OpenELM: An Efficient Language Model Family with Open-source Training
  and Inference Framework
Authors: name: Sachin Mehta, name: Mohammad Hossein Sekhavat, name: Qingqing Cao, name: Maxwell Horton, name: Yanzi Jin, name: Chenfan Sun, name: Iman Mirzadeh, name: Mahyar Najibi, name: Dmitry Belenko, name: Peter Zatloukal, name: Mohammad Rastegari

The reproducibility and transparency of large language models are crucial for
advancing open research, ensuring the trustworthiness of results, and enabling
investigations into data and model biases, as well as potential risks. To this
end, we release OpenELM, a state-of-the-art open language model. OpenELM uses a
layer-wise scaling strategy to efficiently allocate parameters within each
layer of the transformer model, leading to enhanced accuracy. For example, with
a parameter budget of approximately one billion parameters, OpenELM exhibits a
2.36% improvement in accuracy compared to OLMo while requiring 2times fewer
pre-training tokens.
  Diverging from prior practices that only provide model weights and inference
code, and pre-train on private datasets, our release includes the complete
framework for training and evaluation of the language model on publicly
available datasets, including training logs, multiple checkpoints, and
pre-training configurations. We also release code to convert models to MLX
library for inference and fine-tuning on Apple devices. This comprehensive
release aims to empower and strengthen the open research community, paving the
way for future open research endeavors.
  Our source code along with pre-trained model weights and training recipes is
available at https://github.com/apple/corenet. Additionally, \model
models can be found on HuggingFace at:
https://huggingface.co/apple/OpenELM.