KeAP
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
- The University of Hong Kong,ByteDance,JancsiTech,OPPO HealthLab
- Organisation type
- Academia,Industry,Industry,Industry
- Country
- Hong Kong, China
- Published
- 30 January 2023
- Authors
- Hong-Yu Zhou, Yunxiang Fu, Zhicheng Zhang, Cheng Bian, Yizhou Yu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein representation learning
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
- 36,000,000 tokens
"ProteinKG25 (Zhang et al., 2022) provides a knowledge graph that consists of approximately five million triplets, with nearly 600k protein, 50k attribute terms, and 31 relation terms included" Assuming the relation and attribute terms are ~5 tokens and proteins are ~300 tokens 5000000*305=1525000000
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 11
Sources
Where this record came from and when it was last checked.
- Reference
- Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
KeAP was published by The University of Hong Kong,ByteDance,JancsiTech,OPPO HealthLab, in the country recorded as Hong Kong, during January 2023. It comes out of an organisation categorised as academia,Industry,Industry,Industry.
It works in the domain of Biology, and is recorded as performing the task of proteins, Protein representation learning.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
The training set ran to roughly 36,000,000 tokens of text.
Answers
KeAP — common questions
KeAP— 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.
KeAP— 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.
KeAP— who created it?
It was published by The University of Hong Kong,ByteDance,JancsiTech,OPPO HealthLab, based in Hong Kong, an organisation categorised as academia,Industry,Industry,Industry.
KeAP— when was it released?
It was published in January 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
KeAP— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of proteins, Protein representation learning. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
KeAP— 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.
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.