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 United States of America, in August 2020. academia,Academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling.
It builds on GPT-2 (124M), which is why it shares that 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 roughly 7.9 × 10²⁰ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Answers
Transformer+Recurrent Windows of Context — common questions
What is Transformer+Recurrent Windows of Context used for?
Transformer+Recurrent Windows of Context works in Language, and is recorded as handling 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.
How much compute was used to train Transformer+Recurrent Windows of Context?
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.
What GPU do I need to run Transformer+Recurrent Windows of Context?
None. Transformer+Recurrent Windows of Context 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.
Is Transformer+Recurrent Windows of Context open source?
No. Transformer+Recurrent Windows of Context has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Transformer+Recurrent Windows of Context have?
Transformer+Recurrent Windows of Context has 124M parameters. 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.
Who created Transformer+Recurrent Windows of Context?
Transformer+Recurrent Windows of Context was published by Toyota Technological Institute at Chicago,University of Chicago, based in United States of America, categorised as academia,Academia.
When was Transformer+Recurrent Windows of Context released?
Transformer+Recurrent Windows of Context 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.
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