Upload SAE model weights, config, and training state

README.md

@@ -38,19 +38,19 @@ This model is a Sparse Autoencoder trained for interpretability analysis of robo
 - **Learning Rate**: 0.0001
 - **Batch Size**: 16
 - **L1 Penalty**: 0.3
-- **Epochs**: 20
+- **Epochs**: 15
 - **Optimizer**: adam
 
 ## Usage
 
 ```python
-from src.sae.trainer import load_sae_from_hub
+from physical_ai_interpretability.sae import load_sae_from_hub
 
 # Load model from Hub
 model = load_sae_from_hub("villekuosmanen/drop_footbag_into_dice_tower_ood_sae_success")
 
 # Or load using builder
-from src.sae.builder import SAEBuilder
+from physical_ai_interpretability.sae import SAEBuilder
 builder = SAEBuilder(device='cuda')
 model = builder.load_from_hub("villekuosmanen/drop_footbag_into_dice_tower_ood_sae_success")
 ```
@@ -60,7 +60,7 @@ model = builder.load_from_hub("villekuosmanen/drop_footbag_into_dice_tower_ood_s
 This SAE model can be used for OOD detection with LeRobot policies:
 
 ```python
-from src.ood import OODDetector
+from physical_ai_interpretability.ood import OODDetector
 
 # Create OOD detector with Hub-loaded SAE
 ood_detector = OODDetector(

config.json

@@ -14,7 +14,7 @@
   "block_size": 8,
   "batch_size": 16,
   "learning_rate": 0.0001,
-  "num_epochs": 20,
+  "num_epochs": 15,
   "validation_split": 0.1,
   "l1_penalty": 0.3,
   "optimizer": "adam",

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:bf72baba488a66a15b846c7b7cd6b720afb61c1f80e76c8f7d94fde412d675a3
 size 971496408

training_state.pt

@@ -1,3 +1,3 @@
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+oid sha256:8ba5e2c03dacbbf979d83915e1b57c5b4d685d69cda7f7403bf8e989697edae8
 size 1942998303