SHAP implementation for multimodal large language models supporting audio and text input.
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📦 mllm-shap Package
Core engine for multimodal SHAP attribution in text/audio LLM pipelines.
✨ Product Highlights
- exact and approximate SHAP explainers under single API surface
- text/audio-ready connector model with typed abstractions
- configurable normalizers, reducers, similarity backends
- package-level test and docs structure for stable iteration
📊 Package Snapshot
- Main modules:
connectors,shap,utils - Explainability modes: precise + Monte Carlo families + advanced variants
- Target runtime: Python 3.12
💾 Installation
Install from PyPI:
pip install mllm-shap
Install from source:
git clone https://github.com/Pawlo77/MLLM-Shap.git
cd MLLM-Shap/mllm_shap
pip install .
🧱 Package Layout
src/mllm_shap/connectors/- model and chat connectorssrc/mllm_shap/shap/- explainers, attribution internals, result objectssrc/mllm_shap/utils/- utility helperstests/- package verification suitedocs/- documentation sources
🚀 Usage and Ecosystem
- notebook workflows:
../examples/README.md - experiment runner integration:
../experiments/mllm_shapx/README.md - GUI companion for visualization: shap-mllm-explainer
📄 License
MIT License.
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