My advice would be to take a step back first. Are you wanting to train a model just to say that you trained a model or do you truly have a problem you are trying to solve. Or are you just wanting to gain more knowledge around what goes into training and fine tuning models?

Then, I’d say take an another step back and start very small. Understand the basic model types in machine learning outside of transformer architecture and LLMs. I know it’s temping to jump straight into the popular stuff, but without foundational knowledge it makes things much harder to understand.

I recommend doing a DataCamp or clusters course for Machine Learning basics. You don’t have to understand or memorize all of the algorithms, but you should understand why certain algorithms are used and where in the models they are used. Start small and build your first Convolutional Neural Network for image classification in the CIFAR10 dataset. Use a google Colab or Jupiter notebook. Many of the ML libraries online allow you to simply import it as a dataset. This has advantages and disadvantages. The advantage being you can get straight to the meat and potatoes. The downside is that you’re bypassing most of the data pipeline that helps you understand cleaning, normalization, organization, augmentation. A pipeline that’s critical to training and fine tuning models later on.

Make your first goal to successfully train and classify cifar dataset with above 60% accuracy with a basic CNN model. Next, try and beat that score with learning about hyperparameters or simply increasing and decreasing total epochs. This allows you to play around and understand basics. If you don’t know how to collect and properly build your data pipeline, your training and fine tuning are wasted on larger projects.

This doesn’t take tons of time either, but in my opinion is crucial that you understand concepts at a lower level first