First, make sure you are using python 3.8+. If you're using python 3.7, see the Troubleshooting section below.
pip install -r requirements.txt
python train.py <config_file>
For exmaple, to fine-tune OpenLLaMA-7B on the wizard_vicuna_70k_unfiltered dataset, run
python train.py configs/open_llama_7b_qlora_uncensored.yaml
Follow instructions here.
| Model name | Config file | URL |
|---|---|---|
| llama2_7b_openorca_35k | configs/llama2_7b_openorca_35k.yaml | https://huggingface.co/georgesung/llama2_7b_openorca_35k |
| llama2_7b_chat_uncensored | configs/llama2_7b_chat_uncensored.yaml | https://huggingface.co/georgesung/llama2_7b_chat_uncensored |
| open_llama_7b_qlora_uncensored | configs/open_llama_7b_qlora_uncensored.yaml | https://huggingface.co/georgesung/llama2_7b_openorca_35k |
See this Colab notebook.
Blog post describing the process of QLoRA fine tuning: https://georgesung.github.io/ai/qlora-ift/
If you're using python 3.7, you will install transformers 4.30.x, since transformers >=4.31.0 no longer supports python 3.7. If you then install the latest version of peft, the GPU memory consumption will be higher than usual. The work-around is to use an older version of peft to go along with the older transformers version you installed. Update your requirements.txt as follows:
transformers==4.30.2
git+https://github.com/huggingface/peft.git@86290e9660d24ef0d0cedcf57710da249dd1f2f4
Of course, make sure to remove the original lines with transformers and peft, and run pip install -r requirements.txt