Slamming: Training a Speech Language Model on One GPU in a Day
Gallil Maimon, Avishai Elmakies, Yossi Adi
Abstract
We introduce *Slam*, a recipe for training high-quality Speech Language Models (SLMs) on a single academic GPU in 24 hours. We do so through empirical analysis of model initialisation and architecture, synthetic training data, preference optimisation with synthetic data and tweaking all other components. We empirically demonstrate that this training recipe also scales well with more compute getting results on par with leading SLMs in a fraction of the compute cost. We hope these insights will make SLM training and research more accessible. In the context of SLM scaling laws, our results far outperform predicted compute optimal performance, giving an optimistic view to SLM feasibility. See code, data, models, samples - https://pages.cs.huji.ac.il/adiyoss-lab/slamming .
BibTeX
@inproceedings{maimon-etal-2025-slamming,
title = "Slamming: Training a Speech Language Model on One {GPU} in a Day",
author = "Maimon, Gallil and
Elmakies, Avishai and
Adi, Yossi",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.631/",
doi = "10.18653/v1/2025.findings-acl.631",
pages = "12201--12216",
ISBN = "979-8-89176-256-5"
}