Instructions to use chanind/qwen2.5-7B-it-layer-20-saes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SAELens
How to use chanind/qwen2.5-7B-it-layer-20-saes with SAELens:
# pip install sae-lens from sae_lens import SAE sae, cfg_dict, sparsity = SAE.from_pretrained( release = "RELEASE_ID", # e.g., "gpt2-small-res-jb". See other options in https://github.com/jbloomAus/SAELens/blob/main/sae_lens/pretrained_saes.yaml sae_id = "SAE_ID", # e.g., "blocks.8.hook_resid_pre". Won't always be a hook point ) - Notebooks
- Google Colab
- Kaggle
SAEs for use with the SAELens library
This repository contains the following SAEs:
- pile/matryoshka/k-100
- lmsys/matryoshka/k-100
The pile SAE is trained on the monology/pile-uncopyrighted dataset without any chat formatting
The lmsys SAE is trained on the lmsys/lmsys-chat-1m dataset with chat formatting
Load these SAEs using SAELens as below:
from sae_lens import SAE
sae = SAE.from_pretrained("chanind/qwen2.5-7B-it-layer-20-saes", "<sae_id>")
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support