Adaptive Input Transformer + RD
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
- Microsoft Research Asia,Soochow University
- Organisation type
- Industry,Academia
- Country
- China, Taiwan
- Published
- 28 June 2021
- Authors
- Xiaobo Liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, Tie-Yan Liu
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
- FP16
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
- 247M
- Training data
- 103,000,000 tokens
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
- 8.6 × 10¹⁹ FLOP
- How it was established
- Hardware
"We train Transformer model for 50k steps and Adaptive Input Transformer for 286k steps" assuming 1 step took 1 second: 125000000000000 FLOP / sec [assumed precision: bf16] * 286000 seconds * 8 GPUs * 0.3 [assumed utilization] = 8.58e+19 FLOP
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
- 79 hours
- Power draw
- 4.9 kW
" The training is on 8 Tesla V100 GPU cards" 286k steps ~ [assumption] 286000 seconds = 79.4 hours
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
- Open source
train and inference code for translation models, MIT license. no weights https://github.com/dropreg/R-Drop/blob/main/fairseq_src/examples/translation_rdrop/README.md
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
- Likely
- Citations
- 538
- Benchmark data
- Adaptive Input Transformer + RD
"In particular, it yields substantial improvements when applied to fine-tune large-scale pre-trained models, e.g., ViT, RoBERTa-large, and BART, and achieves state-of-the-art (SOTA) performances with the vanilla Transformer model " I don't see absolute SOTA claimes for this model
Sources
Where this record came from and when it was last checked.
- Reference
- R-Drop: Regularized Dropout for Neural Networks
- Last updated
- 25 May 2026
What the numbers mean
Background
Adaptive Input Transformer + RD was published by Microsoft Research Asia,Soochow University, in China, in June 2021. It comes out of industry,Academia.
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.
How it was trained
The training run consumed about 8.6 × 10¹⁹ FLOP, on NVIDIA V100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
It was trained on about 103,000,000 tokens of text.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
Adaptive Input Transformer + RD — common questions
When was Adaptive Input Transformer + RD released?
Adaptive Input Transformer + RD was published in June 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 Adaptive Input Transformer + RD used for?
Adaptive Input Transformer + RD 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 Adaptive Input Transformer + RD?
Around 8.6 × 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 Adaptive Input Transformer + RD?
None. Adaptive Input Transformer + RD 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 Adaptive Input Transformer + RD open source?
No. Adaptive Input Transformer + RD has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Adaptive Input Transformer + RD have?
Adaptive Input Transformer + RD has 247M 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 Adaptive Input Transformer + RD?
Adaptive Input Transformer + RD was published by Microsoft Research Asia,Soochow University, based in China, categorised as industry,Academia.
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.