Multipop Adaptive Continuous Stack (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,University of Oxford
- Organisation type
- Industry,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 15 February 2018
- Authors
- Dani Yogatama, Yishu Miao, Gabor Melis, Wang Ling, Adhiguna Kuncoro, 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
- 26M
- 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
- 63
- Benchmark data
- Multipop Adaptive Continuous Stack (WT2)
Sources
Where this record came from and when it was last checked.
- Reference
- Memory Architectures in Recurrent Neural Network Language Models
- Last updated
- 11 February 2026
What the numbers mean
What this model is
Multipop Adaptive Continuous Stack (WT2) was published by DeepMind,University of Oxford, in United Kingdom of Great Britain and Northern Ireland, in February 2018. The organisation is categorised as industry,Academia.
It works in Language, and is recorded as doing language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Multipop Adaptive Continuous Stack (WT2) — common questions
What GPU do I need to run Multipop Adaptive Continuous Stack (WT2)?
None. Multipop Adaptive Continuous Stack (WT2) 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 Multipop Adaptive Continuous Stack (WT2) open source?
No. Multipop Adaptive Continuous Stack (WT2) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Multipop Adaptive Continuous Stack (WT2) have?
Multipop Adaptive Continuous Stack (WT2) has 26M parameters. 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 Multipop Adaptive Continuous Stack (WT2)?
Multipop Adaptive Continuous Stack (WT2) was published by DeepMind,University of Oxford, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry,Academia.
When was Multipop Adaptive Continuous Stack (WT2) released?
Multipop Adaptive Continuous Stack (WT2) was published in February 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.
What is Multipop Adaptive Continuous Stack (WT2) used for?
Multipop Adaptive Continuous Stack (WT2) works in Language, and is recorded as handling language modeling. 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.