Penzai
A JAX research toolkit for building, editing, and visualizing neural networks as legible, functional pytree data structures.
What is Penzai?
Penzai is a JAX research toolkit that enables building, editing, and visualizing neural networks as legible, functional pytree data structures. It solves the problem of making trained models easily inspectable and modifiable for research tasks like reverse-engineering, ablation studies, and model surgery.
Machine learning researchers and practitioners working on model interpretability, neural network analysis, and architectural experimentation, particularly those using JAX-based workflows.
Developers choose Penzai for its unique focus on post-training model manipulation, its declarative combinator-based approach that exposes full model structure, and its integrated visualization tools that make complex models understandable.
Overview
A JAX research toolkit for building, editing, and visualizing neural networks.
Use Cases
Best For
- Reverse-engineering and ablating components in trained neural networks
- Inspecting and probing internal model activations for interpretability research
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