T2R 75% + Pretrain (WT-103)
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
- Organisation type
- Academia,Industry,Industry
- 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
- 668.9M
- Training data
- tokens
- Epochs
- 34.47
Vocabulary size unclear, ~260k if they directly use WT 103 Assumed tied embeddings Remaining parameters: 32 layers 8 attention heads (H) 128 head dimensions (N, D) 1024 model (embedding) dimensions (W, M) 4096 feedforward dimensions "We partition the training data into non-overlapping blocks of 512 contiguous tokens" MLP layer: 2*1024*4096=8388608 Att layer: H×(W×(2×D + N) + N×M) 8*(1024*(2*128+128)+128*1024)=4194304 Total non embedding: 32*(8388608+4194304)=402653184 Embedding: 260000*1024…
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
- 1.4 × 10¹⁹ FLOP
- How it was established
- Operation counting,Hardware
Table 4, "We found that we could speed up training by reducing the warm-up steps, total update steps, maximum and minimum rates, and batch size to 8K steps, 142K steps, 5 ⋅ 10−6, 0.5, and 25K tokens " 142K updates, 25k token batches Training compute: 6*668893184*142000*25000=1.4247425e+19 GPU hour estimate: 95*60*60*125000000000000*0.3=1.2825e+19 Geometric mean: 13517515512289972224 Epochs: 142000*25000/103000000=34.4660194175
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
- Wall-clock time
- 12 hours
- Power draw
- 4.9 kW
95h GPU time / 8 GPUs ~11.8h wall clock time
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
- Confident
- Citations
- 94
- Benchmark data
- T2R 75% + Pretrain
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
About this model
T2R 75% + Pretrain (WT-103) was published by University of Washington,Microsoft,DeepMind, in United States of America, in March 2021. The organisation is categorised as academia,Industry,Industry.
It works in Language, and is recorded as doing 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 1.4 × 10¹⁹ FLOP, on NVIDIA V100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
T2R 75% + Pretrain (WT-103) — common questions
When was T2R 75% + Pretrain (WT-103) released?
T2R 75% + Pretrain (WT-103) 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.
What is T2R 75% + Pretrain (WT-103) used for?
T2R 75% + Pretrain (WT-103) 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 T2R 75% + Pretrain (WT-103)?
Around 1.4 × 10¹⁹ FLOP, on 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.
What GPU do I need to run T2R 75% + Pretrain (WT-103)?
None. T2R 75% + Pretrain (WT-103) 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 T2R 75% + Pretrain (WT-103) open source?
No. T2R 75% + Pretrain (WT-103) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does T2R 75% + Pretrain (WT-103) have?
T2R 75% + Pretrain (WT-103) has 668.9M parameters. Vocabulary size unclear, ~260k if they directly use WT 103 Assumed tied embeddings Remaining parameters: 32 layers 8 attention heads (H) 128 head dimensions (N, D) 1024 model (embedding) dimensions (W, M) 4096 feedforward dimensions "We partition the training data into non-overlapping blocks of 512 contiguous tokens" MLP layer: 2*1024*4096=8388608 Att layer: H×(W×(2×D + N) + N×M) 8*(1024*(2*128+128)+128*1024)=4194304 Total non embedding: 32*(8388608+4194304)=402653184 Embedding: 260000*1024=266240000 Total: 402653184+266240000=668893184. 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 T2R 75% + Pretrain (WT-103)?
T2R 75% + Pretrain (WT-103) was published by University of Washington,Microsoft,DeepMind, based in United States of America, categorised as academia,Industry,Industry.
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