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README.md

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+---
+language: en
+tags:
+- clip
+- vision
+- transformers
+- interpretability
+- sparse autoencoder
+- sae
+- mechanistic interpretability
+license: apache-2.0
+library_name: torch
+pipeline_tag: feature-extraction
+metrics:
+- type: explained_variance 
+  value: 85.0
+  pretty_name: Explained Variance %
+  range:
+    min: 0
+    max: 100
+- type: l0
+  value: 78.387
+  pretty_name: L0 
+---
+
+# CLIP-B-32 Sparse Autoencoder x64 vanilla - L1:8e-05
+
+![Explained Variance](https://img.shields.io/badge/Explained%20Variance-85.0%25-blue)
+![Sparsity](https://img.shields.io/badge/Active%20Features-7838.7%-green)
+
+### Training Details
+
+- Base Model: CLIP-ViT-B-32 (LAION DataComp.XL-s13B-b90K)
+- Layer: 10
+- Component: hook_mlp_out
+
+### Model Architecture
+
+- Input Dimension: 768
+- SAE Dimension: 49,152
+- Expansion Factor: x64 (vanilla architecture)
+- Activation Function: ReLU
+- Initialization: encoder_transpose_decoder
+- Context Size: 50 tokens
+
+### Performance Metrics
+
+- L1 Coefficient: 8e-05
+- L0 Sparsity: 78.3869
+- Explained Variance: 0.8497 (84.97%)
+
+### Training Configuration
+
+- Learning Rate: 0.0004
+- LR Scheduler: Cosine Annealing with Warmup (200 steps)
+- Epochs: 10
+- Gradient Clipping: 1.0
+- Device: NVIDIA Quadro RTX 8000
+
+**Experiment Tracking:**
+- Weights & Biases Run ID: mgwvh02m
+- Full experiment details: https://wandb.ai/perceptual-alignment/clip/runs/mgwvh02m/overview
+- Git Commit: e22dd02726b74a054a779a4805b96059d83244aa
+
+## Citation
+
+```bibtex
+@misc{2024josephsparseautoencoders,
+    title={Sparse Autoencoders for CLIP-ViT-B-32},
+    author={Joseph, Sonia},
+    year={2024},
+    publisher={Prisma-Multimodal},
+    url={https://huggingface.co/Prisma-Multimodal},
+    note={Layer 10, hook_mlp_out, Run ID: mgwvh02m}
+}