Fairseq + UID: variance
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
- Google AI,ETH Zurich,University of Cambridge
- Organisation type
- Industry,Academia,Academia
- Country
- United States of America, Switzerland, United Kingdom of Great Britain and Northern Ireland
- Published
- 15 May 2021
- Authors
- Jason Wei, Clara Meister, Ryan Cotterell
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
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
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.
- Record confidence
- Unknown
- Citations
- 23
- Benchmark data
- Fairseq + UID: variance
Sources
Where this record came from and when it was last checked.
- Reference
- A Cognitive Regularizer for Language Modeling
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Fairseq + UID: variance was published by Google AI,ETH Zurich,University of Cambridge, in United States of America, in May 2021. The organisation is categorised as industry,Academia,Academia.
It works in Language, and is recorded as doing language modeling/generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Fairseq + UID: variance — common questions
Is Fairseq + UID: variance open source?
No. Fairseq + UID: variance has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Fairseq + UID: variance have?
No parameter count has been published for Fairseq + UID: variance, which is why no memory or speed figure appears on this page.
Who created Fairseq + UID: variance?
Fairseq + UID: variance was published by Google AI,ETH Zurich,University of Cambridge, based in United States of America, categorised as industry,Academia,Academia.
When was Fairseq + UID: variance released?
Fairseq + UID: variance was published in May 2021. 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 Fairseq + UID: variance used for?
Fairseq + UID: variance works in Language, and is recorded as handling language modeling/generation. 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 Fairseq + UID: variance?
None. Fairseq + UID: variance 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.