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wassnameandClaudypoo
a43630cec2
config.HUB_LENS_FILE maps HF model -> the authors' pre-fitted Jacobian lens on the Hub (neuronpedia/jacobian-lens, raw Salesforce-wikitext, n=1000). Loading one beats fitting locally: same estimator, 1000 prompts, zero compute. Our Jacobian already wraps jlens.JacobianLens, so their .pt loads through Jacobian.from_pretrained with no format change (verified: n1000 4B loads, d_model=2560, layers [0..30]). jacobian.py: - steer_band(model, lo=0.3, hi=0.9): pre-fitted lenses span every layer; steering all of them over-drives the residual, so restrict to the mid-depth band run-524 used. - lens_topk reuses jlens.vis._meaningful_token_mask so j-space readouts hide punctuation/single-char/special tokens (per the walkthrough these trail the interesting word tokens on Qwen). Verified: Eiffel Tower resolves city->Paris clean. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
71 lines
3.2 KiB
Python
71 lines
3.2 KiB
Python
"""Repo-local paths, slug/cache conventions, and the fit corpus, shared by
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scripts/ and notebooks.
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NOT imported by the jsteer library (which stays path-agnostic so `pip install
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jsteer` never needs a repo root). The corpus content is jlens's own WikiText
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(`load_wikitext_prompts`, not hand-rolled), but wrapped in the model's chat
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template -- see chat_corpus for why.
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"""
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from pathlib import Path
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import torch
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from loguru import logger
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from tqdm.auto import tqdm
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# Configure loguru once, on import, so every script/notebook that imports config
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# gets the same compact format. Routed through tqdm.write so log lines don't
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# break a live progress bar (e.g. the fit bar).
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logger.remove()
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logger.add(lambda m: tqdm.write(m, end=""), colorize=True,
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format="<level>{level.icon}</level> {message}", level="INFO")
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for _lvl, _icon in (("INFO", "I"), ("WARNING", "W"), ("ERROR", "E"), ("DEBUG", "D")):
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logger.level(_lvl, icon=_icon)
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ROOT = Path(__file__).resolve().parent
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ART = ROOT / "artifacts"
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DEVICE = "cuda"
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DTYPE = torch.bfloat16
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# The authors publish pre-fitted Jacobian lenses on the Hub (raw Salesforce-
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# wikitext, n=1000 where the _n1000 suffix is present). Loading one beats fitting
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# locally: identical estimator, 1000 prompts, zero compute. Keyed by HF model id;
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# see github.com/anthropics/jacobian-lens walkthrough.ipynb.
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LENS_REPO = "neuronpedia/jacobian-lens"
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LENS_REVISION = "qwen-n1000"
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HUB_LENS_FILE = {
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"Qwen/Qwen3.5-4B": "qwen3.5-4b/jlens/Salesforce-wikitext/Qwen3.5-4B_jacobian_lens_n1000.pt",
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"Qwen/Qwen3.6-27B": "qwen3.6-27b/jlens/Salesforce-wikitext/Qwen3.6-27B_jacobian_lens_n1000.pt",
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"Qwen/Qwen3-4B": "qwen3-4b/jlens/Salesforce-wikitext/Qwen3-4B_jacobian_lens.pt",
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"Qwen/Qwen3-8B": "qwen3-8b/jlens/Salesforce-wikitext/Qwen3-8B_jacobian_lens.pt",
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"Qwen/Qwen3-14B": "qwen3-14b/jlens/Salesforce-wikitext/Qwen3-14B_jacobian_lens.pt",
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"Qwen/Qwen3-32B": "qwen3-32b/jlens/Salesforce-wikitext/Qwen3-32B_jacobian_lens.pt",
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}
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def hub_lens_file(model_name: str) -> str:
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"""Filename of the authors' pre-fitted lens for `model_name` inside LENS_REPO.
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KeyError (fail fast) if they don't publish one -- then fit locally via fit.py."""
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return HUB_LENS_FILE[model_name]
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def chat_corpus(tok, n_prompts: int) -> list[str]:
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"""jlens's WikiText prompts wrapped in the chat template. Fitting on chat-
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formatted text (not raw documents) puts J closer to the distribution the
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model steers in; run-524's verified vectors were fit this way too. Called
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via a lambda in fit_cached, so it only downloads WikiText on a cache miss."""
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from jlens.examples import load_wikitext_prompts
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return [tok.apply_chat_template([{"role": "user", "content": p}],
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add_generation_prompt=True, tokenize=False,
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enable_thinking=True)
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for p in load_wikitext_prompts(n_prompts)]
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def slug(model_name: str) -> str:
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"""'Qwen/Qwen3-0.6B' -> 'qwen3-0.6b': a filesystem-safe cache stem."""
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return model_name.split("/")[-1].lower()
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def cache_path(model_name: str, suffix: str = "jac") -> Path:
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"""Where the fitted Jacobian for `model_name` is cached."""
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return ART / f"{slug(model_name)}.{suffix}"
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