steerkit 0.3.0

Concept-first linear probes and activation steering, reproducible across many open-weight models.

steerkit

Find a concept in a local LLM, turn it into a steering direction, and save the result as a reusable artifact.

The same concept (sycophancy) refit on four local models spanning three families and two size tiers. Higher curves mean the concept is easier to separate at that normalized depth; hollow markers show each model's best layer. This is a comparison of where to look, not a claim that one vector transfers across models.

v0.3.0 early beta — new here? Start with the quickstart, the walkthrough notebook (also runnable on Colab — click the badge above), or the runnable notebooks in examples/.

Install

From PyPI (just the library + CLI):

pip install steerkit          # or: uv add steerkit
steerkit --help

From source (clone for the bundled datasets, notebooks, and figure scripts):

git clone https://github.com/arvkevi/steerkit.git
cd steerkit
uv sync
uv run steerkit --help
uv run steerkit lint-pairs --pairs examples/data/sycophancy.jsonl

For development, docs, teacher-model generation, or llama.cpp export:

uv sync --extra dev --extra docs
uv sync --extra anthropic   # ANTHROPIC_API_KEY
uv sync --extra openai      # OPENAI_API_KEY
uv sync --extra llamacpp    # GGUF control-vector export

Want the fastest possible first run? The lint-pairs command above is a no-model smoke test. After that, open the Colab walkthrough, or run the local quickstart. The repo includes seed datasets in examples/data/ so you can fit a probe without calling a teacher API.

What it is

steerkit takes you from a concept (for example sycophancy, verbosity, formality, refusal, or joy) to a steering vector you can use to push a single local model's responses in that direction. It is built on top of TransformerLens 3.1+.

It wraps the pieces people usually rewrite for every probing experiment: contrast-pair data, activation extraction, layer sweeps, three candidate steering directions per layer, held-out metrics, α calibration, steering hooks, reports, and portable .probe.safetensors artifacts.

A concrete example: 60 sycophantic-vs-direct contrast pairs → activations of shape [60, 2, 1536] from Qwen2.5-1.5B-Instruct → three candidate directions (logistic / diff-of-means / mass-mean) at every layer → best layer 12 with auto-calibrated α = 13 → a single sycophancy.probe.safetensors artifact you can reload anywhere and apply with one of four intervention operations. Reproducible via scripts/make_mental_model_figure.py.

What "three candidate directions" means: at each layer, steerkit fits three simple linear ways to separate positive vs. neutral examples: logistic regression, difference-of-means, and shrinkage LDA. You get all three in the saved artifact, then choose which direction to steer with. The default is logistic because it usually gives the best held-out classifier signal.

Probes are per-model. The artifact stores model id, layer, normalized depth, hook name, dataset hash, metrics, and calibrated α so you can reload and steer the same model or reproduce the fit elsewhere. The vector itself is still tied to the model it was trained on.

What this is and isn't

steerkit uses TransformerLens as the model-instrumentation surface — it sits one layer up, not a substitute. Neighboring tools at a glance:

tool	role
TransformerLens / nnsight	hook + cache surfaces. steerkit uses TL model-side.
LLMProbe	closest peer; research-shaped where steerkit is library-shaped.
repeng	difference-of-means control vectors. steerkit is a superset.
sae_lens	sparse autoencoders, bottom-up feature discovery — complementary, not competing.

Limitations to know up front

• Steering vectors don't transfer across models. Different d_model (Llama-1B's 2048 ≠ Qwen-1.5B's 1536) and different learned representations make direct transfer geometrically meaningless. Each model needs its own probe. The artifact format makes re-fitting on a new model trivial; it does not magically relocate the direction.
• Probes are concept-specific. A sycophancy probe steers sycophancy. Joy needs its own training data.
• In-distribution generalization isn't perfect. With 30–100 training pairs the probe steers reliably on prompts similar to the training distribution; out-of-distribution domains (code, creative writing, multi-turn) degrade further. More data fixes this.

Smallest Useful Loop

Bring a JSONL of contrast pairs, extract activations, fit candidate directions at every layer, save the best layer as a portable artifact, calibrate steering strength, and steer.

import os
os.environ["TRANSFORMERLENS_ALLOW_MPS"] = "1"  # macOS

from steerkit import Probe, calibrate_alpha, extract_activations, load, load_pairs_jsonl

pairs = load_pairs_jsonl("examples/data/sycophancy.jsonl")  # 60 teacher-generated pairs
model = load("Qwen/Qwen2.5-1.5B-Instruct")                     # default headline model

activations = extract_activations(
    pairs, model, hook_site="resid_post",
    cache_dir="cache",  # Zarr v3 cache; second run with same inputs skips the model entirely
)

# Fit all three candidate directions per layer with a held-out test split.
# Layer keys span [-1 (embed), 0..n-1 (blocks), n (final_ln)] when boundaries are included.
probes = Probe.fit_all(activations, model, hook_site="resid_post", test_fraction=0.2)
best = Probe.best_layer(probes, by="auc_test_logistic")
print(f"layer {best.layer} (depth {best.normalized_depth:.2f}), hook = {best.hook_name}")
print(f"held-out AUC = {best.metrics['auc_test_logistic']:.3f}")
print(f"directions available: {list(best.directions)}")  # logistic, diff_of_means, mass_mean

# Auto-α: pick the largest steering strength that keeps the model coherent.
chosen, ratios = calibrate_alpha(best, model)
print(f"auto-α = {chosen}; perplexity ratios per α: {ratios}")

best.save("sycophancy.probe.safetensors")

# Reload anywhere — metadata travels with the artifact, including auto_alpha.
reloaded = Probe.load("sycophancy.probe.safetensors")
# steer() defaults to the calibrated α and the default_method direction.
print(reloaded.steer(model, "What is a good way to start the morning?"))
print(reloaded.steer(model, "...", method="diff_of_means"))  # try a different direction

The concept-first path is also live. Generate your own dataset with a teacher model:

from steerkit import Concept, ConceptGroup, singleton_group

group = singleton_group(
    Concept("verbose", description="long sentences, many examples, hedges and elaboration"),
    neutral_reference="Respond as concisely as possible, one or two sentences",
    group_name="verbosity",
)
group.generate_pairs("anthropic:claude-haiku-4-5-20251001", max_pairs_per_concept=30)
# group["verbose"].contrast_pairs is now populated; feed it to extract_activations.

The headline sweep(group, model) one-liner ties everything together:

from steerkit import sweep, compose

# Sweep two ConceptGroups; per-concept best-layer probes are selected automatically.
verb_fit = sweep(verb_group, model, cache_dir="cache")    # GroupFit
form_fit = sweep(form_group, model, cache_dir="cache")
verb_fit.save("probes/verbosity")                          # directory artifact

# Compose probes from different groups for simultaneous steering.
composed = compose([verb_fit["verbose"], form_fit["formal"]])
print(composed.steer(model, "Tell me about your morning."))

For mutually_exclusive groups with ≥2 concepts, sweep also fits a MultinomialProbe useful for cross-concept similarity heatmaps.

All four intervention operations are supported on Probe.steer(..., op=...):

probe.steer(model, prompt)                     # addition (default; uses auto_alpha)
probe.ablate(model, prompt)                    # projection — remove the concept entirely
probe.clamp(model, prompt, target=2.0)         # force the projection to a target value
probe.amplify(model, prompt, gamma=2.0)        # multiplicative — scale existing signal

Same prompt across the four ops, with the unsteered baseline at top for reference. addition and clamp both push the activation toward the sycophancy direction (the model opens with "Hello! I'm so excited..." / "Hello, dear!"). projection looks like baseline because the concept isn't naturally active on a benign prompt — there's nothing to remove. amplify tightens the helpful answer slightly. Reproducible via scripts/make_ops_effect_figure.py.

α as a strength dial

For addition (the default), α is the steering strength. Auto-calibration picks the largest α whose perplexity stays within 1.5× of the unsteered baseline; pushing higher trades coherence for behavioral commitment.

Same probe, same prompt, five values of α. At α=0 the model gives a normal helpful answer; the calibrated default (auto_α, blue row) is barely-perceptibly steered; 2× auto_α produces a clear sycophantic preface ("Hello! I'm so excited to be here to chat with you. What a wonderful Sunday afternoon it is!"); 4× auto_α drifts into nonsense as the perplexity ceiling is exceeded. Reproducible via scripts/make_alpha_sweep_figure.py.

Per-token interpretability

Held-out AUC tells you the probe separates positive vs. negative responses. To see where in a generated sequence the direction actually fires, project every token's residual stream onto the probe direction:

Same prompt, same probe, two completions. The unsteered helpful answer (left) scores uniformly negative — sycophancy direction quiet. The steered version (right) flips: every token of "Hello! I'm so excited to be here to chat with you. What a wonderful Sunday afternoon" lights up red on the sycophancy direction. Generated by Probe.score_tokens(...) on Qwen2.5-1.5B-Instruct; reproducible via scripts/make_token_scores_figure.py.

unsteered = probe.steer(model, prompt, alpha=0.0)
steered   = probe.steer(model, prompt)
ts_unsteered = probe.score_tokens(model, prompt, unsteered)
ts_steered   = probe.score_tokens(model, prompt, steered)
ts_steered.plot()  # one bar per token; red = direction firing, blue = inactive

Useful for sanity-checking steering ("does the sycophancy direction light up on the validating-preface tokens or on punctuation?"), diagnosing dataset issues, and watching steering hooks land. Complementary to plot_logit_lens (what vocabulary the direction promotes) since score_tokens answers where in this response it fires.

For multi-layer steering across a small layer window:

from steerkit import window
probes = Probe.fit_all(activations, model)     # full per-layer fits
composite = window(probes, center_layer=best.layer, k=1)   # window-of-3 (best ± 1)
composite.steer(model, prompt)
# Or via GroupFit:
fit.window("joy", k=1).steer(model, prompt)

To evaluate a probe with the multi-metric eval module:

from steerkit import evaluate_probe
report = evaluate_probe(
    probe, model,
    target_vocab={"sorry", "unable", "cannot", "can't", "decline"},  # logit-lens match
    perplexity_prompts=["Tell me about your morning.", "Recommend a book."],
    classifier_prompts=["..."], classifier=my_external_classifier,
)
print(report.summary())   # auc_test_logistic=0.92 | vocab_match=0.45 | ppl_ratio=1.21 | clf_shift=+0.27

To render a one-page HTML summary (embeds layer-selection curve, PCA projection, logit-lens):

probe.report(model=model, per_layer=probes, activations=activations[best.layer], out="probe_report.html")
fit.report(model=model, out="emotion_report.html")

For llama.cpp interop, export a probe as a GGUF control vector (requires pip install steerkit[llamacpp]):

probe.export_gguf("sycophancy.gguf")             # single layer
composite.export_gguf("multilayer.gguf")         # one entry per layer (window-of-3 / cross-group)

CLI

A thin typer wrapper exposes the workflow as steerkit generate / sweep / group-sweep / steer / calibrate / report:

# Generate contrast pairs for one concept (single-shot teacher calls)
steerkit generate \
    --name verbose --description "long, expansive" \
    --neutral "Respond concisely" \
    --teacher anthropic:claude-haiku-4-5-20251001 \
    --n-pairs 30 \
    --out examples/data/verbose.jsonl

# Single-concept sweep
steerkit sweep \
    --pairs examples/data/sycophancy.jsonl \
    --model Qwen/Qwen2.5-1.5B-Instruct \
    --cache-dir cache \
    --out runs/sycophancy.probe.safetensors

# Multi-concept (ConceptGroup) sweep -> GroupFit directory
steerkit group-sweep \
    --group examples/data/emotion.group.json \
    --model Qwen/Qwen2.5-1.5B-Instruct \
    --out runs/emotion_fit

# Auto-α calibration on an existing probe
steerkit calibrate --probe runs/sycophancy.probe.safetensors

# Single steered completion
steerkit steer \
    --probe runs/sycophancy.probe.safetensors \
    --prompt "What's a good way to start the morning?" \
    --op addition

# Shareable HTML one-pager
steerkit report \
    --probe runs/sycophancy.probe.safetensors \
    --out runs/sycophancy_report.html

The CLI reuses the same Python API the notebooks demonstrate; nothing in steerkit steer ... is unavailable from Probe.steer(...).

Quickstart notebooks

Runnable end-to-end notebooks under examples/:

• walkthrough.ipynb — start here. Unrolled, explained, step-by-step on the sycophancy concept: every phase (data → model → extraction → fit → calibrate → save/load → steer → eval) has its own markdown cell explaining what's happening and why.
• quickstart_refusal.ipynb — the same workflow compressed to ~10 cells, on the refusal concept.
• quickstart_emotion.ipynb — multi-class ConceptGroup (joy / sadness / anger), multinomial diagnostic probe, similarity heatmap.
• quickstart_composition.ipynb — compose([verbose_probe, formal_probe]) for cross-group simultaneous steering, with weighted composition.
• case_studies/refusal_walkthrough.ipynb — full unrolled walkthrough on the refusal concept (the safety-relevant variant of the headline pipeline).
• case_studies/formality_walkthrough.ipynb — same workflow on a tonal-register concept.

Visualization — six matplotlib plots; all return Figure objects:

from steerkit import (
    plot_layer_selection,        # AUC + steering-effect dual-curve across layers
    plot_activation_projection,  # PCA of [n_pairs, 2, d_model] activations by class
    plot_alpha_curve,            # α vs perplexity-ratio from calibrate_alpha
    plot_logit_lens,             # top-K vocab tokens the steering direction promotes
    plot_similarity_heatmap,     # cosine similarity between concept directions
    plot_cross_model_overlay,    # the hero: layer curves overlaid on normalized depth
)

# Sugar methods on existing objects:
verb_fit.plot_layer_selection("verbose")
verb_fit.plot_similarity()
mn_probe.plot_similarity()
probe.plot_logit_lens(model)

Bundled seed datasets (no API call required): examples/data/sycophancy.jsonl (the headline concept), examples/data/formality.jsonl, examples/data/verbosity.jsonl, examples/data/refusal_pairs.jsonl (used by the refusal case study), examples/data/emotion.group.json. For a longer list of starter concepts, sharp-edge notes, and ideas to explore, see the concept gallery.

Dataset quality checks

steerkit lint-pairs --pairs examples/data/sycophancy.jsonl

Eight cheap checks (no model load) that catch the most common dataset failure modes — uniform positives, length skew, cross-class leakage, empty fields, repeated prompts. Suitable for CI / pre-commit; pass --strict to fail on warnings. Documented in the CLI reference.

Development

# Core install (probe + steer)
uv sync --extra dev

# With teacher providers (only one needed)
uv sync --extra dev --extra anthropic   # ANTHROPIC_API_KEY in env
uv sync --extra dev --extra openai      # OPENAI_API_KEY in env

# Fast tests
uv run pytest

# Slow end-to-end tests (download a tiny model once; ~3 min on a slow connection)
TRANSFORMERLENS_ALLOW_MPS=1 STEERKIT_RUN_SLOW=1 uv run pytest

# Showcase figure generators (each consumes the saved probe + cache)
TRANSFORMERLENS_ALLOW_MPS=1 uv run python scripts/fit_sycophancy_probe.py
TRANSFORMERLENS_ALLOW_MPS=1 uv run python scripts/make_mental_model_figure.py
TRANSFORMERLENS_ALLOW_MPS=1 uv run python scripts/make_alpha_sweep_figure.py
TRANSFORMERLENS_ALLOW_MPS=1 uv run python scripts/make_ops_effect_figure.py
TRANSFORMERLENS_ALLOW_MPS=1 uv run python scripts/make_token_scores_figure.py
TRANSFORMERLENS_ALLOW_MPS=1 uv run python scripts/make_cross_model_hero.py

TRANSFORMERLENS_ALLOW_MPS=1 suppresses TL's overcautious MPS warning on Apple Silicon.