CrystalCoder Training Code

This repository contains the training code for CrystalCoder, a 7B-parameter language model pretrained on code and natural language.

Pretraining

Code

The training of Phase 1-3 is completed using Cerebras's Model Zoo on Cerebras CS-2 hardware.

To launch the training, you can use the following command:

git clone https://github.com/Cerebras/modelzoo
cd modelzoo

python modelzoo/transformers/pytorch/gpt3/run.py CSX \
  --mode train \
  --num_csx 16 \
  --num_workers_per_csx 1 \ # only needed for rel-1.9, not needed for rel-2.0
  --params <path to params.yaml> \
  --python_paths <path to modelzoo> \
  --mount_dirs <mounts needed for modelzoo and data sets> \
  --model_dir <path to model dir> \
  --checkpoint_path <path to initalization checkpoint> \

In the script, you need to specify different --params for different phases. The params files we used for each phase are in the params folder.

The --checkpoint_path is optional, and you can use it to specify the initialization checkpoint for the training. For example, for phase 2, you need to use the last checkpoint of phase 1 as the initialization checkpoint.

Dataset

The processed dataset for each phase is available at CrystalCoderDatasets.

If you plan to process the dataset yourself from scratch, you can refer to our CrystalCoder data preprocessing code.

Instruction Tuning

We are currently working on instruction-tuned versions of CrystalCoder! The model, training code, and datasets will be released after completion.