QLoRA is all you need (Fast and lightweight model fine-tuning)

Learning and sharing my process with QLoRA (quantized low rank adapters) fine-tuning. In this case, I use a custom-made reddit dataset, but you can use anything you want.

I referenced a LOT of stuff in this video, I will do my best to link everything, but let me know if I forget anything.

Resources:
WSB-GPT-7B Model: https://huggingface.co/Sentdex/WSB-GPT-7B
WSB-GPT-13B Model: https://huggingface.co/Sentdex/WSB-GPT-13B
WSB Training data: https://huggingface.co/datasets/Sentdex/wsb_reddit_v002

Code:
QLoRA Repo: https://github.com/artidoro/qlora
qlora.py: https://github.com/artidoro/qlora/blob/main/qlora.py
Simple qlora training notebook: https://colab.research.google.com/drive/1VoYNfYDKcKRQRor98Zbf2-9VQTtGJ24k?usp=sharing
qlora merging/dequantizing code: https://gist.github.com/ChrisHayduk/1a53463331f52dca205e55982baf9930

Referenced …

case dataset everything fine-tuning low model fine-tuning process qlora reddit video