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Model Details
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schneewolflabs
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nbeerbower/Wichtel-Qwen3.6-27B
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No capability damage. ARC-Challenge (299 tasks, paired McNemar against the base) is flat at every scale, despite this being the largest adapter in the family by effective delta:
| scale | ARC | delta | p |
|---|---|---|---|
| 0.25 | 64.88 | +0.00 | 1.000 |
| 0.50 | 64.55 | −0.33 | 1.000 |
| 0.75 | 65.22 | +0.33 | 1.000 |
| 1.00 | 64.55 | −0.33 | 1.000 |
Worth noting against the family: ‖B·A‖ = 18.95 here, versus 7.81 for a sibling adapter that cost 4 ARC points at scale 1.0 (p = 0.036). Delta magnitude does not predict damage — what the adapter changes matters more than how much. This one moves tool-call behaviour, which ARC never exercises.
Agent behaviour changed, mostly for the worse. Ten held-out tasks from a repository that contributed no training data, graded by running the repo's own test suite:
| outcome | Wichtel | +Lehrling |
|---|---|---|
| passed | 9/10 | 7/10 |
| self, passed | 7 | 7 |
| escalated, passed | 2 | 0 |
| self, failed | 0 | 3 |
| escalated, failed | 1 | 0 |
Every task the base delegated, this attempts alone. Two of those turned out fine — including one the base had escalated and still failed. Three did not, and they failed in the same way.
Thinking mode is untouched. An 8-prompt probe at temperature 0, matched 12k token cap:
| Wichtel | +Lehrling | |
|---|---|---|
| unclosed think blocks | 0/8 | 0/8 |
| total reasoning | 6,872 words | 6,914 (1.01x) |
This was not the expected result. The training targets contain no <think> blocks at all —
Claude Code does not export thinking — so 372 steps of never deliberating looked likely to
suppress it. It did not, and the probable reason is format_type: raw: the prompts are
pre-rendered conversations that never contain the <think> opener, so the model was never shown a
context where it sat inside a think block and produced nothing. The reasoning pathway was simply
never in the gradient.
Trained through a chat template with enable_thinking: true instead, the same data would likely
suppress reasoning — the model would see the opener followed by an immediate close, 372 times. The
raw format isolated the two behaviours by accident.
Practically: this composes with a reasoning adapter rather than competing with one.
The three regressions are not scattered. All three explored and never edited:
markdown
metrics_words: 10 tools — read, glob, execute x5 ... no write, no edit"[Agent reached maximum turns without producing a final response]"metrics_content_words: 6 tools — read, glob, read, read, git_log, read(empty response)
The function under test was left holding raise NotImplementedError, untouched.
This is a data composition problem rather than a training failure. Claude Code explores heavily
before committing to an edit, and with one sample per assistant turn that exploration dominates
the dataset — Read, Glob, Grep, Bash vastly outnumber Write, Edit and Agent. The
adapter learned the reconnaissance half of the behaviour and not the decisive half.
The obvious next experiment: filter the source dataset to samples whose target is a mutating
action — Write, Edit, or Agent — and retrain. That is a filter on the existing 24k, not new
data, and it is testable on the same ten tasks.
Three lost, two won on ten tasks is p = 1.000. Nothing here is statistically significant, and the direction should not be over-read. What raises it above noise is that the three failures share one mechanism and one file family rather than scattering — a pattern that usually survives more data, though that remains to be shown.
Not strongly indicated, with one caveat: no eval loss was recorded despite eval_steps: 0.5,
so the standard check was unavailable.
| base | nbeerbower/Wichtel-Qwen3.6-27B |
| method | SFT, LoRA r=32 α=64 dropout 0.05 |
| data | 6,000 shuffled samples of 24,395 |
| schedule | 1 epoch, 372 steps, lr 2e-4 cosine, warmup 0.05 |
| batch | 1 × grad-accum 16 |
| sequence | max_length 4096, max_prompt_length 3600 |
| format | raw — the prompts are pre-rendered Qwen3 conversations |
The learning rate was too high. Loss bottomed at 0.947 by step 16, climbed to 1.212 by step 56 as the LR sat near its 2e-4 peak, and only recovered as cosine decay took hold — roughly the first 120 steps were spent undoing the first 40. 5e-5 would likely reach a lower final loss in the same budget. The 2e-4 was inherited from a sibling run that was installing a capability the base lacked entirely, where aggressive updates are appropriate; nudging behaviour a model already has is a different problem.
Subsampled to 6,000 because the full 24,395 samples at ~3,300 tokens each is ~79M tokens per epoch, which on the training hardware is 15 days. At 6,000 it is 3.3 days.