RNA language models predict mutations that improve RNA function
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
- NERSC, Lawrence Berkeley National Laboratory,University of California San Francisco,University of California (UC) Berkeley
- Organisation type
- Government,Academia,Academia
- Country
- United States of America
- Published
- 16 September 2024
- Authors
- Yekaterina Shulgina, Marena I Trinidad, Conner J Langeberg, Hunter Nisonoff, Seyone Chithrananda, Petr Skopintsev, Amos J Nissley, Jaymin Patel, Ron S Boger, Honglue Shi, Peter H Yoon, Erin E Doherty, Tara Pande, Aditya M Iyer, Jennifer A Doudna, Jamie H D Cate
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Mutation 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
- tokens
89,000,000 + 274,000,000 = 363,000,000 tokens Dataset 1 (23S rRNA): 89M tokens Dataset 2 (GARNET): 274M tokens Total combined: 363M tokens (3.63e8)
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
- 12
Sources
Where this record came from and when it was last checked.
- Reference
- RNA language models predict mutations that improve RNA function
- Last updated
- 1 December 2025
What the numbers mean
Background
RNA language models predict mutations that improve RNA function was published by NERSC, Lawrence Berkeley National Laboratory,University of California San Francisco,University of California (UC) Berkeley, in the country recorded as United States of America, during September 2024. The publishing organisation is categorised as government,Academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of mutation prediction.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
RNA language models predict mutations that improve RNA function — common questions
RNA language models predict mutations that improve RNA function— 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.
RNA language models predict mutations that improve RNA function— who created it?
It was published by NERSC, Lawrence Berkeley National Laboratory,University of California San Francisco,University of California (UC) Berkeley, based in United States of America, an organisation categorised as government,Academia,Academia.
RNA language models predict mutations that improve RNA function— when was it released?
It was published in September 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.
RNA language models predict mutations that improve RNA function— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of mutation prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
RNA language models predict mutations that improve RNA function— 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.
RNA language models predict mutations that improve RNA function— 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.
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.