---
license: mit
---

OpenAI's GPT2-Small SAEs reformatted for easy loading from SAE Lens. 

Links
- [Paper](https://cdn.openai.com/papers/sparse-autoencoders.pdf)
- [Original File Loading](https://github.com/openai/sparse_autoencoder/blob/lg-training/sparse_autoencoder/paths.py)

```python
import torch
from transformer_lens import HookedTransformer
from sae_lens import SAE, ActivationsStore

torch.set_grad_enabled(False)
model = HookedTransformer.from_pretrained("gpt2-small")
sae, cfg, sparsity  = SAE.from_pretrained(
  "gpt2-small-resid-post-v5-32k", # to see the list of available releases, go to: https://github.com/jbloomAus/SAELens/blob/main/sae_lens/pretrained_saes.yaml
  "blocks.11.hook_resid_post" # change this to another specific SAE ID in the release if desired. 
)

# For loading activations or tokens from the training dataset.
activation_store = ActivationsStore.from_sae(
    model=model,
    sae=sae,
    streaming=True,
    # fairly conservative parameters here so can use same for larger
    # models without running out of memory.
    store_batch_size_prompts=8,
    train_batch_size_tokens=4096,
    n_batches_in_buffer=4,
    device=device,
)

```