2 个仓库
Libraries providing a suite of glassbox models and post-hoc explanation tools for human-understandable AI.
Distinct from Machine Learning: The primary identity of the project, bridging the gap between model training and explainability.
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Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training inherently transparent models and applying post-hoc explanation techniques to make machine learning predictions human-understandable. The framework distinguishes itself by integrating differential privacy into the training of interpretable models to prevent sensitive data from leaking through explanations. It also features a visualization tool for rendering interactive decision paths and model behavior. The library covers model explainability through feature importance calcu
Provides a collection of glassbox models and post-hoc explanation techniques to make predictions human-understandable.
TransformerLens is a library for mechanistic interpretability research designed to reverse engineer the learned algorithms within large language models. It provides a standardized framework for wrapping diverse transformer architectures, allowing researchers to extract, manipulate, and analyze internal activations and weights through a consistent interface. The project distinguishes itself through a comprehensive system of activation hooks that can capture, patch, and ablate internal tensors during the forward pass. It includes specialized utilities for decomposing fused projections, material
Provides a comprehensive suite of tools for reverse engineering learned algorithms in LLMs through internal activation analysis.