MMLSTM (WT-103)
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
- 75M
- Training data
- 103,000,000 tokens
- Epochs
- 50
Table VII
size of WT 103
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
- 2.3 × 10¹⁸ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 75000000 parameters * 103000000 tokens * 50 epochs [assumption] = 2.3175e+18 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
- Likely
- Citations
- 19
- Benchmark data
- MMLSTM
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 (WT-103) was published by Beijing University of Posts and Telecommunications,University of West London, in China, in December 2019. It comes out of 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 2.3 × 10¹⁸ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Around 103,000,000 tokens went into training it.
Answers
MMLSTM (WT-103) — common questions
Is MMLSTM (WT-103) open source?
No. MMLSTM (WT-103) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does MMLSTM (WT-103) have?
MMLSTM (WT-103) has 75M parameters. Table VII. 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 MMLSTM (WT-103)?
MMLSTM (WT-103) was published by Beijing University of Posts and Telecommunications,University of West London, based in China, categorised as academia,Academia.
When was MMLSTM (WT-103) released?
MMLSTM (WT-103) 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.
What is MMLSTM (WT-103) used for?
MMLSTM (WT-103) 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 MMLSTM (WT-103)?
Around 2.3 × 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.
What GPU do I need to run MMLSTM (WT-103)?
None. MMLSTM (WT-103) 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.
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.