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