Towards Isolated Interventions via Almost Orthogonal Features in Language Models
Paper • 2602.04718 • Published
How to use moritzmiller/sae-icm-final-checkpoint-llama-3.2-1b-1e-04 with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")
model = PeftModel.from_pretrained(base_model, "moritzmiller/sae-icm-final-checkpoint-llama-3.2-1b-1e-04")Stage-2 artifacts for the paper Towards Isolated Interventions via Almost Orthogonal Features in Language Models (arXiv:2602.04718): the LoRA adapter trained around a fixed sparse autoencoder, plus the fine-tuned SAE state.
meta-llama/Llama-3.2-1B1e-04adapter_model.safetensors, adapter_config.json, sae_state.safetensors (keys W_enc.weight, W_enc.bias, W_dec.weight, W_dec.bias)Load with src/poet/load_hub.py from https://github.com/mrtzmllr/sae-icm.
Base model
meta-llama/Llama-3.2-1B