Proposed: Enhancing Task-Specific Model Performance through Fine-Tuning with Axolotl
Metadata
- Status: Proposed
- Deciders: V-Sekai, fire
- Tags: V-Sekai, Model Fine-tuning, Axolotl
The Backdrop
We have multiple tasks each with a minimum of 100 samples and we want to fine-tune the latest models with our data. We are considering using the Axolotl library in a mode that batches the inputs.
The Challenge
The challenge is to effectively use these samples to fine-tune the model for each task without overfitting, while also ensuring that the model generalizes well to unseen data.
The Strategy
Data Preparation: First, we need to prepare our data for each task. This involves cleaning the data and splitting it into training and validation sets.
Model Selection: Next, we select the latest models that we want to fine-tune. These could be models from Hugging Face’s model hub or any other source.
Fine-tuning with Axolotl: We will use the Axolotl library for fine-tuning. We can batch-mask our inputs to efficiently utilize our computational resources. This process will be repeated for each task.
Evaluation: After fine-tuning, we evaluate the model on our validation set for each task to see how well it performs.
The Upside
By fine-tuning the latest models on our specific tasks, we can potentially achieve better performance than using pre-trained models out-of-the-box. Also, Axolotl provides an easy-to-use interface for fine-tuning models.
The Downside
Fine-tuning models requires computational resources and time. Also, with only 100 samples per task, there’s a risk of overfitting.
The Road Not Taken
An alternative approach could be to use data augmentation techniques to increase the size of our dataset before fine-tuning.
The Infrequent Use Case
If we have tasks that are infrequently performed, it might not be worth the effort to fine-tune a model specifically for these tasks.
In Core and Done by Us?
Yes, this strategy involves us actively participating in the fine-tuning process.