AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB)

Training compute

4.9×10¹⁶ FLOP

Parameters

22M

Published

May 3, 2018

AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB) is an AI model developed by Columbia University, New York University (NYU) and Princeton University (United States), first published in May 2018. It works in the language domain, on tasks such as language modeling.

Training it took an estimated 4.9×10¹⁶ FLOP of compute (estimation method: operation counting). The model has 22,000,000 parameters. It was trained on roughly 929K datapoints.

Access: Unreleased. Its weights are not openly released. The reference paper has 27 citations. Epoch AI rates the confidence of this record as confident.

Full record

Organization: Columbia University, New York University (NYU), Princeton University
Country of organization: United States
Domain: Language
Task: Language modeling
Training compute: 4.9×10¹⁶ FLOP
Compute estimation method: Operation counting
Parameters: 22,000,000
Dataset size: 929K
Model accessibility: Unreleased
Open weights: No
Citations: 27
Epoch confidence: Confident

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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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