metadata
library_name: pytorch
datasets:
- marplan6/batgrad
tags:
- battery
- timeseries
- foundation-models
- template
license: mit
batgrad model
A checkpoint for exploring the
batgrad training and inference workflow.
Download it with:
uv run scripts/hf_assets.py download --ckpt init_baseline
The checkpoint is written to
outputs/checkpoints/init_baseline.pt. It contains weights for
the hybrid attention/Mamba-3 model, the complete experiment configuration,
training step, checkpoint format version, and creating batgrad Git commit.
Load it with batgrad.ml.checkpoint.load_checkpoint(). Mamba-3 checkpoints
require Linux and CUDA. The checkpoint can be evaluated on any compatible
normalized batgrad dataset. (~10M parameters, ~4GB VRAM inference)