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wassname
09349894ce
- plot: Panel A now tracks top-moving trait (care for love demo, auth for authority) instead of hardcoded auth_nats; Panel C already did this, Panel A now consistent - README: update table with new run (lam decay extends saturation r4→r6), refresh diary from new run's outputs, update trajectory plot - AGENTS.md: correct gotchas -- tau<operating_KL is the key constraint (tau=2.0 not 4.0); QLoRA + bs=3 ga=2 is the right default for better heal gradient estimates Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
41 lines
2.6 KiB
Markdown
41 lines
2.6 KiB
Markdown
**This is novel ML research.** Not in your training data. Extrapolate carefully. Read `spec.md` first.
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## What this is
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Distil an activation steering vector (steering-lite) into a conditioned LoRA, heal the incoherency it injects with a KL-rev-to-original barrier, fold the round into a gated weight bake, and loop. Eval on tinymfv (auth/care axis + coherence). Full design and the three uncertainty gates are in `spec.md`.
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## Workflow
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- Inherit global rules from `~/.claude/CLAUDE.md`.
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- `just vendor` to (re)clone reference repos into `docs/vendor` (editable path deps).
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- `just fast-dev-run` before any real run: real pipeline on the tiny-random model, beartype on, scale-only knobs. If a bug slips past it, strengthen the gate, do not add a `tests/` dir.
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- `just run` for a real run on gemma-3-1b-it (RTX 3090, 24GB).
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- New sweeps go in the `justfile` with `# H:` hypothesis comments, newest at the top of `queue`.
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- `tail docs/RESEARCH_JOURNAL.md` for latest context.
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## Reuse, do not reinvent (docs/vendor)
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- steering-lite: `Vector.train(...).calibrate(target_kl=...)`, mean-diff vector + iso-KL dose.
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- iso-kl-figure: coefficient calibration and KL/coherence measurement.
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- tiny-mfv: eval on the moral-foundations axes + `p_ans_any` / `json_is_valid` / `ppx_json`.
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- w2schar-mini (NOT a dep, needs py3.13): copy `src/csm/ws/{adapter,bake,history}.py` for the conditioned LoRA + gated bake, and port `src/csm/plot.py` `_build_scatter` for the Care-vs-Authority HTML map. The base stays pristine at gate 0 = our KL anchor.
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## Code style
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- `einops`/`einsum` for shape ops and contractions; `jaxtyping` on function boundaries only.
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- `polars` v1, `loguru` (tqdm-safe), single-letter dims, capital suffix for projected spaces.
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- Fail fast, crash loudly. No defensive guards, no fallbacks, no silent skips.
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- One objective + one constraint (barrier), never competing losses. See `spec.md` Loss.
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- Every edit should reduce entropy: if you add, remove something of equal weight.
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## Gotchas
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- Use QLoRA + train_bs=3 + grad_accum=2 (eff_bs=6). The larger effective batch gives better heal
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SFT gradient estimates. 4-bit decode is ~3x slower than bf16 but the convergence win is worth it.
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Only skip QLoRA if targeting a model too large for the GPU in bf16.
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- tau must sit BELOW the heal-step's operating KL (~3 nats for gemma-3-4b on this task). If
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tau > operating_KL, relu(div - tau) = 0 and the barrier silently fires no gradient. Symptom:
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coherence drops fast and coh_floor early-stop fires at r1. Fix: tau=2.0.
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- The heal KL step masks completion positions BEFORE log_softmax (full [B, L-1, ~262k] OOMs on a
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3090 at bs>1). Keep this regardless of dtype.
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