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Haiping-UoM/NexuST
NexuST is a machine learning model from Haiping-UoM. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Paper: NexuST: A Hierarchical Foundation Model for Spatial Transcriptomics (bioRxiv, 2026).
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Updated Sep 30, 2026
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.ckpt815 MB · 100%
From the Hugging Face model README
Paper: NexuST: A Hierarchical Foundation Model for Spatial Transcriptomics (bioRxiv, 2026).
This repository contains the NexuST checkpoint saved at training step 10,000.
| File | Training step | Size |
|---|---|---|
NexuST-step10000.ckpt | 10,000 | 815,205,507 bytes |
The uploaded file is byte-identical to the original training artifact
step_step=010000.ckpt; only the repository filename was changed.
This is the step-10,000 checkpoint, not the separately saved best-validation-loss checkpoint.
SHA256: 178de8d6ad027c2bb699de9e25ebf0dc76915d63701590752fbd8ed85e647d7e
from huggingface_hub import hf_hub_download
checkpoint_path = hf_hub_download(
repo_id="Haiping-UoM/NexuST",
filename="NexuST-step10000.ckpt",
)
This example downloads the checkpoint. Model construction and inference require the corresponding NexuST code, configuration, and vocabulary; a verified loading example is not yet included in this repository.
The H5AD release is available in the dataset repository
HumanST-46M,
with separate train/ and val/ directories.
This is an initial checkpoint release. Architecture details, preprocessing and inference instructions, evaluation results, and license terms have not yet been documented here.
If you use NexuST or HumanST-46M in your research, please cite:
@article{liu2026nexust,
title = {NexuST: A Hierarchical Foundation Model for Spatial Transcriptomics},
author = {Liu, Haiping and Zhao, Qian and Lin, Lijing and Zou, Zhiyong and Cai, Wenhao and Sun, Jingyuan and Zhou, Yuxi and Alvarez, Mauricio A. and Gilmore, Andrew and Rattray, Magnus and Frangi, Alejandro F. and Zhou, Hongpeng},
journal = {bioRxiv},
year = {2026},
doi = {10.64898/2026.09.22.753590}
}