Create README.md

README.md

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+---
+library_name: saelens
+---
+
+SAE panels and SAEBench results from the paper "Are Sparse Autoencoder Benchmarks Reliable?"
+
+This repo is split into 2 panels, a cross-architecture panel consisting of 4 SAEs (K=50 Matryoska, k=100 Matryoshka, k=50 BatchTopK, k=100 BatchTopK),
+and a Matryoshka panel consisting of 4 Matryoshka SAEs verying the number of Matryoshka prefixes from 1 to 4 (n-1, n-2, n-3, n-4). Each SAE in the Matryoshka panel
+is trained 3 times with different seeds (so 12 SAEs total). The cross-architecture panel is trained for 1.5B tokens, while the Matryoshka panel is trained for 300M tokens.
+
+Within each SAE dir, there are a number of snapshots of the SAE taken throughout training. Each of these snapshot dirs include the following:
+
+- SAE weights (`sae_weights.safetensors`) and `cfg.json` for loading with SAELens
+- SAEBench raw result JSON files for all SAEBench metrics
+
+To load an SAE snapshot using SAELens, run the following:
+
+```python
+from sae_lens import SAE
+
+sae = SAE.from_pretrained("decoderesearch/sae-snapshot-panels", "path/to/snapshot")
+```
+
+For instance, to load the SAE snapshot for the K=100 BatchTopK SAE after 500M tokens of training, you would run:
+
+```python
+sae = SAE.from_pretrained(
+  "decoderesearch/sae-snapshot-panels",
+  "cross-arch-panel/gemma-2-2b/batchtopk/k-100/seed-0/snapshots/step-12207-tokens-50000000",
+)
+```