CT-MoS (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
- 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
- 24M
- Training data
- 929,000 tokens
- Epochs
- 1,000
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
- Chips used
- 1
- Power draw
- 201 W
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 (PTB)
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
Where it came from
CT-MoS (PTB) 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.
Training and provenance
The training set ran to roughly 929,000 tokens.
Answers
CT-MoS (PTB) — common questions
Who created CT-MoS (PTB)?
CT-MoS (PTB) was published by National Tsing Hua University,Google, based in Taiwan, categorised as academia,Industry.
When was CT-MoS (PTB) released?
CT-MoS (PTB) 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 (PTB) used for?
CT-MoS (PTB) works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run CT-MoS (PTB)?
None. CT-MoS (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 CT-MoS (PTB) open source?
No. CT-MoS (PTB) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does CT-MoS (PTB) have?
CT-MoS (PTB) has 24M 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.
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.