wassname (Michael J Clark) wassname

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

    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):

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

PEFT Adapter papers

Orthogonal methods: OFT, BOFT, HRA, ROAD, GOFT (Givens), OFTv2 Low-rank methods: LoRA, AdaLoRA, LoHa, LoKr, RandLoRA, VBLoRA, FourierFT, DeLoRA Scaling methods: IA3, VeRA Prompt-based: Prompt Tuning, Prefix Tuning, P-Tuning, Adaption Prompt, Multitask Prompt Tuning, CPT Specialized: MiSS, SHiRA, C3A, LN Tuning, Poly, XLoRA


	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>

	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

	"""
	Stopping criteria: regexp

	ref:
	- https://huggingface.co/docs/transformers/v4.56.1/en/main_classes/text_generation#transformers.GenerationMixin.generate.stopping_criteria
	- https://github.com/huggingface/transformers/blob/e8a6eb3304033fdd9346fe3b3293309fe50de238/tests/generation/test_stopping_criteria.py#L51
	"""

	from transformers import StopStringCriteria, StoppingCriteriaList, EosTokenCriteria

	# 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