The report card for steering vectors — effect size, side effects, a dose-response curve, and per-layer sensitivity for any concept direction.
A real Qwen2.5-7B formality vector. Effect changes with the injection coefficient while perplexity holds — then falls off the coherence cliff. steerbench marks the sweet spot and the cliff automatically.
The hero stays the real 7B curve — largest dynamic range, and it anchors the shape narrative below. For a one-command, in-repo reproducible version on a cheap T4 (no A100), see the small-model canonical run in
RESULTS.mdviaexperiments/reproduce.sh: Qwen2.5-1.5B and 0.5B reproduce the same interior-optimum dose-response shape, and frac ≈ 0.61 of depth is a high-effect coherent injection layer on both (the argmax on 1.5B; the start of the coherent plateau on 0.5B, whose argmax is the last layer — seeRESULTS.mdfor the honest breakdown).
Render the four-part card from a repeng vector — CPU-only, no model download:
git clone https://github.com/bamdadd/steerbench.git && cd steerbench
uv sync --extra vectors --extra report
uv run steer-report data/examples/formality_qwen2.5-7b.gguf --out report_out
open report_out/report.html # the four-part card (self-contained HTML)steer-report summarises the vector (per-layer norms), then reads the committed
sweep CSVs in artifacts/ and renders the card on CPU. Drop the vector argument
to render from the CSVs alone. Zero-install alternative: the
Colab notebook (badge above) does the
same in well under 10 minutes.
The GPU sweep that produces the CSVs lives behind steerbench[gpu] (Modal),
reached via steer-report --run <function>; the core never imports it.
flowchart LR
subgraph S["steerbench"]
direction TB
D["dose sweep (α grid)"]
L["layer sweep (every depth)"]
X["side-effect slices (MMLU / GSM8K)"]
end
subgraph R["report card"]
direction TB
R1["effect size"]
R2["side effects"]
R3["dose-response: sweet spot + cliff"]
R4["layer sensitivity"]
end
V["steering vector<br/>repeng .gguf or .pt"] --> S
S --> R
1. A concept is a direction. As a model reads text, every layer keeps its running "thoughts" as a big list of numbers — the residual stream, a conveyor belt each layer adds to. A concept like formality or ocean shows up as a direction in that space. steerbench consumes directions extracted by repeng — it does not reimplement extraction.
2. The nudge has two dials. You steer by adding that direction into the model's live internal state mid-generation — plain vector addition:
current thoughts + α · (formality direction) → nudged thoughts
Two dials: where (which layer — depth) and how much (α — strength).
Pick them wrong and you either get no effect, or you push α too high and the
output collapses into repetitive nonsense — the coherence cliff.
3. The problem steerbench solves. Anyone injecting a concept must choose
where and how much to nudge without breaking the model — and "looks
fine" is not a measurement. steerbench answers it objectively: dose-response
sweeps α to find the sweet spot and the cliff; the layer sweep injects at
every depth to find where the vector actually works (a coherent plateau, versus
the degenerate-trap layers that look strong but are just broken text); and
side effects re-check held-out benchmarks (MMLU / GSM8K) so you know the
nudge didn't quietly break everything else.
The tool exists because the science needed it.
introspection-scaling — the
project steerbench was built for — asks whether models can introspect on
injected concepts as they scale. To run that cleanly it must inject at a
known-good layer and strength on every model in the ladder. steerbench produces
that report card — the sweet spot, the cliff, the safe layer — so the injection
is calibrated, not guessed. Concretely: introspection-scaling injects/reads at
frac ≈ 0.61 of depth, and steerbench's layer sweep finds that depth to be a
high-effect, coherent formality-injection layer on Qwen2.5-0.5B, 1.5B and 7B
— the coherent argmax on 1.5B and 7B, and on the coherent back-half plateau on
0.5B (see RESULTS.md) — so the depth hyperparameter is measured
across a ~14× size range, not assumed.
One extraction recipe (repeng PCA-diff, 3 seeds, A100 via Modal) run across models and concepts. Each (model, concept) cell is n = 1 vector with coarse behavioural proxies, so these are existence claims, not statistical interactions. Every effect is measured within-concept against that cell's own baseline; the formality and sentiment proxies are on different scales and are never compared to each other.
The finding: architectures differ in dose-response shape — measured on the
coherence-gated alpha_norm axis (the residual-fraction dose; model-independent,
unlike the proxy scale):
- Qwen2.5-7B — interior optimum. Formality effect rises with dose, peaks
around
alpha_norm≈ 0.04–0.055, then the coherence cliff takes over and the effect reverses past it (the hero above). - Llama-3.1-8B — monotonic-to-cliff. No interior turn; the best usable effect
is the last coherent dose (
alpha_norm≈ 0.197). It needs a ~3–4× higher normalized dose than Qwen (≈ 3.6× vs Qwen formality at ~0.055; ≈ 2.3× vs Qwen sentiment at 0.087). A dose tuned on Qwen underdrives Llama. (Llama-3.1-8B = NousResearch mirror.)
Both formality vectors behind this are stable at adequate data — injection-layer cosine across re-extractions 0.97 (Qwen) and 0.83 (Llama) at a 90% subsample of the 69 contrastive pairs — so the shape difference is a real architectural property, not extraction variance. Honest caveat: formality extraction is data-hungry — cosine falls to 0.55–0.64 at a 70% subsample — so vector stability itself depends on dataset size and should be measured, not assumed (another face of vgel/repeng#78).
A cautionary case — the report card catches a bad vector. On Mistral-7B,
the formality dose-response came back flat in both directions (positive
alpha never clears the 4.83 baseline; negative alpha barely moves it despite
downward headroom). That looks like "this model won't take formality steering"
— but a re-extraction stability check showed why: the formality direction is
unstable (injection-layer cosine ≈ −0.13, sign-flipping run to run:
−0.81, −0.29, +0.69), a low-SNR vector, not a model property. Crucially, this is
not just the data-hunger above: at the same 70% subsample the sentiment
vector on the same model is rock-stable (cosine 0.95) and steers fine, and
verbosity extracts stably too (cosine 0.93). Mistral extracts two of the
three concepts cleanly and fails on formality alone — so this is not "Mistral is
hard to extract from," it is formality-specific. Lesson: a flat curve can
mean bad vector or stubborn model, and you cannot tell by eyeballing one
generation — the report card plus a stability check distinguishes them. This is
the repeng extraction-instability failure mode
(vgel/repeng#78).
Coherence gates every peak. We deliberately do not headline layer/depth patterns: Llama's formality peak sits at perplexity ≈ 5.0 (borderline), and Mistral's apparent formality spike at layer 2 (effect 15.6) lands at perplexity ≈ 8.5 — degenerate text the gate rejects rather than reports as a peak.
Qwen M0 full run: M0_REPORT.md. Cross-model sweep complete — three concepts (formality, sentiment, verbosity) across Qwen2.5-7B, Llama-3.1-8B, and Mistral-7B (Gemma 2 9B gated, substituted).
Consumes vectors produced by repeng — extraction is not reimplemented. Ships benign example concepts (formality, sentiment, verbosity).
Two bugs in repeng — the non-determinism bug found while building steerbench, and the one-line fix (vgel/repeng#79).
Contributions welcome — start with the good first issues (each has file:line pointers and acceptance criteria). See CONTRIBUTING.md for setup and the checks a PR must pass.
MIT.