Persona Steering Template Library

Evaluated persona/template candidates for steering-vector and preference-pair experiments.

Dataset: https://huggingface.co/datasets/wassname/persona-steering-template-library

What This Measures

How do we know if a persona template is good? We want on-axis variation, but not off-axis variation.

If we choose honest and dishonest personas, use a template like You are a {{ persona }} assistant, and ask The Eiffel Tower is in, we want the completions to vary on the honest/dishonest axis. in Paris versus in Berlin shows on-axis variation. in Paris versus I refuse to answer is not good, because it is confounded by refusal. Other confounds include length, verbosity, confidence, style, and language.

So we try persona/template pairs on one model. We use another model as a judge, which rates on-axis and off-axis variation. The final score rewards on-axis variation and penalizes off-axis variation, style movement, persona echo, and refusals.

This field is pre-scientific in a way: it is still an art. I collected a wide sampling of what people have used, minimally measured it, and put it here to make it accessible to more people and agents.

The dataset has persona templates in Jinja2 format, scores for each measured template/persona-pair cell, and source attribution where known.

Use

Start with the scores split on Hugging Face.

Important columns:

template_jinja
score
persona_pair_id
axis
source_id
source_type

Then check judged_examples to see the paired completions behind the score.

Score

100
* strict_pass_rate
* clamp(mean_axis_delta / 8)
* clamp((7 - mean_off_axis_problem) / 6)
* clamp((6 - mean_max_style_abs_delta) / 6)
* (1 - persona_echo_rate)
* (1 - refusal_or_ai_break_rate)

High score means the template/persona-pair cell repeatedly moved the intended axis while staying comparatively clean on off-axis, style, persona-echo, and refusal checks.

Provenance

Sources are marked in the dataset as source_id and source_type. Some entries come from papers, some from associated code/trait files, and some from wassname anecdotes/design notes.

Appendix: Run

uv sync
uv run python scripts/validate_persona_axes_openrouter.py \
  --dry-run \
  --axes data/persona_pairs_v2_candidates.jsonl \
  --templates data/templates_v2_candidates.txt \
  --family data/scenarios_v2_candidates.jsonl \
  --n 2 \
  --out out/dryrun.json

uv run python scripts/build_hf_dataset.py \
  --out /tmp/persona-steering-template-library-hf

Citation

@misc{wassname_persona_steering_template_library_2026,
  title = {Persona Steering Template Library},
  author = {Wassname},
  year = {2026},
  url = {https://github.com/wassname/persona-steering-template-library}
}

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