CRL
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
- Ulm University
- Country
- Germany
- Published
- 31 May 2022
- Authors
- Pedro Hermosilla, Timo Ropinski
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein folding prediction, Protein classification, Protein-ligand binding affinity prediction, Protein structure similarity 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.
- Training data
- 952,724 tokens
"We collected 476, 362 different protein chains, each composed of at least 25 of amino acids."
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 144 hours
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 61
Sources
Where this record came from and when it was last checked.
- Reference
- Contrastive Representation Learning for 3D Protein Structures
- Last updated
- 25 May 2026
What the numbers mean
Background
CRL was published by Ulm University, in the country recorded as Germany, during May 2022.
It works in the domain of Biology, and is recorded as performing the task of protein folding prediction, Protein classification, Protein-ligand binding affinity prediction, Protein structure similarity prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training consumed a corpus of around 952,724 tokens of text.
Answers
CRL — common questions
CRL— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
CRL— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
CRL— who created it?
It was published by Ulm University, based in Germany.
CRL— when was it released?
It was published in May 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
CRL— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein folding prediction, Protein classification, Protein-ligand binding affinity prediction, Protein structure similarity 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.
CRL— what GPU do I need to run it?
None. This 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.