Stacked-LSTM+PruningNo
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
- University of Electronic Science and Technology of China,Chinese University of Hong Kong (CUHK),Peng Cheng Laboratory
- Organisation type
- Academia,Academia,Academia
- Country
- China, Hong Kong
- Published
- 17 June 2019
- Authors
- Liangjian Wen, Xuanyang Zhang, Haoli Bai, Zenglin Xu
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
- 6.2M
- Training data
- 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.
- Citations
- 43
- Benchmark data
- Stacked-LSTM+Pruning
Sources
Where this record came from and when it was last checked.
- Reference
- Structured Pruning of Recurrent Neural Networks through Neuron Selection
- Last updated
- 25 May 2026
What the numbers mean
Background
Stacked-LSTM+PruningNo was published by University of Electronic Science and Technology of China,Chinese University of Hong Kong (CUHK),Peng Cheng Laboratory, in China, in June 2019. It comes out of academia,Academia,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.
Answers
Stacked-LSTM+PruningNo — common questions
What GPU do I need to run Stacked-LSTM+PruningNo?
None. Stacked-LSTM+PruningNo 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 Stacked-LSTM+PruningNo open source?
No. Stacked-LSTM+PruningNo has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Stacked-LSTM+PruningNo have?
Stacked-LSTM+PruningNo has 6.2M 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 Stacked-LSTM+PruningNo?
Stacked-LSTM+PruningNo was published by University of Electronic Science and Technology of China,Chinese University of Hong Kong (CUHK),Peng Cheng Laboratory, based in China, categorised as academia,Academia,Academia.
When was Stacked-LSTM+PruningNo released?
Stacked-LSTM+PruningNo was published in June 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 Stacked-LSTM+PruningNo used for?
Stacked-LSTM+PruningNo 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.
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.