Transformer-XL DeFINE (141M)
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 Washington,Allen Institute for AI
- Organisation type
- Academia,Research collective
- Country
- United States of America
- Published
- 27 November 2019
- Authors
- Sachin Mehta, Rik Koncel-Kedziorski, Mohammad Rastegari, Hannaneh Hajishirzi
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Numerical format
- FP32
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
- 141M
- Training data
- 103,000,000 tokens
- Epochs
- 20
Table 2b
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
- 1.7 × 10¹⁸ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 141000000 parameters * 103000000 tokens * 20 epochs [assumption based on number of epochs for LSTMs] = 1.74276e+18 FLOP _______________ older estimation: 6.2 × 10^18 (no explantion how it was calculated)
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.
- Why it is tracked
- SOTA improvement
- Record confidence
- Speculative
- Citations
- 29
- Benchmark data
- Transformer-XL DeFINE (141M)
"Compared to state-of-the-art methods including adaptive input representations, this technique results in a 6% to 20% drop in perplexity" Table 2a
Sources
Where this record came from and when it was last checked.
- Reference
- DeFINE: DEep Factorized INput Token Embeddings for Neural Sequence Modeling
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Transformer-XL DeFINE (141M) was published by University of Washington,Allen Institute for AI, in the country recorded as United States of America, during November 2019. The category the publisher falls under is academia,Research collective.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Training it took a computation budget of roughly 1.7 × 10¹⁸ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 103,000,000 tokens of text.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
Transformer-XL DeFINE (141M) — common questions
Transformer-XL DeFINE (141M)— how many parameters does it have?
It has a parameter count of 141M. Table 2b. 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 DeFINE (141M)— who created it?
It was published by University of Washington,Allen Institute for AI, based in United States of America, an organisation categorised as academia,Research collective.
Transformer-XL DeFINE (141M)— when was it released?
It was published in November 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 DeFINE (141M)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of 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.
Transformer-XL DeFINE (141M)— how much compute was used to train it?
Training consumed around 1.7 × 10¹⁸ FLOP. 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 DeFINE (141M)— 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 DeFINE (141M)— 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.