AI models
DiffStk-MRNN
Training compute
2.8×10¹⁴ FLOP
Parameters
1M
Published
Apr 4, 2020
DiffStk-MRNN is an AI model developed by Pennsylvania State University and Rochester Institute of Technology (United States), first published in April 2020. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 2.8×10¹⁴ FLOP of compute (estimation method: operation counting). The model has 1,010,000 parameters. It was trained on roughly 912.3K datapoints.
Access: Unreleased. Its weights are not openly released. The reference paper has 14 citations. Epoch AI rates the confidence of this record as likely.
Full record
- Organization
- Pennsylvania State University, Rochester Institute of Technology
- Country of organization
- United States
- Domain
- Language
- Task
- Language modeling
- Training compute
- 2.8×10¹⁴ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 1,010,000
- Dataset size
- 912.3K
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 14
- Epoch confidence
- Likely
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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