Quick Start#


We provide a docker image and singularity image for the dependencies of FMEngine. You can download the docker image by running:

docker pull xzyaoi/fmsys:0.0.3

or download the singularity image here.

Please note that the images are built without fmengine installed. You are advised to clone the fmengine and bind the directory to the container.

Training preparation#

  • Prepare checkpoints. As the first step, you will need to split a large model checkpoint into smaller pieces for each layer. This can be done by running the following command:

python scripts/conversions/llama/from_hf.py \
--model_name_or_path meta-llama/Llama-2-7b-hf  \
--output_dir path_to_outdir/llama2-7b \
--mp_world_size 1

You can download pre-configured checkpoints here: Google Drive.

  • Prepare datasets. We now only supports .jsonl format, which is a list of json objects, each of which contains a text field. For example, a sample of the dataset can be:

{"text": "I love this movie!"}
{"text": "I hate this movie!"}
{"text": "I don't know."}


In /scripts, we show some examples of training scripts, for example, to finetune a pythia-2.8b model, you can run the following command:

deepspeed --num_gpus 4 --num_nodes 1 starter.py \
    --output_dir .cache/models \
    --init_ckpt /pretrained_weights/pythia-160m-deduped \
    --data_path /datasets/quantitative_natural_instructions/train/all.train.jsonl \
    --max_seq_len 1024 \
    --train_steps 1000 \
    --eval_steps 10 \
    --save_steps 100 \
    --log_steps 1 \
    --pipe_parallel_size 1 \
    --model_parallel_size 1 \
    --use_flash_attn true \
    --deepspeed_config ./configs/pythia.json

You are also advised to read ./configs/pythia.json for the deepspeed configuration, which convers the learning rate, batch size, etc.