diff --git a/README.md b/README.md
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+---
+pretty_name: Whittle distillation set (Qwen3.8-27B reasoning traces + top-20 logprobs)
+license: other
+language:
+- en
+task_categories:
+- text-generation
+tags:
+- knowledge-distillation
+- teacher-logprobs
+- reasoning
+- chain-of-thought
+- tool-use
+- agentic
+- qwen
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*.parquet
+---
+
+# Whittle distillation set: Qwen3.8-27B traces with top-20 logprobs
+
+About **25,000 complete, quality-filtered answers** from **Qwen3.8-27B** (thinking on), each with the teacher's **top-20 log-probabilities at every answer token**. It is built for **logit-level knowledge distillation**: a student can match the teacher's full per-token distribution, not just its sampled text. We use it to train [Whittle-Qwen-3.8-35B-A3B](https://huggingface.co/logic65/Whittle-Qwen-3.8-35B-A3B).
+
+- **25,178 rows**, **78.0M tokens**, of which **25.4M are answer tokens with top-20 logprobs** (about 507M token/logprob pairs).
+- Covers maths, grade-school maths, code review and explanation, open chat, strict JSON extraction, long-document reading, and **agentic tool use**. Agent episodes were **executed in a sandbox and kept only when hidden unit tests passed**.
+- Every trace is **complete**: it stopped by itself, closed its thinking block and ended on `<|im_end|>`. It is **correct wherever it could be graded**.
+
+```python
+from datasets import load_dataset
+ds = load_dataset("logic65/whittle-distill-qwen3.8-27b", split="train")
+row = ds[0]
+row["kind"], row["prompt"][:200], row["reasoning"][:200], row["answer"][:200]
+# logit-level targets: for answer position j (predicting input_ids[answer_start + j])
+#   candidates = row["topk_ids"][j]; logprobs = row["topk_logprobs"][j]   (20 each, highest first; renormalise before use)
+```
+
+---
+
+## Columns
+
+| Column | Type | Meaning |
+|---|---|---|
+| `id` | string | stable id (`kind-#####`; agent episode turns get `/turnN`) |
+| `kind` | string | `math`, `gsm`, `code`, `chat`, `json`, `longctx`, `agent1` (single tool step), `agentx` (turn of an executed agent episode) |
+| `source` | string | where the prompt came from (see *Sources*) |
+| `prompt` | string | the full prompt as rendered with the Whittle / Qwen chat template (system, tools, earlier turns), ending with `<|im_start|>assistant\n<think>\n` |
+| `response` | string | the teacher's full raw answer: reasoning, `</think>`, visible answer or tool calls, `<|im_end|>` |
+| `reasoning` | string | the thinking part only |
+| `answer` | string | the visible answer only (empty when the turn is only a tool call) |
+| `tool_calls` | string (JSON) | parsed tool calls `[{"name": ..., "arguments": {...}}]`, if any |
+| `input_ids` | list[int32] | exact token ids of `prompt + response` |
+| `answer_start` | int32 | index in `input_ids` where the response begins |
+| `topk_ids` | list[list[int32]] | `[n_answer_tokens][20]`: teacher's top-20 candidate token ids at each answer position, highest first |
+| `topk_logprobs` | list[list[float32]] | `[n_answer_tokens][20]`: their **raw** log-probabilities (before temperature / top-p) |
+| `n_prompt_tokens`, `n_answer_tokens` | int32 | lengths |
+
+`topk_*[j]` is the teacher's distribution **predicting `input_ids[answer_start + j]`**. The token actually sampled is always among the 20; we checked every row. Tokenizer: [logic65/Whittle-Qwen-3.8-35B-A3B](https://huggingface.co/logic65/Whittle-Qwen-3.8-35B-A3B) (the Qwen3.8 tokenizer, 248,320 tokens, `<|im_end|>` = 248046).
+
+---
+
+## Composition
+
+| Kind | Rows | Median answer tokens | Source | Content |
+|---|---:|---:|---|---|
+| `math` | 4,013 | 671 | MATH train, levels 2–5 | step-by-step solutions, graded `\boxed{}` answer |
+| `gsm` | 1,443 | 264 | GSM8K train | word problems, graded final number |
+| `code` | 4,487 | 2,160 | open-source files | review, explain, write tests, edge cases, refactor |
+| `chat` | 1,334 | 1,283 | oasst2 | open-ended questions (mostly English) |
+| `json` | 2,000 | 144 | synthetic | strict schema extraction, exact typed fields |
+| `longctx` | 1,470 | 197 | open-source files | find one function in a 24k–139k-character file (prompts up to ~42k tokens) |
+| `agent1` | 2,409 | 145 | synthetic | one next step in a Claude-Code-style tool session |
+| `agentx` | 8,022 turns (1,495 episodes) | 81 per turn | synthetic, executed | multi-turn coding episodes with real Read / Write / Edit / Bash results |
+
+The GSM8K **test** split is not included, nor are any held-out evaluation problems.
+
+---
+
+## How it was made
+
+- **Teacher:** `Qwen/Qwen3.8-27B-FP8` (official block-128 FP8 build) on vLLM 0.30.
+  - Checked against the bf16 teacher on 69,922 answer positions: **98.1% top-1 agreement**, total variation 0.016 over the top-20, 98.2% shared probability mass.
+- **Sampling:** thinking on, temperature 0.6, top-p 0.95, top-k 20.
+  - Reasoning effort was the teacher template's maximum (*xhigh*) for all kinds except code. Code used the default effort with focused instructions, because open-ended code requests at *xhigh* ran past 16k tokens without finishing.
+  - Maximum answer length: 16,384 tokens (8,192 for grade-school maths, JSON and agent turns).
+- **Context distillation.** The teacher answered with its *xhigh* "reason carefully" instruction. The `prompt` column is rendered **without** it, so a student trained on these rows learns careful reasoning by default.
+- **Filtering:** 19,500 prompts went in and **18,651 (96%) were kept**. A trace is kept only if **all** of these hold:
+  1. it finished with its own end-of-turn token;
+  2. the think block is closed;
+  3. no tool markup leaked into the answer;
+  4. it is not degenerate (minimum lengths);
+  5. every sampled token appears in its own top-20;
+  6. the answer is correct:
+     - maths: normalised `\boxed{}` comparison;
+     - GSM: final number;
+     - JSON: exact fields with strict types;
+     - long-context: exact file and parameters;
+     - single agent steps: the right action (e.g. fix the implementation, never the test);
+     - agent episodes: hidden unit tests pass **and** the original tests still pass.
+- **Agent episodes:** the teacher's Read / Write / Edit / Bash calls ran against a real sandbox, with Claude-Code-style tool results and a command allowlist, for up to 16 rounds. Every assistant turn of a passing episode is its own row. In half of the rows, earlier turns keep their reasoning; in the other half it is stripped, as many clients do.
+- **Privacy scan:** every row was scanned for credentials, tokens and private network addresses before release. No rows needed removal.
+
+---
+
+## Sources and licences
+
+| Source | Kinds | Licence |
+|---|---|---|
+| MATH (Hendrycks et al.), train split | `math` | MIT |
+| GSM8K, train split | `gsm` | MIT |
+| OpenAssistant oasst2 (first turns) | `chat` | Apache-2.0 |
+| Permissively licensed open-source code (licence-checked) | `code`, `longctx` | each file's own permissive licence |
+| Synthetic (generated by our scripts) | `json`, `agent1`, `agentx` | released with this dataset |
+| Qwen3.8-27B outputs | all answers and logprobs | Apache-2.0 (Qwen model licence) |
+
+The sources are mixed, so the dataset is marked `license: other`. Check each source's terms for your use.
+
+---
+
+## Limitations
+
+- **FP8 teacher:** 98.1% top-1 agreement with bf16, not 100%.
+- **Top-20 only:** the long tail of the distribution is not stored.
+- **Grader strictness:** a few correct answers were filtered out, for example `(e)`-style letter answers. Maths or chat answers that hit the 16k cap were dropped, so the very longest reasoning is slightly under-represented.
+- **Synthetic agent tasks** are small single-file Python projects. They teach tool discipline, not large-codebase navigation.
+- **Prompt rendering:** prompts use the Qwen chat template with thinking on. If your student uses a different template, re-render from `reasoning` / `answer` / `tool_calls`. The logprobs belong to the teacher's tokenisation of `response`.
+
+## Citation
+
+```
+@misc{whittle-distill-2026,
+  title  = {Whittle distillation set: Qwen3.8-27B reasoning traces with top-20 logprobs},
+  author = {logic65},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/logic65/whittle-distill-qwen3.8-27b}
+}
+```
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