GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2)
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 of the Negev
- 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
- 38M
- Training data
- 2,000,000 tokens
- Epochs
- 1,000
38M (Table 2)
" The model was trained for 1000 epochs until the validation score stopped improving."
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
- 4.6 × 10¹⁷ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 38000000 parameters * 2000000 tokens * 1000 epochs = 4.56e+17 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.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 4
- Benchmark data
- GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2)
"Our GL-LSTM model overcame the state-of-the-art results with only two layers and 19M parameters, and further improved the state-of-the-art results with the third layer phase"
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
Where it came from
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2) was published by Ben-Gurion University of the Negev, in Israel, in August 2017. academia is the category the publisher falls under.
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.
Training and provenance
The training run consumed about 4.6 × 10¹⁷ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 2,000,000 tokens went into training it.
Its inclusion criterion is sOTA improvement.
Answers
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2) — common questions
What GPU do I need to run GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2)?
None. GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2) 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 (WT2) open source?
No. GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2) 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 (WT2) have?
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2) has 38M parameters. 38M (Table 2). 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 (WT2)?
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2) was published by Ben-Gurion University of the Negev, based in Israel, categorised as academia.
When was GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2) released?
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2) 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 (WT2) used for?
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2) works in Language, and is recorded as handling language modeling. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2)?
Around 4.6 × 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.
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.