Copyright Continuum Labs - 2023
Parameter-Efficient Fine-Tuning (PEFT) is a technique used to fine tune neural language models
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The concept of fine-tuning pre-trained models has become a cornerstone for achieving enhanced performance on specific tasks.
However, as these models grow in complexity and size, traditional fine-tuning methods demand an increasingly hefty computational costs.
Parameter-Efficient Fine-Tuning (PEFT) has been developed to optimise AI model performance efficiently, catering to scenarios where extensive retraining or large-scale parameter updates are not viable.
Before describing the process, it is worthwhile spending some time on the architecture of neural language model.
Neural language models are built using Transformer architectures, which consist of multiple layers of self-attention and feed-forward neural networks. Each layer contains a large number of parameters, contributing to the model's ability to understand and generate complex language patterns. When you add up multiple layers of parameters - you end up with billions of 'parameters'.
Parameters in neural language models are numerical values that define the behaviour of the model. They are the core elements that the model adjusts during the training process to learn from data.
Typically, these parameters are the weights and biases in the neural network's layers.
Weights determine how much influence one node (or neuron) in a layer has on another in the subsequent layer.
Biases are added to the output of weighted node inputs and provide additional flexibility to the model, allowing it to better fit the data.
These models contain billions of 'parameters' - and each one takes up memory in a computer. Some of the larger language models require up to 600GB of memory to operate (not disk space).
Traditional fine-tuning methods involve updating all the parameters based on a specific task or dataset. However, this approach can be resource-intensive due to the vast number of parameters and can lead to issues like overfitting on smaller datasets.
Parameter Efficient Fine Tuning aims to modify only a small fraction of the model's parameters during the fine-tuning process. This approach seeks to retain most of the pre-trained knowledge of the model while adapting it to specific tasks or datasets, making it more efficient and resource-friendly.
Fine-tuning involves adjusting a pre-trained model further on a new task using new data.
Traditionally, this process updates all layers and parameters of the model, requiring significant computational resources and time, particularly for larger models. This method, while effective, is not always practical or necessary for achieving optimal results on the new task.
On the flip side, PEFT focuses on training only a crucial subset of the model's parameters, significantly reducing the computation required for fine-tuning.
By identifying and updating the most impactful parameters for the new task, PEFT offers a more resource-efficient pathway to model optimisation.
| Feature | Parameter-efficient Fine-tuning | Standard Fine-tuning |
|---|---|---|
Goal | Improve performance on a specific task with limited data and computation | Improve performance on a specific task with ample data and computation |
Training Data | Small dataset (fewer examples) | Large dataset (many examples) |
Training Time | Faster, as only a subset of parameters is updated | Longer, due to updating the entire model |
Computational Resources | Fewer required | Larger required |
Model Parameters | Modifies only a small subset | Re-trains the entire model |
Overfitting | Less prone | More prone |
Training Performance | Good enough, though not as high as full fine-tuning | Typically better than PEFT |
Use Cases | Ideal for low-resource settings | Ideal for high-resource settings |
PEFT not only streamlines the fine-tuning process but also addresses several limitations of the traditional approach:
Reduced Computational and Storage Costs: By updating a minimal number of parameters, PEFT significantly cuts down on computational and storage demands.
Overcoming Catastrophic Forgetting: Traditional fine-tuning risks the model forgetting previously learned information. PEFT mitigates this by limiting parameter updates.
Efficiency in Low-data Regimes: PEFT has shown superior performance and generalisation in scenarios with limited data, making it ideal for niche applications.
Portability: The compact nature of PEFT modifications facilitates easy deployment and application across multiple tasks, without the need to overhaul the entire model.
Comparable Performance: Despite its efficiency, PEFT can achieve results on par with traditional fine-tuning, ensuring no compromise on model effectiveness.
As the demand for sophisticated AI applications grows, so does the need for more efficient model training and fine-tuning methods.
PEFT represents a significant leap forward, offering a viable solution that balances performance with computational efficiency.
Whether you're working in a resource-constrained environment or looking to optimise a vast pre-trained model for a new task, PEFT provides a pathway to achieving high-quality results without the traditional costs.
