Transformer-XL + RMS dynamic eval
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 Edinburgh
- Organisation type
- Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 17 April 2019
- Authors
- Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals
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
- 257M
- Training data
- 103,000,000 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
- Open source
Apache for code: https://github.com/benkrause/dynamiceval-transformer wt103 train script: https://github.com/benkrause/dynamiceval-transformer/blob/master/tf/sota/wt103.sh
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 47
- Benchmark data
- Transformer-XL + RMS dynamic eval
"By applying dynamic evaluation to Transformer-XL models, we improve the state of the art on enwik8 from 0.99 to 0.94 bits/char, text8 from 1.08 to 1.04 bits/char, and WikiText-103 from 18.3 to 16.4 perplexity points."
Sources
Where this record came from and when it was last checked.
- Reference
- Dynamic Evaluation of Transformer Language Models
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Transformer-XL + RMS dynamic eval was published by University of Edinburgh, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during April 2019. The publishing organisation is categorised as academia.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
The training set ran to roughly 103,000,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Transformer-XL + RMS dynamic eval — common questions
Transformer-XL + RMS dynamic eval— what is it used for?
It works in the domain of Language, and is recorded as handling the task of 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.
Transformer-XL + RMS dynamic eval— 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 + RMS dynamic eval— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Transformer-XL + RMS dynamic eval— how many parameters does it have?
It has a parameter count of 257M. 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 + RMS dynamic eval— who created it?
It was published by University of Edinburgh, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia.
Transformer-XL + RMS dynamic eval— when was it released?
It was published in April 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.
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.