AWD-LSTM-MoS + dynamic evaluation (WT2, 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
- 35M
- Training data
- 2,000,000 tokens
- Epochs
- 8,000
35M (Table 2)
max sequence length from https://github.com/zihangdai/mos/blob/master/main.py is 110 tokens batch size 15 (table 8) epochs: 8000 (from https://github.com/zihangdai/mos/blob/master/main.py)
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
- 3.4 × 10¹⁸ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 35000000 parameters * 2000000 tokens * 8000 epochs = 3.36e+18 FLOP _____________ in the algorithmic progress paper the estimation was 4.37 × 10^17 FLOP based on 1000 epochs assumption
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.
- Why it is tracked
- SOTA improvement
- Record confidence
- Likely
- Citations
- 416
- Benchmark data
- AWD-LSTM-MoS + dynamic evaluation (WT2, 2017)
"Experimental results confirm that the proposed method significantly improves state-of-the-art language models, achieving a perplexity of 55.31 and 62.89 on the test set of Penn Treebank and WikiText-2"
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
What this model is
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017) was published by Carnegie Mellon University (CMU), in the country recorded as United States of America, during November 2017. The publishing organisation is categorised as academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required arithmetic totalling around 3.4 × 10¹⁸ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 2,000,000 tokens of text.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017) — common questions
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017)— how much compute was used to train it?
Training consumed around 3.4 × 10¹⁸ 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.
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017)— 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.
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017)— how many parameters does it have?
It has a parameter count of 35M. 35M (Table 2). 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.
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017)— who created it?
It was published by Carnegie Mellon University (CMU), based in United States of America, an organisation categorised as academia.
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017)— when was it released?
It 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.
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.