interpkit 0.2.0

Mech interp for any HuggingFace model.

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Mech interp for any HuggingFace model.

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Why InterpKit?

Mechanistic interpretability tooling today is fragmented. Each library supports a narrow set of architectures, and moving to a different model family usually means rewriting hook code from scratch.

InterpKit provides a single, consistent interface for mech interp operations across any HuggingFace model — transformers, SSMs, vision models, and more — with zero annotation required.

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Install

pip install interpkit

# For linear probe support:
pip install interpkit[probe]

Or install from source for development:

git clone https://github.com/davidezani/InterpKit.git
cd InterpKit
pip install -e ".[dev]"

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Quickstart

import interpkit

model = interpkit.load("gpt2")

# One-command model overview — runs DLA, logit lens, attention, attribution
# and surfaces the most interesting findings automatically
model.scan("The capital of France is")

# Or run individual operations:
model.inspect()                                        # module tree
model.dla("The capital of France is")                  # direct logit attribution
model.trace("...Paris...", "...Rome...", top_k=20)     # causal tracing
model.lens("The capital of France is")                 # logit lens (all positions)
model.attribute("The capital of France is")            # gradient saliency
model.decompose("The capital of France is")            # residual stream decomposition

Works the same on any HF architecture:

model = interpkit.load("state-spaces/mamba-370m")
model = interpkit.load("google/vit-base-patch16-224")
model = interpkit.load("bert-base-uncased")

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Operations

Operation	What it does	Works on
scan	One-command model overview: runs DLA, lens, attention, attribution and surfaces key findings	LMs
dla	Direct Logit Attribution — decompose output logits by head and MLP contribution	LMs
inspect	Module tree with types, param counts, shapes	Any model
patch	Activation patching at a module, head, or position	Any model
trace	Causal tracing — module-level or position-aware (Meng et al.) heatmap	Any model
attribute	Gradient saliency over inputs (returns scores programmatically)	Any model
lens	Logit lens — project activations to vocabulary at all positions	LMs (auto-detected)
activations	Extract raw activation tensors at any module	Any model
head_activations	Decompose attention output into per-head contributions	Transformers
ablate	Zero/mean ablate a component and measure effect	Any model
attention	Visualize attention patterns per layer/head	Transformers
steer	Extract and apply steering vectors	LMs
probe	Linear probe on activations	Any model
diff	Compare activations between two models	Any model
features	SAE feature decomposition	Any model
decompose	Residual stream decomposition — per-component norms	Transformers
ov_scores	OV circuit analysis — W_OV matrix per head	Transformers
qk_scores	QK circuit analysis — W_QK matrix per head	Transformers
composition	Q/K/V composition scores between heads in two layers	Transformers
find_circuit	Automated circuit discovery via iterative ablation	Transformers
batch	Run any operation over a dataset with result aggregation	Any model

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Scan — One-Command Model Overview

The fastest way to understand what a model is doing on an input. Runs DLA, logit lens, attention analysis, and gradient attribution, then surfaces the most interesting findings in a ranked summary:

model.scan("The capital of France is")
# Output:
#   Predictions: "the" (8.5%), "now" (4.8%), "a" (4.6%)
#   Key Findings (ranked by significance):
#     1. Top contributor to "the": L11.attn (+204.701)
#     2. Top attention head: L11.H0 (+149.850)
#     3. Most salient input token: "is" (score 12.435)
#     4. Answer "the" first appears at layer 9/12

model.scan("The capital of France is", save="scan")  # exports scan_dla.png, scan_lens.png, etc.

Direct Logit Attribution (DLA)

Answers the fundamental question: why does the model predict this token? Decomposes the output logit by component (attention block + MLP per layer) and by individual attention head:

result = model.dla("The capital of France is")
# result["contributions"]      — per-component logit contributions, sorted
# result["head_contributions"] — per-head breakdown
# result["target_token"]       — the token being attributed

# Attribute a specific token
model.dla("The capital of France is", token="Paris")

# Save a bar chart
model.dla("The capital of France is", save="dla.png")

Causal Tracing

# Module-level tracing (default) — rank modules by causal effect
model.trace("...Paris...", "...Rome...", top_k=20)

# Position-aware tracing (Meng et al. 2022) — (layer x position) heatmap
model.trace("...Paris...", "...Rome...", mode="position", save="trace.png")

Logit Lens

Now analyses all token positions by default, producing the classic (layers x positions) heatmap:

model.lens("The capital of France is")                      # all positions
model.lens("The capital of France is", position=-1)         # last position only
model.lens("The capital of France is", save="lens.png")     # 2D heatmap export

Attribution

Returns scores programmatically (no longer just prints):

result = model.attribute("The capital of France is")
result["tokens"]   # ["The", "capital", "of", "France", "is"]
result["scores"]   # [8.88, 11.15, 7.24, 7.37, 12.43]
result["target"]   # 262

Activation Patching

Supports module-level, head-level, and position-level patching:

# Module-level (original)
model.patch(clean, corrupted, at="transformer.h.8.mlp")

# Head-level — patch only attention head 3
model.patch(clean, corrupted, at="transformer.h.8", head=3)

# Position-level — patch only positions 3 and 4
model.patch(clean, corrupted, at="transformer.h.8", positions=[3, 4])

# Combined — patch head 3 at positions 3 and 4
model.patch(clean, corrupted, at="transformer.h.8", head=3, positions=[3, 4])

Head-Level Activations

Decompose an attention module's output into per-head contributions, optionally projected through W_O into residual-stream space:

result = model.head_activations("The capital of France is", at="transformer.h.8")
result["head_acts"]   # tensor (num_heads, batch, seq, d_model)
result["num_heads"]   # 12
result["head_dim"]    # 64

Activations, Ablation, Attention

# Extract raw activations
act = model.activations("The capital of France is", at="transformer.h.8.mlp")
acts = model.activations("...", at=["transformer.h.0", "transformer.h.8.mlp"])

# Ablation — zero or mean
result = model.ablate("The capital of France is", at="transformer.h.8.mlp")
result = model.ablate("...", at="transformer.h.8.mlp", method="mean")

# Attention patterns
model.attention("The capital of France is")                   # all layers
model.attention("The capital of France is", layer=8, head=3)  # single head

Residual Stream Decomposition

Break down the residual stream at any position into contributions from each attention block and MLP:

result = model.decompose("The capital of France is")
# result["components"] — list of {"name": "L8.attn", "type": "attn", "norm": 8.94, ...}
# result["residual"]   — final residual stream vector

OV / QK Circuit Analysis

Analyse the effective weight matrices of attention heads:

# OV circuit: what does each head write to the residual stream?
model.ov_scores(layer=8)
# Per-head Frobenius norm, top singular values, approximate rank of W_OV

# QK circuit: what does each head attend to?
model.qk_scores(layer=8)

# Composition: how much does head j in layer 4 compose with head i in layer 8?
model.composition(src_layer=4, dst_layer=8, comp_type="q")  # Q-composition
model.composition(src_layer=4, dst_layer=8, comp_type="k")  # K-composition
model.composition(src_layer=4, dst_layer=8, comp_type="v")  # V-composition

Circuit Discovery

Automatically find the minimal set of components that explain a behaviour:

circuit = model.find_circuit(
    "The Eiffel Tower is in Paris",
    "The Eiffel Tower is in Rome",
    threshold=0.05,
)
# circuit["circuit"]       — components in the circuit, sorted by effect
# circuit["excluded"]      — components not in the circuit
# circuit["verification"]  — faithfulness check (how much output is preserved
#                            when all non-circuit components are ablated)

Batch / Dataset Operations

Run any operation over a dataset of examples with automatic result aggregation:

# Generic batch runner
results = model.batch("trace", [
    {"clean": "...Paris...", "corrupted": "...Rome..."},
    {"clean": "...Berlin...", "corrupted": "...Madrid..."},
], op_kwargs={"top_k": 10})
# results["summary"]["ranked_modules"] — modules ranked by mean effect across examples

# Convenience: trace over a dataset
results = model.trace_batch(dataset, clean_col="clean", corrupted_col="corrupted")

# Convenience: DLA over a list of texts
results = model.dla_batch(["The capital of France is", "The CEO of Apple is"])
# results["summary"]["ranked_components"] — components ranked by mean contribution

Steering

vector = model.steer_vector("Love", "Hate", at="transformer.h.8")
model.steer("The weather today is", vector=vector, at="transformer.h.8", scale=2.0)

Linear Probe

result = model.probe(
    texts=["The cat sat", "The dog ran", "A bird flew", "A fish swam"],
    labels=[0, 0, 1, 1],
    at="transformer.h.8",
)
print(result["accuracy"])

Model Diff

base = interpkit.load("gpt2")
finetuned = interpkit.load("my-finetuned-gpt2")
interpkit.diff(base, finetuned, "The capital of France is")

SAE Features

Decompose activations into interpretable features using pre-trained Sparse Autoencoders from HuggingFace:

model.features(
    "The capital of France is",
    at="transformer.h.8",
    sae="jbloom/GPT2-Small-SAEs-Reformatted",
)

No SAELens dependency — weights are loaded directly via safetensors.

Activation Cache

Avoid redundant forward passes when exploring the same input with multiple operations:

model.cache("The capital of France is")  # one forward pass, cache all layers
model.activations("The capital of France is", at="transformer.h.8.mlp")  # instant
model.activations("The capital of France is", at="transformer.h.0.mlp")  # instant

model.clear_cache()  # free memory

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Visualizations

Pass save="path.png" to export a static matplotlib figure, or html="path.html" for an interactive visualization:

model.attention("hello world", layer=0, head=0, save="attention.png")
model.trace("...Paris...", "...Rome...", save="trace.png")
model.trace("...Paris...", "...Rome...", mode="position", save="position_trace.png")
model.lens("The capital of France is", save="lens.png")
model.steer("The weather is", vector=vector, at="transformer.h.8", save="steer.png")
model.attribute("The capital of France is", save="attribution.png")
model.dla("The capital of France is", save="dla.png")
model.scan("The capital of France is", save="scan")
interpkit.diff(base, finetuned, "...", save="diff.png")

# Interactive HTML — self-contained files with hover tooltips, filters, and sliders
model.attention("hello world", html="attention.html")
model.trace("...Paris...", "...Rome...", html="trace.html")
model.attribute("The capital of France is", html="attribution.html")

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CLI

interpkit inspect gpt2
interpkit scan gpt2 "The capital of France is"
interpkit dla gpt2 "The capital of France is"
interpkit trace gpt2 --clean "...Paris..." --corrupted "...Rome..." --top-k 20
interpkit trace gpt2 --clean "...Paris..." --corrupted "...Rome..." --mode position --save trace.png
interpkit lens gpt2 "The capital of France is"
interpkit lens gpt2 "The capital of France is" --position -1
interpkit attention gpt2 "The capital of France is" --layer 8 --save attention.png
interpkit attribute gpt2 "The capital of France is"
interpkit steer gpt2 "The weather is" --positive Love --negative Hate --at transformer.h.8
interpkit ablate gpt2 "The capital of France is" --at transformer.h.8.mlp
interpkit decompose gpt2 "The capital of France is"
interpkit diff gpt2 my-finetuned-gpt2 "The capital of France is" --save diff.png
interpkit features gpt2 "The capital of France is" --at transformer.h.8 --sae jbloom/GPT2-Small-SAEs-Reformatted

# Interactive HTML output
interpkit attention gpt2 "hello world" --html attention.html
interpkit trace gpt2 --clean "...Paris..." --corrupted "...Rome..." --html trace.html
interpkit attribute gpt2 "The capital of France is" --html attribution.html

# Vision models — auto-preprocessed
interpkit attribute microsoft/resnet-50 cat.jpg --target 281

Run interpkit with no arguments for a full command reference.

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TransformerLens interop

Already using TransformerLens? Pass your HookedTransformer directly into InterpKit — it auto-detects the model and extracts the tokenizer:

from transformer_lens import HookedTransformer
import interpkit

tl_model = HookedTransformer.from_pretrained("gpt2")
model = interpkit.load(tl_model)

# All InterpKit operations work on TL models
model.scan("The capital of France is")
model.dla("The capital of France is")
model.trace("The Eiffel Tower is in Paris", "The Eiffel Tower is in Rome", top_k=20)
model.attention("The capital of France is", save="attention.png")
model.steer("The weather is", vector=vector, at="blocks.8", scale=2.0)

Translate between native and TL hook point names:

interpkit.to_tl_name("transformer.h.8.mlp")       # -> "blocks.8.mlp"
interpkit.to_native_name("blocks.8.attn", model.arch_info)  # -> "transformer.h.8.attn"
interpkit.list_tl_hooks(tl_model)                  # -> ["blocks.0.hook_resid_pre", ...]

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Local models

import torch.nn as nn
import interpkit

my_model = MyCustomModel()
interpkit.register(my_model, layers=["blocks.0", "blocks.1"], output_head="head")
model = interpkit.load(my_model, tokenizer=my_tokenizer)
model.trace(input_a, input_b, top_k=10)

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Examples

See the examples/ directory for Jupyter notebooks:

Notebook	Topics
01_quickstart	Inspect, scan, DLA, trace, lens, attribution, patching, ablation
02_attention_patterns	Per-head heatmaps, layer filtering, HTML export
03_steering_vectors	Extract and apply steering vectors at different layers/scales
04_sae_features	Sparse Autoencoder feature decomposition
05_caching_and_probing	Activation cache, linear probes across layers
06_model_comparison	Diff two models, side-by-side tracing and logit lens
07_vision_models	ResNet/ViT attribution, ablation, activations
08_dla_and_circuits	DLA, head activations, residual decomposition, OV/QK analysis, composition, circuit discovery
09_scan_and_batch	Auto-scan, batch operations, dataset workflows

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License

MIT