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---
license: cc-by-4.0
library_name: saelens
---
# 1. Gemma Scope
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.
See our [landing page](https://huggingface.co./google/gemma-scope) for details on the whole suite. This is a specific set of SAEs:
# 2. What Is `gemma-scope-9b-pt-res`?
- `gemma-scope-`: See 1.
- `9b-pt-`: These SAEs were trained on Gemma v2 9B base model.
- `res`: These SAEs were trained on the model's residual stream.
- We include experimental SAEs trained on token embeddings in the ./embedding folder.
# 3. How can I use these SAEs straight away?
```python
from sae_lens import SAE # pip install sae-lens
sae, cfg_dict, sparsity = SAE.from_pretrained(
release = "gemma-scope-9b-pt-res-canonical",
sae_id = "layer_0/width_16k/canonical",
)
```
See https://github.com/jbloomAus/SAELens for details on this library.
# 4. Point of Contact
Point of contact: Arthur Conmy
Contact by email:
```python
''.join(list('moc.elgoog@ymnoc')[::-1])
```
HuggingFace account:
https://huggingface.co./ArthurConmyGDM
# 5. Citation
Paper: https://arxiv.org/abs/2408.05147
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