from src.sae.trainer import load_sae_from_hub

# Load model from Hub
model = load_sae_from_hub("villekuosmanen/box_flip_franka_mini_ood_sae")

# Or load using builder
from src.sae.builder import SAEBuilder
builder = SAEBuilder(device='cuda')
model = builder.load_from_hub("villekuosmanen/box_flip_franka_mini_ood_sae")

Out-of-Distribution Detection

This SAE model can be used for OOD detection with LeRobot policies:

python

from src.ood import OODDetector

# Create OOD detector with Hub-loaded SAE
ood_detector = OODDetector(
    policy=your_policy,
    sae_hub_repo_id="villekuosmanen/box_flip_franka_mini_ood_sae"
)

# Fit threshold and use for detection
ood_detector.fit_ood_threshold_to_validation_dataset(validation_dataset)
is_ood, error = ood_detector.is_out_of_distribution(observation)

Files

model.safetensors: The trained SAE model weights
config.json: Training and model configuration
training_state.pt: Complete training state (optimizer, scheduler, metrics)
ood_params.json: OOD detection parameters (if fitted)

Citation

If you use this model in your research, please cite:

bibtex

@misc{sae_model,
  title={Sparse Autoencoder for Mh2 Step 1 Flip},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/villekuosmanen/box_flip_franka_mini_ood_sae}
}

Framework

This model was trained using the physical-ai-interpretability framework with LeRobot.