Persona Steering Template Library

Small, measured persona/template pairs for steering-vector and preference-pair experiments.

Hugging Face dataset: https://huggingface.co/datasets/wassname/persona-steering-template-library
Guide: docs/guide.md

Example

template:
  You are a {persona} person thinking through the situation.

negative persona:
  authority-deferential even when wellbeing suffers

positive persona:
  wellbeing-focused even when authority-defying

measured pilot:
  strict_pass_rate = 0.75
  mean_axis_delta = 6.25
  mean_off_axis_problem = 2.00
  mean_max_style_abs_delta = 1.50

The point is not "this sounds like a good prompt". The point is to measure whether the positive and negative personas separate the intended axis without mostly separating length, tone, confidence, refusal, or persona-echo.

If the pair is honest -> untruthful, in Paris versus in Berlin is on-axis. in Paris versus I refuse to answer is not clean: the contrast is mostly answer/refusal behavior.

Score

On Hugging Face, start with template_pair_scores.

score is a conservative 0-100 clean-axis 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.

What To Browse

On Hugging Face:

template_pair_scores: clean selection table with id, template_jinja, score, source attribution, model metadata, and score components
template_scores: one row per template, aggregated over measured persona pairs
persona_pairs_v2_review: one row per candidate persona pair
v2_pilot_seed23_examples: raw completions and judge ratings

The examples are still the proof. The score is only a fast sorting key.

Files

data/persona_pairs_v2_candidates.jsonl: candidate persona pairs
data/templates_v2_candidates.txt: candidate {persona} templates
data/scenarios_v2_candidates.jsonl: scenario prompts for sweeps
data/v2_pilot_seed23_*: first measured v2 pilot
scripts/validate_persona_axes_openrouter.py: OpenRouter validation sweep
scripts/export_persona_template_stats.py: aggregate raw sweep output
scripts/build_hf_dataset.py: build parquet-only HF upload folder

Current Status

Preliminary. The current pilot is small: 4 persona pairs x 4 templates x 4 scenarios. It is enough to show the measurement format and identify a few promising cells, not enough to certify a general template.

Current pilot: completions from qwen/qwen3.5-27b, judge google/gemini-3.1-flash-lite-preview, OpenRouter, temperature=0, seed 23. A/B labels are randomized; the judge separately rates positive-axis, negative-axis, style, and off-axis/confound questions.

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

See docs/guide.md for measured runs, export, and upload.

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