> For the complete documentation index, see [llms.txt](https://ubiai.gitbook.io/llm-guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ubiai.gitbook.io/llm-guide/supervised-fine-tuning-strategies/parameter-efficient-fine-tuning-peft.md).

# Parameter-Efficient Fine-Tuning (PEFT)

Parameter-efficient fine-tuning (PEFT) is a method designed to adapt large pre-trained models for specific tasks while minimizing the number of parameters that need to be updated. Unlike traditional fine-tuning approaches, such as full fine-tuning or "half fine-tuning," where you freeze some layers and update the rest of the model, PEFT focuses on freezing most of the model's parameters while only modifying a small subset of them. This could include the addition of task-specific adapters or updates to certain layers, significantly reducing the number of parameters that need to be trained. 

The concept of **Parameter-Efficient Fine-Tuning (PEFT)** has significantly lowered the barriers to applying large language models (LLMs) in development. This has sparked a wide range of research into various methods for achieving PEFT. These methods can be classified into three main categories:

1. **Selective Fine-Tuning**: This approach focuses on updating a carefully chosen subset of a pre-trained model's parameters, rather than fine-tuning the entire model. This method enables more efficient adaptation to specific tasks.
2. **Additive Fine-Tuning**: New modules are added to the pre-trained model for fine-tuning. These modules are then trained to incorporate domain-specific knowledge, allowing the model to adapt to new tasks while preserving the original model's capabilities.
3. **Reparameterization**: Where a low-dimensional representation is created for specific model components. This reduces the complexity of the fine-tuning process by working with a smaller set of parameters.

In this section, we will be exploring a few of the most effective techniques for applying parameter-efficient fine-tuning (PEFT).

{% content-ref url="/pages/oWB96kBqRwv4YVHaULxx" %}
[LoRA (Low-Rank Adaptation)](/llm-guide/supervised-fine-tuning-strategies/parameter-efficient-fine-tuning-peft/lora-low-rank-adaptation.md)
{% endcontent-ref %}

{% content-ref url="/pages/jpfsgbULq7u8Yd92IMXY" %}
[QLoRA (Quantized LoRA)](/llm-guide/supervised-fine-tuning-strategies/parameter-efficient-fine-tuning-peft/qlora-quantized-lora.md)
{% endcontent-ref %}

{% content-ref url="/pages/9KDYnEsup1jz4sINEKdt" %}
[DoRA (Decomposed  Low-Rank Adaptation)](/llm-guide/supervised-fine-tuning-strategies/parameter-efficient-fine-tuning-peft/dora-decomposed-low-rank-adaptation.md)
{% endcontent-ref %}

{% content-ref url="/pages/86cdmkocvbmRZbkmPWrc" %}
[NEFTune (Noise-Enhanced Fine-Tuning)](/llm-guide/supervised-fine-tuning-strategies/parameter-efficient-fine-tuning-peft/neftune-noise-enhanced-fine-tuning.md)
{% endcontent-ref %}