We release bigram subnetworks as described in Chang and Bergen (2025). These are sparse subsets of model parameters that recreate bigram predictions (next token predictions conditioned only on the current token) in Transformer language models. This repository contains the bigram subnetwork for openai-community/gpt2.

A subnetwork file is a pickled Python dictionary that maps the original model parameter names to numpy binary masks with the same shapes as the original model parameters (1: keep, 0: drop). For details on usage, see: https://github.com/tylerachang/bigram-subnetworks. For details on how these subnetworks were trained, see Chang and Bergen (2025).

For minimal usage, download the code at https://github.com/tylerachang/bigram-subnetworks (or just the file circuit_loading_utils.py) and run in Python:

from circuit_loading_utils import load_bigram_subnetwork_dict, load_subnetwork_model
mask_dict = load_bigram_subnetwork_dict('openai-community/gpt2')
model, tokenizer, config = load_subnetwork_model('openai-community/gpt2', mask_dict)