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  GitHub - PaulPauls/llama3_interpretability_sae: A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible.

  • 7 users
  • github.com/PaulPauls
  • テクノロジー
  • 2024/11/22

  Modern LLMs encode concepts by superimposing multiple features into the same neurons and then interpeting them by taking into account the linear superposition of all neurons in a layer. This concept of giving each neuron multiple interpretable meanings they activate depending on the context of other neuron activations is called superposition. Sparse Autoencoders (SAEs) are models that are inserted

  
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  GitHub - MAIF/shapash: 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

  • 5 users
  • github.com/MAIF
  • テクノロジー
  • 2021/03/10

  Shapash is a Python library designed to make machine learning interpretable and comprehensible for everyone. It offers various visualizations with clear and explicit labels that are easily understood by all. With Shapash, you can generate a Webapp that simplifies the comprehension of interactions between the model's features, and allows seamless navigation between local and global explainability.

  • 機械学習
  • Python

  
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  Learning Interpretability Tool

  • 5 users
  • pair-code.github.io
  • テクノロジー
  • 2021/04/10

  The Learning Interpretability Tool (🔥LIT) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. The Learning Interpretability Tool (🔥LIT) is for researchers and practitioners looking to understand NLP model behavior through a visual, interactive, and extensible tool. Use LIT to ask and answer questions like: What kind of examples does my model perform

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