Layer Normalization: Handwriting sequence generation
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
- 21 July 2016
- Authors
- Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image generation
- Base model
- RNN+weight noise+dynamic eval
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
- 3.7M
- Training data
- 8,525,300 tokens
The total number of weights was increased to approximately 3.7M.
12179*700=8525300 There are, in total, 12179 handwriting line sequences. The input string is typically more than 25 characters and the average handwriting line has a length around 700.
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.
- How it was established
- Operation counting
- Fine-tuning compute
- 1.9 × 10¹⁴ FLOP
=8525300*3700000*6=1.8926166e+14
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Layer Normalization
- Last updated
- 28 November 2025
What the numbers mean
About this model
Layer Normalization: Handwriting sequence generation was published by University of Toronto, in Canada, in July 2016. academia is the category the publisher falls under.
It works in Image generation, and is recorded as doing image generation.
It builds on RNN+weight noise+dynamic eval, which is why it shares that model's general shape and size.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
It was trained on about 8,525,300 tokens of text.
Answers
Layer Normalization: Handwriting sequence generation — common questions
What GPU do I need to run Layer Normalization: Handwriting sequence generation?
None. Layer Normalization: Handwriting sequence generation 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 Layer Normalization: Handwriting sequence generation open source?
The licensing for Layer Normalization: Handwriting sequence generation was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Layer Normalization: Handwriting sequence generation have?
Layer Normalization: Handwriting sequence generation has 3.7M parameters. The total number of weights was increased to approximately 3.7M. 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 Layer Normalization: Handwriting sequence generation?
Layer Normalization: Handwriting sequence generation was published by University of Toronto, based in Canada, categorised as academia.
When was Layer Normalization: Handwriting sequence generation released?
Layer Normalization: Handwriting sequence generation was published in July 2016. 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 Layer Normalization: Handwriting sequence generation used for?
Layer Normalization: Handwriting sequence generation works in Image generation, and is recorded as handling image generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.