TF-LM-discourse LSTM (WT2)
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- ESAT - PSI
- Organisation type
- Academia
- Country
- Belgium
- Published
- 1 May 2018
- Authors
- Lyan Verwimp, Hugo Van hamme, Patrick Wambacq
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.
- Training data
- tokens
- Epochs
- 39
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
code and weights, MIT license: https://github.com/lverwimp/tf-lm
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 12
- Benchmark data
- TF-LM-discourse LSTM (WT2)
Sources
Where this record came from and when it was last checked.
- Reference
- TF-LM: TensorFlow-based Language Modeling Toolkit
- Last updated
- 11 February 2026
What the numbers mean
About this model
TF-LM-discourse LSTM (WT2) was published by ESAT - PSI, in Belgium, in May 2018. It comes out of academia.
It works in Language, and is recorded as doing language modeling.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.
Answers
TF-LM-discourse LSTM (WT2) — common questions
What GPU do I need to run TF-LM-discourse LSTM (WT2)?
We cannot say. TF-LM-discourse LSTM (WT2) has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is TF-LM-discourse LSTM (WT2) open source?
Its weights are published, so TF-LM-discourse LSTM (WT2) can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does TF-LM-discourse LSTM (WT2) have?
No parameter count has been published for TF-LM-discourse LSTM (WT2), which is why no memory or speed figure appears on this page.
Who created TF-LM-discourse LSTM (WT2)?
TF-LM-discourse LSTM (WT2) was published by ESAT - PSI, based in Belgium, categorised as academia.
When was TF-LM-discourse LSTM (WT2) released?
TF-LM-discourse LSTM (WT2) was published in May 2018. 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 TF-LM-discourse LSTM (WT2) used for?
TF-LM-discourse LSTM (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.
Where can I download TF-LM-discourse LSTM (WT2)?
The weights for TF-LM-discourse LSTM (WT2) are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
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.