|
--- |
|
license: cc-by-4.0 |
|
library_name: saelens |
|
--- |
|
|
|
⚠️ WARNING: We have small labelling issues, and some SAEs appear twice in this repo. |
|
|
|
# 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-2b-pt-res`? |
|
|
|
- `gemma-scope-`: See 1. |
|
- `2b-pt-`: These SAEs were trained on Gemma v2 2B 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. Which SAE is in the [Neuronpedia demo](https://www.neuronpedia.org/gemma-scope)? |
|
|
|
https://huggingface.co/google/gemma-scope-2b-pt-res/tree/main/layer_20/width_16k/average_l0_71 |
|
|
|
See also 4.: |
|
|
|
# 4. 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-2b-pt-res-canonical", |
|
sae_id = "layer_0/width_16k/canonical", |
|
) |
|
``` |
|
|
|
See https://github.com/jbloomAus/SAELens for details on this library. |
|
|
|
# 5. Point of Contact |
|
|
|
Point of contact: Arthur Conmy |
|
|
|
Contact by email: |
|
|
|
```python |
|
''.join(list('moc.elgoog@ymnoc')[::-1]) |
|
``` |
|
|
|
HuggingFace account: |
|
https://huggingface.co/ArthurConmyGDM |
|
|
|
# 6. Citation |
|
|
|
Paper: https://arxiv.org/abs/2408.05147 |
|
|
