---
license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - knowledge-distillation
  - research-preview
  - anti-repetition
pretty_name: Whittle teacher-complete answers (top-32 logprobs)
---

# Whittle teacher32: complete answers with per-token teacher logprobs

**Research preview.** Part of the Whittle compression campaign, a personal
research project. The compute for this project is self funded and donations
decide whether the next round happens: https://ko-fi.com/davida81328

## What this is

Complete answers generated by Qwen3.8-27B (UD-Q5_K_XL via llama.cpp), each
ending on a real end-of-turn token because the answer is finished, with the
teacher's top-32 logprobs captured at every generated position. Built to
distil the one signal our five-run study showed compressed students are
missing: WHEN an answer is complete. The full story is in
WHITTLE_FINDINGS.md on the model repo, including the two design catches
this dataset exists to fix: an audit found 11 of 12 evaluation prompts
inside the earlier training list, and a first draft whose every answer was
long would have taught a length prior instead of content-conditioned
stopping.

## Families

| family | count basis | why it is here |
|---|---|---|
| enum | 25 prompts x 2 seeds | numbered lists that end after item N |
| short | 14 x 2 | one-sentence answers, the early-stop anchor |
| medium | 8 x 2 | explanations at natural length |
| code | 5 x 2 | fenced code answers |
| convo | 8 scripts x 2 seeds | 5-6 turn conversations, late-turn enumeration asks (the measured failure site) |
| extra | 70 x 1 | structured outputs (SQL, HTML, markdown tables, JSON) harvested from the released model's real failure prompts |

All prompts are disjoint from the evaluation gate. Rows that did not end on
a clean EOS within their token budget were skipped and logged, never banked.

## Format

- `teacher_complete.jsonl`: one row per answer or conversation:
  `{key, family, prompt|convo, seed, input_ids, spans, idx, val}` where
  `spans` are [start, end) token ranges of assistant answers, `idx`/`val`
  are per-position top-32 teacher token ids and logprobs (position p holds
  the distribution that produced token p+1; positions outside assistant
  spans are junk-filled and must be masked).
- `teacher_complete.npz`: trainer cache (row_ids, lengths, idx, val, topk).
- `gen_teacher_v2.py`: the exact generator, for provenance.
- `extra_prompts.json`: the harvested structured-output prompts.

## Teacher and capture

Teacher: Qwen/Qwen3.8-27B, Unsloth UD-Q5_K_XL GGUF, served by llama.cpp
with thinking disabled. Logprobs are the pre-sampling softmax over the full
vocabulary, top-32 per position, captured in the same generation call.
Sampling: temperature 0.6, top_p 0.9, top_k 40, fixed seeds.

## License

Apache 2.0, matching the teacher model's license. Prompts are original to
this project or generated for its earlier on-policy harvest.