---
license: other
license_name: mixed-upstream
license_link: https://huggingface.co/datasets/weilan55/k3f_regen_data#licensing
task_categories:
- text-generation
language:
- en
- zh
tags:
- speculative-decoding
- draft-model
- on-policy
- multi-turn
size_categories:
- 1M<n<10M
configs:
- config_name: v5_train
  data_files: v5_train.jsonl
- config_name: v6_train
  data_files: v6_train.jsonl
- config_name: longchat_allturn
  data_files: longchat_allturn.jsonl
---

# K3-Flash regenerated corpora for draft-model training

On-policy responses generated by **`moonshotai/Kimi-K3-Flash`** over prompts taken
from public datasets, built for training speculative-decoding draft models
(EAGLE-3 / DSpark style). The prompts are upstream; **every assistant turn here is
K3-Flash's own output**, which is what makes the data on-policy for a draft that
has to predict this target.

No production or service traffic is included. Every row's `metadata.source` names
its upstream public dataset.

## Files

| file | rows | what it is |
|---|---|---|
| `v5_train.jsonl` | 293,182 | depth-matched sample, deliberately skewed toward **bounded** asks (69.3% vs 45.4% in the internal baseline) |
| `v6_train.jsonl` | 1,013,914 | the full deduplicated pool, no ratio shaping |
| `longchat_allturn.jsonl` | 476,318 | **all-turn** regeneration of 47,651 long conversations: every intermediate assistant turn regenerated against the model's own earlier replies |

### Schema

`v5_train` / `v6_train` — one conversation per row:

```json
{"id": "...", "conversations": [{"role": "user", "content": "..."},
                                {"role": "assistant", "content": "..."}],
 "metadata": {"source": "WildChat-1M", "batch": "E", "n_assistant_turns": 4,
              "depth_band": "4-7", "ask_shape": "bounded",
              "supervised_kind": "text_only", "tail_is_assistant": true}}
```

`longchat_allturn` — one **generation** per row, pretokenized:

```json
{"id": "lc_<conv>_gen2", "primary_id": "lc_<conv>",
 "input_ids": [...], "loss_mask": [0,...,0,1,...,1],
 "conversations": [...prefix..., {"role": "assistant", "content": "..."}],
 "metadata": {"turn_index": 2, "n_turns": 9, "source": "WildChat-1M"}}
```

`loss_mask` is 1 exactly over the completion tokens, so `input_ids` is the true
on-policy sequence and the boundary is exact rather than re-derived. Rows of one
conversation share a `primary_id`; `turn_index` k was generated against a prefix
whose earlier assistant turns are the model's own outputs, not the source's.

## Upstream prompt sources

| source | rows in v6 | upstream |
|---|---|---|
| OpenCodeInstruct | 365,160 | `nvidia/OpenCodeInstruct` |
| kimi-mtp-dataset | 172,412 | `lightseekorg/kimi-mtp-dataset` |
| WildChat-1M | 148,706 | `allenai/WildChat-1M` |
| aya_dataset | 111,545 | `CohereLabs/aya_dataset` |
| ultrachat_200k | 99,738 | `HuggingFaceH4/ultrachat_200k` |
| kimi-mtp-llava-mm | 97,094 | llava subset of kimi-mtp |
| glaive-function-calling-v2 | 19,259 | `glaiveai/glaive-function-calling-v2` |

## Known limitations

**Multimodal rows carry no pixels.** The 97,094 `kimi-mtp-llava-mm` rows in
`v6_train` use image parts shaped `{"type":"image","url":...,"path":...}`. Common
loaders look for `item["image"]` or `item["image_url"]["url"]`, so with those this
resolves to no image and the row trains as text with a bare `<image>` placeholder.
Affected rows are flagged `metadata.mm_image_unreadable: true`. The original COCO
URLs are in the `url` field if you want to rehydrate them.

**`tail_is_assistant: false` rows** end on a non-assistant message, so a
last-turn-only supervision rule has no target on them. Flagged, not removed.

**Acceptance is dominated by the supervised turn's kind, not by topic.** Measured
on these corpora's family: turns that emit a tool call draft at tau ~4.0-4.3 while
plain-text turns sit at ~2.0-3.0. Within plain text, a *bounded* ask (translate,
list, extract, fix) drafts +0.4 to +0.65 above an open-ended one; conversation
depth is monotonically worse (1 turn ~2.74, 10+ turns ~2.02). CJK text, output
length, and markdown structure were each tested and did **not** explain the gap.
These corpora are ~99.9% plain-text-supervised, so they exercise the hard end.

**`v6_train` and `longchat_allturn` overlap** on 29,567 conversations (62% of
longchat) — both draw from WildChat/ultrachat. Deduplicate by first-user-turn if
you mix them.

**Held-out rows** matching an internal eval split were removed during the build;
this is not a general-purpose decontamination against public benchmarks.

## Licensing

Each row's prompt inherits its upstream dataset's license (ODC-BY for WildChat,
MIT for ultrachat, and the respective terms for OpenCodeInstruct, aya, glaive and
kimi-mtp — check each before redistributing). The assistant turns are generated
output from `moonshotai/Kimi-K3-Flash` and are subject to that model's license.
Verify both chains apply to your use case.