Transformer-XL+AdamP
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
- Naver AI Lab,Naver Clova
- Organisation type
- Industry,Industry
- Country
- Korea (Republic of)
- Published
- 15 June 2020
- Authors
- Byeongho Heo, Sanghyuk Chun, Seong Joon Oh, Dongyoon Han, Sangdoo Yun, Gyuwan Kim, Youngjung Uh, Jung-Woo Ha
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
- 257M
- 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
repo here, looks like code for AdamP but not training code for the model: https://github.com/clovaai/AdamP
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 114
- Benchmark data
- Transformer-XL+AdamP
Sources
Where this record came from and when it was last checked.
- Reference
- AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
- Last updated
- 28 November 2025
What the numbers mean
Background
Transformer-XL+AdamP was published by Naver AI Lab,Naver Clova, in Korea (Republic of), in June 2020. industry,Industry is the category the publisher falls under.
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-XL+AdamP — common questions
What is Transformer-XL+AdamP used for?
Transformer-XL+AdamP works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Transformer-XL+AdamP?
None. Transformer-XL+AdamP 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-XL+AdamP open source?
No. Transformer-XL+AdamP has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Transformer-XL+AdamP have?
Transformer-XL+AdamP has 257M 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 Transformer-XL+AdamP?
Transformer-XL+AdamP was published by Naver AI Lab,Naver Clova, based in Korea (Republic of), categorised as industry,Industry.
When was Transformer-XL+AdamP released?
Transformer-XL+AdamP was published in June 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.
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.