RNNLM + Dynamic KL Regularization
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
- Northwestern University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 January 2018
- Authors
- Thanapon Noraset, David Demeter, Doug Downey
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
- 13.3M
- Training data
- tokens
- Epochs
- 20
2-layer LSTM, 650 hidden units per layer, embedding size = 650. Penn Treebank standard vocab = ~10k words (from Zaremba et al. 2014) 2 * (4 * (650*650 + 650*650 + 650)) + (650*10000)+10000 = 13275200
Penn Treebank known to have 930k tokens
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
- 5 × 10¹⁴ FLOP
((8*650*(650+650))*2)*2 * 930000 tokens/epoch * 20 epochs = 5.029e14. batch size assumed from prior literature
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
code, but not experiment code: https://github.com/northanapon/seqmodel/tree/aaai18
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
- 9
- Benchmark data
- RNNLM + Dynamic KL Regularization
Sources
Where this record came from and when it was last checked.
- Reference
- Controlling Global Statistics in Recurrent Neural Network Text Generation
- Last updated
- 28 November 2025
What the numbers mean
What this model is
RNNLM + Dynamic KL Regularization was published by Northwestern University, in the country recorded as United States of America, during January 2018. The publishing organisation is categorised as academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training run consumed about 5 × 10¹⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
RNNLM + Dynamic KL Regularization — common questions
RNNLM + Dynamic KL Regularization— how many parameters does it have?
It has a parameter count of 13.3M. 2-layer LSTM, 650 hidden units per layer, embedding size = 650. Penn Treebank standard vocab = ~10k words (from Zaremba et al. 2014) 2 * (4 * (650*650 + 650*650 + 650)) + (650*10000)+10000 = 13275200. 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.
RNNLM + Dynamic KL Regularization— who created it?
It was published by Northwestern University, based in United States of America, an organisation categorised as academia.
RNNLM + Dynamic KL Regularization— when was it released?
It was published in January 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
RNNLM + Dynamic KL Regularization— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
RNNLM + Dynamic KL Regularization— how much compute was used to train it?
Training consumed around 5 × 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.
RNNLM + Dynamic KL Regularization— 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.
RNNLM + Dynamic KL Regularization— is it open source?
No. Its weights have not been 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.