LSTM (WT103)
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
- Facebook AI Research
- Organisation type
- Industry
- Country
- United States of America, France
- Published
- 13 December 2016
- Authors
- Edouard Grave, Armand Joulin, Nicolas Usunier
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.
- Training data
- 103,000,000 tokens
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
- 308
- Benchmark data
- LSTM (WT103)
Sources
Where this record came from and when it was last checked.
- Reference
- Improving Neural Language Models with a Continuous Cache
- Last updated
- 25 May 2026
What the numbers mean
Background
LSTM (WT103) was published by Facebook AI Research, in United States of America, in December 2016. industry is the category the publisher falls under.
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.
How it was trained
Around 103,000,000 tokens went into training it.
Answers
LSTM (WT103) — common questions
When was LSTM (WT103) released?
LSTM (WT103) was published in December 2016. 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 LSTM (WT103) used for?
LSTM (WT103) 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.
What GPU do I need to run LSTM (WT103)?
None. LSTM (WT103) 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 LSTM (WT103) open source?
No. LSTM (WT103) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does LSTM (WT103) have?
No parameter count has been published for LSTM (WT103), which is why no memory or speed figure appears on this page.
Who created LSTM (WT103)?
LSTM (WT103) was published by Facebook AI Research, based in United States of America, categorised as industry.
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.