---
license: mit
---

# Gemma 2b Residual Stream SAEs. 

This is a "quick and dirty" SAE release to unblock researchers. These SAEs have not been extensively studied or characterized. 
However, I will try to update the readme here when I add SAEs here to reflect what I know about them. 

These SAEs were trained with [SAE Lens](https://github.com/jbloomAus/SAELens) and the library version is stored in the cfg.json.

All training hyperparameters are specified in cfg.json.

They are loadable using SAE via a few methods. The preferred method is to use the following:

EDIT: This chunk is out of date. Please see SAE Lens tutorials for up to date syntax for loading pretrained SAEs. 
```python
import torch
from transformer_lens import HookedTransformer
from sae_lens import SparseAutoencoder, ActivationsStore

torch.set_grad_enabled(False)
model = HookedTransformer.from_pretrained("gemma-2b")
sparse_autoencoder = SparseAutoencoder.from_pretrained(
  "gemma-2b-res-jb", # to see the list of available releases, go to: https://github.com/jbloomAus/SAELens/blob/main/sae_lens/pretrained_saes.yaml
  "blocks.0.hook_resid_post" # change this to another specific SAE ID in the release if desired. 
)
activation_store = ActivationsStore.from_config(model, sparse_autoencoder.cfg)
```

## Resid Post 0

Stats:
- 16384 Features (expansion factor 8)
- CE Loss score of 99.1% (2.647 without SAE, 2.732 with the SAE)
- Mean L0 54 (in practice L0 is log normal distributed and is heavily right tailed).
- Dead Features: We think this SAE may have ~2.5k dead features. 

Notes:
- This SAE was trained with methods from the Anthropic [April Update](https://transformer-circuits.pub/2024/april-update/index.html#training-saes) excepting activation normalization. 
- It is likely under-trained.


## Resid Post 6

Stats:
- 16384 Features (expansion factor 8) achieving a CE Loss score of
- CE Loss score of 95.33% (2.647 without SAE, 3.103 with the SAE)
- Mean L0 53 (in practice L0 is log normal distributed and is heavily right tailed).
- Dead Features: We think this SAE may have up to 7k dead features. 

Notes:
- This SAE was trained with methods from the Anthropic [April Update](https://transformer-circuits.pub/2024/april-update/index.html#training-saes)
  - Excepting activation normalization. 
  - We increased the learning rate here by one order of magnitude in order to explore whether this resulted in faster training (in particular, a lower L0 more quickly)
    - We find in practice that the drop in L0 is accelerated but this results is significantly more dead features (likely causing worse reconstruction)
- As above, it is likely under-trained.

## Resid Post 12

Stats:
- 16384 Features (expansion factor 8) achieving a CE Loss score of
- CE Loss score of 95.99% (2.563 without SAE, 2.96 with the SAE)
- Mean L0 52 (in practice L0 is log normal distributed and is heavily right tailed).
- Dead Features: Less than 200 dead features. 

Notes:
- This SAE was trained with methods from the Anthropic [April Update](https://transformer-circuits.pub/2024/april-update/index.html#training-saes)
  - **With activation normalization**. This means that activations should be multiplied by a constant such that E(|X|) = sqrt(2048)
- As above, it is likely under-trained.