> 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/neftune-noise-enhanced-fine-tuning.md).

# NEFTune (Noise-Enhanced Fine-Tuning)

### **What Is NEFTune?**

Noise-Enhanced Fine-Tuning (NEFTune) is another effective technique for improving the fine-tuning process of language models. NEFTune uses the well-known regularization technique of introducing random noise to improve model generalization during the fine-tuning process. This approach aims      to reduce overfitting and enhance the robustness of pre-trained models.

### The NEFTune Process Explained

{% stepper %}
{% step %}
**Targeting the Embedding Layers**

The embedding layers are responsible for converting input tokens into vector representations. These embeddings are a crucial foundation for the model's understanding of the input.
{% endstep %}

{% step %}
**Adding Gaussian Noise**

During fine-tuning, small amounts of Gaussian noise are applied specifically to the embedding layers. This controlled disturbance helps the model generalize better and avoid overfitting.
{% endstep %}
{% endstepper %}

**Why NEFT is Effective**

* **Encourages Generalization**: By perturbing the embeddings, the model learns to focus on higher-level features within the training data rather than memorizing fine-grained details.
* **Prevents Overfitting**: NEFTune reduces the risk of overfitting to the training set, particularly for smaller datasets, by forcing the model to adapt to slight variations in the data.
* **Improves Performance**: Adding noise has been shown to improve the fine-tuned model's ability to perform on unseen data, yielding better results on downstream tasks.

**Implementing NEFT is also very simple:**

To implement NEFTune, you can incorporate it directly into your training process by modifying the trainer's configuration. The key is to add the neftune\_noise\_alpha parameter, which specifies the noise intensity added to the embeddings. 

```python
trainer = SFTTrainer(
    #Some Parameters Here
     neftune_noise_alpha = 5
)
```

When fine-tuning the model, this noise is integrated into the token embedding layer, This is exactly what allows the model to generalize better and  learn higher-level features from the dataset.