Selfish-RNN (ON-LSTM)
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
- Eindhoven University of Technology
- Organisation type
- Academia
- Country
- Netherlands
- Published
- 22 January 2021
- Authors
- Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
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
- 25.2M
- Training data
- tokens
- Epochs
- 1,000
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open (non-commercial)
code, no clear license https://github.com/Shiweiliuiiiiiii/Selfish-RNN
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 43
- Benchmark data
- Selfish-RNN (ON-LSTM)
Sources
Where this record came from and when it was last checked.
- Reference
- Selfish Sparse RNN Training
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
Selfish-RNN (ON-LSTM) was published by Eindhoven University of Technology, in Netherlands, in January 2021. It comes out of academia.
It works in Language, and is recorded as doing language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
Selfish-RNN (ON-LSTM) — common questions
How many parameters does Selfish-RNN (ON-LSTM) have?
Selfish-RNN (ON-LSTM) has 25.2M 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.
Who created Selfish-RNN (ON-LSTM)?
Selfish-RNN (ON-LSTM) was published by Eindhoven University of Technology, based in Netherlands, categorised as academia.
When was Selfish-RNN (ON-LSTM) released?
Selfish-RNN (ON-LSTM) was published in January 2021. 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 Selfish-RNN (ON-LSTM) used for?
Selfish-RNN (ON-LSTM) works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Selfish-RNN (ON-LSTM)?
None. Selfish-RNN (ON-LSTM) 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 Selfish-RNN (ON-LSTM) open source?
No. Selfish-RNN (ON-LSTM) has not had its weights published, so it exists only as a service controlled by its owner.
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.