

Transformer Debugger (TDB)
Overview :
Transformer Debugger combines automated explainability and sparse autoencoding techniques, allowing for rapid exploration before writing code and enabling intervention in the forward pass to observe its impact on specific behaviors. It identifies and explains the activation reasons of specific components (neurons, attention heads, autoencoder latent representations) within the model, showcasing automatically generated explanations for why these components are strongly activated, and tracks connections between components to help discover circuits.
Target Users :
Used by researchers and developers to investigate and understand the behavior of language models, as well as debug and optimize them.
Use Cases
Use TDB to investigate why a model outputs specific words in response to a particular prompt
Explore why attention heads focus on specific words
Understand the activation patterns of neurons in the model using TDB
Features
Automate the explanation of small language model behavior
Intervene in the forward pass to observe changes in model behavior
Identify and explain the activation reasons of specific components in the model
Track connections between components to discover circuits within the model
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