Brain2Qwerty
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
- Meta AI,Universite de Technologie de Compiègne – CNRS,Basque Center on Cognition
- Organisation type
- Industry,Academia,Academia
- Country
- United States of America, France, Spain
- Published
- 18 February 2025
- Authors
- Jarod Lévy,, Mingfang (Lucy) Zhang, Svetlana Pinet, Jérémy Rapin, Hubert Banville, Stéphane d’Ascoli, Jean-Rémi King
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language 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
- 400M
- Training data
- tokens
- Epochs
- 100
a total of ∼ 400M parameters (258M for Conv, 138M for Trans)
The model is trained for 100 epochs with a batch size of 128 Custom dataset collected from 35 participants typing 128 unique Spanish sentences
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
- 1.6 × 10¹⁸ FLOP
- How it was established
- Hardware
125000000000000 FLOP / GPU / sec [V100 reported, bf16 assumed] * 1 GPU * 12 hours * 3600 sec / hour * 0.3 [assumed utilization] = 1.62e+18 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
- 1
- Wall-clock time
- 12 hours
- Power draw
- 324 W
Training was conducted on a single NVIDIA Tesla V100 Volta GPU with 32 GB of memory. The total runtime for training one model is ∼12 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
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Brain-to-Text Decoding: A Non-invasive Approach via Typing
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Brain2Qwerty was published by Meta AI,Universite de Technologie de Compiègne – CNRS,Basque Center on Cognition, in the country recorded as United States of America, during February 2025. The category the publisher falls under is industry,Academia,Academia.
It works in the domain of Language, and is recorded as performing the task of language 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 arithmetic totalling around 1.6 × 10¹⁸ FLOP, on hardware recorded as NVIDIA V100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Brain2Qwerty — common questions
Brain2Qwerty— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language 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.
Brain2Qwerty— how much compute was used to train it?
Training consumed around 1.6 × 10¹⁸ 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.
Brain2Qwerty— 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.
Brain2Qwerty— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Brain2Qwerty— how many parameters does it have?
It has a parameter count of 400M. a total of ∼ 400M parameters (258M for Conv, 138M for Trans). 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.
Brain2Qwerty— who created it?
It was published by Meta AI,Universite de Technologie de Compiègne – CNRS,Basque Center on Cognition, based in United States of America, an organisation categorised as industry,Academia,Academia.
Brain2Qwerty— when was it released?
It was published in February 2025.
The other direction
Looking at it from the other side?
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