Importance of higher-order epistasis in large protein sequence-function relationships
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
- University of Florida
- Organisation type
- Academia
- Country
- United States of America
- Published
- 24 September 2024
- Authors
- Palash Sethi a, Juannan Zhou
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein function 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
- 165,428 tokens
Initial calculations by dataset: 1. GRB-1: 129,320 * 0.8 * 33 = 3,414,048 2. GRB-3-abundance: 31,936 * 0.8 * 15 = 383,235 3. GRB-3-binding: 25,967 * 0.8 * 15 = 311,610 4. AAV2-Capsid: 42,328 * 0.8 * 28 = 948,136 5. CreiLOV: 165,428 * 0.8 * 15 = 1,985,130 6. cgreGFP: 26,165 * 0.8 * 234 = 4,898,088 7. ppluGFP: 32,260 * 0.8 * 221 = 5,703,568 8. His3-S2: 116,935 * 0.8 * 28 = 2,619,344 9. His3-S5: 92,408 * 0.8 * 31 = 2,291,706 10. His3-S12: 62,305 * 0.8 * 19 = 947,036 Final sum: 3,414,048 + 383,235 …
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 A100
- Chips used
- 1
- Power draw
- 433 W
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
- Importance of higher-order epistasis in large protein sequence-function relationships
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Importance of higher-order epistasis in large protein sequence-function relationships was published by University of Florida, in United States of America, in September 2024. The organisation is categorised as academia.
It works in Biology, and is recorded as doing protein function prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Around 165,428 tokens went into training it.
Answers
Importance of higher-order epistasis in large protein sequence-function relationships — common questions
What is Importance of higher-order epistasis in large protein sequence-function relationships used for?
Importance of higher-order epistasis in large protein sequence-function relationships works in Biology, and is recorded as handling protein function prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Importance of higher-order epistasis in large protein sequence-function relationships?
None. Importance of higher-order epistasis in large protein sequence-function relationships 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 Importance of higher-order epistasis in large protein sequence-function relationships open source?
The licensing for Importance of higher-order epistasis in large protein sequence-function relationships 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 Importance of higher-order epistasis in large protein sequence-function relationships have?
No parameter count has been published for Importance of higher-order epistasis in large protein sequence-function relationships, which is why no memory or speed figure appears on this page.
Who created Importance of higher-order epistasis in large protein sequence-function relationships?
Importance of higher-order epistasis in large protein sequence-function relationships was published by University of Florida, based in United States of America, categorised as academia.
When was Importance of higher-order epistasis in large protein sequence-function relationships released?
Importance of higher-order epistasis in large protein sequence-function relationships 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.
The other direction
Looking at it from the other side?
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