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wassnameandClaudypoo
220f5018ac
Adds this session's evidence: uat_prefitted_4b.py (proves the n1000 lens steers, word>random), calib_c_prefitted.py + calib_persona.py (how the demo Cs were chosen: word knee ~0.5, mean_diff ~1), run_nb.py (nbclient notebook executor, bypasses the broken global nbconvert config). Removes u4_step3_guard.sh / retry.sh; fit.py gains --out for scratch fits to non-canonical paths. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
48 lines
2.1 KiB
Python
48 lines
2.1 KiB
Python
"""Calibrate C for the persona variants on the pre-fitted n1000 lens. (Claude)
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persona_vector / persona_topk_vector / mean_diff have different residual scales than the
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word vector (they contrast persona activations, not a single unembedding row), so their
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coherence knee differs. Sweep C on each to pick the demo coefficients.
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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 torch
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from loguru import logger
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import config # noqa: F401
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from jsteer import Jacobian, show_steer
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from steering_lite import Vector, MeanDiffC
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MODEL = "Qwen/Qwen3.5-4B"
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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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optimist = [
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"Things usually work out better than people expect, and today is no exception.",
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"Every setback I have hit this year turned into a door I could not have planned for.",
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"The team is behind schedule, but honestly the hard part is done and the rest is downhill.",
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"I love how much there is to look forward to this month.",
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]
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pessimist = [
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"Things usually go worse than people expect, and today is no exception.",
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"Every setback this year just confirmed that planning is pointless.",
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"The team is behind schedule, and frankly the hardest part has not even started.",
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"I dread how much is crammed into this month.",
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]
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DEMO = "Give me your honest assessment of how the project is going."
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vp = jac.persona_vector(model, tok, optimist, pessimist, layers=band)
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vt = jac.persona_topk_vector(model, tok, optimist, pessimist, k=8, layers=band)
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vm = Vector.train(model, tok, optimist, pessimist, MeanDiffC(layers=tuple(band)))
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for name, v in (("persona_vector", vp), ("persona_topk", vt), ("mean_diff", vm)):
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logger.info(f"=== {name}: C sweep for coherence knee ===")
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show_steer(jac, model, tok, v, DEMO, Cs=(0, 0.5, 1.0, 2.0), max_new_tokens=40)
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