TransformerXL-LayerFusion-CA
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 Liverpool,University of Southern California
- Organisation type
- Academia,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland, United States of America
- Published
- 29 July 2020
- Authors
- James O' Neill, Greg Ver Steeg, Aram Galstyan
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.
- 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
- 7
- Benchmark data
- TransformerXL-LayerFusion-CA
Sources
Where this record came from and when it was last checked.
- Reference
- Compressing Deep Neural Networks via Layer Fusion
- Last updated
- 25 May 2026
What the numbers mean
Background
TransformerXL-LayerFusion-CA was published by University of Liverpool,University of Southern California, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during July 2020. It comes out of an organisation categorised as academia,Academia.
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
TransformerXL-LayerFusion-CA — common questions
TransformerXL-LayerFusion-CA— 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.
TransformerXL-LayerFusion-CA— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
TransformerXL-LayerFusion-CA— 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.
TransformerXL-LayerFusion-CA— who created it?
It was published by University of Liverpool,University of Southern California, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia,Academia.
TransformerXL-LayerFusion-CA— when was it released?
It was published in July 2020. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
TransformerXL-LayerFusion-CA— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.