Year: 2025
Type: conference-paper
Abstract: We introduce Slam, a recipe for training highquality 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.
Authors: Gallil Maimon, Avishai Elmakies, Yossi Adi
Institution: Hebrew University of Jerusalem
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Israel Science Foundation
OpenAlex ID: W4412888269