2-layer skip-LSTM + dropout tuning (WT2)
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
- DeepMind
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 23 May 2018
- Authors
- Gábor Melis, Charles Blundell, Tomáš Kočiský, Karl Moritz Hermann, Chris Dyer, Phil Blunsom
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
- 5.4M
- Training data
- tokens
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.
- Citations
- 14
- Benchmark data
- 2-layer skip-LSTM + dropout tuning (WT2)
Sources
Where this record came from and when it was last checked.
- Reference
- Pushing the bounds of dropout
- Last updated
- 11 February 2026
What the numbers mean
What this model is
2-layer skip-LSTM + dropout tuning (WT2) was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during May 2018. The publishing organisation is categorised as industry.
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.
Answers
2-layer skip-LSTM + dropout tuning (WT2) — common questions
2-layer skip-LSTM + dropout tuning (WT2)— how many parameters does it have?
It has a parameter count of 5.4M. 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.
2-layer skip-LSTM + dropout tuning (WT2)— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
2-layer skip-LSTM + dropout tuning (WT2)— when was it released?
It was published in May 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
2-layer skip-LSTM + dropout tuning (WT2)— 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.
2-layer skip-LSTM + dropout tuning (WT2)— 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.
2-layer skip-LSTM + dropout tuning (WT2)— 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.