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---

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

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"""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.

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Principles

• don't use many tokens
• make it so a dumb summary LLM can easily 1) see problems 2) have clues to diagnose
• timing information

example of good log

• with single line should statement inline in log that make it clear how it should look, distinguish from subtle failures, and give principled clue for diagnosis
• table short have longest and least important lines last, so that humans can read it even with wrap around e.g short numeric columns first. long text columns last, notes or desc last
• use tabulate plain for token effecient, not logging each step or epoch but just table

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    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.

https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py#L416

class RepetitionPenaltyLogitsProcessor(LogitsProcessor):

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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)

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"""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>

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# 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

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GPU + uv in Claude Code sandbox (Linux/NVIDIA)

Problem: Claude Code's sandbox (bubblewrap) blocks GPU devices and the uv package cache.

Solution: bwrap wrapper at ~/.local/bin/bwrap

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import torch


def generate_with_input_logits(model, tokenizer, batch2, **kwargs):
    """
    problem: generate does not return logits for inputs, but we need them for nll

    but forward -> generate with past key values does, and it doesn't recompute the input logits

    so this is a helper that does both