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README.md

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+---
+library_name: sae_lens
+tags:
+  - sparse-autoencoder
+  - mechanistic-interpretability
+  - sae
+---
+
+# Sparse Autoencoders for Unknown
+
+This repository contains 1 Sparse Autoencoder(s) (SAE) trained using [SAELens](https://github.com/jbloomAus/SAELens).
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| **Base Model** | `Unknown` |
+| **Architecture** | `topk` |
+| **Input Dimension** | 3584 |
+| **SAE Dimension** | 16384 |
+| **Training Dataset** | `Unknown` |
+
+## Available Hook Points
+
+| Hook Point |
+|------------|
+| `sae` |
+
+## Usage
+
+```python
+from sae_lens import SAE
+
+# Load an SAE for a specific hook point
+sae, cfg_dict, sparsity = SAE.from_pretrained(
+    release="rufimelo/secure_code_qwen_coder_topk_cl_16384",
+    sae_id="sae"  # Choose from available hook points above
+)
+
+# Use with TransformerLens
+from transformer_lens import HookedTransformer
+
+model = HookedTransformer.from_pretrained("Unknown")
+
+# Get activations and encode
+_, cache = model.run_with_cache("your text here")
+activations = cache["sae"]
+features = sae.encode(activations)
+```
+
+## Files
+
+- `sae/cfg.json` - SAE configuration
+- `sae/sae_weights.safetensors` - Model weights
+- `sae/sparsity.safetensors` - Feature sparsity statistics
+
+## Training
+
+These SAEs were trained with SAELens version 6.26.2.

sae/cfg.json

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+{"d_in": 3584, "d_sae": 16384, "dtype": "float32", "device": "cuda", "apply_b_dec_to_input": true, "normalize_activations": "none", "reshape_activations": "none", "metadata": {"sae_lens_version": "6.26.2", "sae_lens_training_version": "6.26.2"}, "decoder_init_norm": 0.1, "k": 64, "use_sparse_activations": false, "aux_loss_coefficient": 1.0, "rescale_acts_by_decoder_norm": true, "contrastive_weight": 0.1, "contrastive_temperature": 0.07, "contrastive_mode": "infonce", "triplet_margin": 1.0, "use_feature_contrastive": true, "architecture": "topk", "hook_name": "blocks.0.hook_resid_post"}

sae/sae_weights.safetensors

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+size 469842240

sae/sparsity.safetensors

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