Fresh-eyes caught stale framing: the module/chat_corpus docstrings still sold chat-
templated fitting as the corpus convention. The default demo path now loads the
authors' pre-fitted raw-wikitext lens; chat_corpus only feeds the local-fit fallback,
and chat-vs-raw was never compared head-to-head (run-524 used chat), so it's flagged
unresolved rather than claimed better.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
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>
- Jacobian.fit wraps prompts in tqdm (jlens has no bar; safe since fit only
enumerate/len's them), logs the full first prompt (special tokens on, SHOULD
line) and a done-summary -- token-efficient-logging style, both tqdm intervals set
- config.py sets up loguru on import (compact single-char icons, routed through
tqdm.write so bars survive), so every script/notebook importing config gets it
- notebooks drop their manual logger setup and import config in cell 1
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The verified run-524 vectors were fit on chat-templated prompts (u4_prompts.json),
so fitting raw WikiText diverged from what worked. Now:
- config.chat_corpus wraps jlens WikiText in the chat template (fit J where we steer)
- jsteer.demo.show_steer generates through apply_chat_template(enable_thinking) with
the model's own generation_config sampling, splits </think>, shows lens_topk j-space
readout + reasoning + answer as Tufte small-multiples per C
- word_steering.ipynb rewired to Qwen3.5-4B, dim_batch=4 (3090-safe 4B), show_steer
- fit.py defaults to Qwen3.5-4B + chat_corpus
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
