Fine-tuning

Overview

This screenshot showcases the interface for fine-tuning models within the Edge AI SDK. Fine-tuning is a technique used to optimize pre-trained models for specific tasks. By fine-tuning, you can improve the accuracy and performance of a model in a particular application using a smaller dataset.

Key Features

The interface provides three main fine-tuning options:

• Text-to-Text (Full Parameter): Finetune popular text-to-text models using full parameter adjustment. This method involves updating all parameters in the model, typically achieving the highest accuracy but requiring more computational resources and data.

• Text-to-Text (LoRA): Finetune popular text-to-text models using LoRA (Low-Rank Adaptation). LoRA is a more efficient fine-tuning method that freezes the original weights of the pre-trained model and only trains a small number of additional parameters. This significantly reduces computational and memory requirements.

• Text-to-Image: Finetune popular text-to-image models. This option allows you to customize the output of text-to-image models for specific needs. This feature is under development and will be available in a future release.

Usage

Users can select the appropriate fine-tuning option based on their specific task and resources.

• If the highest possible accuracy is needed and sufficient computational resources are available, "Text-to-Text (Full Parameter)" can be used.

• If computational resources are limited, or a more efficient fine-tuning process is desired, "Text-to-Text (LoRA)" can be used.

• If there is a need to customize the output of text-to-image models, "Text-to-Image" can be used.