AI models
CT-MoS + DynamicEval (WT2)
Training compute
5.4×10¹⁷ FLOP
Parameters
45M
Published
Dec 25, 2020
CT-MoS + DynamicEval (WT2) is an AI model developed by National Tsing Hua University and Google (Taiwan and United States), first published in December 2020. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 5.4×10¹⁷ FLOP of compute (estimation method: operation counting). The model has 45,000,000 parameters. It was trained on roughly 2M datapoints.
Access: Unreleased. Its weights are not openly released. The reference paper has 36 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- National Tsing Hua University, Google
- Country of organization
- Taiwan, United States
- Domain
- Language
- Task
- Language modeling
- Training compute
- 5.4×10¹⁷ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 45,000,000
- Dataset size
- 2M
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 36
- Epoch confidence
- Confident
More from National Tsing Hua University,Google
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
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