AI models
Tensor-Transformer(1core)+PN (WT103)
Training compute
1.6×10¹⁸ FLOP
Parameters
85.3M
Published
Mar 17, 2020
Tensor-Transformer(1core)+PN (WT103) is an AI model developed by University of California (UC) Berkeley (United States), first published in March 2020. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 1.6×10¹⁸ FLOP of compute (estimation method: operation counting). The model has 85,300,000 parameters. It was trained on roughly 103M datapoints.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 60 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- University of California (UC) Berkeley
- Country of organization
- United States
- Domain
- Language
- Task
- Language modeling
- Training compute
- 1.6×10¹⁸ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 85,300,000
- Dataset size
- 103M
- Numerical format
- FP32
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 60
- Epoch confidence
- Confident
More from University of California (UC) Berkeley
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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