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wassnameandClaude Sonnet 4.6
a48430b075
- data.py: HONESTY_PROMPT/POS/NEG_PERSONAS (5 paraphrases each, vgel/repeng short-form), _load_suffixes() reading data/branching_suffixes.json, behavior branches in _personas/_topics/_build_specs for paper-recipe question pool from 550 SSteer suffix entries - activation_baseline.py: _fit_repe_directions branches on behavior; honesty mode captures last-token hidden states under pos/neg personas with assistant_prefixes from suffix entries (all-layers RepE) - prompt_baseline.py: paired engineered_prompt_honest + _dishonest (AxBench J.2), both as plain strings - evals/smoke.py: behavior field in SmokeCfg - data/branching_suffixes.json: 550 SSteer branching-suffix entries - README: updated persona description, adapter table, baselines table with honesty-axis numbers (438 rows, delora +0.237 best) - RESEARCH_JOURNAL.md: 2026-04-27 axis-switch entry - fork_plan.md: open design question resolved as option 2 (honesty axis) - HANDOVER.md: overnight handover notes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
"""Smoke: full pipeline on a tiny random model. CPU-feasible. ~1 min.
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Set BEARTYPE=1 to enable jaxtyping runtime shape/dtype checks via the
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jaxtyping import hook (autochars2 pattern). Catches dim errors early.
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Pipeline exercised: data gen -> train pos -> train neg -> diff -> alpha sweep eval.
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"""
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from __future__ import annotations
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import os
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import sys
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from pathlib import Path
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# Install jaxtyping+beartype import hook BEFORE importing ws.* — this makes
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# every Float[Tensor, "..."] annotation in ws/* a runtime check.
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if os.environ.get("BEARTYPE"):
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from jaxtyping import install_import_hook
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# Returned manager auto-installs; keep ref alive for the process lifetime.
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_hook = install_import_hook("ws", "beartype.beartype")
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print("[smoke] BEARTYPE=1: jaxtyping runtime checks ENABLED for ws.*", flush=True)
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import tyro
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from dataclasses import dataclass
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from ws.replicate import Cfg, main as replicate_main
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@dataclass
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class SmokeCfg:
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model: str = "katuni4ka/tiny-random-qwen3" # or any tiny-random LM
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max_steps: int = 2
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out: Path = Path("out/smoke")
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adapter: str = "lora"
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behavior: str = "sycophancy"
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def main(cfg: SmokeCfg) -> None:
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print(f"[smoke] model={cfg.model} adapter={cfg.adapter} behavior={cfg.behavior} max_steps={cfg.max_steps}")
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rcfg = Cfg(
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model=cfg.model,
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behavior=cfg.behavior,
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adapter=cfg.adapter,
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max_steps=cfg.max_steps,
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out=cfg.out,
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coeffs=(-1.0, 0.0, 1.0),
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rank=4, # tiny model, tiny rank
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n_topics=2, # 2×1×2 = 4 pairs
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n_personas=1,
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n_samples=2,
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)
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replicate_main(rcfg)
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print("[smoke] OK", flush=True)
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if __name__ == "__main__":
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main(tyro.cli(SmokeCfg))
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