Layer Normalization: The Attentive Reader
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
- Language
- Task
- Question answering
- Base model
- The Attentive Reader
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
- tokens
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: The Attentive Reader was published by University of Toronto, in the country recorded as Canada, during July 2016. The publishing organisation is categorised as academia.
It works in the domain of Language, and is recorded as performing the task of question answering.
It builds on The Attentive Reader. Most models at this scale are adapted from an existing base rather than built from nothing.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
Layer Normalization: The Attentive Reader — common questions
Layer Normalization: The Attentive Reader— what is it used for?
It works in the domain of Language, and is recorded as handling the task of question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Layer Normalization: The Attentive Reader— 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: The Attentive Reader— 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.
Layer Normalization: The Attentive Reader— 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: The Attentive Reader— who created it?
It was published by University of Toronto, based in Canada, an organisation categorised as academia.
Layer Normalization: The Attentive Reader— 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.
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.