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