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CLIP Sparse Autoencoder Checkpoint
This model is a sparse autoencoder trained on CLIP's internal representations.
Model Details
Architecture
- Layer: 9
- Layer Type: hook_resid_post
- Model: open-clip:laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K
- Dictionary Size: 49152.0
- Input Dimension: 768.0
- Expansion Factor: 64.0
- CLS Token Only: False
Training
- Training Images: 1299936.0000
- Learning Rate: 0.0108
- L1 Coefficient: 0.0000
- Batch Size: 4096.0
- Context Size: 49.0
Performance Metrics
Sparsity
L0 (Active Features): 854.8915
Dead Features: 0.0000
Mean Passes Since Fired: 305.1165
Reconstruction
- Explained Variance: 1.0000
- Explained Variance Std: 0.0000
- MSE Loss: 0.0000
- L1 Loss: 426.1071
- Overall Loss: 0.0000
Training Details
- Training Duration: 3993 seconds
- Final Learning Rate: 0.0000
- Warm Up Steps: 200.0
- Gradient Clipping: 1.0
Additional Information
- Original Checkpoint Path: /network/scratch/p/praneet.suresh/imgnet_checkpoints/30fae0c6-tinyclip_sae_16_hyperparam_sweep_lr/n_images_1300020.pt
- Wandb Run: https://wandb.ai/perceptual-alignment/vanilla-imagenet-spatial_only-sweep/runs/ofxu3mk8
- Random Seed: 42.0
- Downloads last month
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