Specifically, the ones related to Tensorflow checkpoints and Huggingface models.

To kick things off, let’s start with Tensorflow checkpoints. These are essentially saved versions of your model’s weights during training. They can be really helpful when it comes to resuming training or loading a pre-trained model for fine-tuning purposes. However, there’s one major downside they take up a lot of space!

Imagine you have a model with millions (or billions) of parameters and you want to save checkpoints every few epochs. Well, those files can quickly add up and before you know it, your hard drive is filled to the brim. And let’s not forget about the time it takes to load these massive files back into memory during training or inference!

If you ever need to transfer a model from one machine to another (say, for deployment purposes), those checkpoints can be a real pain. You have to make sure they’re compatible with the new system and that all of your dependencies are installed correctly. And if something goes wrong during this process? Well, let’s just say it’s not pretty!

Now, let’s move on to Huggingface models. These pre-trained language models have become incredibly popular in recent years due to their impressive performance and ease of use. However, they also come with some limitations that you should be aware of. For starters, these models can be really large! Depending on the specific model you choose, it could take hours (or even days) just to download and load into memory. And once you’ve loaded it in, you might notice a significant decrease in performance due to all of that extra data being stored in RAM.

If you ever need to fine-tune one of these models for your own specific use case (say, for sentiment analysis or text classification), you’re going to have to do some serious tweaking. And let’s not forget about the time it takes to train a new model from scratch this can be incredibly expensive in terms of both money and resources!

Later!