google-deepmind/penzai

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

1901 stars · 70 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Penzai is a JAX research toolkit for building, editing, and visualizing neural networks as legible, functional pytree data structures. It includes tools for model surgery, interpretability research, activation inspection, and interactive visualization of deeply nested model structures.

Use cases

• reverse-engineer and ablate components of trained neural networks
• inspect and probe internal activations of transformer models
• perform model surgery on pretrained Gemma, Llama, Mistral, or GPT-NeoX models
• visualize deeply nested JAX pytrees and NDArrays in notebooks
• build and train custom neural network architectures declaratively
• debug neural network architectures interactively
• fine-tune large language models with editable structures

When to choose

• you need to modify or analyze trained models rather than just train them
• you want interpretability research tooling for transformer LLMs in JAX
• you prefer models as inspectable, editable pytree data structures
• you need interactive visualization of model internals in IPython or Colab

When to avoid

• you need a production training framework with mature ecosystem like PyTorch
• you want a simple high-level API like Keras for standard workflows
• your project is not based on JAX or the Python ML ecosystem
• you need distributed large-scale training infrastructure out of the box

Facets

library · maturity active

machine-learning deep-learning data-visualization developer-tools machine-learning deep-learning large-language-models developer-tools python jvm jax interpretability model-surgery transformers pytrees neural-networks fine-tuning research-toolkit

Member repositories

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markdown · JSON · MCP: product_card(name="google-deepmind/penzai")