Modalities, a PyTorch-native Framework For Large-scale LLM Training and Research

• URL: http://arxiv.org/abs/2602.08387v1
• Date: Mon, 09 Feb 2026 08:39:41 GMT
• Title: Modalities, a PyTorch-native Framework For Large-scale LLM Training and Research
• Abstract summary: Modalities is an end-to-end PyTorch-native framework for large-scale model training.<n>It integrates data-driven LLM research with large-scale model training from two angles.
• Score: 33.56269788349648
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Today's LLM (pre-) training and research workflows typically allocate a significant amount of compute to large-scale ablation studies. Despite the substantial compute costs of these ablations, existing open-source frameworks provide limited tooling for these experiments, often forcing researchers to write their own wrappers and scripts. We propose Modalities, an end-to-end PyTorch-native framework that integrates data-driven LLM research with large-scale model training from two angles. Firstly, by integrating state-of-the-art parallelization strategies, it enables both efficient pretraining and systematic ablations at trillion-token and billion-parameter scale. Secondly, Modalities adopts modular design with declarative, self-contained configuration, enabling reproducibility and extensibility levels that are difficult to achieve out-of-the-box with existing LLM training frameworks.

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