Transformer + Average Attention Network
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 Electronic Science and Technology of China
- Organisation type
- Academia
- Country
- China
- Published
- 1 January 2019
- Authors
- Jian Guo Zhang, Jian Ping Li, Huang Li
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
- 126
- Benchmark data
- Transformer + Average Attention Network
Sources
Where this record came from and when it was last checked.
- Reference
- Language Modeling with Transformer
- Last updated
- 28 November 2025
What the numbers mean
About this model
Transformer + Average Attention Network was published by University of Electronic Science and Technology of China, in China, in January 2019. It comes out of 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
Transformer + Average Attention Network — common questions
Who created Transformer + Average Attention Network?
Transformer + Average Attention Network was published by University of Electronic Science and Technology of China, based in China, categorised as academia.
When was Transformer + Average Attention Network released?
Transformer + Average Attention Network was published in January 2019. 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 Transformer + Average Attention Network used for?
Transformer + Average Attention Network 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 Transformer + Average Attention Network?
None. Transformer + Average Attention Network 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 Transformer + Average Attention Network open source?
No. Transformer + Average Attention Network has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Transformer + Average Attention Network have?
No parameter count has been published for Transformer + Average Attention Network, which is why no memory or speed figure appears on this page.
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.