# Model Prefrontal Lobotomy

A local research workflow for capturing a language model's internal activations and directly editing its weights to suppress a chosen behavior, without any training step.

Model Prefrontal Lobotomy is a small research sandbox for activation capture and weight ablation on Hugging Face Transformers models, using Qwen3-1.7B as its default source model. It collects last-token hidden states from selected transformer layers for a set of target prompts and a set of control prompts, computes the difference between the two mean activations, and treats that difference as a direction to remove from the model.

For each selected layer, the script directly edits the down-projection weight of that layer's feed-forward network so it emits less of that direction back into the residual stream. This is a direct weight edit rather than training: there is no optimizer, gradient step, or dataset loop involved, and the edited checkpoint is written to a separate output directory so the original model is left untouched. A companion chat script loads either the edited or the original checkpoint for manual comparison.

It requires Python 3.10 or newer and runs on CPU, CUDA, or Apple Silicon's MPS backend.

Website: https://github.com/goldenplums2003/model_brain_surgery