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Scripts to train Magpie models

Requirements

Hardware

  • 4 GPU @ 80GB VRAM

If you have fewer GPUs, please add gradient_accumulation_steps in the config file accordingly.

If you are using GPUs with less VRAM, please consider using deepspeed zero3 or FSDP.

Software

We use Axolotl for supervised fine-tuning, and alignment-handbook for DPO.

Supervised Fine-tuning

Install Axolotl

Environment: Python >=3.10 and Pytorch >=2.1.1. You can setup the environment using Conda.

Note: We found that the latest Axolotl may encounter some bugs during the training, so we use this commit in our experiments. Please also manually install FastChat for the latest Llama3 conversation template support.

git clone https://github.com/lm-sys/FastChat
cd FastChat
pip install -e .
cd ../

git clone https://github.com/OpenAccess-AI-Collective/axolotl
cd axolotl
git reset --hard 7c2bf3091f5e73c787afe839dfdcc8220b770a1a

pip3 install packaging ninja
pip3 install -e '.[flash-attn,deepspeed]'

Note 2: They have fixed that. Please use the latest version.

Run

Go to the directory where you placed the SFT YAML file, then run the following command:

accelerate launch -m axolotl.cli.train your_config_name.yaml

DPO

Install Alignment Handbook

conda create -n handbook python=3.10 && conda activate handbook

git clone https://github.com/huggingface/alignment-handbook.git
cd alignment-handbook
python -m pip install .

python -m pip install flash-attn --no-build-isolation

Run

Please change num_processes if you are not using 4 GPUs.

ACCELERATE_LOG_LEVEL=info accelerate launch --num_processes 4 --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_dpo.py your_config_name.yaml

Magpie Recipes