R-Transformer
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
- Michigan State University,TAL Education Group (Xueersi)
- Organisation type
- Academia,Industry
- Country
- United States of America, China
- Published
- 12 July 2019
- Authors
- Zhiwei Wang, Yao Ma, Zitao Liu, Jiliang Tang
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
- 15.8M
- Training data
- 912,344 tokens
- Epochs
- 100
https://colab.research.google.com/drive/1afDpFvO3Dtry2Mdg8C1aUBV_sMF7LGb7?usp=sharing
default number of epochs in the code: https://github.com/DSE-MSU/R-transformer/tree/master/language_word
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 8.6 × 10¹⁵ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 15800000 parameters * 912344 tokens * 100 epochs = 8.6490211e+15 FLOP
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/DSE-MSU/R-transformer/tree/master/language_word
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
- 116
- Benchmark data
- R-Transformer
Sources
Where this record came from and when it was last checked.
- Reference
- R-Transformer: Recurrent Neural Network Enhanced Transformer
- Last updated
- 25 May 2026
What the numbers mean
Background
R-Transformer was published by Michigan State University,TAL Education Group (Xueersi), in United States of America, in July 2019. The organisation is categorised as academia,Industry.
It works in Language, and is recorded as doing language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The training run consumed about 8.6 × 10¹⁵ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 912,344 tokens went into training it.
Answers
R-Transformer — common questions
What GPU do I need to run R-Transformer?
None. R-Transformer 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 R-Transformer open source?
No. R-Transformer has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does R-Transformer have?
R-Transformer has 15.8M parameters. https://colab.research.google.com/drive/1afDpFvO3Dtry2Mdg8C1aUBV_sMF7LGb7?usp=sharing. 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 R-Transformer?
R-Transformer was published by Michigan State University,TAL Education Group (Xueersi), based in United States of America, categorised as academia,Industry.
When was R-Transformer released?
R-Transformer was published in July 2019. 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 R-Transformer used for?
R-Transformer works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train R-Transformer?
Around 8.6 × 10¹⁵ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
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.