Compress-LSTM (4.6M)
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
- Samsung R&D Institute Russia,National Research University Higher School of Economics
- Organisation type
- Industry,Academia
- Country
- Russia
- Published
- 6 February 2019
- Authors
- Artem M. Grachev, Dmitry I. Ignatov, Andrey V. Savchenko
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
- 4.6M
- Training data
- 929,000 tokens
- Epochs
- 90
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
- 43
- Benchmark data
- Compress-LSTM (4.6M)
Sources
Where this record came from and when it was last checked.
- Reference
- Compression of Recurrent Neural Networks for Efficient Language Modeling
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Compress-LSTM (4.6M) was published by Samsung R&D Institute Russia,National Research University Higher School of Economics, in Russia, in February 2019. The organisation is categorised as industry,Academia.
It works in Language, and is recorded as doing language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Around 929,000 tokens went into training it.
Answers
Compress-LSTM (4.6M) — common questions
How many parameters does Compress-LSTM (4.6M) have?
Compress-LSTM (4.6M) has 4.6M 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 Compress-LSTM (4.6M)?
Compress-LSTM (4.6M) was published by Samsung R&D Institute Russia,National Research University Higher School of Economics, based in Russia, categorised as industry,Academia.
When was Compress-LSTM (4.6M) released?
Compress-LSTM (4.6M) was published in February 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 Compress-LSTM (4.6M) used for?
Compress-LSTM (4.6M) 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 Compress-LSTM (4.6M)?
None. Compress-LSTM (4.6M) 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 Compress-LSTM (4.6M) open source?
No. Compress-LSTM (4.6M) has not had its weights published, so it exists only as a service controlled by its owner.
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.