Transformer-XL + AutoDropout (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
- Google Research
- Organisation type
- Industry
- Country
- United States of America
- Published
- 5 January 2021
- Authors
- Hieu Pham, Quoc V. Le
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
- 24M
- Training data
- tokens
- Epochs
- 192
"We use the same model size as specified by Dai et al. (2019)."
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 5.8 × 10¹⁶ FLOP
"We train every configuration from scratch for 160,000 steps, using a batch size of 16 and a segment length of 70" Total tokens: 160000*16*70=179200000 Epochs: 179200000/929000=192 Training flop: 6*24000000*179200000=2.58048e+16 GPU hour estimate: 40*60*4*45000000000000*0.3=1.296e+17 Geometric mean: 57829941034035304
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- Google TPU v2
- Chips used
- 4
- Wall-clock time
- 1 hours
- Power draw
- 2.3 kW
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
404: https://github.com/google-research/google-research/tree/master/auto_dropout
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
- 63
- Benchmark data
- Transformer-XL + AutoDropout (PTB)
Sources
Where this record came from and when it was last checked.
- Reference
- AutoDropout: Learning Dropout Patterns to Regularize Deep Networks
- Last updated
- 25 May 2026
What the numbers mean
Background
Transformer-XL + AutoDropout (PTB) was published by Google Research, in the country recorded as United States of America, during January 2021. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training it took a computation budget of roughly 5.8 × 10¹⁶ FLOP, on hardware recorded as Google TPU v2. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Transformer-XL + AutoDropout (PTB) — common questions
Transformer-XL + AutoDropout (PTB)— who created it?
It was published by Google Research, based in United States of America, an organisation categorised as industry.
Transformer-XL + AutoDropout (PTB)— when was it released?
It was published in January 2021. 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 + AutoDropout (PTB)— 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.
Transformer-XL + AutoDropout (PTB)— how much compute was used to train it?
Training consumed around 5.8 × 10¹⁶ FLOP, on hardware recorded as Google TPU v2. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Transformer-XL + AutoDropout (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 + AutoDropout (PTB)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Transformer-XL + AutoDropout (PTB)— how many parameters does it have?
It has a parameter count of 24M. "We use the same model size as specified by Dai et al. (2019).". 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.
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.