#
Model merge

PEFT provides several internal utilities for merging LoRA adapters with the TIES and DARE methods.

####
peft.utils.merge_utils.prune

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( tensor: Tensor density: float method: typing.Literal['magnitude', 'random'] rescale: bool = False ) →
`torch.Tensor`

Parameters

- tensor (`torch.Tensor`) —The tensor to prune.
- density (`float`) —The fraction of values to preserve. Should be in [0,1].
- method (`str`) —The method to use to prune. Should be one of [“magnitude”, “random”].
- rescale (`bool`) —Whether to rescale the result to preserve the expected value of the original tensor.

Returns

`torch.Tensor`

The pruned tensor.

Prune the values of task tensors based on the `method`.

####
peft.utils.merge_utils.calculate_majority_sign_mask

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( tensor: Tensor method: typing.Literal['total', 'frequency'] = 'total' ) →
`torch.Tensor`

Parameters

- tensor (`torch.Tensor`) —The tensor to get the mask from.
- method (`str`) —The method to use to get the mask. Should be one of [“total”, “frequency”].

Returns

`torch.Tensor`

The majority sign mask.

Get the mask of the majority sign across the task tensors. Task tensors are stacked on dimension 0.

####
peft.utils.merge_utils.disjoint_merge

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( task_tensors: Tensor majority_sign_mask: Tensor ) →
`torch.Tensor`

Parameters

- task_tensors (`torch.Tensor`) —The task tensors to merge.
- majority_sign_mask (`torch.Tensor`) —The mask of the majority sign across the task tensors.

Returns

`torch.Tensor`

The merged tensor.

Merge the task tensors using disjoint merge.

####
peft.utils.merge_utils.task_arithmetic

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( task_tensors: list weights: Tensor ) →
`torch.Tensor`

Parameters

- task_tensors(`List[torch.Tensor]`) —The task tensors to merge.
- weights (`torch.Tensor`) —The weights of the task tensors.

Returns

`torch.Tensor`

The merged tensor.

Merge the task tensors using `task arithmetic`.

####
peft.utils.merge_utils.ties

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( task_tensors: list weights: Tensor density: float majority_sign_method: typing.Literal['total', 'frequency'] = 'total' ) →
`torch.Tensor`

Parameters

- task_tensors(`List[torch.Tensor]`) —The task tensors to merge.
- weights (`torch.Tensor`) —The weights of the task tensors.
- density (`float`) —The fraction of values to preserve. Should be in [0,1].
- majority_sign_method (`str`) — The method to use to get the majority sign mask. Should be one of [“total”, “frequency”].

Returns

`torch.Tensor`

The merged tensor.

Merge the task tensors using `ties`.

####
peft.utils.merge_utils.dare_linear

< source >

( task_tensors: list weights: Tensor density: float ) →
`torch.Tensor`

Parameters

- task_tensors(`List[torch.Tensor]`) —The task tensors to merge.
- weights (`torch.Tensor`) —The weights of the task tensors.
- density (`float`) —The fraction of values to preserve. Should be in [0,1].

Returns

`torch.Tensor`

The merged tensor.

Merge the task tensors using `dare linear`.

####
peft.utils.merge_utils.dare_ties

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( task_tensors: list weights: Tensor density: float majority_sign_method: typing.Literal['total', 'frequency'] = 'total' ) →
`torch.Tensor`

Parameters

- task_tensors(`List[torch.Tensor]`) —The task tensors to merge.
- weights (`torch.Tensor`) —The weights of the task tensors.
- density (`float`) —The fraction of values to preserve. Should be in [0,1].
- majority_sign_method (`str`) — The method to use to get the majority sign mask. Should be one of [“total”, “frequency”].

Returns

`torch.Tensor`

The merged tensor.

Merge the task tensors using `dare ties`.

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