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wassnameandClaudypoo
eba1ba4f5a
wassname read the demo text and caught that the JSON-object gate over-credited
degenerate methods: persona_vector scored rubric ans=9 while its actual generation had
collapsed into wedding-jewelry loops. Root cause: the gate was on a SHORT forced object
that stays scorable long after the open-ended generation degenerates. Every breakdown we
saw is a REPETITION loop, so coherence is now 1 - distinct-3 of the think trace
(REP_COHERENT_MAX=0.35, from the empirical gap in rep_metric_check.py over 40+ real
generations: coherent <0.3, degenerate >0.6). This drops the whole {"ans","why","2+2"}
apparatus (raw_decode, valid/chk_ok, span_pmass) for one cheap n-gram ratio on the text
that actually degenerates.
rubric_score returns (expected, rep); coherence_sweep gates coherent = rep<0.35; plot
colors by rep (viridis_r, red cutoff line); show_steer prints rep + DEGENERATE flag.
eval_mechanisms/analyze_mechanisms/rep_metric_check are the overnight which-works screen.
Removed uat_coherence_break (tested the removed JSON gate).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
45 lines
2.1 KiB
Python
45 lines
2.1 KiB
Python
"""Demo artifact: coherence_sweep that WALKS PAST the coherence edge, so the table +
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plot show the full dose-response (ans rises with C) colored by coherence (valid-object
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fraction) with a red-edged incoherent boundary. (Claude) step=0.25 reaches the ~C=1.0
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break found in uat_coherence_break; n_samples=3 for BMA error bars.
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uv run python scripts/scratch/sweep_artifact.py
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"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
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import config # noqa: E402
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import torch # noqa: E402
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from loguru import logger # noqa: E402
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from tabulate import tabulate # noqa: E402
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from transformers import AutoModelForCausalLM, AutoTokenizer # noqa: E402
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from jsteer import Jacobian # noqa: E402
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from jsteer.demo import coherence_sweep, plot_sweep # noqa: E402
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MODEL = "Qwen/Qwen3.5-4B"
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OUT = "/tmp/claude-1000/sweep_json_coherence.png"
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tok = AutoTokenizer.from_pretrained(MODEL)
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model = AutoModelForCausalLM.from_pretrained(MODEL, dtype=torch.bfloat16).to("cuda").eval()
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jac = Jacobian.from_pretrained(config.LENS_REPO, filename=config.hub_lens_file(MODEL),
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revision=config.LENS_REVISION)
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band = jac.steer_band(model)
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v = jac.word_vector(model, tok, ["happy", "joy"], layers=band)
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RUBRIC = "On a scale of 0 (bleak) to 9 (joyful), how is your outlook on the week?"
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rows = coherence_sweep(model, tok, v, RUBRIC, step=0.25, max_steps=5, n_samples=3,
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max_new_tokens=384)
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logger.info("\n" + tabulate(rows, headers="keys", tablefmt="github", floatfmt="+.2f"))
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fig = plot_sweep(rows, title="joy steer: rubric ans vs C (colored by coherence)")
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fig.savefig(OUT, dpi=110, bbox_inches="tight")
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logger.info(f"wrote {OUT}")
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# UAT: the sweep must contain BOTH coherent (low rep) and degenerate (red-edge)
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# rows, so the plot shows the dose-response AND the breakdown edge.
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coh = [r for r in rows if r["coherent"]]
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inc = [r for r in rows if not r["coherent"]]
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logger.info(f"\nUAT: coherent rows={len(coh)}, incoherent (edge) rows={len(inc)} at "
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f"C={[r['C'] for r in inc]} (SHOULD have >=1 of each -> plot shows the edge)")
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