Transformer+Recurrent Windows of Context
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
- Toyota Technological Institute at Chicago,University of Chicago
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 16 August 2020
- Authors
- Davis Yoshida, Allyson Ettinger, Kevin Gimpel
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Base model
- GPT-2 (124M)
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
- 124M
- Training data
- tokens
- Epochs
- 2
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
- 7.9 × 10²⁰ FLOP
- How it was established
- Operation counting
- Fine-tuning compute
- 1.5 × 10¹⁷ FLOP
base model compute (speculative confidence): 7.936e+20 FLOP fine-tune compute: 1.53264e+17 FLOP 7.936e+20 FLOP + 1.53264e+17 FLOP = 7.9375326e+20 FLOP
6 FLOP / token / parameter * 124000000 parameters * 103000000 tokens * 2 epochs = 1.53264e+17 FLOP
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.
- Record confidence
- Speculative
- Citations
- 7
- Benchmark data
- Transformer+Recurrent Windows of Context
Sources
Where this record came from and when it was last checked.
- Reference
- Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size
- Last updated
- 28 November 2025
What the numbers mean
Background
Transformer+Recurrent Windows of Context was published by Toyota Technological Institute at Chicago,University of Chicago, in the country recorded as United States of America, during August 2020. The category the publisher falls under is academia,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
It builds on GPT-2 (124M). That is why it shares the base model's general shape and size.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Training it took a computation budget of roughly 7.9 × 10²⁰ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Transformer+Recurrent Windows of Context — common questions
Transformer+Recurrent Windows of Context— 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+Recurrent Windows of Context— how much compute was used to train it?
Training consumed around 7.9 × 10²⁰ 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+Recurrent Windows of Context— 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+Recurrent Windows of Context— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Transformer+Recurrent Windows of Context— how many parameters does it have?
It has a parameter count of 124M. 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+Recurrent Windows of Context— who created it?
It was published by Toyota Technological Institute at Chicago,University of Chicago, based in United States of America, an organisation categorised as academia,Academia.
Transformer+Recurrent Windows of Context— when was it released?
It was published in August 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.