# Parameters Guide

## Fine-Tuning Parameters Guide

Adjusting the fine-tuning configuration parameters allows you to customize the training process to better suit your data and use case. Here's a list of the supported parameters and their impact on the fine-tuning process:

### **Learning Rate**

* Determines how quickly the model learns.
* Lower rates are ideal for minor adjustments, while higher rates speed up learning but risk overshooting.
* Default value: **0.0001**

### **Batch Size**

* Specifies how much data is processed simultaneously.
* Smaller batch sizes can improve accuracy but take longer to train.
* Default value: **16**

### **Epochs**

* Indicates how many times the model will iterate through the entire dataset.
* More epochs can improve accuracy but increase computation time.
* Default value: **10** epochs

### **LoRA Rank**

* The rank of the low-rank adaptation matrix.
* Higher ranks can capture more information but require more memory and computation.
* Default value: **16**