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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>
36 lines
1.4 KiB
Python
36 lines
1.4 KiB
Python
"""Calibrate the steering coefficient C for the pre-fitted n1000 Qwen3.5-4B lens. (Claude)
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The n1000 raw lens has a different residual scale than our old 128-chat fit, so C=6
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over-drives it (generation degenerates to 'joyjoyjoy'). Sweep small C on the word
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vector to find the coherent-but-steered sweet spot, and show the random control at the
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same C. Picks the Cs the demo notebooks should use.
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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 huggingface_hub import hf_hub_download
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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 loguru-on-import
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from jsteer import Jacobian, show_steer
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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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logger.info(f"pre-fitted {jac!r}; steer band {band}")
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v = jac.word_vector(model, tok, ["happy", "joy"], layers=band)
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vr = jac.random_vector(seed=0, layers=band)
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msg = "Describe how your week has been going."
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logger.info("=== WORD (happy/joy): fine small-C sweep for the coherence knee (C=2 already over-drives) ===")
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show_steer(jac, model, tok, v, msg, Cs=(0, 0.5, 1.0, 1.5), max_new_tokens=50)
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