Pretrain a Transformer on Language Modeling

A minimal yet efficient implementation of causal language modeling in PyTorch.

It features a custom torch-compilable Transformer model implementation supporting RoPE, GLU, and RMSNorm. It supports distributed training via Distributed Data Parallel (DDP).

A dedicated script is included for downloading, tokenizing, and chunking data, making data preparation seamless.

🛠 Installation

We recommend running plainLM in a dedicated Python environment. To install dependencies in an Anaconda environment, execute:

conda create --name plainLM python=3.12 -y && conda activate plainLM && cd plainLM
pip install .

📚 Data

We provide a script for downloading, tokenizing, chunking and saving Hugging Face datasets: data/datasets/prepare.py. You can specify any HF dataset and tokenizer. To avoid downloading the entire corpus, we support streaming, tokenizing, and chunking data on-the-fly. We provide an example for FineWebEdu-100BT in data/datasets/prepare_finewebedu_100BT.sh.

⚡️ Usage

Specify hyperparameters in config.yaml and launch training as follows:

Single GPU/CPU:

  python train.py --config=config/config.yaml

Multiple GPUs:

  torchrun --nnodes=1 --nproc_per_node=4 train.py --config=code/config/sweep.yaml

Run a sweep in parallel on a SLURM or Condor HPC cluster:

1. Define hyperparameter sweep: create a single YAML file with lists of hyperparameter values. Each value in the list will represent a different configuration, e.g.:

   lr: [0.1, 0.01]
   wd: [0.1, 0.2, 0.5]
   beta1: 0.9
   ...

2. Submit the sweep: Submit a job-array, where each job executes the same python script and reads the same configuration, but with a different job_idx. We use job_idx to map a job to its hyperparameters; job_idx should range from 0 to n-1, where n is the number of Cartesian product configurations in the YAML. This is done automatically by cluster/slurm.sh and cluster/condor.sub. Python takes care of assigning the corresponding configuration to each job based on the value of job_idx.

📂 Structure

plainLM/
├── cluster/             # HPC scripts (SLURM & Condor)
├── config/              # Configuration files for training and model setup
├── data/                # Everything regarding data preparation and data stream
│   └── datasets/        # Data preprocessing files to download, tokenize, chunk and save data
│   └── dataloaders.py   # Dataloader utilities
│   └── datasamplers.py  # Custom stateful distributed samplers
├── engine/              # Core implementation of the model engine: a torch.nn.Module implementing training steps and evaluations
├── models/              # Model architectures
├── optim/               # Optimization utilities
├── checkpoint_utils.py  # Checkpoint utilities
├── torch_utils.py       # PyTorch utilities (DDP, seed, TF32...)
├── train.py             # Main training script ⭐️
└── utils.py             # Miscellaneous helper functions

☑️ TODO

• FSDP2 support, ZeRO-2 and tensor parallel compatibility
• dummy data
• unit tests
• add seed to DistributedSampler

Citation

@misc{ajroldi2024plainlm,
  author = {Niccolò Ajroldi},
  title = {plainLM: Language Model Pretraining in PyTorch},
  year = {2024},
  howpublished = {\url{https://github.com/Niccolo-Ajroldi/plainLM}}
}

Credits

This project was inspired by:

• Cramming by Jonas Geiping
• GPT-NeoX by EleutherAI
• NanoGPT by Andrej Karpathy

Huge thanks to these projects for their contributions to open-source language model pretraining!

Published works using plainLM

Some recent projects using plainLM:

• Orvieto, A., & Gower, R. (2025). In search of Adam’s secret sauce ArXiv.
• Ajroldi, N., Orvieto, A., & Geiping, J. (2025). When, where and why to average weights? In Proceedings of ICML 2025.
• Srećković, T., Geiping, J., & Orvieto, A. (2025). Is your batch size the problem? Revisiting the Adam-SGD gap in language modeling. ArXiv.
• Belloni, A., Noci, L., & Orvieto, A. (2025). Universal Dynamics of Warmup Stable Decay: Understanding WSD Beyond Transformers. [MOSS Workshop, ICML 2025].(https://icml.cc/virtual/2025/47679)