Densely Connected LSTM + Var. Dropout
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
- Ghent University
- Organisation type
- Academia
- Country
- Belgium
- Published
- 19 July 2017
- Authors
- Fréderic Godin, Joni Dambre, Wesley De Neve
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
- 23M
- Training data
- 929,000 tokens
- Epochs
- 100
23M (Table 1)
"We trained for 100 epochs and used early stopping."
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
- 1.3 × 10¹⁶ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 23000000 parameters * 929000 tokens * 100 epochs = 1.28202e+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.
- Record confidence
- Confident
- Citations
- 7
- Benchmark data
- Densely Connected LSTM + Var. Dropout
Sources
Where this record came from and when it was last checked.
- Reference
- Improving Language Modeling using Densely Connected Recurrent Neural Networks
- Last updated
- 28 November 2025
What the numbers mean
Background
Densely Connected LSTM + Var. Dropout was published by Ghent University, in the country recorded as Belgium, during July 2017. It comes out of an organisation categorised as 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
The training run consumed about 1.3 × 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.
Answers
Densely Connected LSTM + Var. Dropout — common questions
Densely Connected LSTM + Var. Dropout— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Densely Connected LSTM + Var. Dropout— how many parameters does it have?
It has a parameter count of 23M. 23M (Table 1). 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.
Densely Connected LSTM + Var. Dropout— who created it?
It was published by Ghent University, based in Belgium, an organisation categorised as academia.
Densely Connected LSTM + Var. Dropout— when was it released?
It was published in July 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Densely Connected LSTM + Var. Dropout— 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.
Densely Connected LSTM + Var. Dropout— how much compute was used to train it?
Training consumed around 1.3 × 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.
Densely Connected LSTM + Var. Dropout— 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.
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.