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README.md
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# CLIP Sparse Autoencoder Checkpoint
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This model is a sparse autoencoder trained on CLIP's internal representations.
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## Model Details
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### Architecture
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- **Layer**: 4
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- **Layer Type**: hook_resid_post
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- **Model**: open-clip:laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K
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- **Dictionary Size**: 49152.0
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- **Input Dimension**: 768.0
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- **Expansion Factor**: 64.0
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- **CLS Token Only**: False
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### Training
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- **Training Images**: 1299936.0000
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- **Learning Rate**: 0.0002
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- **L1 Coefficient**: 0.0000
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- **Batch Size**: 4096.0
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- **Context Size**: 49.0
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## Performance Metrics
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### Sparsity
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- **L0 (Active Features)**: 965.4101
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- **Dead Features**: 0.0000
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- **Mean Passes Since Fired**: 2.1601
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### Reconstruction
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- **Explained Variance**: 0.9987
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- **Explained Variance Std**: 0.0032
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- **MSE Loss**: 0.0000
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- **L1 Loss**: 1456.6568
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- **Overall Loss**: 0.0001
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## Training Details
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- **Training Duration**: 3927 seconds
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- **Final Learning Rate**: 0.0000
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- **Warm Up Steps**: 200.0
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- **Gradient Clipping**: 1.0
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## Additional Information
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- **Original Checkpoint Path**: /network/scratch/p/praneet.suresh/imgnet_checkpoints/c8de8491-tinyclip_sae_16_hyperparam_sweep_lr/n_images_1300020.pt
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- **Wandb Run**: https://wandb.ai/perceptual-alignment/vanilla-imagenet-spatial_only-sweep/runs/7dfdq3cz
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- **Random Seed**: 42.0
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