Parameter efficient fine-tuning PEFT: Adapters in PyTorch
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Master Parameter Efficient Fine-Tuning (PEFT) techniques with PyTorch. Learn how to apply adapters for efficient model tuning, optimizing performance without retraining from scratch.
At a Glance
Apply parameter-efficient fine-tuning (PEFT) in PyTorch using adapters! This hands-on project walks you through fine-tuning a transformer-based neural network using a bottleneck adapter that improves the efficiency of training and storage. Upon completion, you will have enhanced your skills in incorporating adapters and fine-tuning pre-existing models, and you will have gained insights into the advantages and disadvantages of different fine-tuning methods.
A look at the project ahead
- Efficient training: During the training process, a significantly smaller number of weights must be updated. This leads to a more efficient training process compared to full fine-tuning.
- Efficient storage: The models can be stored compactly by only saving the weights for the adapter’s layers and the output layer. This is because the weights in the original model, except for the output layer, remain unchanged.
- Reduced overfitting: Adapter-based PEFT techniques, which preserve the original weights, are less prone to overfitting. This is largely due to the fact that the adapted model retains a substantial part of the original model’s structure.
Learning objectives
- Understand how adapters work
- Apply adapters to linear layers in a neural network
- Train a neural network in a parameter efficient way by training just the adapted layers
What you’ll need
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