OpenELM: An Efficient Language Model Family with Open Training and Inference Framework

TL;DR

OpenELM is a transparent, open-source language model framework that improves accuracy and efficiency through layer-wise scaling, providing comprehensive training resources to foster open research.

Contribution

It introduces a complete open training and inference framework for large language models, enhancing transparency and reproducibility in the field.

Findings

OpenELM achieves 2.36% higher accuracy with fewer pre-training tokens.

The layer-wise scaling strategy improves model efficiency and performance.

The release includes training logs, checkpoints, and conversion tools for open research.

Abstract

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 $2\times$ 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 \url{https://github.com/apple/corenet}. Additionally, \model models can be found on HuggingFace at: \url{https://huggingface.co/apple/OpenELM}.