AFP-Deep
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
- Nanjing University,Yangzhou University
- Organisation type
- Academia,Academia
- Country
- China
- Published
- 24 September 2024
- Authors
- Jiashun Wu, Yan Liu, Yiheng Zhu, Dong-Jun Yu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein design, Protein property 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
AFP920 has that size of training dataset
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
- Improving Antifreeze Proteins Prediction with Protein Language Models and Hybrid Feature Extraction Networks
- Last updated
- 28 November 2025
What the numbers mean
What this model is
AFP-Deep was published by Nanjing University,Yangzhou University, in the country recorded as China, during September 2024. It comes out of an organisation categorised as academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of protein design, Protein property prediction.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
AFP-Deep — common questions
AFP-Deep— 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.
AFP-Deep— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein design, Protein property prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
AFP-Deep— 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.
AFP-Deep— 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.
AFP-Deep— 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.
AFP-Deep— who created it?
It was published by Nanjing University,Yangzhou University, based in China, an organisation categorised as academia,Academia.
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.