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---
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. A method that currently works (but may be replaced shortly by a more convenient method) would be the following:
```python
import torch
from sae_lens.training.session_loader import LMSparseAutoencoderSessionloader
torch.set_grad_enabled(False)
path = "path/to/folder_containing_cfgjson_and_safetensors_file"
model, sae, activation_store = LMSparseAutoencoderSessionloader.load_pretrained_sae(
path, device = "cuda",
)
```
## 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. |