DiffStk-MRNN
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
- Pennsylvania State University,Rochester Institute of Technology
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 4 April 2020
- Authors
- Ankur Mali, Alexander Ororbia, Daniel Kifer, Clyde Lee Giles
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
- 1M
- Training data
- 912,344 tokens
- Epochs
- 50
All models trained on this dataset consisted of 100 hidden units Okay, let's break this down: - RNN with 1 hidden layer - 100 hidden units - Vocabulary size = 10,000 The RNN layer will have: - Input weight matrix U: dimensions (vocab size x hidden size) = (10,000 x 100) = 1,000,000 parameters - Recurrent weight matrix W: dimensions (hidden size x hidden size) = (100 x 100) = 10,000 parameters - Bias vector b: (hidden size) = (100) = 100 parameters So the total parameters is: U = 1,000,0…
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
- 2.8 × 10¹⁴ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 1010000 parameters * 912 344 tokens * 50 epochs = 2.7644023e+14 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
- Unreleased
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
- 14
- Benchmark data
- DiffStk-MRNN
Sources
Where this record came from and when it was last checked.
- Reference
- Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack
- Last updated
- 1 December 2025
What the numbers mean
Background
DiffStk-MRNN was published by Pennsylvania State University,Rochester Institute of Technology, in United States of America, in April 2020. academia,Academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Training it took roughly 2.8 × 10¹⁴ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Around 912,344 tokens went into training it.
Answers
DiffStk-MRNN — common questions
What is DiffStk-MRNN used for?
DiffStk-MRNN 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.
How much compute was used to train DiffStk-MRNN?
Around 2.8 × 10¹⁴ 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.
What GPU do I need to run DiffStk-MRNN?
None. DiffStk-MRNN 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 DiffStk-MRNN open source?
No. DiffStk-MRNN has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DiffStk-MRNN have?
DiffStk-MRNN has 1M parameters. All models trained on this dataset consisted of 100 hidden units Okay, let's break this down: - RNN with 1 hidden layer - 100 hidden units - Vocabulary size = 10,000 The RNN layer will have: - Input weight matrix U: dimensions (vocab size x hidden size) = (10,000 x 100) = 1,000,000 parameters - Recurrent weight matrix W: dimensions (hidden size x hidden size) = (100 x 100) = 10,000 parameters - Bias vector b: (hidden size) = (100) = 100 parameters So the total parameters is: U = 1,000,000 W = 10,000 b = 100 Total parameters = U + W + b = 1,000,000 + 10,000 + 100 = 1,010,100 Therefore, the total number of parameters for this 1-layer RNN with 100 hidden units and 10,000 vocab size is 1,010,100. 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 DiffStk-MRNN?
DiffStk-MRNN was published by Pennsylvania State University,Rochester Institute of Technology, based in United States of America, categorised as academia,Academia.
When was DiffStk-MRNN released?
DiffStk-MRNN was published in April 2020. 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.