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--- |
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license: cc-by-4.0 |
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library_name: saelens |
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--- |
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# 1. Gemma Scope |
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Gemma Scope is a comprehensive, open suite of sparse autoencoders for Gemma 2 9B and 2B. Sparse Autoencoders are a "microscope" of sorts that can help us break down a model’s internal activations into the underlying concepts, just as biologists use microscopes to study the individual cells of plants and animals. |
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See our [landing page](https://huggingface.co/google/gemma-scope) for details on the whole suite. This is a specific set of SAEs: |
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# 2. What Is `gemma-scope-9b-pt-res`? |
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- `gemma-scope-`: See 1. |
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- `9b-pt-`: These SAEs were trained on Gemma v2 9B base model. |
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- `res`: These SAEs were trained on the model's residual stream. |
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- We include experimental SAEs trained on token embeddings in the ./embedding folder. |
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# 3. How can I use these SAEs straight away? |
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```python |
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from sae_lens import SAE # pip install sae-lens |
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sae, cfg_dict, sparsity = SAE.from_pretrained( |
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release = "gemma-scope-9b-pt-res-canonical", |
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sae_id = "layer_0/width_16k/canonical", |
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) |
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``` |
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See https://github.com/jbloomAus/SAELens for details on this library. |
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# 4. Point of Contact |
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Point of contact: Arthur Conmy |
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Contact by email: |
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```python |
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''.join(list('moc.elgoog@ymnoc')[::-1]) |
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``` |
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HuggingFace account: |
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https://huggingface.co/ArthurConmyGDM |
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# 5. Citation |
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Paper: https://arxiv.org/abs/2408.05147 |
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