AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB)
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
- Columbia University,New York University (NYU),Princeton University
- Organisation type
- Academia,Academia,Academia
- Country
- United States of America
- Published
- 3 May 2018
- Authors
- Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei
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
- 22M
- Training data
- 929,000 tokens
- Epochs
- 400
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
- 4.9 × 10¹⁶ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 22000000 parameters * 929000 tokens * 400 epochs = 4.90512e+16 FLOP
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
- Citations
- 27
- Benchmark data
- AWD-LSTM-MoS+Noisin+dynamic evaluation
Sources
Where this record came from and when it was last checked.
- Reference
- Noisin: Unbiased Regularization for Recurrent Neural Networks
- Last updated
- 25 May 2026
What the numbers mean
Background
AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) was published by Columbia University,New York University (NYU),Princeton University, in United States of America, in May 2018. The organisation is categorised as academia,Academia,Academia.
It works in Language, and is recorded as doing language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Producing it required around 4.9 × 10¹⁶ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Around 929,000 tokens went into training it.
Answers
AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) — common questions
Who created AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB)?
AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) was published by Columbia University,New York University (NYU),Princeton University, based in United States of America, categorised as academia,Academia,Academia.
When was AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) released?
AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) was published in May 2018. 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 AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) used for?
AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB)?
Around 4.9 × 10¹⁶ FLOP. 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 AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB)?
None. AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) 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 AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) open source?
No. AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) have?
AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) has 22M 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.
The other direction
Looking at it from the other side?
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