PARM
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
- Oncode Institute,UMC Utrecht,Netherlands Cancer Institute,University of Groningen,Radboud University Medical Center
- Organisation type
- Academia,Academia
- Country
- Netherlands
- Published
- 15 July 2024
- Authors
- Lucía Barbadilla-Martínez, Noud Klaassen, Vinícius H. Franceschini-Santos, Jérémie Breda, Miguel Hernandez-Quiles, Tijs van Lieshout, Carlos G. Urzua Traslaviña, Hatice Yücel, Minh Chau Luong Boi, Celia Hermana-Garcia-Agullo, Sebastian Gregoricchio, Wilbert Zwart, Emile Voest, Lude Franke, Michiel Vermeulen, Jeroen de Ridder, Bas van Steensel
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM), Nucleotide generation
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
- tokens
- Epochs
- 10
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 6000 Ada Generation
- Chips used
- 1
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- The regulatory grammar of human promoters uncovered by MPRA-trained deep learning
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
PARM was published by Oncode Institute,UMC Utrecht,Netherlands Cancer Institute,University of Groningen,Radboud University Medical Center, in Netherlands, in July 2024. academia,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM), Nucleotide generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
PARM — common questions
How many parameters does PARM have?
No parameter count has been published for PARM, which is why no memory or speed figure appears on this page.
Who created PARM?
PARM was published by Oncode Institute,UMC Utrecht,Netherlands Cancer Institute,University of Groningen,Radboud University Medical Center, based in Netherlands, categorised as academia,Academia.
When was PARM released?
PARM was published in July 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is PARM used for?
PARM works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM), Nucleotide generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run PARM?
None. PARM 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.
Is PARM open source?
The licensing for PARM was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.