AI models
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017)
Training compute
3.4×10¹⁸ FLOP
Parameters
35M
Published
Nov 10, 2017
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017) is an AI model developed by Carnegie Mellon University (CMU) (United States), first published in November 2017. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 3.4×10¹⁸ FLOP of compute (estimation method: operation counting). The model has 35,000,000 parameters. It was trained on roughly 2M datapoints.
Access: Unreleased. Its weights are not openly released. The reference paper has 416 citations. Epoch AI rates the confidence of this record as likely.
Full record
- Organization
- Carnegie Mellon University (CMU)
- Country of organization
- United States
- Domain
- Language
- Task
- Language modeling
- Training compute
- 3.4×10¹⁸ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 35,000,000
- Dataset size
- 2M
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 416
- Epoch confidence
- Likely
More from Carnegie Mellon University (CMU)
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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