---
date: "2024-10-30T08:44:13.000Z"
title: "2024-10-30"
tags: ["language_models","google_colab"]
draft: false
---

I wanted to get more hands-on with the language model trained in chapter 12 of the FastAI course, so I got some Google Colab credits and actually ran the training on an A100.
It cost about $2.50 and took about 1:40, but generally worked quite well.
There was a minor issue with auto-saving the notebook, probably due to my use of [this workaround](https://stackoverflow.com/questions/73413086/how-to-prevent-fastai-fastbook-from-requesting-access-to-google-drive-when-runni) to avoid needing to give Colab full Google Drive access.
Regardless, I was still able to train the language model, run sentence completions, then using the fine-tuned language model as an encoder to build a sentiment classifier.
Seeing how long this process took, then seeing it work helped me build a bit more intuition about what to expect when training models.
I was also a bit surprised how fast the next token prediction and classification inference were.
I might try out a smaller fine-tune on my local machine now that I have a better sense of what this process looks like end to end.