Transformer-XL-ptb
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
- Carnegie Mellon University (CMU),Google Brain
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 9 January 2019
- Authors
- Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov
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.
- Parameters
- 24M
- Training data
- tokens
24M, Table 5
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
- Open source
Apache 2: https://github.com/kimiyoung/transformer-xl PTB model not in downloads
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 4,320
- Benchmark data
- Transformer-XL-ptb
Sources
Where this record came from and when it was last checked.
- Reference
- Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
- Last updated
- 25 May 2026
What the numbers mean
About this model
Transformer-XL-ptb was published by Carnegie Mellon University (CMU),Google Brain, in the country recorded as United States of America, during January 2019. The category the publisher falls under is academia,Industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
Transformer-XL-ptb — common questions
Transformer-XL-ptb— how many parameters does it have?
It has a parameter count of 24M. 24M, Table 5. 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.
Transformer-XL-ptb— who created it?
It was published by Carnegie Mellon University (CMU),Google Brain, based in United States of America, an organisation categorised as academia,Industry.
Transformer-XL-ptb— when was it released?
It 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.
Transformer-XL-ptb— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
Transformer-XL-ptb— 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.
Transformer-XL-ptb— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.