Godseye1311/DecayFormer
AI model by Godseye1311
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Trained weights of DecayFormer, a compact hybrid state space / attention model that forecasts a battery cell's full state of health (SOH) trajectory from its first 100 cycles, for all 22 aging-condition-exclusive folds of the BatteryLife benchmark as defined by BatteryMFormer. One model per fold; 791,529 parameters. Code, reproducibility notebooks and documentation: the DecayFormer GitHub repository. Test MAPE (%) from BatteryMFormer's released evaluation (evaluatemodel.py, fp16), on each fold's own test set. Mean ± std across folds: All 22 models share one configuration: dmodel 96, 3 encoder blocks, 4 attention heads, dropout 0.1; AdamW at learning rate 5e-4 with a constant schedule; batch 128 (one GPU, equal to BatteryMFormer's 2 x 64 recipe); up to 300 epochs with early stopping on validation MAPE; fp16; each fold's own…
Read the full model card ↗ · Preview checked 2026-11-13T08:11:30.789Z
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Model overview
Godseye1311/DecayFormer is a task-unspecified model repository published by Godseye1311 on Hugging Face. The source reports the pytorch library.
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Model facts
- Task
- Not reported
- Library
- pytorch
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
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- Reported safetensors parameters
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- Architecture
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- License
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- Access
- Not gated by Hugging Face
- Created
- 2026-11-13T06:43:13.000Z
- Last modified
- 2026-11-13T06:43:15.000Z
Compatibility and lineage
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-13T06:57:58.356Z. Daily imports are snapshots, not real-time monitoring.
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