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CircuitKit

CircuitKit

Discover, evaluate, and intervene on circuits in transformer models.
One call takes a model + task to a discovered circuit, a 6-pillar faithfulness score, and a intervened HuggingFace checkpoint.

Python 3.10+ PyTorch 2.0+ License: LSAL v1.2 (source-available) Docs


CircuitKit is a framework for mechanistic interpretability. Given a model and a task, it discovers the circuit driving that behaviour, evaluates how faithful it is, and lets you act on it (prune, quantize, edit, steer, or fine-tune), then export a reloadable HuggingFace checkpoint.

No GPU required for the quickstart — GPT-2 runs on CPU in a few minutes.

Quick start

# CPU-only, no GPU needed:
pip install -e .

# For benchmarking, add: pip install -e ".[benchmarks]"
from circuitkit import Pipeline

pipe = Pipeline("gpt2", task="ioi")
pipe.discover(algorithm="eap-ig", sparsity=0.3)
pipe.evaluate()
pipe.prune()
pipe.export("./checkpoint")

Also works as a CLI and YAML config.

What is a circuit?

A circuit is the minimal set of attention heads and MLP layers in a transformer that drives a specific behaviour. Most interp tooling stops at "here is a subgraph with attribution scores." CircuitKit goes further: it prunes (or quantizes) the model down to that subgraph, exports a reloadable HuggingFace checkpoint, and measures how faithful the pruned model stays. Because the circuit is task-specific, this produces a task-specialized checkpoint — not a general-purpose compressed model.

What you can do

Capability What it means
Discover 13 algorithms across maturity tiers: 6 stable (EAP, EAP-IG, EAP-GP, ACDC, IBCircuit, CD-T), tested across the GPT-2, Llama, Gemma, and Qwen families, and 7 research
Evaluate 6-pillar faithfulness: causal patching, ablation, stability, robustness, baselines, generalization
Prune Structural weight pruning down to the circuit
Quantize Circuit-aware mixed-precision quantization (3/4-bit + protect tiers)
Edit ROME / MEMIT knowledge editing at circuit-identified components
Steer Activation steering at inference (no retraining)
Fine-tune Circuit-restricted LoRA — only circuit components update
Benchmark lm-evaluation-harness integration for compressed checkpoints

Why CircuitKit?

Instead of stitching together… …CircuitKit gives you
A separate repo per discovery algorithm, plus a pruning script and lm-eval-harness — wired together by hand One Pipeline: discover → evaluate → prune → export → benchmark
One-off data formats per tool Standard circuit artifact + HuggingFace checkpoint
GPT-2-only tooling Llama-3, Gemma, Qwen — with GQA, RoPE, chat templates
One faithfulness score 6-pillar evaluation suite

Next steps

Getting Started Install, quickstart, core concepts
User Guide Pipeline, custom data, evaluation, selectors, tasks
Algorithms EAP, ACDC, IBCircuit, CD-T — with stability tiers
Applications Pruning, quantization, editing, steering, fine-tuning
Examples Runnable scripts and notebooks (all CPU-friendly)
API Reference Full API and CLI reference

Tests

pip install -e ".[dev]"
pytest tests/ -q

Citation

@software{circuitkit2026,
  title  = {CircuitKit: Circuit Discovery, Evaluation, and Application Toolkit
            for Mechanistic Interpretability},
  author = {Seth, Pratinav and Gosalia, Hem and Kasliwal, Aditya
            and Sankarapu, Vinay Kumar},
  year   = {2026},
  version = {1.0.0},
  url    = {https://github.com/Lexsi-Labs/CircuitKIT}
}

License

Lexsi Labs Source Available License (LSAL) v1.2: free for academic research and teaching on MIT-like terms; use by any organization requires written acknowledgement or permission (Section 1A); commercial use requires a separate license; responsible-use conditions apply. See LICENSE.md.

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