---
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