Repress
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- DeepGenomics
- Organisation type
- Industry
- Country
- Canada
- Published
- 16 May 2025
- Authors
- Bhargav Kanuparthi, Sara E. Pour, Scott D. Findlay Omar Wagih, Jahir M. Gutierrez, Rory Gao, Jeff Wintersinger, Junru Lin, Martino Gabra, Emma Bohn, Tammy Lau, Chris Cole, Andrew Jung, Albi Celaj, Fraser Soares, Rachel Gray, Brandon Vaz, Kate Delfosse, Varun Lodaya, Sakshi Bhargava, Diane Ly, Farhan Yusuf, Ken Kron, Greg Hoffman, Shreshth Gandhi, Brendan J. Frey
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Cell Biology, RNA structure prediction, Protein-RNA binding affinity prediction
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 133M
- Training data
- tokens
- Epochs
- 60
133M
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA RTX A6000
- Chips used
- 1
- Power draw
- 323 W
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
Smaller mode: Creative Commons Attribution-NonCommercial 4.0 International Public License https://github.com/deepgenomics/repress "he version of REPRESS released in this repository has 8356199 parameters"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Sequence based prediction of cell type specific microRNA binding and mRNA degradation for therapeutic discovery
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Repress was published by DeepGenomics, in Canada, in May 2025. It comes out of industry.
It works in Biology, and is recorded as doing cell Biology, RNA structure prediction, Protein-RNA binding affinity prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Repress — common questions
Is Repress open source?
No. Repress has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Repress have?
Repress has 133M parameters. 133M. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Repress?
Repress was published by DeepGenomics, based in Canada, categorised as industry.
When was Repress released?
Repress was published in May 2025.
What is Repress used for?
Repress works in Biology, and is recorded as handling cell Biology, RNA structure prediction, Protein-RNA binding affinity prediction. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run Repress?
None. Repress is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.