AWD-LSTM-MoS + dynamic evaluation (PTB, 2017)
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
- Carnegie Mellon University (CMU)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 10 November 2017
- Authors
- Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, William W. Cohen
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
- tokens
- Epochs
- 1,000
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 3
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
MIT code: https://github.com/zihangdai/mos
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
- 416
- Benchmark data
- AWD-LSTM-MoS + dynamic evaluation (PTB, 2017)
Sources
Where this record came from and when it was last checked.
- Reference
- Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
AWD-LSTM-MoS + dynamic evaluation (PTB, 2017) was published by Carnegie Mellon University (CMU), in United States of America, in November 2017. The organisation is categorised as academia.
It works in Language, and is recorded as doing language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
AWD-LSTM-MoS + dynamic evaluation (PTB, 2017) — common questions
When was AWD-LSTM-MoS + dynamic evaluation (PTB, 2017) released?
AWD-LSTM-MoS + dynamic evaluation (PTB, 2017) was published in November 2017. 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 + dynamic evaluation (PTB, 2017) used for?
AWD-LSTM-MoS + dynamic evaluation (PTB, 2017) 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.
What GPU do I need to run AWD-LSTM-MoS + dynamic evaluation (PTB, 2017)?
None. AWD-LSTM-MoS + dynamic evaluation (PTB, 2017) 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 + dynamic evaluation (PTB, 2017) open source?
No. AWD-LSTM-MoS + dynamic evaluation (PTB, 2017) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AWD-LSTM-MoS + dynamic evaluation (PTB, 2017) have?
AWD-LSTM-MoS + dynamic evaluation (PTB, 2017) 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.
Who created AWD-LSTM-MoS + dynamic evaluation (PTB, 2017)?
AWD-LSTM-MoS + dynamic evaluation (PTB, 2017) was published by Carnegie Mellon University (CMU), based in United States of America, categorised as 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.