeftModel for causal language modeling.

class peft.PeftModelForCausalLM

< >

( model: torch.nn.Module peft_config: PeftConfig adapter_name: str = 'default' )

Parameters

Peft model for causal language modeling.

Example:

>>> from transformers import AutoModelForCausalLM
>>> from peft import PeftModelForCausalLM, get_peft_config

>>> config = {
...     "peft_type": "PREFIX_TUNING",
...     "task_type": "CAUSAL_LM",
...     "inference_mode": False,
...     "num_virtual_tokens": 20,
...     "token_dim": 1280,
...     "num_transformer_submodules": 1,
...     "num_attention_heads": 20,
...     "num_layers": 36,
...     "encoder_hidden_size": 1280,
...     "prefix_projection": False,
...     "postprocess_past_key_value_function": None,
... }

>>> peft_config = get_peft_config(config)
>>> model = AutoModelForCausalLM.from_pretrained("gpt2-large")
>>> peft_model = PeftModelForCausalLM(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 1843200 || all params: 775873280 || trainable%: 0.23756456724479544

PeftModelForSeq2SeqLM

A PeftModel for sequence-to-sequence language modeling.

class peft.PeftModelForSeq2SeqLM

< >

( model: torch.nn.Module peft_config: PeftConfig adapter_name: str = 'default' )

Parameters

Peft model for sequence-to-sequence language modeling.

Example:

>>> from transformers import AutoModelForSeq2SeqLM
>>> from peft import PeftModelForSeq2SeqLM, get_peft_config

>>> config = {
...     "peft_type": "LORA",
...     "task_type": "SEQ_2_SEQ_LM",
...     "inference_mode": False,
...     "r": 8,
...     "target_modules": ["q", "v"],
...     "lora_alpha": 32,
...     "lora_dropout": 0.1,
...     "fan_in_fan_out": False,
...     "enable_lora": None,
...     "bias": "none",
... }

>>> peft_config = get_peft_config(config)
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> peft_model = PeftModelForSeq2SeqLM(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 884736 || all params: 223843584 || trainable%: 0.3952474242013566

PeftModelForQuestionAnswering

A PeftModel for question answering.

class peft.PeftModelForQuestionAnswering

< >

( model: torch.nn.Module peft_config: PeftConfig adapter_name: str = 'default' )

Parameters

Peft model for extractive question answering.

Attributes:

  • config (PretrainedConfig) — The configuration object of the base model.
  • cls_layer_name (str) — The name of the classification layer.

Example:

>>> from transformers import AutoModelForQuestionAnswering
>>> from peft import PeftModelForQuestionAnswering, get_peft_config

>>> config = {
...     "peft_type": "LORA",
...     "task_type": "QUESTION_ANS",
...     "inference_mode": False,
...     "r": 16,
...     "target_modules": ["query", "value"],
...     "lora_alpha": 32,
...     "lora_dropout": 0.05,
...     "fan_in_fan_out": False,
...     "bias": "none",
... }

>>> peft_config = get_peft_config(config)
>>> model = AutoModelForQuestionAnswering.from_pretrained("bert-base-cased")
>>> peft_model = PeftModelForQuestionAnswering(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 592900 || all params: 108312580 || trainable%: 0.5473971721475013

PeftModelForFeatureExtraction

A PeftModel for getting extracting features/embeddings from transformer models.

class peft.PeftModelForFeatureExtraction

< >

( model: torch.nn.Module peft_config: PeftConfig adapter_name: str = 'default' )

Parameters

Peft model for extracting features/embeddings from transformer models

Attributes:

Example:

>>> from transformers import AutoModel
>>> from peft import PeftModelForFeatureExtraction, get_peft_config

>>> config = {
...     "peft_type": "LORA",
...     "task_type": "FEATURE_EXTRACTION",
...     "inference_mode": False,
...     "r": 16,
...     "target_modules": ["query", "value"],
...     "lora_alpha": 32,
...     "lora_dropout": 0.05,
...     "fan_in_fan_out": False,
...     "bias": "none",
... }
>>> peft_config = get_peft_config(config)
>>> model = AutoModel.from_pretrained("bert-base-cased")
>>> peft_model = PeftModelForFeatureExtraction(model, peft_config)
>>> peft_model.print_trainable_parameters()

Utilities

peft.get_peft_model

< >

( model: PreTrainedModel peft_config: PeftConfig adapter_name: str = 'default' mixed: bool = False )

Parameters

  • model (transformers.PreTrainedModel) — Model to be wrapped.
  • peft_config (PeftConfig) — Configuration object containing the parameters of the Peft model.
  • adapter_name (str, optional, defaults to "default") — The name of the adapter to be injected, if not provided, the default adapter name is used (“default”).
  • mixed (bool, optional, defaults to False) — Whether to allow mixing different (compatible) adapter types.

Returns a Peft model object from a model and a config.

peft.prepare_model_for_kbit_training

< >

( model use_gradient_checkpointing = True gradient_checkpointing_kwargs = None )

Parameters

  • model (transformers.PreTrainedModel) — The loaded model from transformers
  • use_gradient_checkpointing (bool, optional, defaults to True) — If True, use gradient checkpointing to save memory at the expense of slower backward pass.
  • gradient_checkpointing_kwargs (dict, optional, defaults to None) — Keyword arguments to pass to the gradient checkpointing function, please refer to the documentation of torch.utils.checkpoint.checkpoint for more details about the arguments that you can pass to that method. Note this is only available in the latest transformers versions (> 4.34.1).

Note this method only works for transformers models.

This method wraps the entire protocol for preparing a model before running a training. This includes: 1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm head to fp32

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