AI models
PeptideBERT
Training compute
4.9×10¹⁶ FLOP
Published
Aug 28, 2023
PeptideBERT is an AI model developed by Carnegie Mellon University (CMU) (United States), first published in August 2023. It works in the biology domain, on tasks such as proteins and protein property prediction.
Training it took an estimated 4.9×10¹⁶ FLOP of compute (estimation method: hardware). It was trained on roughly 4.2M datapoints. Training ran on 1 NVIDIA GeForce GTX 1080 Ti for about 4.07 hours.
Access: Open weights (unrestricted). Its weights are openly available. It is built on top of ProtBERT-UniRef. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Carnegie Mellon University (CMU)
- Country of organization
- United States
- Domain
- Biology
- Task
- Proteins, Protein property prediction
- Training compute
- 4.9×10¹⁶ FLOP
- Compute estimation method
- Hardware
- Dataset size
- 4.2M
- Training hardware
- NVIDIA GeForce GTX 1080 Ti
- Chips used
- 1
- Training time
- 4 h
- Chip-hours
- 4
- Training power draw
- 273 W
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Base model
- ProtBERT-UniRef
- Epoch confidence
- Confident
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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