RNN+weight noise+dynamic eval
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 Toronto
- Organisation type
- Academia
- Country
- Canada
- Published
- 4 August 2013
- Authors
- Alex Graves
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
- 54M
- Training data
- 929,000 tokens
- Epochs
- 14
"the word-level network had 10,000 inputs and outputs and around 54M weights"
14 epochs (Table 1)
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
- 4.2 × 10¹⁵ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 54000000 parameters * 929000 tokens * 14 epochs = 4.213944e+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
- Unreleased
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
- 4,734
- Benchmark data
- RNN+weight noise+dynamic eval
Sources
Where this record came from and when it was last checked.
- Reference
- Generating Sequences With Recurrent Neural Networks
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
RNN+weight noise+dynamic eval was published by University of Toronto, in the country recorded as Canada, during August 2013. It comes out of an organisation 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 4.2 × 10¹⁵ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 929,000 tokens of text.
Answers
RNN+weight noise+dynamic eval — common questions
RNN+weight noise+dynamic eval— how many parameters does it have?
It has a parameter count of 54M. "the word-level network had 10,000 inputs and outputs and around 54M weights". 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.
RNN+weight noise+dynamic eval— who created it?
It was published by University of Toronto, based in Canada, an organisation categorised as academia.
RNN+weight noise+dynamic eval— when was it released?
It was published in August 2013. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
RNN+weight noise+dynamic eval— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
RNN+weight noise+dynamic eval— how much compute was used to train it?
Training consumed around 4.2 × 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.
RNN+weight noise+dynamic eval— 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.
RNN+weight noise+dynamic eval— 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.