GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB)
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
- Ben-Gurion University
- Organisation type
- Academia
- Country
- Israel
- Published
- 29 August 2017
- Authors
- Ziv Aharoni, Gal Rattner, Haim Permuter
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
- 26M
- Training data
- 929,000 tokens
- Epochs
- 1,000
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
- 1.5 × 10¹⁷ FLOP
6*26000000*929000*1000=1.44924e+17
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
- Benchmark data
- GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB)
Sources
Where this record came from and when it was last checked.
- Reference
- Gradual Learning of Recurrent Neural Networks
- Last updated
- 11 February 2026
What the numbers mean
What this model is
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB) was published by Ben-Gurion University, in Israel, in August 2017. It comes out of 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
Producing it required around 1.5 × 10¹⁷ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
The training set ran to roughly 929,000 tokens.
Answers
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB) — common questions
When was GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB) released?
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB) was published in August 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.
What is GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB) used for?
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB) works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB)?
Around 1.5 × 10¹⁷ 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 GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB)?
None. GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB) 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 GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB) open source?
No. GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB) have?
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB) has 26M 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 GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB)?
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB) was published by Ben-Gurion University, based in Israel, categorised as academia.
The other direction
Looking at it from the other side?
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