FAIRSEQ Adaptive Inputs
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
- Facebook AI Research,Google Brain
- Organisation type
- Industry,Industry
- Country
- United States of America, France
- Published
- 1 April 2019
- Authors
- Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
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
- Epochs
- 180
"The first model has 16 blocks, inner dimension 4K and embedding dimension 1K" 247M as in Baevski and Auli (2019) Transformer
assuming same number of epochs as in Baevski and Auli (2019) Transformer - 180
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
- 3.2 × 10¹⁹ FLOP
- How it was established
- Operation counting,Hardware
6 FLOP / parameter / token * 247000000 parameters * 103000000 tokens * 180 epochs = 2.747628e+19 FLOP for translation model, Table 2: 31330000000000 FLOP / second / GPU * 128 GPUs * 8.5 hours * 3600 seconds / hour * 0.3 [assumed precision] = 3.6814003e+19 FLOP sqrt(2.747628e+19*3.6814003e+19) = 3.1804274e+19 FLOP Speculative confidence since amount of parameters and epochs are assumed as well as hardware estimation is given for another model in the paper __________ In the Algorithmic progr…
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
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
weights and training, Repo is MIT-licensed https://github.com/facebookresearch/fairseq/blob/main/examples/language_model/README.adaptive_inputs.md
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
- 3,381
- Benchmark data
- FAIRSEQ Adaptive Inputs
Sources
Where this record came from and when it was last checked.
- Reference
- fairseq: A Fast, Extensible Toolkit for Sequence Modeling
- Last updated
- 25 May 2026
What the numbers mean
About this model
FAIRSEQ Adaptive Inputs was published by Facebook AI Research,Google Brain, in United States of America, in April 2019. The organisation is categorised as industry,Industry.
It works in Language, and is recorded as doing language modeling/generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Producing it required around 3.2 × 10¹⁹ FLOP of arithmetic, on NVIDIA V100, which is a statement about the training budget rather than about inference.
The training set ran to roughly 103,000,000 tokens.
Answers
FAIRSEQ Adaptive Inputs — common questions
Who created FAIRSEQ Adaptive Inputs?
FAIRSEQ Adaptive Inputs was published by Facebook AI Research,Google Brain, based in United States of America, categorised as industry,Industry.
When was FAIRSEQ Adaptive Inputs released?
FAIRSEQ Adaptive Inputs was published in April 2019. 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 FAIRSEQ Adaptive Inputs used for?
FAIRSEQ Adaptive Inputs works in Language, and is recorded as handling language modeling/generation. 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 FAIRSEQ Adaptive Inputs?
Around 3.2 × 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 FAIRSEQ Adaptive Inputs?
None. FAIRSEQ Adaptive Inputs 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 FAIRSEQ Adaptive Inputs open source?
No. FAIRSEQ Adaptive Inputs has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does FAIRSEQ Adaptive Inputs have?
FAIRSEQ Adaptive Inputs has 247M parameters. "The first model has 16 blocks, inner dimension 4K and embedding dimension 1K" 247M as in Baevski and Auli (2019) Transformer. 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.
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.