MMLSTM (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
- Beijing University of Posts and Telecommunications,University of West London
- Organisation type
- Academia,Academia
- Country
- China, United Kingdom of Great Britain and Northern Ireland
- Published
- 5 December 2019
- Authors
- Kai Shuang, Rui Li, Mengyu Gu, Jonathan Loo, Sen Su
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
- 21.3M
- Training data
- 929,000 tokens
- Epochs
- 500
Table II
size of PTB
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
- 5.8 × 10¹⁶ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 21300000 parameters * 912344 tokens * 500 epochs [assumption] = 5.8298782e+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.
- Why it is tracked
- SOTA improvement
- Record confidence
- Likely
- Citations
- 19
"In experiments, we demonstrate the language model with MMLSTMs surpasses the existing state-of-the-art model on Penn Treebank (PTB) and WikiText-2 (WT2) datasets"
Sources
Where this record came from and when it was last checked.
- Reference
- Major–Minor Long Short-Term Memory for Word-Level Language Model
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
MMLSTM (PTB) was published by Beijing University of Posts and Telecommunications,University of West London, in the country recorded as China, during December 2019. It comes out of an organisation categorised as academia,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Producing it required arithmetic totalling around 5.8 × 10¹⁶ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 929,000 tokens of text.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
MMLSTM (PTB) — common questions
MMLSTM (PTB)— how much compute was used to train it?
Training consumed around 5.8 × 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.
MMLSTM (PTB)— 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.
MMLSTM (PTB)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
MMLSTM (PTB)— how many parameters does it have?
It has a parameter count of 21.3M. Table II. 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.
MMLSTM (PTB)— who created it?
It was published by Beijing University of Posts and Telecommunications,University of West London, based in China, an organisation categorised as academia,Academia.
MMLSTM (PTB)— when was it released?
It was published in December 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.
MMLSTM (PTB)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.