Download Dataset

• If you download the data from the zip file, skip it.

pip install gdown
mkdir data

export data_path=data
gdown https://drive.google.com/drive/folders/19_sIUa6wbpVpTRv232tygxU0-Au2z_GQ -O $data_path --folder

export raw_data_path=raw-data
gdown https://drive.google.com/drive/folders/1leDy1PBXGdtlpY-IdUpEOtPHpCScZnfQ -O $raw_data_path --folder

Install

conda create -n llm_train python=3.10
conda activate llm_train

Install torch

• Please refer: (https://pytorch.org/get-started/previous-versions/)

conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=11.8 -c pytorch -c nvidia

Install packages for training

python -m pip install .
pip install sentence_transformers

Train

You have to specify your GPU number.

Required Files:

• data/train.csv
• data/eval.csv

Save the model weight in model/final-model

sh run_sft_lora.sh

Inference

You have to specify your GPU number.

Required Files:

• model/final-model (train the model using above train command or download the model weights)
• raw-data/eval.csv (download using the 'Download Dataset' command)
• raw-data/sample_submission.csv (download using the 'Download Dataset' command)

There are two output files.

• result/output.csv: file used for dacon submission that the model answer is embedded.
• result/output-text.json: file that allows you to check answers in text form, consisting of questions and answers.

sh inference_lora.sh

Citation

@misc{alignment_handbook2023,
  author = {Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Shengyi Huang and Kashif Rasul and Alexander M. Rush and Thomas Wolf},
  title = {The Alignment Handbook},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/alignment-handbook}}
}