In this blog, we’ll explore Parameter Efficient Fine Tuning (PEFT), a technique designed to address the limitations of traditional fine-tuning methods like full fine-tuning and final layer fine-tuning. Fine-tuning only the final layer often results in sub-optimal outcomes, while updating all layers can be extremely time-consuming and resource intensive. To overcome these challenges, researchers have developed various parameter-efficient fine-tuning techniques.

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TL;DR

Parameter-Efficient Fine-Tuning (PEFT) is a smart alternative to full fine-tuning for large language models like BERT, GPT-3, and LLaMA. Instead of updating all parameters, PEFT updates only a small subset—greatly reducing memory, compute, and training time. It enables fine-tuning on consumer hardware, supports multi-task learning, and helps preserve a model’s pretrained knowledge (avoiding catastrophic forgetting).

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What is PEFT?

Parameter-Efficient Fine-Tuning (PEFT) is a fine-tuning technique that updates only a small subset of parameters in a pretrained model, while keeping most of the model frozen. This approach significantly reduces memory consumption and computational cost, making it ideal for fine-tuning large-scale transformer models such as BERT, GPT-3, T5, and LLaMA.

Unlike full fine-tuning, which updates all model parameters, PEFT trains only a few additional layers or trainable modules that are added to the model while preserving its original knowledge.

What is Catastrophic forgetting ?

Catastrophic forgetting is a phenomenon observed in machine learning, particularly when fine-tuning large language models (LLMs) like GPT. It occurs when a neural network that has already been trained on one task is updated or fine-tuned on a new task, and in doing so, it starts to lose the information it learned during its original training. This is problematic because the model forgets previously acquired knowledge, which can degrade its performance on tasks it was previously good at.

Popular PEFT techniques include Adapters, LoRA, QLoRA, IA³, Prefix-Tuning, and Prompt-Tuning. While PEFT offers scalability and efficiency, it may not perform the best in every scenario it is best suited for large models or resource-constrained settings.

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Why is PEFT Used ?

Parameter-Efficient Fine-Tuning (PEFT) is designed to optimize the adaptation of large language models (LLMs) by updating only a small subset of parameters, thereby enhancing efficiency without significantly compromising performance.

Key Reasons for Using PEFT:

• Reduces Computational Cost: Full fine-tuning involves adjusting all parameters of a model, which can be computationally expensive. A model such as GPT-4 has an estimated 175 Billion parameters to be trained whereas PEFT focuses on updating a limited number of parameters, significantly lowering GPU memory requirements.

• Enables Fine-Tuning on Consumer Hardware: Large models like GPT-3, LLaMA, or T5 typically require substantial resources for fine-tuning. PEFT allows these models to be fine-tuned on standard consumer-grade GPUs, making the process more accessible.

• Prevents Catastrophic Forgetting (when a model loses previously learned information upon learning new tasks): Full fine-tuning can sometimes cause a model to overwrite its general knowledge. By updating only specific parameters, PEFT helps preserve the model's original capabilities while integrating task-specific adaptations.

• Facilitates Multi-Task Learning: Techniques such as Adapters (small trainable layers added to a model to enable task-specific learning without altering the entire model) enable a single base model to efficiently handle multiple tasks by incorporating different adapter modules for each task.

• Accelerates Training and Deployment: Since fewer parameters are updated, the fine-tuning process is faster, and the resulting models are smaller, simplifying deployment.

When is PEFT Used?

PEFT is particularly useful in scenarios where:

• Resource Constraints Exist: When full fine-tuning is too resource-intensive, PEFT offers a more efficient alternative by reducing the number of trainable parameters, thereby decreasing memory and computational requirements.

• Rapid Training is Needed: Updating fewer parameters allows for quicker training times, enabling faster model adaptation to new tasks.

• Multiple Tasks Must Be Handled Efficiently: Utilizing PEFT techniques like adapters allows a model to be fine-tuned for various tasks without extensive retraining.

• Limited Data is Available: PEFT is advantageous in low-data scenarios, as it requires fewer data points to achieve effective fine-tuning.

• Preservation of Pretrained Capabilities is Crucial: By updating only a subset of parameters, the fine-tuned model retains its original knowledge while incorporating task-specific adaptations.

Examples of PEFT Applications:

• Fine-Tuning GPT-3.5 for Customer Service Chatbots (Low-Rank Adaptation or LoRA): Low-Rank Matrices (LoRA) (simplified matrices used to efficiently update model weights, reducing memory usage) enable GPT-3.5 to be adapted for customer service applications by updating only essential parameters. This approach allows the model to understand and respond to customer queries effectively without the need for extensive computational resources.

• Adapting BERT for Finance Document Classification (Adapters): By integrating adapters, BERT can be fine-tuned to recognize and categorize financial documents. These small, trainable layers allow BERT to handle domain-specific language pertinent to the financial sector without modifying the entire model.

• Tuning LLaMA with Minimal GPU Usage (Quantized Low-Rank Adaptation or QLoRA): Quantized Low-Rank Adaptation (QLoRA) combines low-rank adaptation with quantization techniques to enable efficient fine-tuning of LLaMA models on consumer-grade hardware. This method reduces memory requirements while maintaining model performance.

• Optimizing Large Language Models for Personalized Assistants (Prefix-Tuning): Prefix-Tuning involves prepending trainable continuous vectors (prefixes) to the input of a model, allowing it to adapt to specific tasks such as personalized assistance without modifying the model's original parameters.

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When Not to Use PEFT?

1. You Need Maximum Performance on a Specific Task

If your goal is to squeeze out every last bit of performance for a single, high-stakes task (e.g., a production-critical model in finance or healthcare), full fine-tuning may outperform PEFT — since all parameters are optimized for that task.

PEFT trades some task-specific performance for efficiency and modularity.

2. You're Working with a Small Model

If your base model is already small (like DistilBERT or TinyGPT), the benefits of PEFT — saving memory or compute — are minimal. In such cases, full fine-tuning might be just as efficient and yield better results.

3. You Need Deep Architectural Changes

PEFT assumes you're fine-tuning within the structure of a pretrained model. If you need to change the architecture itself (e.g., adding new layers, modifying attention mechanisms), PEFT won’t help — you’ll need full control of all weights and gradients.

4. You’re Doing Zero-Shot or Few-Shot Inference Only

If you're not training at all, just prompting models in zero-shot or few-shot mode then PEFT isn't relevant. It’s a training technique, not an inference one.

5. The Task Requires Global Model Changes

Some tasks (e.g., major domain shifts, like converting a general language model into a code-only model) may require altering many layers of the model. PEFT might not adapt the model deeply enough for such drastic shifts.

6. You’re Using a Poorly Supported Model Architecture

PEFT techniques like LoRA, Adapters, or Prefix-Tuning are well-supported on popular models (e.g., BERT, T5, LLaMA). If you're working with a custom or uncommon architecture, PEFT tooling might not exist yet — making implementation tricky or impossible.

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Pros and Cons of Parameter-Efficient Fine-Tuning (PEFT)

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Common PEFT Techniques

PEFT encompasses several techniques, each designed to make fine-tuning more efficient while targeting different resource constraints and use cases:

• Adapters (e.g., Houlsby, Compacter, BitFit)
  Small trainable layers inserted between frozen layers of a transformer. Ideal for multi-task learning, as each task can have its own adapter module without retraining the entire model.

• LoRA (Low-Rank Adaptation)
  Introduces low-rank matrices to fine-tune weight updates efficiently. Greatly reduces memory consumption while retaining strong performance. Especially useful for fine-tuning large models.

• QLoRA (Quantized LoRA)
  Combines LoRA with quantization techniques to further shrink memory usage. Tailored for training large models on low-resource hardware (e.g., single GPUs or laptops).

• IA³ (Infused Adapter Layers)
  Unlike traditional adapters, IA³ modifies the internal scaling of existing activations rather than adding new layers. This makes it lightweight and effective for certain tasks.

• Prefix-Tuning
  Trains a set of task-specific prefix embeddings that are prepended to the model’s input. These prefixes influence model behavior without altering internal parameters.

• Prompt-Tuning & P-Tuning
  Instead of updating model weights, these techniques learn soft prompts—continuous vectors—that steer model outputs. They’re especially effective for low-data or few-shot tasks.

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Conclusion

In conclusion, Parameter Efficient Fine Tuning (PEFT) offers a compelling approach to optimizing large language models by reducing memory and computational demands while maintaining performance. By selectively updating a small subset of parameters, PEFT enables efficient fine-tuning on consumer hardware, supports multi-task learning, and prevents catastrophic forgetting.

In upcoming blogs, I will delve deeper into specific PEFT techniques such as Adapters, LoRA, and QLoRA, exploring their unique advantages and applications in greater detail. Stay tuned!