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hbliu-24/RNA-SE-checkpoints
RNA-SE-checkpoints is a machine learning model from hbliu-24. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This model repository provides four pretrained inference checkpoints for RNA-SE, an RNA secondary-structure model combining a variational autoencoder (VAE) with a diffusion transformer (DiT).
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Updated Sep 25, 2026
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From the Hugging Face model README
This model repository provides four pretrained inference checkpoints for RNA-SE, an RNA secondary-structure model combining a variational autoencoder (VAE) with a diffusion transformer (DiT).
Download: checkpoints.zip
Model definitions, configurations and usage instructions: RNA-StructEnsemble on GitHub
| File inside the archive | Model |
|---|---|
bprna_1m/bp_vae.pt | bpRNA ContactVAE |
bprna_1m/bp_dit.pt | bpRNA DiT |
eternabench_cm/CM_vae.pt | EternaBench-CM ContactVAE |
eternabench_cm/dit.pt | EternaBench-CM DiT |
VAE exports retain model parameters. DiT exports also retain inference configuration, exponential moving average (EMA) state and latent normalization information. These are not complete training-resumption checkpoints and cannot restore the full training state.
Place the archive contents in the GitHub checkout's checkpoints/ directory.
The resulting files are:
checkpoints/
├── bprna_1m/
│ ├── bp_vae.pt
│ └── bp_dit.pt
└── eternabench_cm/
├── CM_vae.pt
└── dit.pt
The archive already contains the dataset-specific directories. Preserve the
filenames and use the corresponding VAE/DiT pair for each dataset. Data and
RNA-FM features are distributed separately in bpRNA_data.zip and CM_data.zip.
Use the linked GitHub code for sampling and evaluation. This repository does
not provide automatic loading through transformers.from_pretrained or a
hosted inference service.