EpiScan
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
- Sun Yat-sen University,Guangzhou National Laboratory
- Organisation type
- Academia,Government
- Country
- China
- Published
- 9 September 2024
- Authors
- Chuan Wang, Jiangyuan Wang, Wenjun Song, Guanzheng Luo, Taijiao Jiang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Antibody epitope 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.
- Parameters
- 288.9K
- Training data
- tokens
Loaded model from https://github.com/ gzBiomedical/EpiScan and counted parameters.
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 GeForce RTX 2080 Ti 11GB
- Chips used
- 1
- Power draw
- 271 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
- EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information
- Last updated
- 28 November 2025
What the numbers mean
Background
EpiScan was published by Sun Yat-sen University,Guangzhou National Laboratory, in the country recorded as China, during September 2024. The category the publisher falls under is academia,Government.
It works in the domain of Biology, and is recorded as performing the task of antibody epitope prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
EpiScan — common questions
EpiScan— who created it?
It was published by Sun Yat-sen University,Guangzhou National Laboratory, based in China, an organisation categorised as academia,Government.
EpiScan— 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.
EpiScan— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of antibody epitope prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
EpiScan— 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.
EpiScan— 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.
EpiScan— how many parameters does it have?
It has a parameter count of 288.9K. Loaded model from https://github.com/ gzBiomedical/EpiScan and counted parameters. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
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.