File size: 1,974 Bytes
016b363 f8afffd 068c017 016b363 617c6ee 2159c7f 67b2a69 016b363 bd3fe55 67b2a69 bd3fe55 67b2a69 884074a 016b363 bd3fe55 016b363 0127b34 016b363 db1f4e0 63d9867 389d928 63d9867 016b363 bd3fe55 016b363 2232397 63d9867 2232397 0127b34 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
---
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",
)
```
This uses **canonical** SAEs, those with average L0 closest to 100, which we expect to be reasonably useful for most tasks. The exact defined here is determined by this file in the SAELens repo, snappshotted on 22nd October 2024: https://github.com/jbloomAus/SAELens/blob/a470460/sae_lens/pretrained_saes.yaml#L1667
See https://github.com/jbloomAus/SAELens for more 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
|