CT-MoS (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
- Google,National Tsing Hua University
- Organisation type
- Industry,Academia
- Country
- United States of America, Taiwan
- Published
- 25 December 2020
- Authors
- Pei-Hsin Wang, Sheng-Iou Hsieh, Shih-Chieh Chang, Yu-Ting Chen, Jia-Yu Pan, Wei Wei, Da-Chang Juan
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
- 45M
- Training data
- 2,000,000 tokens
- Epochs
- 1,000
45M Table 2
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
- 5.4 × 10¹⁷ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 45000000 parameters * 2000000 tokens * 1000 epochs = 5.4e+17 FLOP
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce GTX 1080 Ti
- Chips used
- 4
- Power draw
- 2.0 kW
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
- 36
- Benchmark data
- CT-MoS (WT2)
"Experimental results confirm that the proposed method significantly improves state-of-the-art language models, achieving a perplexity of 55.31 and 62.89 on the test set of Penn Treebank and WikiText-2"
Sources
Where this record came from and when it was last checked.
- Reference
- Contextual Temperature for Language Modeling
- Last updated
- 25 May 2026
What the numbers mean
What this model is
CT-MoS (WT2) was published by Google,National Tsing Hua University, in United States of America, in December 2020. industry,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.
How it was trained
Producing it required around 5.4 × 10¹⁷ FLOP of arithmetic, on NVIDIA GeForce GTX 1080 Ti, which is a statement about the training budget rather than about inference.
The training set ran to roughly 2,000,000 tokens.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
CT-MoS (WT2) — common questions
How many parameters does CT-MoS (WT2) have?
CT-MoS (WT2) has 45M parameters. 45M 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 CT-MoS (WT2)?
CT-MoS (WT2) was published by Google,National Tsing Hua University, based in United States of America, categorised as industry,Academia.
When was CT-MoS (WT2) released?
CT-MoS (WT2) was published in December 2020. 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 CT-MoS (WT2) used for?
CT-MoS (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 CT-MoS (WT2)?
Around 5.4 × 10¹⁷ FLOP, on NVIDIA GeForce GTX 1080 Ti. 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 CT-MoS (WT2)?
None. CT-MoS (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 CT-MoS (WT2) open source?
No. CT-MoS (WT2) has not had its weights published, so it exists only as a service controlled by its owner.
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.