Kesselman List of Estimative Words (fig 5.2) (gwern finds this usefull for LLMs and so do I). These are probability words calibrated to meaning where the general public understand them to be, with ambiouso words avboided.
Words of estimative probability (WEP or WEPs) are terms used by intelligence analysts in the production of analytic reports to convey the likelihood of a future event occurring.
| Word | Certainty |
|---|---|
| Almost Certain | 86-99% |
| Highly Likely | 71-85% |
| """General candidate-slot label placement for matplotlib: polygon-aware, short-leader, gist-ready. | |
| The problem: matplotlib has no label placer that (a) tries every side of a marker and keeps the | |
| nearest clear slot, (b) draws a leader line ONLY when the label had to move far, and (c) treats a | |
| filled polygon (a convex-hull region) as something to avoid. adjustText (Phlya/adjustText) relaxes | |
| by force and parks labels in local minima; textalloc (ckjellson/textalloc) does candidate placement | |
| but only against points/lines/other-text, and always/never draws lines. This is a small placer that | |
| does all three, generalised so ONE call handles both marker labels and region labels. | |
| The generalisation (wassname's idea): every label owns a SET of 1..N candidate anchor points, and we |
| """Minimal greedy non-overlapping label placement for plotly. | |
| `place_labels` returns plotly annotation dicts that place text labels around | |
| labelled scatter points without overlapping each other or nearby obstacle | |
| markers. No maintained plotly-native label de-overlap exists as of 2025 | |
| (plotly.js issue #4674 open since 2020); this is textalloc / D3-Labeler in | |
| miniature, with two deliberate differences: | |
| 1. Determinism. The canvas is fixed (fig_w/fig_h/margins) and labels are | |
| placed in input order, so a given (points, obstacles) set always yields |
name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays
| """minicache — anycache-spirit single-file disk cache. | |
| Why not anycache: | |
| - in machine learning you can't has `model` so we have `exclude=["model", "tok"]`` | |
| - you sometimes want to cach `tensor` so we use cloudpickle and to compress if possible (TODO check it works) | |
| - we add cloudpickle backend. Handles lambdas, closures, dataclasses, pandas, | |
| jax pytrees, torch tensors — everything stdlib pickle chokes on. | |
| Why not just `functools.lru_cache + pickle.dump`: same arg-hashing problem. | |
| - gzip on disk. ~3× smaller files. cloudpickle output streams cleanly | |
| through `gzip.open`, no extra tooling. |
Principles
example of good log
repetition_penalty (`float`, *optional*):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://huggingface.co/papers/1909.05858) for more details.
class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
| def classify_linear_sublayers( | |
| model, | |
| block_layers: list[str], | |
| ) -> dict[str, list[str]]: | |
| """Classify all Linear sublayers in each block by their residual-stream role. | |
| For a Linear layer with weight shape [out_features, in_features]: | |
| - residual_write : out_features == d_model (writes TO residual stream) | |
| - residual_read : in_features == d_model (reads FROM residual stream) |
| """Reusable guided-rollout primitive: think → forced-close-think → JSON choice. | |
| One rollout, three numbers. The same primitive backs: | |
| - calibrate()'s coherence + format + rep measurement | |
| - probe replay at edit time | |
| - post-keep probe regeneration | |
| - (future) DD eval (once _measure_logratios is ported to this) | |
| Substrate: | |
| <user_prompt + schema_hint> |
| # Smoke test: demo on task 0 showing steered outputs at -1, 0, +1 | |
| smoke *ARGS: | |
| BEARTYPE=1 {{ PY }} ssteer_v3.py --quick {{ ARGS }} 2>&1 | tee /tmp/smoke.log | tail -80 |