eFold
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
- Harvard Medical School,Stanford University,Columbia University,University of Strasbourg
- Organisation type
- Academia,Academia,Academia,Academia
- Country
- United States of America, France
- Published
- 4 April 2024
- Authors
- Silvi Rouskin, Alberic de Lajart, Yves Martin des Taillades, Colin Kalicki, Federico Fuchs Wightman, Justin Aruda, Dragui Salazar, Matthew Allan, Casper L'Esperance-Kerckhoff, Alex Kashi, Fabrice Jossinet
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)
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
pretraining: "over 120,000 unique sequences and structures” (bpRNA + Ribonanza); "The final dataset contains 227,000 sequences up to 512 in length." (synthetic from RNAcentral + RNAstructure Fold) finetuning: "1,456 mRNA and 1,098 pri-miRNA structures" after filtering (RNAndria)
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
- Diverse Database and Machine Learning Model to Narrow the Generalization Gap in RNA Structure Prediction
- Last updated
- 28 November 2025
What the numbers mean
What this model is
eFold was published by Harvard Medical School,Stanford University,Columbia University,University of Strasbourg, in United States of America, in April 2024. academia,Academia,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).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
eFold — common questions
What GPU do I need to run eFold?
None. eFold 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 eFold open source?
The licensing for eFold was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does eFold have?
No parameter count has been published for eFold, which is why no memory or speed figure appears on this page.
Who created eFold?
eFold was published by Harvard Medical School,Stanford University,Columbia University,University of Strasbourg, based in United States of America, categorised as academia,Academia,Academia,Academia.
When was eFold released?
eFold was published in April 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 eFold used for?
eFold works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). These are the areas it was designed around; they describe intent rather than a hard boundary.
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.