Layer Normalization: Draw
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
- Draw
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.
- Training data
- 39,200,000 tokens
The dataset has been split into 50,000 training, 10,000 validation and 10,000 test
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Layer Normalization
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Layer Normalization: Draw was published by University of Toronto, in the country recorded as Canada, during July 2016. The category the publisher falls under is academia.
It works in the domain of Image generation, and is recorded as performing the task of image generation.
Its starting point was an existing base model, Draw. That is the usual way a specialised model is produced.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
It was trained on a corpus of about 39,200,000 tokens of text.
Answers
Layer Normalization: Draw — common questions
Layer Normalization: Draw— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Layer Normalization: Draw— who created it?
It was published by University of Toronto, based in Canada, an organisation categorised as academia.
Layer Normalization: Draw— when was it released?
It 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.
Layer Normalization: Draw— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of image generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Layer Normalization: Draw— 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.
Layer Normalization: Draw— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.