Layer Normalization: Skip Thoughts
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
- Language modeling
- Base model
- Skip-Thoughts
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
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 720 hours (30 days)
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
Where it came from
Layer Normalization: Skip Thoughts was published by University of Toronto, in Canada, in July 2016. academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling.
It builds on Skip-Thoughts, 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.
Answers
Layer Normalization: Skip Thoughts — common questions
Is Layer Normalization: Skip Thoughts open source?
The licensing for Layer Normalization: Skip Thoughts 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: Skip Thoughts have?
No parameter count has been published for Layer Normalization: Skip Thoughts, which is why no memory or speed figure appears on this page.
Who created Layer Normalization: Skip Thoughts?
Layer Normalization: Skip Thoughts was published by University of Toronto, based in Canada, categorised as academia.
When was Layer Normalization: Skip Thoughts released?
Layer Normalization: Skip Thoughts 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: Skip Thoughts used for?
Layer Normalization: Skip Thoughts works in Language, and is recorded as handling 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.
What GPU do I need to run Layer Normalization: Skip Thoughts?
None. Layer Normalization: Skip Thoughts 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.
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.