semvec/pypi-packages huggingface dataset:
mllm-shap 0.1.9
Shap implementation for Multi Modal Large Language Models with audio and text on input.
License: MIT License
Requires Python: <3.13,>=3.12
Keywords: shapley-value, llms, mllms, audio, text, xai

# Welcome to MLLM-SHAP

**MLLM-SHAP** is a Python package designed to **interpret the predictions of large language models (LLMs)** using **SHAP (SHapley Additive exPlanations)** values.
It helps you understand the contribution of input features to model outputs, enabling **transparent and explainable AI workflows**.
This work also has companion GUI visualization tools for easier interpretation of results, which is available at the official [shap-mllm-explainer](https://github.com/mvishiu11/shap-mllm-explainer) repository.

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## ✨ Key Features

- Integration with **audio and text models**, supporting multi-modal inputs and outputs.
- Flexible aggregation strategies: *mean*, *sum*, *max*, *min*, etc.
- Multiple similarity metrics (*cosine*, *euclidean*, etc.) for embedding analysis.
- Customizable SHAP calculation algorithms: *exact*, *Monte Carlo approximations*, and more.
- Examples showcasing common explainability pipelines in [`examples/`](https://github.com/Pawlo77/MLLM-Shap/tree/main/examples) on the official GitHub repository.

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## 📊 Visualization & Examples

If you’re interested in GUI visualization of SHAP values, check out the section  **Extension - GUI Visualization** in the docs.

For more advanced CLI usages, refer to:

- The [official GitHub repository examples](https://github.com/Pawlo77/MLLM-Shap/tree/main/examples)
- Or explore more advanced pipelines from exemplary [research projects](https://github.com/Pawlo77/MLLM-Shap/tree/main/experiments)

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## 🤖 Supported LLM Integrations

- [Liquid-Audio](https://github.com/Liquid4All/liquid-audio/)