CreativityNeuro identifies task-specific neurons using Wanda-style importance scoring and selectively upscales weights associated with creative thinking. The modification formula is:

W_new = W × (1 + α × mask)

Where mask identifies weights important for creative tasks but not for routine/associative tasks.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("priorcomputers/llama-3.2-3b-instruct-cn-problem-kr0.01-a0.1-creative")
tokenizer = AutoTokenizer.from_pretrained("priorcomputers/llama-3.2-3b-instruct-cn-problem-kr0.01-a0.1-creative")

# Use like any other model
outputs = model.generate(...)

Citation

If you use this model, please cite:

bibtex

@misc{creativityneuro2025,
  title={CreativityNeuro: Mechanistic Interpretability for LLM Creativity},
  author={Prior Computers},
  year={2025},
  url={https://huggingface.co/priorcomputers}
}

creativityschapiro/llama-3.2-3b-instruct-cn-problem-kr0.01-a0.1-creative

llama-3.2-3b-instruct-cn-problem-kr0.01-a0.1-creative

Model Details

What is CreativityNeuro?

Usage

Citation

creativityschapiro/
llama-3.2-3b-instruct-cn-problem-kr0.01-a0.1-creative