# CLIP Sparse Autoencoder Checkpoint

This model is a sparse autoencoder trained on CLIP's internal representations.

## Model Details

### Architecture
- **Layer**: 4
- **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**: 0.0002
- **L1 Coefficient**: 0.0000
- **Batch Size**: 4096.0
- **Context Size**: 49.0

## Performance Metrics

### Sparsity
- **L0 (Active Features)**: 965.4101
- **Dead Features**: 0.0000

- **Mean Passes Since Fired**: 2.1601

### Reconstruction
- **Explained Variance**: 0.9987
- **Explained Variance Std**: 0.0032
- **MSE Loss**: 0.0000
- **L1 Loss**: 1456.6568
- **Overall Loss**: 0.0001

## Training Details
- **Training Duration**: 3927 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/c8de8491-tinyclip_sae_16_hyperparam_sweep_lr/n_images_1300020.pt
- **Wandb Run**: https://wandb.ai/perceptual-alignment/vanilla-imagenet-spatial_only-sweep/runs/7dfdq3cz
- **Random Seed**: 42.0