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wassnameandClaudypoo
339c67fefe
calib_pos_C (fluent +C knee), smoke_sweep (coherence_sweep sampling gives ans_std>0), proto_lens_slice (reference compute_slice readout prototype: auto-tracked tokens incl 巴黎, Paris rank->0 at the model row). Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
48 lines
2.0 KiB
Python
48 lines
2.0 KiB
Python
"""Find the fluent positive-C knee for the word_steering demo prompt. (Claude)
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The committed notebook claimed C~0.5 stays fluent and C~1 spams, but at seed=0 the
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"describe your week" free-form already degenerates into happy-spam at C=0.5. Sweep
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low C to find where the free-form stays coherent, and log the rubric number so we
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can pick a C that is BOTH fluent and visibly steered.
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uv run python scripts/scratch/calib_pos_C.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 transformers import AutoModelForCausalLM, AutoTokenizer # noqa: E402
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from jsteer import Jacobian # noqa: E402
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from jsteer.demo import chat_input, rubric_score # noqa: E402
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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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v = jac.word_vector(model, tok, ["happy", "joy"], layers=band)
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prompt = chat_input(tok, "Describe how your week has been going.")
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enc = tok(prompt, return_tensors="pt").to(model.device)
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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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@torch.no_grad()
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def gen_tail(C):
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torch.manual_seed(0)
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with v(model, C=C):
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out = model.generate(**enc, max_new_tokens=400, pad_token_id=tok.eos_token_id)
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ans, pmass = rubric_score(model, tok, RUBRIC, max_new_tokens=400, seed=0)
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txt = tok.decode(out[0][enc.input_ids.shape[1]:], skip_special_tokens=True)
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ans_only = txt.split("</think>")[-1].strip().replace("\n", " ")[:220]
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return ans_only, ans, pmass
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for C in (0.0, 0.2, 0.3, 0.4, 0.5):
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tail, ans, pmass = gen_tail(C)
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logger.info(f"\nC={C:+g} rubric ans={ans:.2f}/9 pmass={pmass:.2f}\n free-form answer: {tail!r}")
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