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, and supports the PromptEncoder for prompt learning.

PeftModel

class peft.PeftModel

< >

( model peft_config: PeftConfig adapter_name = 'default' )

Parameters

  • model (PreTrainedModel) — The base transformer model used for Peft.
  • peft_config (PeftConfig) — The configuration of the Peft model.

Base model encompassing various Peft methods.

Attributes:

disable_adapter

< >

( )

Disables the adapter module.

forward

< >

( *args **kwargs )

Forward pass of the model.

from_pretrained

< >

( model model_id adapter_name = 'default' is_trainable = False **kwargs )

Parameters

  • model (PreTrainedModel) — The model to be adapted. The model should be initialized with the from_pretrained method from the 🤗 Transformers library.
  • model_id (str or os.PathLike) — The name of the Lora configuration to use. Can be either:
    • A string, the model id of a Lora configuration hosted inside a model repo on the Hugging Face Hub.
    • A path to a directory containing a Lora configuration file saved using the save_pretrained method (./my_lora_config_directory/).

Instantiate a LoraModel from a pretrained Lora configuration and weights.

get_base_model

< >

( )

Returns the base model.

get_prompt

< >

( batch_size )

Returns the virtual prompts to use for Peft. Only applicable when peft_config.peft_type != PeftType.LORA.

get_prompt_embedding_to_save

< >

( adapter_name )

Returns the prompt embedding to save when saving the model. Only applicable when peft_config.peft_type != PeftType.LORA.

print_trainable_parameters

< >

( )

Prints the number of trainable parameters in the model.

save_pretrained

< >

( save_directory **kwargs )

Parameters

  • save_directory (str) — Directory where the adapter model and configuration files will be saved (will be created if it does not exist).
  • 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 LoraModel.from_pretrained class method, and also used by the LoraModel.push_to_hub method.

set_adapter

< >

( adapter_name )

Sets the active adapter.

PeftModelForSequenceClassification

A PeftModel for sequence classification tasks.

class peft.PeftModelForSequenceClassification

< >

( model peft_config: PeftConfig adapter_name = 'default' )

Parameters

Peft model for sequence classification tasks.

Attributes:

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 peft_config: PeftConfig = None adapter_name = 'default' )

Parameters

Peft model for token classification tasks.

Attributes:

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 peft_config: PeftConfig adapter_name = '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 peft_config: PeftConfig adapter_name = '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,
...     "merge_weights": False,
...     "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