Zeta.nn.modules.AverageModelMerger Documentation

Introduction

The AverageModelMerger class, found in the zeta.nn.modules library, is a simple yet powerful class to merge multiple models by averaging their weights. It offers a straightforward way to combine models trained in different stages, such as instruction and alignment tuning, leading to improved model performance in certain circumstances.

Class Definition: AverageModelMerger

class AverageModelMerger:
    """
    A class to merge multiple models by averaging their weights.

    Attributes:
    models (List[nn.Module]): A list of PyTorch models to be merged.

    Examples::- Example usage:
    model1 = nn.Linear(in_features=10, out_features=10)
    model2 = nn.Linear(in_features=10, out_features=10)
    model3 = nn.Linear(in_features=10, out_features=10)
    merge = AverageModelMerger([model1, model2, model3])
    mergedim = merge.merge_models()
    print(mergedim)
    """

Class Parameters:

Parameters	Data Type	Default Value	Description
models	List[nn.Module]	N/A	List of PyTorch models to be merged

Class Methods:

Method Name	Description	Parameters	Returns
__init__(self, models: List[nn.Module])	Initializes the AverageModelMerger with a list of models.	models (List[nn.Module])	None
merge_models(self)	Merges the models by averaging their weights.	None	A new model with averaged weights.
_copy_model_structure(model: nn.Module)	Creates a new instance of a model with the same structure as the given model.	model (nn.Module)	A new model with the same structure.

Constructor __init__(self, models: List[nn.Module])

Initializes an instance of the AverageModelMerge class. It takes a list of PyTorch models as input which are to be merged later using the merge_models method.

• models (List[nn.Module]): Models to be merged.

Method merge_models(self) -> nn.Module

This function merges the models by averaging the weights of the PyTorch models.

Returns

nn.Module: A new model with averaged weights.

Method _copy_model_structure(self, model: nn.Module) -> nn.Module

This function creates a new instance of a model with exactly the same structure as the given model.

Parameters - model (nn.Module): The model whose structure is to be copied.

Returns

nn.Module: A new model with exactly the same structure.

Examples of Usage:

Example 1

import torch.nn as nn

from zeta.nn.modules import AverageModelMerger

# Define models
model1 = nn.Linear(in_features=10, out_features=10)
model2 = nn.Linear(in_features=10, out_features=10)
model3 = nn.Linear(in_features=10, out_features=10)

# Initialize AverageModelMerger
merger = AverageModelMerger([model1, model2, model3])

# Merge models
mergedim = merger.merge_models()

# Print merged model
print(mergedim)

Example 2

import torch.nn as nn

from zeta.nn.modules import AverageModelMerger

# Define models
model1 = nn.Conv2d(3, 6, 5)
model2 = nn.Conv2d(3, 6, 5)
model3 = nn.Conv2d(3, 6, 5)

# Initialize AverageModelMerger
merger = AverageModelMerger([model1, model2, model3])

# Merge models
mergedim = merger.merge_models()

# Print merged model
print(mergedim)

Example 3

import torch.nn as nn

from zeta.nn.modules import AverageModelMerger

# Define models
model1 = nn.CrossEntropyLoss()
model2 = nn.CrossEntropyLoss()
model3 = nn.CrossEntropyLoss()

# Initialize AverageModelMerger
merger = AverageModelMerger([model1, model2, model3])

# Merge models
mergedim = merger.merge_models()

# Print merged model
print(mergedim)

All the examples above demonstrate the basic usage of this class. In cases where you have multiple trained models (e.g., resultant from a k-fold cross-validation or models trained on different datasets), you can use this class to merge or average their weights. The resultant model will carry averaged weights, giving a balanced representation of all the models.