Instructions to use itsalissonsilva/Restriction-Learning-Experiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use itsalissonsilva/Restriction-Learning-Experiment with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-1b-it") model = PeftModel.from_pretrained(base_model, "itsalissonsilva/Restriction-Learning-Experiment") - Notebooks
- Google Colab
- Kaggle
File size: 427 Bytes
9fbaf4c | 1 2 3 4 | # Superseded patching capture
The patching rows in results.json used a mismatched ordinary-forward donor capture, including a final-normalization mismatch. They are retained for provenance, not valid causal evidence. Use ../interpretability_patchfix/patching.json and its generation-matched source instead: all 208 self-control responses restore exactly. The steering/probe portions of results.json are separate measurements.
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