CT-MoS + DynamicEval (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
- National Tsing Hua University,Google
- Organisation type
- Academia,Industry
- Country
- Taiwan, United States of America
- 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
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
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
- 36
- Benchmark data
- CT-MoS + DynamicEval (WT2)
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
Background
CT-MoS + DynamicEval (WT2) was published by National Tsing Hua University,Google, in Taiwan, in December 2020. The organisation is categorised as academia,Industry.
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
The training run consumed about 5.4 × 10¹⁷ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
The training set ran to roughly 2,000,000 tokens.
Answers
CT-MoS + DynamicEval (WT2) — common questions
When was CT-MoS + DynamicEval (WT2) released?
CT-MoS + DynamicEval (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 + DynamicEval (WT2) used for?
CT-MoS + DynamicEval (WT2) 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 CT-MoS + DynamicEval (WT2)?
Around 5.4 × 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 CT-MoS + DynamicEval (WT2)?
None. CT-MoS + DynamicEval (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 + DynamicEval (WT2) open source?
No. CT-MoS + DynamicEval (WT2) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does CT-MoS + DynamicEval (WT2) have?
CT-MoS + DynamicEval (WT2) has 45M 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 CT-MoS + DynamicEval (WT2)?
CT-MoS + DynamicEval (WT2) was published by National Tsing Hua University,Google, based in Taiwan, categorised as academia,Industry.
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.