RNS-RNN
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
- University of Notre Dame
- Organisation type
- Academia
- Country
- United States of America
- Published
- 5 September 2021
- Authors
- Brian DuSell, David Chiang
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
- 5.8M
- Training data
- tokens
- Epochs
- 100
- Batch size
- 1,120
Table 1
Penn TreeBank's training split has 912,344 tokens.
Sequence length 35, minibatches of 32
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 here, unclear license: https://github.com/bdusell/nondeterministic-stack-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
- 19
- Benchmark data
- RNS-RNN
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Hierarchical Structures with Differentiable Nondeterministic Stacks
- Last updated
- 25 May 2026
What the numbers mean
Background
RNS-RNN was published by University of Notre Dame, in United States of America, in September 2021. The organisation is categorised as 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
RNS-RNN — common questions
When was RNS-RNN released?
RNS-RNN was published in September 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 RNS-RNN used for?
RNS-RNN 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.
What GPU do I need to run RNS-RNN?
None. RNS-RNN 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 RNS-RNN open source?
No. RNS-RNN has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does RNS-RNN have?
RNS-RNN has 5.8M parameters. Table 1. 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 RNS-RNN?
RNS-RNN was published by University of Notre Dame, based in United States of America, categorised as academia.
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.