| | --- |
| | tags: |
| | - creativityneuro |
| | - llm-creativity |
| | - mechanistic-interpretability |
| | base_model: microsoft/Phi-3.5-mini-instruct |
| | license: apache-2.0 |
| | --- |
| | |
| | # phi-3.5-mini-instruct-cn-problem-kr0.2-a1.0-creative |
| |
|
| | This is a **CreativityNeuro (CN)** modified version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct). |
| |
|
| | ## Model Details |
| |
|
| | - **Base Model**: microsoft/Phi-3.5-mini-instruct |
| | - **Modification**: CreativityNeuro weight scaling |
| | - **Prompt Set**: problem |
| | - **Keep Ratio**: 0.2 (top 20.0% of task-specific weights) |
| | - **Alpha**: 1.0 (scaling strength) |
| | - **Mode**: creative |
| |
|
| | ## What is CreativityNeuro? |
| |
|
| | 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/phi-3.5-mini-instruct-cn-problem-kr0.2-a1.0-creative") |
| | tokenizer = AutoTokenizer.from_pretrained("priorcomputers/phi-3.5-mini-instruct-cn-problem-kr0.2-a1.0-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} |
| | } |
| | ``` |
| |
|
