ProteinINR
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
- Kakao
- Organisation type
- Industry
- Country
- Korea (Republic of)
- Published
- 16 January 2024
- Authors
- Youhan Lee, Hasun Yu, Jaemyung Lee, Jaehoon Kim
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- 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
- tokens
Calculation: 16,384 x 906,458 = 14,879,754,112 ≈ 1.5e10 data points Additional calculation: 300 x 906,458 = 272,000,000 points (sequence data) Final estimate: 1.5e10 data points (dominated by structural data)
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
- 20
Sources
Where this record came from and when it was last checked.
- Reference
- Pre-training Sequence, Structure, and Surface Features for Comprehensive Protein Representation Learning
- Last updated
- 1 January 2026
What the numbers mean
About this model
ProteinINR was published by Kakao, in Korea (Republic of), in January 2024. It comes out of industry.
It works in Biology, and is recorded as doing protein representation learning.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
ProteinINR — common questions
When was ProteinINR released?
ProteinINR was published in January 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 ProteinINR used for?
ProteinINR works in Biology, and is recorded as handling 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.
What GPU do I need to run ProteinINR?
None. ProteinINR 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 ProteinINR open source?
The licensing for ProteinINR 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 ProteinINR have?
No parameter count has been published for ProteinINR, which is why no memory or speed figure appears on this page.
Who created ProteinINR?
ProteinINR was published by Kakao, based in Korea (Republic of), categorised as industry.
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.