T2R + Random Init
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,Microsoft,DeepMind,Allen Institute for AI
- Organisation type
- Academia,Industry,Industry,Research collective
- Country
- United States of America, United Kingdom of Great Britain and Northern Ireland
- Published
- 24 March 2021
- Authors
- Jungo Kasai, Hao Peng, Yizhe Zhang, Dani Yogatama, Gabriel Ilharco, Nikolaos Pappas, Yi Mao, Weizhu Chen, Noah A. Smith
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
- 450M
- Training data
- 103,000,000 tokens
- Epochs
- 205.48
- Batch size
- 74,000
450M source: https://arxiv.org/pdf/2203.12644 table 3
"We train the model with a batch size of about 74K tokens with a total of 286K steps" 74000*286000 / 103000000 = 205.48 epochs
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
- 2.7 × 10¹⁹ FLOP
- How it was established
- Operation counting,Hardware
6 FLOP / token / parameter * 450000000 parameters * 74000 tokens per batch * 286000 steps = 5.71428e+19 FLOP 125000000000000 [assumed bf16] * 98 GPU-hours * 3600 sec / hour * 0.3 [assumed utilization] = 1.323e+19 FLOP geometric mean: sqrt(5.71428e+19*1.323e+19) = 2.749544e+19
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
- NVIDIA V100
- Chips used
- 8
- Chip-hours
- 98
- Power draw
- 4.9 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
There's a repo but it's kind of inscrutable with no docs about T2R, not clear if the training code for this paper is in it: https://github.com/jungokasai/T2R/tree/master
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 94
- Benchmark data
- T2R + Random Init
Sources
Where this record came from and when it was last checked.
- Reference
- Finetuning Pretrained Transformers into RNNs
- Last updated
- 25 May 2026
What the numbers mean
Background
T2R + Random Init was published by University of Washington,Microsoft,DeepMind,Allen Institute for AI, in the country recorded as United States of America, during March 2021. The category the publisher falls under is academia,Industry,Industry,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.
What went into building it
The training run consumed about 2.7 × 10¹⁹ FLOP, on hardware recorded as NVIDIA V100. 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.
Answers
T2R + Random Init — common questions
T2R + Random Init— 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.
T2R + Random Init— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
T2R + Random Init— how many parameters does it have?
It has a parameter count of 450M. 450M source: https://arxiv.org/pdf/2203.12644 table 3. 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.
T2R + Random Init— who created it?
It was published by University of Washington,Microsoft,DeepMind,Allen Institute for AI, based in United States of America, an organisation categorised as academia,Industry,Industry,Research collective.
T2R + Random Init— when was it released?
It was published in March 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.
T2R + Random Init— 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.
T2R + Random Init— how much compute was used to train it?
Training consumed around 2.7 × 10¹⁹ FLOP, on hardware recorded as NVIDIA V100. 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.
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.