PLTNUM
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
- Kyoto University,National Institute of Biomedical Innovation,RIKEN
- Organisation type
- Academia,Government
- Country
- Japan
- Published
- 14 September 2024
- Authors
- Tatsuya Sagawa, Eisuke Kanao, Kosuke Ogata, Koshi Imami, Yasushi Ishihama
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein property prediction
- Base model
- SaProt
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
- 650M
- Training data
- tokens
- Epochs
- 10
Total Datapoints = Number of Proteins × Sequence Length Total Datapoints = 4,162 × 510 = 2,122,620 tokens Final result ≈ 2.1 × 10^6 tokens
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
- Prediction of Protein Half-lives from Amino Acid Sequences by Protein Language Models
- Last updated
- 28 November 2025
What the numbers mean
What this model is
PLTNUM was published by Kyoto University,National Institute of Biomedical Innovation,RIKEN, in the country recorded as Japan, 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 protein property prediction.
Rather than being trained from scratch, it is derived from SaProt. That is the usual way a specialised model is produced.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
PLTNUM — common questions
PLTNUM— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein property prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
PLTNUM— 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.
PLTNUM— 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.
PLTNUM— how many parameters does it have?
It has a parameter count of 650M. 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.
PLTNUM— who created it?
It was published by Kyoto University,National Institute of Biomedical Innovation,RIKEN, based in Japan, an organisation categorised as academia,Government.
PLTNUM— 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.
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.