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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.

This adapter is not trained. To load a trained adapter, check out PeftModel.load_adapter().

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 configuration. 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.

Note: print_trainable_parameters() uses get_nb_trainable_parameters() which is different from num_parameters(only_trainable=True) from huggingface/transformers. get_nb_trainable_parameters() returns (trainable parameters, all parameters) of the Peft Model which includes modified backbone transformer model. For techniques like LoRA, the backbone transformer model is modified in place with LoRA modules. However, for prompt tuning, the backbone transformer model is unmodified. num_parameters(only_trainable=True) returns number of trainable parameters of the backbone transformer model which can be different.

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.

Additionally, this function will set the specified adapter to trainable (i.e., requires_grad=True). If this is not desired, use the following code.

>>> for name, param in model_peft.named_parameters():
...     if ...:  # some check on name (ex. if 'lora' in name)
...         param.requires_grad = False

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()

PeftMixedModel

A PeftModel for mixing different adapter types (e.g. LoRA and LoHa).

class peft.PeftMixedModel

< >

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

Parameters

  • model (torch.nn.Module) — The model to be tuned.
  • config (PeftConfig) — The config of the model to be tuned. The adapter type must be compatible.
  • adapter_name (str, optional, defaults to "default") — The name of the first adapter.

PeftMixedModel for loading mixing different types of adapters for inference.

This class does not support loading/saving, and it shouldn’t usually be initialized directly. Instead, use get_peft_model with the argument mixed=True.

Read the Mixed adapter types guide to learn more about using different adapter types.

Example:

>>> from peft import get_peft_model

>>> base_model = ...  # load the base model, e.g. from transformers
>>> peft_model = PeftMixedModel.from_pretrained(base_model, path_to_adapter1, "adapter1").eval()
>>> peft_model.load_adapter(path_to_adapter2, "adapter2")
>>> peft_model.set_adapter(["adapter1", "adapter2"])  # activate both adapters
>>> peft_model(data)  # forward pass using both adapters

disable_adapter

< >

( )

Disables the adapter module.

forward

< >

( *args: Any **kwargs: Any )

Forward pass of the model.

from_pretrained

< >

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

Parameters

  • model (nn.Module) — The model to be adapted.
  • 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 use for inference
  • config (PeftConfig, optional) — The configuration object to use instead of an automatically loaded configuration. 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 mixed model from a pretrained model and loaded PEFT weights.

Note that the passed model may be modified inplace.

generate

< >

( *args: Any **kwargs: Any )

Generate output.

get_nb_trainable_parameters

< >

( )

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

merge_and_unload

< >

( *args: Any **kwargs: Any )

Parameters

  • progressbar (bool) — whether to show a progressbar indicating the unload and merge process
  • safe_merge (bool) — whether to activate the safe merging check to check if there is any potential Nan in the adapter weights
  • adapter_names (List[str], optional) — The list of adapter names that should be merged. If None, all active adapters will be merged. Defaults to None.

This method merges the adapter layers into the base model. This is needed if someone wants to use the base model as a standalone model.

print_trainable_parameters

< >

( )

Prints the number of trainable parameters in the model.

Note: print_trainable_parameters() uses get_nb_trainable_parameters() which is different from num_parameters(only_trainable=True) from huggingface/transformers. get_nb_trainable_parameters() returns (trainable parameters, all parameters) of the Peft Model which includes modified backbone transformer model. For techniques like LoRA, the backbone transformer model is modified in place with LoRA modules. However, for prompt tuning, the backbone transformer model is unmodified. num_parameters(only_trainable=True) returns number of trainable parameters of the backbone transformer model which can be different.

set_adapter

< >

( adapter_name: Union[str, list[str]] )

Parameters

  • adapter_name (str or List[str]) — The name of the adapter(s) to be activated.

Sets the active adapter(s) for the model.

Note that the order in which the adapters are applied during the forward pass may not be the same as the order in which they are passed to this function. Instead, the order during the forward pass is determined by the order in which the adapters were loaded into the model. The active adapters only determine which adapters are active during the forward pass, but not the order in which they are applied.

Additionally, this function will set the specified adapters to trainable (i.e., requires_grad=True). If this is not desired, use the following code.

>>> for name, param in model_peft.named_parameters():
...     if ...:  # some check on name (ex. if 'lora' in name)
...         param.requires_grad = False

unload

< >

( *args: Any **kwargs: Any )

Gets back the base model by removing all the adapter modules without merging. This gives back the original base model.

Utilities

peft.cast_mixed_precision_params

< >

( model dtype )

Parameters

  • model (torch.nn.Module) — The model to cast the non-trainable parameters of.
  • dtype (torch.dtype) — The dtype to cast the non-trainable parameters to. The dtype can be torch.float16 or

Cast all non-trainable parameters of the model to the given dtype. The dtype can be torch.float16 or torch.bfloat16 as per the mixed-precision training you are performing. The trainable parameters are cast to full precision. This is meant to reduce the GPU memory usage when using PEFT methods by using half-precision dtype for non-trainable parameters. Having the trainable parameters in full-precision preserves training stability when using automatic mixed-precision training.

torch.bfloat16 as per the mixed-precision training you are performing.

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.inject_adapter_in_model

< >

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

Parameters

  • peft_config (PeftConfig) — Configuration object containing the parameters of the Peft model.
  • model (torch.nn.Module) — The input model where the adapter will be injected.
  • 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”).

A simple API to create and inject adapter in-place into a model. Currently the API does not support prompt learning methods and adaption prompt. Make sure to have the correct target_names set in the peft_config object. The API calls get_peft_model under the hood but would be restricted only to non-prompt learning methods.

peft.get_peft_model_state_dict

< >

( model state_dict = None adapter_name = 'default' unwrap_compiled = False save_embedding_layers = 'auto' )

Parameters

  • model (PeftModel) — The Peft model. When using torch.nn.DistributedDataParallel, DeepSpeed or FSDP, the model should be the underlying model/unwrapped model (i.e. model.module).
  • state_dict (dict, optional, defaults to None) — The state dict of the model. If not provided, the state dict of the passed model will be used.
  • adapter_name (str, optional, defaults to "default") — The name of the adapter whose state dict should be returned.
  • unwrap_compiled (bool, optional, defaults to False) — Whether to unwrap the model if torch.compile was used.
  • 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. Based on it sets the boolean flag. This only works for 🤗 transformers models.

Get the state dict of the Peft model.

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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