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Avesed/Qwen3.6-27B-DSpark-data

Qwen3.6-27B DSpark training data (on-policy, clean) On-policy conversations generated by Avesed/Qwen3.6-27B-W4A16 on a sha256-verified checkpoint, used to train Avesed/Qwen3.6-27B-DSpark. Each file is its own dataset config (they use different id schemes — integer vs zh_* string — so the viewer must keep them separate rather than merge into one table). config / file convs lang prompt source pb_pool94k_clean ~86k en PerfectBlend-style instruction mix general_onpolicy… See the full description on the dataset page: https://huggingface.co/datasets/Avesed/Qwen3.6-27B-DSpark-data.

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face
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Qwen3.6-27B DSpark training data (on-policy, clean)

On-policy conversations generated by `Avesed/Qwen3.6-27B-W4A16` on a sha256-verified checkpoint, used to train `Avesed/Qwen3.6-27B-DSpark`.

Each file is its own dataset config (they use different id schemes — integer vs zh_* string — so the viewer must keep them separate rather than merge into one table).

config / fileconvslangprompt source
pb_pool94k_clean~86kenPerfectBlend-style instruction mix
general_onpolicy~416,557en (some zh)Magpie-Pro + WildChat + UltraChat (general chat/instruct)
chinese_onpolicy~100,000zhMagpie-zh (pure-Chinese chat/instruct)

All splits are on-policy (assistant turns = the target model's own outputs). Format: JSONL, {"id", "src", "conversations": [{"role": "user", ...}, {"role": "assistant", ...}]} (pb_pool94k_clean has no src field).

python
from datasets import load_dataset
zh = load_dataset("Avesed/Qwen3.6-27B-DSpark-data", "chinese_onpolicy", split="train")