README.md

metadata

title: Induction Circuit Stability Under Fine-Tuning
emoji: 🔬
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 4.37.2
app_file: app.py
pinned: true
python_version: '3.10'
license: mit
short_description: Visualise induction head circuits change under fine-tuning.
tags:
  - mechanistic-interpretability
  - transformers
  - circuits
  - induction-heads
  - fine-tuning
  - safety

Induction Circuit Stability Under Fine-Tuning

Interactive dashboard accompanying the paper Induction Circuit Stability Under Fine-Tuning: A Mechanistic Interpretability Study.

What this does

Induction heads are attention heads that implement the copy-and-complete pattern: if the model has seen A→B before, an induction head at the second A attends back to B and predicts B will follow. This is the primary in-context learning mechanism in small transformers (Olsson et al., 2022).

This dashboard lets you inspect:

• Attention heatmap — which tokens each head attends to on your input text.
• Induction score grid — per-head induction strength (0 = none, 1 = perfect).
• Circuit diagram — which heads are causally verified circuit members.

Toggle between Pre-fine-tuning and Post-fine-tuning states to see how the circuit changes after training on Python code.

Load time

The dashboard loads ONNX models on first run (pre-cached in this Space). Typical cold-start: < 3 seconds on Hugging Face CPU Basic hardware. Inference per input: < 500 ms.

Links

• GitHub: Mattral/mech-interp-induction
• Paper: [ArXiv link TBD]