Trim Transformer: A transformer for physics models

In plain words

Trim Transformer is an open-source transformer package designed specifically for physics models rather than language processing. The software reduces memory usage by 90% and increases speed 3.5x on standard physics datasets like Navier-Stokes simulations. It addresses a gap in available transformer architectures, which are typically optimized for language tasks. The tool is intended for developers and researchers building physics-based foundation models and computational simulations.

written from the facts on this page · September 2026

From the sources

A linear attention transformer package. Contribute to eg-trim/trim-transformer development by creating an account on GitHub.

trim-transformer is a lightweight PyPI package that replicates the familiar interface of torch.nn.TransformerEncoder , but with an attention function of the form Attn(Q,K,V) = QK^TV, which we call multi-linear attention. This implementation has time complexity O(nd^2), where n is the sequence length and d is the model dimension. Since the time complexity is linear in the sequence length, this implementation is well suited for high sequence length tasks. Attention in this form has shown success in operator learning tasks, see Choose a Transformer: Fourier or Galerkin . This implementation is particularly relevent for training physics models where high sequence length can come from large grid…from github.com

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