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jtcao7/UCE-Modern-100m

AI model by jtcao7

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The 4-layer Universal Cell Embeddings (UCE) model, packaged for Hugging Face transformers by UCE-Modern. UCE maps a single cell's raw gene-expression counts to a 1280-dimensional embedding. It works across species and needs no fine-tuning. These are the original pretrained weights released by snap-stanford/UCE (4layermodel.torch). Only the format has changed. All credit for the model and its training goes to the original authors. For a larger model with stronger embeddings, see the 33-layer UCE-Modern-650m. Dependencies are unpinned, and nothing in the model depends on a particular CUDA version. Any recent PyTorch build should work: CUDA 13, CUDA 12 or CPU only. Tested with PyTorch 2.14 (CUDA 13.0) and transformers 5.20. If you have the UCE-Modern package installed, you can use from ucemodern import UCEProcessor instead of getclassfromdynamicmodule. Before processor.prepareadata, the AnnData must have: Keep all genes; don't subset to highly variable genes. prepareadata filters genes and cells, drops genes without a…

jtcao7/UCE-Modern-100m on Hugging Face A short preview, not the full document.

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  1. UCE-Modern-100m (Universal Cell Embeddings, 4-layer)
  2. Usage
  3. Input data format
  4. Files
  5. Notes
  6. Citation
  7. License

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jtcao7/UCE-Modern-100m is a task-unspecified model repository published by jtcao7 on Hugging Face. A library was not reported.

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mit
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Created
2026-10-22T06:24:18.000Z
Last modified
2026-10-22T06:24:18.000Z

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