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persona-steering-template-l…/scripts/build_hf_dataset.py
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"""Build the Hugging Face dataset folder with parquet-only data files.
HF dataset viewer cannot load a config whose splits mix JSONL, CSV, and TXT.
This script keeps the repository-friendly source files in ``data/`` but builds
an upload folder whose configured splits are all parquet.
"""
from __future__ import annotations
import argparse
import json
import shutil
from pathlib import Path
from typing import Any
import pyarrow as pa
import pyarrow.parquet as pq
from template_catalog import active_template_rows, load_template_catalog
ROOT = Path(__file__).resolve().parents[1]
DATA = ROOT / "data"
V2_PILOT_META = {
"measurement_id": "v2_pilot_seed24",
"generator_model": "qwen/qwen3.5-27b",
"judge_model": "google/gemini-3.1-flash-lite-preview",
"generation_temperature": 0.0,
"seed": 24,
"judge_order": "A/B labels randomized per prompt/template/persona_pair",
"judge_method": (
"separate positive-axis, negative-axis, style, and off-axis/confound "
"calls with deterministic judge temperature"
),
}
SCORE_FORMULA = "100 * on_axis * (1 - off_axis)"
SOURCE_INFO = {
"repeng": {
"type": "code / practitioner examples",
"url": "https://github.com/vgel/repeng",
},
"assistant_axis": {
"type": "associated code / trait files",
"url": "https://github.com/safety-research/assistant-axis",
},
"persona_vectors": {
"type": "associated code / trait files",
"url": "https://github.com/safety-research/persona_vectors",
},
"weight_steering": {
"type": "associated code / trait files",
"url": "https://github.com/safety-research/weight-steering",
},
"sycophancy_literature": {
"type": "paper / literature",
"url": "https://arxiv.org/abs/2310.13548",
},
"persona_steering_skill": {
"type": "repo-authored distillate, not an independent citation",
"url": "https://github.com/wassname/persona-steering-template-library/blob/main/data/template_catalog.yaml",
},
"steer_heal_love": {
"type": "wassname anecdote / design note",
"url": "https://github.com/wassname/w2schar-mini",
},
"wassname_w2schar": {
"type": "wassname w2schar notes",
"url": "https://github.com/wassname/w2schar-mini",
},
"wassname_v2_candidate": {
"type": "repo-authored local candidate",
"url": "https://github.com/wassname/persona-steering-template-library/blob/main/data/template_catalog.yaml",
},
"antipasto3": {
"type": "wassname associated code / template file",
"url": "https://github.com/wassname/AntiPaSTO3/blob/main/antipasto3_jax/data.py",
},
"innerpissa_engineered": {
"type": "wassname associated code / engineered prompting baseline",
"url": "https://github.com/wassname/InnerPiSSA_private/blob/rebuttal/nbs/eval_baseline_prompting_engineered.py",
},
"w2schar_persona_notes": {
"type": "wassname notes / persona-writing guide",
"url": "https://github.com/wassname/w2schar-mini/blob/main/docs/how_to_write_personas.md",
},
}
def _jsonable(value: Any) -> Any:
if isinstance(value, (dict, list)):
return json.dumps(value, ensure_ascii=False, sort_keys=True)
return value
def _read_jsonl(path: Path) -> list[dict[str, Any]]:
rows = []
with path.open() as fh:
for line in fh:
line = line.strip()
if line:
rows.append(json.loads(line))
return rows
def _write_parquet(path: Path, rows: list[dict[str, Any]]) -> None:
if not rows:
table = pa.table({})
else:
keys = list(rows[0])
for row in rows[1:]:
for key in row:
if key not in keys:
keys.append(key)
normalized = [{k: _jsonable(row.get(k)) for k in keys} for row in rows]
table = pa.Table.from_pylist(normalized)
path.parent.mkdir(parents=True, exist_ok=True)
pq.write_table(table, path)
def _template_rows(path: Path) -> list[dict[str, Any]]:
sources = _template_sources()
return [
{
"id": i + 1,
"template": line.strip(),
"template_jinja": _jinja(line.strip()),
"template_format": "jinja2",
"source_id": sources.get(line.strip(), {}).get("source_id", "wassname_v2_candidate"),
"source_type": sources.get(line.strip(), {}).get(
"source_type", _source_type("wassname_v2_candidate")
),
"source_url": sources.get(line.strip(), {}).get(
"source_url", _source_url("wassname_v2_candidate")
),
"source_note": sources.get(line.strip(), {}).get("note", ""),
}
for i, line in enumerate(path.read_text().splitlines())
if line.strip()
]
def _clamp01(x: float) -> float:
return max(0.0, min(1.0, x))
def _on_axis(row: dict[str, Any]) -> float:
return round(_clamp01(float(row.get("mean_axis_delta") or 0.0) / 8.0), 4)
def _off_axis(row: dict[str, Any]) -> float:
return round(_clamp01((float(row.get("mean_off_axis_problem") or 7.0) - 1.0) / 6.0), 4)
def _score(on_axis: float, off_axis: float) -> float:
return round(100.0 * on_axis * (1.0 - off_axis), 1)
def _jinja(template: str) -> str:
return template.replace("{persona}", "{{ persona }}")
def _source_type(source_id: str | None) -> str:
return SOURCE_INFO.get(source_id or "", {}).get("type", "source-listed candidate")
def _source_url(source_id: str | None) -> str:
return SOURCE_INFO.get(source_id or "", {}).get("url", "")
def _template_sources() -> dict[str, dict[str, Any]]:
out = {}
for row in active_template_rows(load_template_catalog()):
out[row["template_runtime"]] = {
"source_id": row["primary_source_id"],
"source_type": row["primary_source_type"],
"source_url": row["primary_source_url"],
"note": row.get("note", ""),
"other_sources": row.get("other_sources", []),
}
return out
def _v2_error_counts() -> dict[tuple[str, str], int]:
out: dict[tuple[str, str], int] = {}
for row in _read_jsonl(DATA / f"{V2_PILOT_META['measurement_id']}_examples.jsonl"):
key = (row.get("template"), row.get("persona_pair"))
if row.get("error"):
out[key] = out.get(key, 0) + 1
return out
def _persona_pairs_by_id() -> dict[str, dict[str, Any]]:
return {row["id"]: row for row in _read_jsonl(DATA / "persona_pairs_pilot_two.jsonl")}
def _template_pair_score_rows() -> list[dict[str, Any]]:
pairs = _persona_pairs_by_id()
errors = _v2_error_counts()
template_sources = _template_sources()
rows = []
for stat in _read_jsonl(DATA / f"{V2_PILOT_META['measurement_id']}_template_pair_stats.jsonl"):
pair = pairs.get(stat["persona_pair"], {})
template_source = template_sources.get(stat["template"], {})
template_source_id = template_source.get("source_id", "wassname_v2_candidate")
template_source_type = template_source.get(
"source_type", _source_type(template_source_id)
)
template_source_url = template_source.get(
"source_url", _source_url(template_source_id)
)
n_success = int(stat.get("n") or 0)
n_errors = errors.get((stat["template"], stat["persona_pair"]), 0)
on_axis = _on_axis(stat)
off_axis = _off_axis(stat)
score = _score(on_axis, off_axis)
source_id = pair.get("source_id", "wassname_v2_candidate")
rows.append({
"id": 0,
"template": _jinja(stat["template"]),
"score": score,
"on_axis": on_axis,
"off_axis": off_axis,
"positive_persona": pair.get("pos"),
"negative_persona": pair.get("neg"),
"contrast": f"{pair.get('neg', '')}->{pair.get('pos', '')}",
"source": source_id,
"source_type": _source_type(source_id),
"source_url": _source_url(source_id),
"template_source": template_source_id,
"template_source_type": template_source_type,
"template_source_url": template_source_url,
"template_source_note": template_source.get("note", ""),
"persona_pair": stat["persona_pair"],
"positive_behavior": pair.get("positive_behavior"),
"negative_behavior": pair.get("negative_behavior"),
"raw_template": stat["template"],
"cell_key": f"{stat['persona_pair']}::{_slug(stat['template'])}",
"template_format": "jinja2",
"measurement_id": V2_PILOT_META["measurement_id"],
"score_formula": SCORE_FORMULA,
"recommended": bool(stat.get("recommended")),
"n_success": n_success,
"n_errors": n_errors,
"n_planned": n_success + n_errors,
"strict_pass_rate": stat.get("strict_pass_rate"),
"mean_axis_delta": stat.get("mean_axis_delta"),
"mean_off_axis_problem": stat.get("mean_off_axis_problem"),
"mean_max_style_abs_delta": stat.get("mean_max_style_abs_delta"),
"mean_abs_word_delta_frac": stat.get("mean_abs_word_delta_frac"),
"persona_echo_rate": stat.get("persona_echo_rate"),
"refusal_or_ai_break_rate": stat.get("refusal_or_ai_break_rate"),
"usable_rate": stat.get("usable_rate"),
**V2_PILOT_META,
})
rows.sort(key=lambda r: (r["score"], r["strict_pass_rate"], r["mean_axis_delta"]), reverse=True)
for i, row in enumerate(rows, start=1):
row["id"] = i
return rows
def _slug(text: str) -> str:
out = "".join(ch.lower() if ch.isalnum() else "_" for ch in text)
while "__" in out:
out = out.replace("__", "_")
return out.strip("_")[:64]
def _template_score_rows(template_pair_scores: list[dict[str, Any]]) -> list[dict[str, Any]]:
by_template: dict[str, list[dict[str, Any]]] = {}
for row in template_pair_scores:
by_template.setdefault(row["raw_template"], []).append(row)
out = []
for template, rows in by_template.items():
best = rows[0]
measured = len(rows)
out.append({
"id": 0,
"template": _jinja(template),
"score": round(sum(float(r["score"]) for r in rows) / measured, 1),
"best_score": best["score"],
"best_persona_pair": best["persona_pair"],
"source": "wassname_v2_candidate",
"source_type": _source_type("wassname_v2_candidate"),
"source_url": _source_url("wassname_v2_candidate"),
"raw_template": template,
"template_key": _slug(template),
"measurement_id": V2_PILOT_META["measurement_id"],
"template_format": "jinja2",
"recommended_cell_count": sum(bool(r["recommended"]) for r in rows),
"measured_persona_pair_count": measured,
"mean_axis_delta": round(
sum(float(r["mean_axis_delta"] or 0) for r in rows) / measured, 4),
"mean_off_axis_problem": round(
sum(float(r["mean_off_axis_problem"] or 0) for r in rows) / measured, 4),
"mean_max_style_abs_delta": round(
sum(float(r["mean_max_style_abs_delta"] or 0) for r in rows) / measured, 4),
**V2_PILOT_META,
})
out.sort(key=lambda r: (r["best_score"], r["score"]), reverse=True)
for i, row in enumerate(out, start=1):
row["id"] = i
return out
def _persona_pair_review_rows(template_pair_scores: list[dict[str, Any]]) -> list[dict[str, Any]]:
pairs = _read_jsonl(DATA / "persona_pairs_pilot_two.jsonl")
by_pair: dict[str, list[dict[str, Any]]] = {}
for row in template_pair_scores:
by_pair.setdefault(row["persona_pair"], []).append(row)
out = []
for i, pair in enumerate(pairs, start=1):
rows = sorted(
by_pair.get(pair["id"], []),
key=lambda r: (
bool(r.get("recommended")),
float(r.get("score") or 0),
float(r.get("strict_pass_rate") or 0),
),
reverse=True,
)
best = rows[0] if rows else {}
recommended = [r["raw_template"] for r in rows if r.get("recommended")]
if recommended:
proof_grade = "pilot_recommended"
elif best:
proof_grade = "pilot_measured_not_promoted"
else:
proof_grade = "candidate_unmeasured"
if best:
proof_summary = (
f"best_template={best['raw_template']}; "
f"score={best['score']}; "
f"n_success={best['n_success']}; "
f"pass={best['strict_pass_rate']}; "
f"axis_delta={best['mean_axis_delta']}; "
f"off_axis={best['mean_off_axis_problem']}; "
f"style={best['mean_max_style_abs_delta']}"
)
else:
proof_summary = "no measured v2 pilot rows yet"
out.append({
"id": i,
"persona_pair": pair["id"],
"contrast": f"{pair['neg']}->{pair['pos']}",
"best_score": best.get("score"),
"proof_grade": proof_grade,
"source": pair.get("source_id"),
"source_type": _source_type(pair.get("source_id")),
"source_url": _source_url(pair.get("source_id")),
"positive_persona": pair["pos"],
"negative_persona": pair["neg"],
"positive_behavior": pair["positive_behavior"],
"negative_behavior": pair["negative_behavior"],
"proof_summary": proof_summary,
"n_measured_template_cells": len(rows),
"best_template": best.get("template"),
"best_n_success": best.get("n_success"),
"best_strict_pass_rate": best.get("strict_pass_rate"),
"best_axis_delta": best.get("mean_axis_delta"),
"best_off_axis_problem": best.get("mean_off_axis_problem"),
"best_max_style_abs_delta": best.get("mean_max_style_abs_delta"),
"recommended_templates": json.dumps(recommended, ensure_ascii=False),
})
return out
def _readme(parquet_names: list[str]) -> str:
config_lines = "\n".join(
f" - split: {name}\n path: parquet/{name}.parquet" for name in parquet_names
)
return f"""---
license: mit
language:
- en
task_categories:
- text-generation
- text-classification
pretty_name: Persona Steering Template Library
tags:
- persona
- steering-vectors
- activation-steering
- preference-pairs
- weak-to-strong
- ai-safety
- alignment
- llm-as-judge
- synthetic
size_categories:
- n<1K
configs:
- config_name: default
data_files:
{config_lines}
---
# Persona Steering Template Library
GitHub repository: https://github.com/wassname/persona-steering-template-library
Evaluated persona/template candidates for steering-vector and preference-pair experiments.
## 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 are kept as audit columns.
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.
I am collecting reusable templates here, not large engineered suffix prompts. Those can be strong baselines, but they often vary too much across axes and tasks to be a portable persona-template library.
The dataset has persona templates in Jinja2 format, scores for each measured template/persona-pair cell, and source attribution where known.
Important: this is a provenance inventory, not a full lit review. See `data/template_catalog.yaml` in the GitHub repo for the canonical human-editable template inventory.
Persona-pair provenance is marked as `source`, `source_type`, and `source_url`. Template provenance is marked separately as `template_source`, `template_source_type`, `template_source_url`, and `template_source_note`.
## Score
Start with `main`.
The main column is `score`, a conservative 0-100 clean-axis score:
```text
score = 100 * on_axis * (1 - off_axis)
```
`on_axis` is normalized from the intended-axis judge rating. `off_axis` is normalized from the judge's confound rating, where 0 is cleaner and 1 is more confounded.
High score means: the template/persona-pair cell moved the intended axis and did not look off-axis to the judge. Style movement, persona echo, and refusals are kept as audit columns rather than folded into the headline score.
Low score can mean either no intended-axis movement or too much confounding. Read the component columns before trusting the score.
## Confounds Audited
The judge audits length, generic helpfulness, harmlessness/refusal, honesty/truthfulness, thoughtfulness/reasoning depth, task-context shift (code/chat/math/think), coding style, multilingual behavior, confidence, hedging, vagueness, warmth, enthusiasm, praise/flattery, sycophancy, chattiness, formality, language shift, incoherence/repetition/rambling, persona echo, and generic off-axis helpfulness.
Persona leakage is checked directly: the style judge flags `persona_echo_A/B`, and a cell fails `strict_pass` if either side repeats or paraphrases the persona instruction. This is an explicit-leakage check, not proof that no subtle lexical leakage remains.
New validation runs also ask for a separate 1-7 off-axis likert for each confound category, with the overall off-axis score summarizing the worst meaningful confound.
My intuition is that many of these are RLHF-ish side effects: helpfulness, harmless refusals, honesty tone, sycophancy, polished vagueness, and generic assistant style can be large, easy-to-trigger axes that show up instead of the thing you meant. - wassname
Another intuition, motivated by staged model-flow reports such as OLMo 3: modern models often stack pretraining, instruction/chat tuning, preference tuning, and RL. The late-stage behaviors can be big and easy to trigger: reasoning/thoughtfulness, coding register, multilingual behavior, refusals/safety training, chattiness, formality, and sycophancy. - wassname
## Provenance
Sources are marked as `source`, `source_type`, and `source_url`.
Do not read every `source_id` as an independent citation. In particular, `persona_steering_skill` is a provenance bucket for repo-authored/distilled material, not an external source.
`data/template_catalog.jsonl`, `data/templates_v2_candidates.txt`, and `data/template_sources.jsonl` are generated runtime artifacts. `data/template_catalog.yaml` is the template source of truth.
## Tables
1. `main`: one row per measured template/persona-pair cell.
2. `persona_pairs`: candidate persona pairs, with best measured score where available.
3. `examples`: paired completions and judge ratings behind the score.
## Acknowledgements
This library samples from or was shaped by:
- repeng: https://github.com/vgel/repeng
- Persona Vectors: https://github.com/safety-research/persona_vectors
- Assistant Axis: https://github.com/safety-research/assistant-axis
- weight-steering: https://github.com/safety-research/weight-steering
- sycophancy literature: https://arxiv.org/abs/2310.13548
- OLMo 3 report: https://arxiv.org/abs/2512.13961
- wassname/w2schar-mini: https://github.com/wassname/w2schar-mini
- wassname/AntiPaSTO3: https://github.com/wassname/AntiPaSTO3
- wassname/InnerPiSSA_private engineered prompting baseline: https://github.com/wassname/InnerPiSSA_private
## Citation
```bibtex
@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}}
}}
@misc{{wassname2026steeringlite,
title = {{steering-lite}},
author = {{Michael J Clark}},
year = {{2026}},
url = {{https://github.com/wassname/steering-lite}}
}}
```
"""
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--out", type=Path, default=Path("/tmp/persona-steering-template-library-hf"))
args = ap.parse_args()
if args.out.exists():
shutil.rmtree(args.out)
parquet_dir = args.out / "parquet"
parquet_dir.mkdir(parents=True)
tables = {
"main": _template_pair_score_rows(),
"examples": _read_jsonl(DATA / f"{V2_PILOT_META['measurement_id']}_examples.jsonl"),
}
tables["persona_pairs"] = _persona_pair_review_rows(tables["main"])
for name, rows in tables.items():
_write_parquet(parquet_dir / f"{name}.parquet", rows)
names = [
"main",
"persona_pairs",
"examples",
]
(args.out / "README.md").write_text(_readme(names))
print(f"built {args.out}")
for name in names:
print(f"{name}: {len(tables[name])} rows")
if __name__ == "__main__":
main()
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