QRNN

Training compute

6.9×10¹⁷ FLOP

Parameters

135M

Published

Feb 1, 2018

QRNN is an AI model developed by Salesforce Research (United States), first published in February 2018. It works in the language domain, on tasks such as language modeling.

Training it took an estimated 6.9×10¹⁷ FLOP of compute (estimation method: operation counting,hardware). The model has 135,000,000 parameters. It was trained on roughly 103M datapoints. Training ran on NVIDIA V100 for about 12 hours.

Access: Unreleased. Its weights are not openly released. The reference paper has 5 citations. Epoch AI rates the confidence of this record as likely.

Full record

Organization: Salesforce Research
Country of organization: United States
Domain: Language
Task: Language modeling
Training compute: 6.9×10¹⁷ FLOP
Compute estimation method: Operation counting, Hardware
Parameters: 135,000,000
Dataset size: 103M
Training hardware: NVIDIA V100
Training time: 12 h
Numerical format: FP32
Model accessibility: Unreleased
Open weights: No
Citations: 5
Epoch confidence: Likely