We present ELMOD — Efficient Language Model for On-Device Deployment — a compact (2.7B) German language model designed for efficient inference on resource-constrained hardware. This work was performed as research project in the scope of the compute time project ELMOD at NHR@FAU. ELMOD was trained on a limited computational budget (55k H100 GPU hours) using exclusively publicly available data. We developed a suite of German-specific data preprocessing, which differ from English-oriented counterparts in their handling of morphological variation, compounding, and orthographic conventions, to ensure high model performance, we developed a suite of German-specific data preprocessing. Furthermore, we introduced an quality filtering and rephrasing step, which increased the instructional quality of the data, improved performance during the annealing phase, and reduced overall compute requirements. Thanks to our architectural model choices as well as our data choices, including prefiltering, our educational-quality filtering and rephrasal to raise the educational-quality, ELMOD is the strongest performer in its size class (<3B), matching the performance of 7B parameter models in German.

For more details, please refer to our paper!

Comparison of German-capable base models with <= 3 parameters, averaged across German base tasks, among which ELMOD-2.7B performs best

ELMOD is a 2.7B dense transformer model.

This models is also compatible with vLLM.

The generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These model should not be used as a definitive source of information. Any generated content should always be critically evaluated.

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This model is licensed under the [Creative Commons Attribution Non Commercial 4.0] (https://spdx.org/licenses/CC-BY-NC-4.0).

This work has been funded by the Free State of Bavaria in the DSgenAI project (Grant Nr.: RMF-SG20-3410-2-18-4). The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität ErlangenNürnberg (FAU) under the NHR project ELMOD: Efficient language models for on-device deployment (Grant Nr.: b239dc). NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the German Research Foundation (DFG) – 440719683.