anthropics/jacobian-lens

Health v2 · maintenance only

Flags: no_releases young

Adoption not part of the score

1836 stars · 261 forks observed · 2026-08-28

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

A Python library implementing the Jacobian lens, an interpretability technique that linearly transports residual-stream activations at any layer into the final-layer basis and decodes them into ranked vocabulary tokens. It is the reference implementation accompanying the 'Verbalizable Representations Form a Global Workspace in Language Models' paper.

Use cases

• inspect what an internal LLM activation is disposed to make the model say
• visualize layer-by-layer token readouts across positions in a prompt
• fit a Jacobian lens on any HuggingFace decoder transformer
• apply a pre-fitted lens to probe model internals
• reproduce results from the global workspace interpretability paper
• study how verbalizable representations evolve across transformer layers

When to choose

• you want to interpret internal activations of open-weights decoder LLMs
• you need a reference implementation of the Jacobian lens technique
• you want to reproduce or extend the global workspace paper's analysis

When to avoid

• you need a maintained, production-supported tool (it is explicitly not maintained and not accepting contributions)
• you need optimized or fast fitting (the implementation is a non-optimized reference)
• you work with non-HuggingFace model formats without adaptation

Facets

library · maturity maintenance

machine-learning nlp developer-tools machine-learning deep-learning large-language-models python cross-platform interpretability mechanistic-interpretability jacobian lens transformers residual-stream global-workspace reference-implementation research-code natural-language-processing research gpu

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